From 14c3195dbb8dcba74088fff10c80b9893ac3c8ba Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Fri, 6 Mar 2026 07:43:06 -0500 Subject: [PATCH 1/8] Feature/thoughts (#778) * initial * adding logging --- application/single_app/app.py | 4 + application/single_app/config.py | 12 + application/single_app/functions_settings.py | 3 + application/single_app/functions_thoughts.py | 256 +++++++++ application/single_app/route_backend_chats.py | 180 ++++-- .../single_app/route_backend_conversations.py | 18 +- .../single_app/route_backend_thoughts.py | 80 +++ .../route_frontend_admin_settings.py | 3 +- .../plugin_invocation_logger.py | 42 +- application/single_app/static/css/chats.css | 156 ++++++ .../single_app/static/images/custom_logo.png | Bin 0 -> 11877 bytes .../static/images/custom_logo_dark.png | Bin 0 -> 13468 bytes .../static/js/admin/admin_settings.js | 7 +- .../static/js/chat/chat-loading-indicator.js | 25 +- .../static/js/chat/chat-messages.js | 14 +- .../static/js/chat/chat-streaming.js | 8 +- .../static/js/chat/chat-thoughts.js | 215 ++++++++ .../single_app/templates/_sidebar_nav.html | 5 + .../single_app/templates/admin_settings.html | 24 +- application/single_app/templates/chats.html | 3 +- .../v0.239.003/PROCESSING_THOUGHTS.md | 153 ++++++ docs/explanation/release_notes.md | 19 + functional_tests/test_thoughts_feature.py | 520 ++++++++++++++++++ 23 files changed, 1692 insertions(+), 55 deletions(-) create mode 100644 application/single_app/functions_thoughts.py create mode 100644 application/single_app/route_backend_thoughts.py create mode 100644 application/single_app/static/images/custom_logo.png create mode 100644 application/single_app/static/images/custom_logo_dark.png create mode 100644 application/single_app/static/js/chat/chat-thoughts.js create mode 100644 docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md create mode 100644 functional_tests/test_thoughts_feature.py diff --git a/application/single_app/app.py b/application/single_app/app.py index 2354b1b5..e0c8cfee 100644 --- a/application/single_app/app.py +++ b/application/single_app/app.py @@ -75,6 +75,7 @@ from route_backend_public_prompts import * from route_backend_user_agreement import register_route_backend_user_agreement from route_backend_conversation_export import register_route_backend_conversation_export +from route_backend_thoughts import register_route_backend_thoughts from route_backend_speech import register_route_backend_speech from route_backend_tts import register_route_backend_tts from route_enhanced_citations import register_enhanced_citations_routes @@ -641,6 +642,9 @@ def list_semantic_kernel_plugins(): # ------------------- API User Agreement Routes ---------- register_route_backend_user_agreement(app) +# ------------------- API Thoughts Routes ---------------- +register_route_backend_thoughts(app) + # ------------------- Extenral Health Routes ---------- register_route_external_health(app) diff --git a/application/single_app/config.py b/application/single_app/config.py index da63c230..934e47af 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -459,6 +459,18 @@ def get_redis_cache_infrastructure_endpoint(redis_hostname: str) -> str: default_ttl=-1 # TTL disabled by default, enabled per-document for auto-cleanup ) +cosmos_thoughts_container_name = "thoughts" +cosmos_thoughts_container = cosmos_database.create_container_if_not_exists( + id=cosmos_thoughts_container_name, + partition_key=PartitionKey(path="/user_id") +) + +cosmos_archived_thoughts_container_name = "archive_thoughts" +cosmos_archived_thoughts_container = cosmos_database.create_container_if_not_exists( + id=cosmos_archived_thoughts_container_name, + partition_key=PartitionKey(path="/user_id") +) + def ensure_custom_logo_file_exists(app, settings): """ If custom_logo_base64 or custom_logo_dark_base64 is present in settings, ensure the appropriate diff --git a/application/single_app/functions_settings.py b/application/single_app/functions_settings.py index 8176939d..4164fab4 100644 --- a/application/single_app/functions_settings.py +++ b/application/single_app/functions_settings.py @@ -205,6 +205,9 @@ def get_settings(use_cosmos=False): 'require_member_of_feedback_admin': False, 'enable_conversation_archiving': False, + # Processing Thoughts + 'enable_thoughts': False, + # Search and Extract 'azure_ai_search_endpoint': '', 'azure_ai_search_key': '', diff --git a/application/single_app/functions_thoughts.py b/application/single_app/functions_thoughts.py new file mode 100644 index 00000000..c6ffe9dd --- /dev/null +++ b/application/single_app/functions_thoughts.py @@ -0,0 +1,256 @@ +# functions_thoughts.py + +import uuid +import time +from datetime import datetime, timezone +from config import cosmos_thoughts_container, cosmos_archived_thoughts_container, cosmos_messages_container +from functions_appinsights import log_event +from functions_settings import get_settings + + +class ThoughtTracker: + """Stateful per-request tracker that writes processing step records to Cosmos DB. + + Each add_thought() call immediately upserts a document so that polling + clients can see partial progress before the final response is sent. + + All Cosmos writes are wrapped in try/except so thought errors never + interrupt the chat processing flow. + """ + + def __init__(self, conversation_id, message_id, thread_id, user_id): + self.conversation_id = conversation_id + self.message_id = message_id + self.thread_id = thread_id + self.user_id = user_id + self.current_index = 0 + settings = get_settings() + self.enabled = settings.get('enable_thoughts', False) + + def add_thought(self, step_type, content, detail=None): + """Write a thought step to Cosmos immediately. + + Args: + step_type: One of search, tabular_analysis, web_search, + agent_tool_call, generation, content_safety. + content: Short human-readable description of the step. + detail: Optional technical detail (function names, params, etc.). + + Returns: + The thought document id, or None if disabled/failed. + """ + if not self.enabled: + return None + + thought_id = str(uuid.uuid4()) + thought_doc = { + 'id': thought_id, + 'conversation_id': self.conversation_id, + 'message_id': self.message_id, + 'thread_id': self.thread_id, + 'user_id': self.user_id, + 'step_index': self.current_index, + 'step_type': step_type, + 'content': content, + 'detail': detail, + 'duration_ms': None, + 'timestamp': datetime.now(timezone.utc).isoformat() + } + self.current_index += 1 + + try: + cosmos_thoughts_container.upsert_item(thought_doc) + except Exception as e: + log_event(f"ThoughtTracker.add_thought failed: {e}", level="WARNING") + return None + + return thought_id + + def complete_thought(self, thought_id, duration_ms): + """Patch an existing thought with its duration after the step finishes.""" + if not self.enabled or not thought_id: + return + + try: + thought_doc = cosmos_thoughts_container.read_item( + item=thought_id, + partition_key=self.user_id + ) + thought_doc['duration_ms'] = duration_ms + cosmos_thoughts_container.upsert_item(thought_doc) + except Exception as e: + log_event(f"ThoughtTracker.complete_thought failed: {e}", level="WARNING") + + def timed_thought(self, step_type, content, detail=None): + """Convenience: add a thought and return a timer helper. + + Usage: + timer = tracker.timed_thought('search', 'Searching documents...') + # ... do work ... + timer.stop() + """ + start = time.time() + thought_id = self.add_thought(step_type, content, detail) + return _ThoughtTimer(self, thought_id, start) + + +class _ThoughtTimer: + """Helper returned by ThoughtTracker.timed_thought() for auto-duration capture.""" + + def __init__(self, tracker, thought_id, start_time): + self._tracker = tracker + self._thought_id = thought_id + self._start = start_time + + def stop(self): + elapsed_ms = int((time.time() - self._start) * 1000) + self._tracker.complete_thought(self._thought_id, elapsed_ms) + return elapsed_ms + + +# --------------------------------------------------------------------------- +# CRUD helpers +# --------------------------------------------------------------------------- + +def get_thoughts_for_message(conversation_id, message_id, user_id): + """Return all thoughts for a specific assistant message, ordered by step_index.""" + try: + query = ( + "SELECT * FROM c " + "WHERE c.conversation_id = @conv_id " + "AND c.message_id = @msg_id " + "ORDER BY c.step_index ASC" + ) + params = [ + {"name": "@conv_id", "value": conversation_id}, + {"name": "@msg_id", "value": message_id}, + ] + results = list(cosmos_thoughts_container.query_items( + query=query, + parameters=params, + partition_key=user_id + )) + return results + except Exception as e: + log_event(f"get_thoughts_for_message failed: {e}", level="WARNING") + return [] + + +def get_pending_thoughts(conversation_id, user_id): + """Return the latest thoughts for a conversation that are still in-progress. + + Used by the polling endpoint. Retrieves thoughts created within the last + 5 minutes for the conversation, grouped by the most recent message_id. + """ + try: + five_minutes_ago = datetime.now(timezone.utc) + from datetime import timedelta + five_minutes_ago = (five_minutes_ago - timedelta(minutes=5)).isoformat() + + query = ( + "SELECT * FROM c " + "WHERE c.conversation_id = @conv_id " + "AND c.timestamp >= @since " + "ORDER BY c.timestamp DESC" + ) + params = [ + {"name": "@conv_id", "value": conversation_id}, + {"name": "@since", "value": five_minutes_ago}, + ] + results = list(cosmos_thoughts_container.query_items( + query=query, + parameters=params, + partition_key=user_id + )) + + if not results: + return [] + + # Group by the most recent message_id + latest_message_id = results[0].get('message_id') + latest_thoughts = [ + t for t in results if t.get('message_id') == latest_message_id + ] + # Return in ascending step_index order + latest_thoughts.sort(key=lambda t: t.get('step_index', 0)) + return latest_thoughts + except Exception as e: + log_event(f"get_pending_thoughts failed: {e}", level="WARNING") + return [] + + +def get_thoughts_for_conversation(conversation_id, user_id): + """Return all thoughts for a conversation.""" + try: + query = ( + "SELECT * FROM c " + "WHERE c.conversation_id = @conv_id " + "ORDER BY c.timestamp ASC" + ) + params = [ + {"name": "@conv_id", "value": conversation_id}, + ] + results = list(cosmos_thoughts_container.query_items( + query=query, + parameters=params, + partition_key=user_id + )) + return results + except Exception as e: + log_event(f"get_thoughts_for_conversation failed: {e}", level="WARNING") + return [] + + +def archive_thoughts_for_conversation(conversation_id, user_id): + """Copy all thoughts for a conversation to the archive container, then delete originals.""" + try: + thoughts = get_thoughts_for_conversation(conversation_id, user_id) + for thought in thoughts: + archived = dict(thought) + archived['archived_at'] = datetime.now(timezone.utc).isoformat() + cosmos_archived_thoughts_container.upsert_item(archived) + + for thought in thoughts: + cosmos_thoughts_container.delete_item( + item=thought['id'], + partition_key=user_id + ) + except Exception as e: + log_event(f"archive_thoughts_for_conversation failed: {e}", level="WARNING") + + +def delete_thoughts_for_conversation(conversation_id, user_id): + """Delete all thoughts for a conversation.""" + try: + thoughts = get_thoughts_for_conversation(conversation_id, user_id) + for thought in thoughts: + cosmos_thoughts_container.delete_item( + item=thought['id'], + partition_key=user_id + ) + except Exception as e: + log_event(f"delete_thoughts_for_conversation failed: {e}", level="WARNING") + + +def delete_thoughts_for_message(message_id, user_id): + """Delete all thoughts associated with a specific assistant message.""" + try: + query = ( + "SELECT * FROM c " + "WHERE c.message_id = @msg_id" + ) + params = [ + {"name": "@msg_id", "value": message_id}, + ] + results = list(cosmos_thoughts_container.query_items( + query=query, + parameters=params, + partition_key=user_id + )) + for thought in results: + cosmos_thoughts_container.delete_item( + item=thought['id'], + partition_key=user_id + ) + except Exception as e: + log_event(f"delete_thoughts_for_message failed: {e}", level="WARNING") diff --git a/application/single_app/route_backend_chats.py b/application/single_app/route_backend_chats.py index e452fed4..abd7a9f4 100644 --- a/application/single_app/route_backend_chats.py +++ b/application/single_app/route_backend_chats.py @@ -28,6 +28,7 @@ from functions_activity_logging import log_chat_activity, log_conversation_creation, log_token_usage from flask import current_app from swagger_wrapper import swagger_route, get_auth_security +from functions_thoughts import ThoughtTracker def get_kernel(): @@ -668,6 +669,18 @@ def result_requires_message_reload(result: Any) -> bool: conversation_item['last_updated'] = datetime.utcnow().isoformat() cosmos_conversations_container.upsert_item(conversation_item) # Update timestamp and potentially title + + # Generate assistant_message_id early for thought tracking + assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}" + + # Initialize thought tracker + thought_tracker = ThoughtTracker( + conversation_id=conversation_id, + message_id=assistant_message_id, + thread_id=current_user_thread_id, + user_id=user_id + ) + # region 3 - Content Safety # --------------------------------------------------------------------- # 3) Check Content Safety (but DO NOT return 403). @@ -679,6 +692,7 @@ def result_requires_message_reload(result: Any) -> bool: blocklist_matches = [] if settings.get('enable_content_safety') and "content_safety_client" in CLIENTS: + thought_tracker.add_thought('content_safety', 'Checking content safety...') try: content_safety_client = CLIENTS["content_safety_client"] request_obj = AnalyzeTextOptions(text=user_message) @@ -836,6 +850,7 @@ def result_requires_message_reload(result: Any) -> bool: # Perform the search + thought_tracker.add_thought('search', f"Searching {document_scope or 'personal'} workspace documents for '{(search_query or user_message)[:50]}'") try: # Prepare search arguments # Set default and maximum values for top_n @@ -899,6 +914,8 @@ def result_requires_message_reload(result: Any) -> bool: }), 500 if search_results: + unique_doc_names = set(doc.get('file_name', 'Unknown') for doc in search_results) + thought_tracker.add_thought('search', f"Found {len(search_results)} results from {len(unique_doc_names)} documents") retrieved_texts = [] combined_documents = [] classifications_found = set(conversation_item.get('classification', [])) # Load existing @@ -1489,6 +1506,7 @@ def result_requires_message_reload(result: Any) -> bool: }), status_code if web_search_enabled: + thought_tracker.add_thought('web_search', f"Searching the web for '{(search_query or user_message)[:50]}'") perform_web_search( settings=settings, conversation_id=conversation_id, @@ -1504,7 +1522,9 @@ def result_requires_message_reload(result: Any) -> bool: agent_citations_list=agent_citations_list, web_search_citations_list=web_search_citations_list, ) - + if web_search_citations_list: + thought_tracker.add_thought('web_search', f"Got {len(web_search_citations_list)} web search results") + # region 5 - FINAL conversation history preparation # --------------------------------------------------------------------- # 5) Prepare FINAL conversation history for GPT (including summarization) @@ -2110,6 +2130,23 @@ def orchestrator_error(e): }) if selected_agent: + thought_tracker.add_thought('agent_tool_call', f"Sending to agent '{getattr(selected_agent, 'display_name', getattr(selected_agent, 'name', 'unknown'))}'") + + # Register callback to write plugin thoughts to Cosmos in real-time + callback_key = f"{user_id}:{conversation_id}" + plugin_logger = get_plugin_logger() + + def on_plugin_invocation(inv): + duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else "" + tool_name = f"{inv.plugin_name}.{inv.function_name}" + thought_tracker.add_thought( + 'agent_tool_call', + f"Agent called {tool_name}{duration_str}", + detail=f"success={inv.success}" + ) + + plugin_logger.register_callback(callback_key, on_plugin_invocation) + def invoke_selected_agent(): return asyncio.run(run_sk_call( selected_agent.invoke, @@ -2120,16 +2157,18 @@ def agent_success(result): msg = str(result) notice = None agent_used = getattr(selected_agent, 'name', 'All Plugins') - + + # Deregister real-time thought callback + plugin_logger.deregister_callbacks(callback_key) + # Get the actual model deployment used by the agent actual_model_deployment = getattr(selected_agent, 'deployment_name', None) or agent_used debug_print(f"Agent '{agent_used}' using deployment: {actual_model_deployment}") - + # Extract detailed plugin invocations for enhanced agent citations - plugin_logger = get_plugin_logger() - # CRITICAL FIX: Filter by user_id and conversation_id to prevent cross-conversation contamination + # (Thoughts already written to Cosmos in real-time by callback) plugin_invocations = plugin_logger.get_invocations_for_conversation(user_id, conversation_id) - + # Convert plugin invocations to citation format with detailed information detailed_citations = [] for inv in plugin_invocations: @@ -2204,6 +2243,7 @@ def make_json_serializable(obj): ) return (msg, actual_model_deployment, "agent", notice) def agent_error(e): + plugin_logger.deregister_callbacks(callback_key) debug_print(f"Error during Semantic Kernel Agent invocation: {str(e)}") log_event( f"Error during Semantic Kernel Agent invocation: {str(e)}", @@ -2244,8 +2284,17 @@ def foundry_agent_success(result): or agent_used ) + # Deregister real-time thought callback + plugin_logger.deregister_callbacks(callback_key) + foundry_citations = getattr(selected_agent, 'last_run_citations', []) or [] if foundry_citations: + # Emit thoughts for Foundry agent citations/tool calls + for citation in foundry_citations: + thought_tracker.add_thought( + 'agent_tool_call', + f"Agent retrieved citation from Azure AI Foundry" + ) for citation in foundry_citations: try: serializable = json.loads(json.dumps(citation, default=str)) @@ -2282,6 +2331,7 @@ def foundry_agent_success(result): return (msg, actual_model_deployment, 'agent', notice) def foundry_agent_error(e): + plugin_logger.deregister_callbacks(callback_key) log_event( f"Error during Azure AI Foundry agent invocation: {str(e)}", extra={ @@ -2360,6 +2410,7 @@ def kernel_error(e): 'on_error': kernel_error }) + thought_tracker.add_thought('generation', 'Generating response...') def invoke_gpt_fallback(): if not conversation_history_for_api: raise Exception('Cannot generate response: No conversation history available.') @@ -2510,8 +2561,8 @@ def gpt_error(e): if hasattr(selected_agent, 'name'): agent_name = selected_agent.name - assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}" - + # assistant_message_id was generated earlier for thought tracking + # Get user_info and thread_id from the user message for ownership tracking and threading user_info_for_assistant = None user_thread_id = None @@ -2672,7 +2723,8 @@ def gpt_error(e): 'web_search_citations': web_search_citations_list, 'agent_citations': agent_citations_list, 'reload_messages': reload_messages_required, - 'kernel_fallback_notice': kernel_fallback_notice + 'kernel_fallback_notice': kernel_fallback_notice, + 'thoughts_enabled': thought_tracker.enabled }), 200 except Exception as e: @@ -3111,10 +3163,27 @@ def generate(): conversation_item['last_updated'] = datetime.utcnow().isoformat() cosmos_conversations_container.upsert_item(conversation_item) - + + # Generate assistant_message_id early for thought tracking + assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}" + + # Initialize thought tracker for streaming path + thought_tracker = ThoughtTracker( + conversation_id=conversation_id, + message_id=assistant_message_id, + thread_id=current_user_thread_id, + user_id=user_id + ) + + def emit_thought(step_type, content, detail=None): + """Add a thought to Cosmos and return an SSE event string.""" + thought_tracker.add_thought(step_type, content, detail) + return f"data: {json.dumps({'type': 'thought', 'step_index': thought_tracker.current_index - 1, 'step_type': step_type, 'content': content})}\n\n" + # Hybrid search (if enabled) combined_documents = [] if hybrid_search_enabled: + yield emit_thought('search', f"Searching {document_scope or 'personal'} workspace documents for '{(search_query or user_message)[:50]}'") try: search_args = { "query": search_query, @@ -3144,8 +3213,10 @@ def generate(): search_results = hybrid_search(**search_args) except Exception as e: debug_print(f"Error during hybrid search: {e}") - + if search_results: + unique_doc_names_stream = set(doc.get('file_name', 'Unknown') for doc in search_results) + yield emit_thought('search', f"Found {len(search_results)} results from {len(unique_doc_names_stream)} documents") retrieved_texts = [] for doc in search_results: @@ -3324,6 +3395,7 @@ def generate(): hybrid_citations_list.sort(key=lambda x: x.get('page_number', 0), reverse=True) if web_search_enabled: + yield emit_thought('web_search', f"Searching the web for '{(search_query or user_message)[:50]}'") perform_web_search( settings=settings, conversation_id=conversation_id, @@ -3339,6 +3411,8 @@ def generate(): agent_citations_list=agent_citations_list, web_search_citations_list=web_search_citations_list, ) + if web_search_citations_list: + yield emit_thought('web_search', f"Got {len(web_search_citations_list)} web search results") # Update message chat type message_chat_type = None @@ -3472,7 +3546,7 @@ def generate(): # Stream the response accumulated_content = "" token_usage_data = None # Will be populated from final stream chunk - assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}" + # assistant_message_id was generated earlier for thought tracking final_model_used = gpt_model # Default to gpt_model, will be overridden if agent is used # DEBUG: Check agent streaming decision @@ -3482,8 +3556,23 @@ def generate(): try: if use_agent_streaming and selected_agent: # Stream from agent using invoke_stream + yield emit_thought('agent_tool_call', f"Sending to agent '{agent_display_name_used or agent_name_used}'") debug_print(f"--- Streaming from Agent: {agent_name_used} ---") - + + # Register callback to persist plugin thoughts to Cosmos in real-time + callback_key = f"{user_id}:{conversation_id}" + plugin_logger_cb = get_plugin_logger() + + def on_plugin_invocation_streaming(inv): + duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else "" + tool_name = f"{inv.plugin_name}.{inv.function_name}" + thought_tracker.add_thought( + 'agent_tool_call', + f"Agent called {tool_name}{duration_str}" + ) + + plugin_logger_cb.register_callback(callback_key, on_plugin_invocation_streaming) + # Import required classes from semantic_kernel.contents.chat_message_content import ChatMessageContent @@ -3539,36 +3628,49 @@ async def stream_agent_async(): try: # Run streaming and collect chunks and usage chunks, stream_usage = loop.run_until_complete(stream_agent_async()) - - # Yield chunks to frontend - for chunk_content in chunks: - accumulated_content += chunk_content - yield f"data: {json.dumps({'content': chunk_content})}\n\n" - - # Try to capture token usage from stream metadata - if stream_usage: - # stream_usage is a CompletionUsage object, not a dict - prompt_tokens = getattr(stream_usage, 'prompt_tokens', 0) - completion_tokens = getattr(stream_usage, 'completion_tokens', 0) - total_tokens = getattr(stream_usage, 'total_tokens', None) - - # Calculate total if not provided - if total_tokens is None or total_tokens == 0: - total_tokens = prompt_tokens + completion_tokens - - token_usage_data = { - 'prompt_tokens': prompt_tokens, - 'completion_tokens': completion_tokens, - 'total_tokens': total_tokens, - 'captured_at': datetime.utcnow().isoformat() - } - debug_print(f"[Agent Streaming Tokens] From metadata - prompt: {prompt_tokens}, completion: {completion_tokens}, total: {total_tokens}") except Exception as stream_error: + plugin_logger_cb.deregister_callbacks(callback_key) debug_print(f"โŒ Agent streaming error: {stream_error}") import traceback traceback.print_exc() yield f"data: {json.dumps({'error': f'Agent streaming failed: {str(stream_error)}'})}\n\n" return + + # Deregister callback (agent completed successfully) + plugin_logger_cb.deregister_callbacks(callback_key) + + # Emit SSE-only events for streaming UI (Cosmos writes already done by callback) + agent_plugin_invocations = plugin_logger_cb.get_invocations_for_conversation(user_id, conversation_id) + for inv in agent_plugin_invocations: + duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else "" + tool_name = f"{inv.plugin_name}.{inv.function_name}" + content = f"Agent called {tool_name}{duration_str}" + yield f"data: {json.dumps({'type': 'thought', 'step_index': thought_tracker.current_index, 'step_type': 'agent_tool_call', 'content': content})}\n\n" + thought_tracker.current_index += 1 + + # Yield chunks to frontend + for chunk_content in chunks: + accumulated_content += chunk_content + yield f"data: {json.dumps({'content': chunk_content})}\n\n" + + # Try to capture token usage from stream metadata + if stream_usage: + # stream_usage is a CompletionUsage object, not a dict + prompt_tokens = getattr(stream_usage, 'prompt_tokens', 0) + completion_tokens = getattr(stream_usage, 'completion_tokens', 0) + total_tokens = getattr(stream_usage, 'total_tokens', None) + + # Calculate total if not provided + if total_tokens is None or total_tokens == 0: + total_tokens = prompt_tokens + completion_tokens + + token_usage_data = { + 'prompt_tokens': prompt_tokens, + 'completion_tokens': completion_tokens, + 'total_tokens': total_tokens, + 'captured_at': datetime.utcnow().isoformat() + } + debug_print(f"[Agent Streaming Tokens] From metadata - prompt: {prompt_tokens}, completion: {completion_tokens}, total: {total_tokens}") # Collect token usage from kernel services if not captured from stream if not token_usage_data: @@ -3650,6 +3752,7 @@ def make_json_serializable(obj): else: # Stream from regular GPT model (non-agent) + yield emit_thought('generation', 'Generating response...') debug_print(f"--- Streaming from GPT ({gpt_model}) ---") # Prepare stream parameters @@ -3818,7 +3921,8 @@ def make_json_serializable(obj): 'agent_citations': agent_citations_list, 'agent_display_name': agent_display_name_used if use_agent_streaming else None, 'agent_name': agent_name_used if use_agent_streaming else None, - 'full_content': accumulated_content + 'full_content': accumulated_content, + 'thoughts_enabled': thought_tracker.enabled } yield f"data: {json.dumps(final_data)}\n\n" diff --git a/application/single_app/route_backend_conversations.py b/application/single_app/route_backend_conversations.py index f267d729..ac25bd8c 100644 --- a/application/single_app/route_backend_conversations.py +++ b/application/single_app/route_backend_conversations.py @@ -8,6 +8,7 @@ from functions_debug import debug_print from swagger_wrapper import swagger_route, get_auth_security from functions_activity_logging import log_conversation_creation, log_conversation_deletion, log_conversation_archival +from functions_thoughts import archive_thoughts_for_conversation, delete_thoughts_for_conversation def register_route_backend_conversations(app): @@ -430,7 +431,14 @@ def delete_conversation(conversation_id): cosmos_archived_messages_container.upsert_item(archived_doc) cosmos_messages_container.delete_item(doc['id'], partition_key=conversation_id) - + + # Archive/delete thoughts for conversation + user_id_for_thoughts = conversation_item.get('user_id') + if archiving_enabled: + archive_thoughts_for_conversation(conversation_id, user_id_for_thoughts) + else: + delete_thoughts_for_conversation(conversation_id, user_id_for_thoughts) + # Log conversation deletion before actual deletion log_conversation_deletion( user_id=conversation_item.get('user_id'), @@ -530,7 +538,13 @@ def delete_multiple_conversations(): cosmos_archived_messages_container.upsert_item(archived_message) cosmos_messages_container.delete_item(message['id'], partition_key=conversation_id) - + + # Archive/delete thoughts for conversation + if archiving_enabled: + archive_thoughts_for_conversation(conversation_id, user_id) + else: + delete_thoughts_for_conversation(conversation_id, user_id) + # Log conversation deletion before actual deletion log_conversation_deletion( user_id=user_id, diff --git a/application/single_app/route_backend_thoughts.py b/application/single_app/route_backend_thoughts.py new file mode 100644 index 00000000..a7624a3f --- /dev/null +++ b/application/single_app/route_backend_thoughts.py @@ -0,0 +1,80 @@ +# route_backend_thoughts.py + +from flask import request, jsonify +from functions_authentication import login_required, user_required, get_current_user_id +from functions_settings import get_settings +from functions_thoughts import get_thoughts_for_message, get_pending_thoughts +from swagger_wrapper import swagger_route, get_auth_security +from functions_appinsights import log_event + + +def register_route_backend_thoughts(app): + + @app.route('/api/conversations//messages//thoughts', methods=['GET']) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + def api_get_message_thoughts(conversation_id, message_id): + """Return persisted thoughts for a specific assistant message.""" + user_id = get_current_user_id() + if not user_id: + return jsonify({'error': 'User not authenticated'}), 401 + + settings = get_settings() + if not settings.get('enable_thoughts', False): + return jsonify({'thoughts': [], 'enabled': False}), 200 + + try: + thoughts = get_thoughts_for_message(conversation_id, message_id, user_id) + # Strip internal Cosmos fields before returning + sanitized = [] + for t in thoughts: + sanitized.append({ + 'id': t.get('id'), + 'step_index': t.get('step_index'), + 'step_type': t.get('step_type'), + 'content': t.get('content'), + 'detail': t.get('detail'), + 'duration_ms': t.get('duration_ms'), + 'timestamp': t.get('timestamp') + }) + return jsonify({'thoughts': sanitized, 'enabled': True}), 200 + except Exception as e: + log_event(f"api_get_message_thoughts error: {e}", level="WARNING") + return jsonify({'error': 'Failed to retrieve thoughts'}), 500 + + @app.route('/api/conversations//thoughts/pending', methods=['GET']) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + def api_get_pending_thoughts(conversation_id): + """Return the latest in-progress thoughts for a conversation. + + Used by the non-streaming frontend to poll for thought updates + while waiting for the chat response. + """ + user_id = get_current_user_id() + if not user_id: + return jsonify({'error': 'User not authenticated'}), 401 + + settings = get_settings() + if not settings.get('enable_thoughts', False): + return jsonify({'thoughts': [], 'enabled': False}), 200 + + try: + thoughts = get_pending_thoughts(conversation_id, user_id) + sanitized = [] + for t in thoughts: + sanitized.append({ + 'id': t.get('id'), + 'step_index': t.get('step_index'), + 'step_type': t.get('step_type'), + 'content': t.get('content'), + 'detail': t.get('detail'), + 'duration_ms': t.get('duration_ms'), + 'timestamp': t.get('timestamp') + }) + return jsonify({'thoughts': sanitized, 'enabled': True}), 200 + except Exception as e: + log_event(f"api_get_pending_thoughts error: {e}", level="WARNING") + return jsonify({'error': 'Failed to retrieve pending thoughts'}), 500 diff --git a/application/single_app/route_frontend_admin_settings.py b/application/single_app/route_frontend_admin_settings.py index 578e1545..8c49522b 100644 --- a/application/single_app/route_frontend_admin_settings.py +++ b/application/single_app/route_frontend_admin_settings.py @@ -809,9 +809,10 @@ def is_valid_url(url): 'require_member_of_safety_violation_admin': require_member_of_safety_violation_admin, # ADDED 'require_member_of_feedback_admin': require_member_of_feedback_admin, # ADDED - # Feedback & Archiving + # Feedback, Archiving & Thoughts 'enable_user_feedback': form_data.get('enable_user_feedback') == 'on', 'enable_conversation_archiving': form_data.get('enable_conversation_archiving') == 'on', + 'enable_thoughts': form_data.get('enable_thoughts') == 'on', # Search (Web Search via Azure AI Foundry agent) 'enable_web_search': enable_web_search, diff --git a/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py b/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py index f982f0a4..bddf9cda 100644 --- a/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py +++ b/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py @@ -11,6 +11,7 @@ import logging import functools import inspect +import threading from typing import Any, Dict, List, Optional, Callable from datetime import datetime from dataclasses import dataclass, asdict @@ -51,24 +52,29 @@ def __init__(self): self.invocations: List[PluginInvocation] = [] self.max_history = 1000 # Keep last 1000 invocations in memory self.logger = get_appinsights_logger() or logging.getLogger(__name__) + self._callbacks: Dict[str, List[Callable[[PluginInvocation], None]]] = {} + self._callback_lock = threading.Lock() def log_invocation(self, invocation: PluginInvocation): """Log a plugin invocation to Application Insights and local history.""" # Add to local history self.invocations.append(invocation) - + # Trim history if needed if len(self.invocations) > self.max_history: self.invocations = self.invocations[-self.max_history:] - + # Enhanced terminal logging self._log_to_terminal(invocation) - + # Log to Application Insights self._log_to_appinsights(invocation) - + # Log to standard logging self._log_to_standard(invocation) + + # Fire registered thought callbacks + self._fire_callbacks(invocation) def _log_to_terminal(self, invocation: PluginInvocation): """Log detailed invocation information to terminal.""" @@ -277,6 +283,34 @@ def clear_history(self): """Clear the invocation history.""" self.invocations.clear() + def register_callback(self, key, callback): + """Register a callback fired on each plugin invocation for the given key. + + Args: + key: A string key, typically f"{user_id}:{conversation_id}". + callback: Called with the PluginInvocation after it is logged. + """ + with self._callback_lock: + if key not in self._callbacks: + self._callbacks[key] = [] + self._callbacks[key].append(callback) + + def deregister_callbacks(self, key): + """Remove all callbacks for the given key.""" + with self._callback_lock: + self._callbacks.pop(key, None) + + def _fire_callbacks(self, invocation): + """Fire matching callbacks for this invocation's user+conversation.""" + key = f"{invocation.user_id}:{invocation.conversation_id}" + with self._callback_lock: + callbacks = list(self._callbacks.get(key, [])) + for cb in callbacks: + try: + cb(invocation) + except Exception as e: + log_event(f"Plugin invocation callback error: {e}", level="WARNING") + # Global instance _plugin_logger = PluginInvocationLogger() diff --git a/application/single_app/static/css/chats.css b/application/single_app/static/css/chats.css index 38e11c3a..6a64bbc1 100644 --- a/application/single_app/static/css/chats.css +++ b/application/single_app/static/css/chats.css @@ -1676,4 +1676,160 @@ mark.search-highlight { 100% { transform: scale(1.05); } +} + +/* ============================================= + Processing Thoughts + ============================================= */ + +/* Loading indicator thought text */ +.thought-live-text { + font-style: italic; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + max-width: 300px; +} + +/* Toggle button in message footer */ +.thoughts-toggle-btn { + font-size: 0.9rem; + color: #6c757d; + padding: 0 0.25rem; + border: none; + background: none; + cursor: pointer; + transition: color 0.15s ease-in-out; +} + +.thoughts-toggle-btn:hover { + color: #ffc107; +} + +/* Collapsible container inside message bubble */ +.thoughts-container { + max-height: 300px; + overflow-y: auto; + font-size: 0.85rem; +} + +/* Timeline wrapper */ +.thoughts-list { + position: relative; + padding-left: 1.25rem; +} + +/* Vertical timeline line */ +.thoughts-list::before { + content: ''; + position: absolute; + left: 0.5rem; + top: 0.25rem; + bottom: 0.25rem; + width: 2px; + background: linear-gradient(to bottom, #0d6efd, #6ea8fe); + border-radius: 1px; +} + +/* Individual thought step */ +.thought-step { + display: flex; + align-items: flex-start; + padding-left: 0.75rem; + padding-top: 0.25rem; + padding-bottom: 0.25rem; + position: relative; +} + +/* Timeline node dot */ +.thought-step::before { + content: ''; + position: absolute; + left: -1rem; + top: 0.55rem; + width: 8px; + height: 8px; + border-radius: 50%; + background-color: #0d6efd; + border: 2px solid #fff; + box-shadow: 0 0 0 1px #0d6efd; + z-index: 1; +} + +/* Last thought step gets a slightly different dot */ +.thought-step:last-child::before { + background-color: #198754; + box-shadow: 0 0 0 1px #198754; +} + +.thought-step i { + flex-shrink: 0; + margin-top: 2px; +} + +/* Streaming cursor thought badge pulse animation */ +.animate-pulse { + animation: thought-pulse 1.5s ease-in-out infinite; +} + +/* Streaming thought display (before content arrives) */ +.streaming-thought-display { + display: flex; + align-items: center; + padding: 0.5rem 0; +} + +/* Light mode: use darker, more readable colors */ +.streaming-thought-display .badge { + background-color: rgba(13, 110, 253, 0.08) !important; + color: #0a58ca !important; + border-color: rgba(13, 110, 253, 0.25) !important; +} + +/* Dark mode: lighter accent colors */ +[data-bs-theme="dark"] .streaming-thought-display .badge { + background-color: rgba(13, 202, 240, 0.15) !important; + color: #6edff6 !important; + border-color: rgba(13, 202, 240, 0.3) !important; +} + +@keyframes thought-pulse { + 0%, 100% { + opacity: 1; + } + 50% { + opacity: 0.6; + } +} + +/* Dark mode overrides */ +[data-bs-theme="dark"] .thoughts-toggle-btn { + color: #adb5bd; +} + +[data-bs-theme="dark"] .thoughts-toggle-btn:hover { + color: #ffc107; +} + +[data-bs-theme="dark"] .thought-step { + /* Dark mode dot border matches dark background */ +} + +[data-bs-theme="dark"] .thought-step::before { + border-color: #212529; + background-color: 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zJBJ<{*ItlT2oZ4=+-%->@w+|@w(F}R;q`iiTW1C$gw`9WR>Q!m;-iunmY7{ zMsQu}*Bcut)qCX1_?K;$h&wvic^pq8o^D$CsAw1;{OF4Tf*^(i$#CjuL+zS{=HV^3 zG!9ShzWu!eK(wKIkL7Ys-pD_bMo-5O-M}oJ36H8CP6ZJDk6D^s+XQq%lxHh%!oesUyJeb#5 zW`q#Nm8HcT27Skk!3JDZyr)Z+2rAP;f0~sl3rURyk%o%!JgnxJSz`f#rD|13165_I z9?K=F>S;_=WJt{pAMkewrxi}SUjua%My?%~~iP52o96I@STwMhe$KKrI>hSVEyTfMCf>Ir?ox8xz zrYbRre3(hidDEVOKBe5kf={T#A0zG|!)?cJQlt?*2D?Aq#P8D-x*h(0&D=lF;#~msKI&%!X?&sokF)+C*!o{#nhEPg aj13{1xX1-4^7hveoPvysbhV^u=>Gr|kZNTB literal 0 HcmV?d00001 diff --git a/application/single_app/static/js/admin/admin_settings.js b/application/single_app/static/js/admin/admin_settings.js index 85719128..87478db0 100644 --- a/application/single_app/static/js/admin/admin_settings.js +++ b/application/single_app/static/js/admin/admin_settings.js @@ -3827,11 +3827,12 @@ function checkOptionalFeaturesEnabled(stepNumber) { return endpoint && key; } - case 11: // User feedback and archiving - // Check if feedback is enabled + case 11: // User feedback, archiving, and thoughts + // Check if feedback, archiving, or thoughts is enabled const feedbackEnabled = document.getElementById('enable_user_feedback')?.checked; const archivingEnabled = document.getElementById('enable_conversation_archiving')?.checked; - return feedbackEnabled || archivingEnabled; + const thoughtsEnabled = document.getElementById('enable_thoughts')?.checked; + return feedbackEnabled || archivingEnabled || thoughtsEnabled; case 12: // Enhanced citations and image generation // Check if enhanced citations or image generation is enabled diff --git a/application/single_app/static/js/chat/chat-loading-indicator.js b/application/single_app/static/js/chat/chat-loading-indicator.js index c1ab20c8..e3a77ff5 100644 --- a/application/single_app/static/js/chat/chat-loading-indicator.js +++ b/application/single_app/static/js/chat/chat-loading-indicator.js @@ -39,12 +39,35 @@ export function showLoadingIndicatorInChatbox() { - AI is typing... + AI is typing... `; chatbox.appendChild(loadingIndicator); chatbox.scrollTop = chatbox.scrollHeight; } +/** + * Update the loading indicator text with a thought step. + * Called by the thought polling handler to replace "AI is typing..." dynamically. + * @param {string} text - The thought content to display. + * @param {string} iconClass - Bootstrap Icon class (e.g. 'bi-search'). + */ +export function updateLoadingIndicatorText(text, iconClass) { + const textEl = document.getElementById("loading-indicator-text"); + if (!textEl) return; + + if (iconClass) { + textEl.innerHTML = `${text}`; + } else { + textEl.textContent = text; + } + + // Scroll chatbox to keep indicator visible + const chatbox = document.getElementById("chatbox"); + if (chatbox) { + chatbox.scrollTop = chatbox.scrollHeight; + } +} + export function hideLoadingIndicatorInChatbox() { const loadingIndicator = document.getElementById("loading-indicator"); if (loadingIndicator) { diff --git a/application/single_app/static/js/chat/chat-messages.js b/application/single_app/static/js/chat/chat-messages.js index d4c54790..81469dc9 100644 --- a/application/single_app/static/js/chat/chat-messages.js +++ b/application/single_app/static/js/chat/chat-messages.js @@ -20,6 +20,7 @@ import { saveUserSetting } from "./chat-layout.js"; import { isStreamingEnabled, sendMessageWithStreaming } from "./chat-streaming.js"; import { getCurrentReasoningEffort, isReasoningEffortEnabled } from './chat-reasoning.js'; import { areAgentsEnabled } from './chat-agents.js'; +import { createThoughtsToggleHtml, attachThoughtsToggleListener, startThoughtPolling, stopThoughtPolling } from './chat-thoughts.js'; // Conditionally import TTS if enabled let ttsModule = null; @@ -743,11 +744,13 @@ export function appendMessage( const metadataContainerId = `metadata-${messageId || Date.now()}`; const metadataContainerHtml = ``; - + + const thoughtsHtml = createThoughtsToggleHtml(messageId); + const footerContentHtml = `
${copyAndFeedbackHtml}
-
${citationToggleHtml}
`; @@ -760,6 +763,7 @@ export function appendMessage(
${senderLabel}
${mainMessageHtml} ${citationContentContainerHtml} + ${thoughtsHtml.containerHtml} ${metadataContainerHtml} ${footerContentHtml} @@ -816,6 +820,9 @@ export function appendMessage( } }); } + + // Attach thoughts toggle listener + attachThoughtsToggleListener(messageDiv, messageId, currentConversationId); const maskBtn = messageDiv.querySelector(".mask-btn"); if (maskBtn) { @@ -1516,6 +1523,7 @@ export function actuallySendMessage(finalMessageToSend) { } // Regular non-streaming fetch + startThoughtPolling(currentConversationId); fetch("/api/chat", { method: "POST", headers: { @@ -1547,6 +1555,7 @@ export function actuallySendMessage(finalMessageToSend) { }) .then((data) => { // Only successful responses reach here + stopThoughtPolling(); hideLoadingIndicatorInChatbox(); console.log("--- Data received from /api/chat ---"); @@ -1688,6 +1697,7 @@ export function actuallySendMessage(finalMessageToSend) { } }) .catch((error) => { + stopThoughtPolling(); hideLoadingIndicatorInChatbox(); console.error("Error sending message:", error); diff --git a/application/single_app/static/js/chat/chat-streaming.js b/application/single_app/static/js/chat/chat-streaming.js index faf6f59e..d2b5b218 100644 --- a/application/single_app/static/js/chat/chat-streaming.js +++ b/application/single_app/static/js/chat/chat-streaming.js @@ -5,6 +5,7 @@ import { loadUserSettings, saveUserSetting } from './chat-layout.js'; import { showToast } from './chat-toast.js'; import { updateSidebarConversationTitle } from './chat-sidebar-conversations.js'; import { applyScopeLock } from './chat-documents.js'; +import { handleStreamingThought } from './chat-thoughts.js'; let streamingEnabled = false; let currentEventSource = null; @@ -207,8 +208,11 @@ export function sendMessageWithStreaming(messageData, tempUserMessageId, current handleStreamError(tempAiMessageId, data.partial_content || accumulatedContent, data.error); return; } - - if (data.content) { + + if (data.type === 'thought') { + handleStreamingThought(data); + // Continue reading โ€” don't fall through to content handling + } else if (data.content) { // Append chunk to accumulated content accumulatedContent += data.content; updateStreamingMessage(tempAiMessageId, accumulatedContent); diff --git a/application/single_app/static/js/chat/chat-thoughts.js b/application/single_app/static/js/chat/chat-thoughts.js new file mode 100644 index 00000000..a780bd3f --- /dev/null +++ b/application/single_app/static/js/chat/chat-thoughts.js @@ -0,0 +1,215 @@ +// chat-thoughts.js + +import { updateLoadingIndicatorText } from './chat-loading-indicator.js'; +import { escapeHtml } from './chat-utils.js'; + +let thoughtPollingInterval = null; +let lastSeenThoughtIndex = -1; + +// --------------------------------------------------------------------------- +// Icon map: step_type โ†’ Bootstrap Icon class +// --------------------------------------------------------------------------- +function getThoughtIcon(stepType) { + const iconMap = { + 'search': 'bi-search', + 'tabular_analysis': 'bi-table', + 'web_search': 'bi-globe', + 'agent_tool_call': 'bi-robot', + 'generation': 'bi-lightning', + 'content_safety': 'bi-shield-check' + }; + return iconMap[stepType] || 'bi-stars'; +} + +// --------------------------------------------------------------------------- +// Polling (non-streaming mode) +// --------------------------------------------------------------------------- + +/** + * Start polling for pending thoughts while waiting for a non-streaming response. + * @param {string} conversationId - The current conversation ID. + */ +export function startThoughtPolling(conversationId) { + if (!conversationId) return; + if (!window.appSettings?.enable_thoughts) return; + + stopThoughtPolling(); // clear any previous interval + lastSeenThoughtIndex = -1; + + thoughtPollingInterval = setInterval(() => { + fetch(`/api/conversations/${conversationId}/thoughts/pending`, { + credentials: 'same-origin' + }) + .then(r => r.json()) + .then(data => { + if (data.thoughts && data.thoughts.length > 0) { + const latest = data.thoughts[data.thoughts.length - 1]; + if (latest.step_index > lastSeenThoughtIndex) { + lastSeenThoughtIndex = latest.step_index; + const icon = getThoughtIcon(latest.step_type); + updateLoadingIndicatorText(latest.content, icon); + } + } + }) + .catch(() => { /* ignore polling errors */ }); + }, 2000); +} + +/** + * Stop the thought polling interval. + */ +export function stopThoughtPolling() { + if (thoughtPollingInterval) { + clearInterval(thoughtPollingInterval); + thoughtPollingInterval = null; + } + lastSeenThoughtIndex = -1; +} + +// --------------------------------------------------------------------------- +// Streaming handler +// --------------------------------------------------------------------------- + +/** + * Handle a streaming thought event received via SSE. + * Updates the streaming message placeholder with a styled thought indicator. + * When actual content starts streaming, updateStreamingMessage() will overwrite this. + * @param {object} thoughtData - { step_index, step_type, content } + */ +export function handleStreamingThought(thoughtData) { + // Find the streaming message's content area + const messageElement = document.querySelector('[data-message-id^="temp_ai_"]'); + if (!messageElement) return; + + const contentElement = messageElement.querySelector('.message-text'); + if (!contentElement) return; + + const icon = getThoughtIcon(thoughtData.step_type); + // Replace entire content with styled thought indicator (visually distinct from AI response) + contentElement.innerHTML = `
+ + ${escapeHtml(thoughtData.content)} + +
`; +} + +// --------------------------------------------------------------------------- +// Per-message collapsible: toggle button + container HTML +// --------------------------------------------------------------------------- + +/** + * Create HTML for the thoughts toggle button and hidden container. + * Returns an object with { toggleHtml, containerHtml }. + * @param {string} messageId + */ +export function createThoughtsToggleHtml(messageId) { + if (!window.appSettings?.enable_thoughts) { + return { toggleHtml: '', containerHtml: '' }; + } + + const containerId = `thoughts-${messageId || Date.now()}`; + const toggleHtml = ``; + const containerHtml = `
Loading thoughts...
`; + + return { toggleHtml, containerHtml }; +} + +/** + * Attach event listener for the thoughts toggle button inside a message div. + * @param {HTMLElement} messageDiv + * @param {string} messageId + * @param {string} conversationId + */ +export function attachThoughtsToggleListener(messageDiv, messageId, conversationId) { + const toggleBtn = messageDiv.querySelector('.thoughts-toggle-btn'); + if (!toggleBtn) return; + + toggleBtn.addEventListener('click', () => { + const targetId = toggleBtn.getAttribute('aria-controls'); + const container = messageDiv.querySelector(`#${targetId}`); + if (!container) return; + + // Store scroll position + const scrollContainer = document.getElementById('chat-messages-container'); + const currentScroll = scrollContainer?.scrollTop || window.pageYOffset; + + const isExpanded = !container.classList.contains('d-none'); + if (isExpanded) { + container.classList.add('d-none'); + toggleBtn.setAttribute('aria-expanded', 'false'); + toggleBtn.title = 'Show processing thoughts'; + toggleBtn.innerHTML = ''; + } else { + container.classList.remove('d-none'); + toggleBtn.setAttribute('aria-expanded', 'true'); + toggleBtn.title = 'Hide processing thoughts'; + toggleBtn.innerHTML = ''; + + // Lazy-load thoughts on first expand + if (container.innerHTML.includes('Loading thoughts')) { + loadThoughtsForMessage(conversationId, messageId, container); + } + } + + // Restore scroll position + setTimeout(() => { + if (scrollContainer) { + scrollContainer.scrollTop = currentScroll; + } else { + window.scrollTo(0, currentScroll); + } + }, 10); + }); +} + +// --------------------------------------------------------------------------- +// Fetch + render thoughts for a message +// --------------------------------------------------------------------------- + +/** + * Fetch thoughts for a specific message from the API and render them. + * @param {string} conversationId + * @param {string} messageId + * @param {HTMLElement} container + */ +function loadThoughtsForMessage(conversationId, messageId, container) { + fetch(`/api/conversations/${conversationId}/messages/${messageId}/thoughts`, { + credentials: 'same-origin' + }) + .then(r => r.json()) + .then(data => { + if (!data.enabled) { + container.innerHTML = '
Processing thoughts are disabled.
'; + return; + } + if (!data.thoughts || data.thoughts.length === 0) { + container.innerHTML = '
No processing thoughts recorded for this message.
'; + return; + } + container.innerHTML = renderThoughtsList(data.thoughts); + }) + .catch(err => { + console.error('Error loading thoughts:', err); + container.innerHTML = '
Failed to load processing thoughts.
'; + }); +} + +/** + * Render a list of thought steps as HTML. + * @param {Array} thoughts + * @returns {string} HTML string + */ +function renderThoughtsList(thoughts) { + let html = '
'; + thoughts.forEach(t => { + const icon = getThoughtIcon(t.step_type); + const durationStr = t.duration_ms != null ? `(${t.duration_ms}ms)` : ''; + html += `
+ + ${escapeHtml(t.content || '')} + ${durationStr} +
`; + }); + html += '
'; + return html; +} diff --git a/application/single_app/templates/_sidebar_nav.html b/application/single_app/templates/_sidebar_nav.html index a0bceee8..33a89b04 100644 --- a/application/single_app/templates/_sidebar_nav.html +++ b/application/single_app/templates/_sidebar_nav.html @@ -287,6 +287,11 @@ GPT Configuration +
  • + + Processing Thoughts + +
  • Embeddings diff --git a/application/single_app/templates/admin_settings.html b/application/single_app/templates/admin_settings.html index 7d01f7da..54b77285 100644 --- a/application/single_app/templates/admin_settings.html +++ b/application/single_app/templates/admin_settings.html @@ -1580,6 +1580,27 @@
    + +
    +
    + Processing Thoughts +
    +

    When enabled, real-time processing steps are shown to users during chat responses and persisted for later review.

    +
    + + + +
    +
    +
    @@ -3217,9 +3238,10 @@
    + - +

    diff --git a/application/single_app/templates/chats.html b/application/single_app/templates/chats.html index b6c212cc..e845cc53 100644 --- a/application/single_app/templates/chats.html +++ b/application/single_app/templates/chats.html @@ -1039,7 +1039,8 @@

    + + +
    +
    + +
    + + +
    +
    +
    + +
    +
    Advanced
    +

    Advanced settings are typically not required. Expand below if you need to customize metadata or additional fields.

    - - -
    Optional metadata for this action.
    +
    -
    - - -
    Additional configuration fields specific to this action type.
    +
    +
    + + +
    Optional metadata for this action.
    +
    +
    + + +
    Additional configuration fields specific to this action type.
    +
    @@ -777,6 +802,15 @@
    background-color: #f8f9fa; } +/* Advanced toggle chevron animation */ +#plugin-advanced-toggle-icon { + transition: transform 0.3s ease; +} +#plugin-advanced-collapse.show ~ .mb-3 #plugin-advanced-toggle-icon, +[aria-expanded="true"] #plugin-advanced-toggle-icon { + transform: rotate(180deg); +} + .sql-connection-config, .sql-auth-config { background-color: white; diff --git a/application/single_app/templates/group_workspaces.html b/application/single_app/templates/group_workspaces.html index e14fe8a3..62937fc3 100644 --- a/application/single_app/templates/group_workspaces.html +++ b/application/single_app/templates/group_workspaces.html @@ -763,33 +763,42 @@

    Group Workspace

    You do not have permission to manage group agents.
    -
    +
    +
    + + + + +
    - - - - - - - - - - - - - -
    Display NameDescriptionActions
    - - Select a group to load agents. -
    +
    + + + + + + + + + + + + + +
    Display NameDescriptionActions
    + + Select a group to load agents. +
    +
    +
    @@ -813,33 +822,42 @@

    Group Workspace

    -
    +
    +
    + + + + +
    - - - - - - - - - - - - - -
    Display NameDescriptionActions
    - - Select a group to load actions. -
    +
    + + + + + + + + + + + + + +
    Display NameDescriptionActions
    + + Select a group to load actions. +
    +
    +
    @@ -851,6 +869,22 @@

    Group Workspace

    + + +
    Personal Workspace
    +
    + + + + +
    - - - - - - - -
    Display NameDescriptionActions
    - - Loading agents... -
    + +
    + + + + + + + +
    Display NameDescriptionActions
    + + Loading agents... +
    +
    + +
    @@ -730,16 +741,27 @@

    Personal Workspace

    +
    + + + + +
    - - - - - -
    Display NameDescriptionActions
    + +
    + + + + + +
    Display NameDescriptionActions
    +
    + +
    @@ -754,6 +776,24 @@

    Personal Workspace

    + + +
    /thoughts/pending` every 2 seconds to show current processing step in the loading indicator. - * **Streaming Mode**: Thought events embedded in SSE stream as `{"type": "thought", ...}` payloads, displayed as animated badges on the streaming placeholder. - * **Per-Message History**: Each assistant message footer includes a lightbulb toggle button. Clicking it opens a collapsible section showing all recorded processing steps with icons and durations. - * **Lazy Loading**: Thoughts are fetched from the API on first toggle click, not preloaded on page render. - * **Cosmos DB Persistence**: Thoughts stored in dedicated `thoughts` container (partition key: `/user_id`), with `archive_thoughts` container for archived conversations. - * **Admin Toggle**: `enable_thoughts` setting in Admin Settings > Optional Features > Processing Thoughts section. Disabled by default. - * **Archive Support**: Thoughts are archived or deleted alongside conversations during single and bulk delete operations. - * **8 Instrumentation Points**: content_safety, search (2), tabular_analysis, web_search (2), agent_tool_call, generation. - * **Files Created**: `functions_thoughts.py`, `route_backend_thoughts.py`, `static/js/chat/chat-thoughts.js`. - * **Files Modified**: `config.py`, `functions_settings.py`, `app.py`, `route_backend_chats.py`, `route_backend_conversations.py`, `admin_settings.html`, `admin_settings.js`, `chat-messages.js`, `chat-streaming.js`, `chat-loading-indicator.js`, `chats.css`. - * (Ref: `PROCESSING_THOUGHTS.md`) +* **Agent & Action User Tracking (created_by / modified_by)** + * All agent and action documents (personal, group, and global) now include `created_by`, `created_at`, `modified_by`, and `modified_at` fields that track which user created or last modified the entity. + * On updates, the original `created_by` and `created_at` values are preserved while `modified_by` and `modified_at` are refreshed with the current user and timestamp. + * New optional `user_id` parameter added to `save_group_agent`, `save_global_agent`, `save_group_action`, and `save_global_action` for caller-supplied user tracking (backward-compatible, defaults to `None`). + * (Ref: `functions_personal_agents.py`, `functions_group_agents.py`, `functions_global_agents.py`, `functions_personal_actions.py`, `functions_group_actions.py`, `functions_global_actions.py`) + +* **Activity Logging for Agent & Action CRUD Operations** + * Every create, update, and delete operation on agents and actions now generates an activity log record in the `activity_logs` Cosmos DB container and Application Insights. + * Six new logging functions: `log_agent_creation`, `log_agent_update`, `log_agent_deletion`, `log_action_creation`, `log_action_update`, `log_action_deletion`. + * Activity records include: `user_id`, `activity_type`, `entity_type` (agent/action), `operation` (create/update/delete), `workspace_type` (personal/group/global), and `workspace_context` (group_id when applicable). + * Logging is fire-and-forget โ€” failures never break the CRUD operation. + * All personal, group, and admin routes for both agents and actions are wired up. + * (Ref: `functions_activity_logging.py`, `route_backend_agents.py`, `route_backend_plugins.py`) + +### **(v0.239.008)** + +#### User Interface Enhancements + +* **List/Grid View Toggle for Agents and Actions** + * Added a list/grid view toggle to all four workspace areas: personal agents, personal actions, group agents, and group actions. + * **Grid View**: Large cards with type icon, humanized name, truncated description, and action buttons (Chat, View, Edit, Delete as applicable). + * **List View**: Improved table layout with fixed column widths (28%/47%/25%), humanized display names, and truncated descriptions with hover tooltips for full text. + * **View Button**: New eye-icon button on every agent and action that opens a read-only detail modal with gradient-header summary cards (Basic Information, Model Configuration, Instructions for agents; Basic Information, Configuration for actions). + * **Name Humanization**: Display names are now automatically parsed โ€” underscores and camelCase/PascalCase boundaries are converted to properly spaced, title-cased words (e.g., `myCustomAgent` โ†’ `My Custom Agent`). + * **Persistent Preference**: View mode selection (list/grid) is saved per area in localStorage and restored on page load. + * New shared utility module `view-utils.js` provides reusable functions for all four workspace areas. + * (Ref: `view-utils.js`, `workspace_agents.js`, `workspace_plugins.js`, `plugin_common.js`, `group_agents.js`, `group_plugins.js`, `workspace.html`, `group_workspaces.html`, `styles.css`) + +### **(v0.239.007)** + +#### New Features + +* **Chat with Agent Button for Group Agents** + * Added a "Chat" button to each group agent row, allowing users to quickly select a group agent and navigate to the chat page. + * (Ref: `group_agents.js`, `group_workspaces.html`) + +### **(v0.239.006)** + +#### User Interface Enhancements + +* **Hidden Deprecated Action Types** + * Deprecated action types (`sql_schema`, `ui_test`, `queue_storage`, `blob_storage`, `embedding_model`) are now hidden from the action creation wizard type selector. Existing actions of these types remain functional. + * (Ref: `plugin_modal_stepper.js`) + +### **(v0.239.005)** + +#### Bug Fixes + +* **Group SQL Action/Plugin Save Failure** + * Fixed group SQL actions (sql_query and sql_schema types) failing to save correctly due to missing endpoint placeholder. Group routes now apply the same `sql://sql_query` / `sql://sql_schema` endpoint logic as personal action routes. + * Fixed Step 4 (Advanced) dynamic fields overwriting Step 3 (Configuration) SQL values with empty strings during form data collection. SQL types now skip the dynamic field merge entirely since Step 3 already provides all necessary configuration. + * Fixed auth type definition schemas (`sql_query.definition.json`, `sql_schema.definition.json`) only allowing `connection_string` auth type, blocking `user`, `identity`, and `servicePrincipal` types that the UI and runtime support. + * Fixed `__Secret` key suffix mismatch in additional settings schemas where `connection_string__Secret` and `password__Secret` didn't match the runtime's expected `connection_string` and `password` field names. Also removed duplicate `azuresql` enum value. + * (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `sql_query.definition.json`, `sql_schema.definition.json`, `sql_query_plugin.additional_settings.schema.json`, `sql_schema_plugin.additional_settings.schema.json`) + +#### New Features + +* **SQL Test Connection Button** + * Added a "Test Connection" button to the SQL Database Configuration section (Step 3) of the action wizard, allowing users to validate database connectivity before saving. + * Supports all database types: SQL Server, Azure SQL (with managed identity), PostgreSQL, MySQL, and SQLite. + * Shows inline success/failure alerts with a 15-second timeout cap and sanitized error messages. + * New backend endpoint: `POST /api/plugins/test-sql-connection`. + * (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `_plugin_modal.html`) + +#### User Interface Enhancements + +* **Advanced Settings Collapse Toggle** + * Step 4 (Advanced) content is now hidden behind a collapsible toggle button ("Show Advanced Settings") instead of being displayed by default. Reduces visual noise for most users. + * For SQL action types, the redundant additional fields UI in Step 4 is hidden entirely since all SQL configuration is already handled in Step 3. + * Step 5 (Summary) no longer shows the raw additional fields JSON dump for SQL types, since that data is already shown in the SQL Database Configuration summary card. + * (Ref: `_plugin_modal.html`, `plugin_modal_stepper.js`) ### **(v0.239.001)** From b8a91b0134b6393ca6c3a4dca04b6b955a1d7f17 Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Fri, 6 Mar 2026 07:46:50 -0500 Subject: [PATCH 3/8] Feature tabular data (#780) * initial tabular data support * working with workspaces, aligned with chat * add popup modal view for xlsx * added tabular data analysis as a mini-agent within chats to process tab data without full agent * Update route_backend_chats.py * SK Mini-Agent Performance Optimization * fixed inline sk trigger bug --- CLAUDE.md | 3 +- application/single_app/config.py | 10 +- application/single_app/functions_content.py | 101 +++- application/single_app/functions_documents.py | 385 ++++++++++--- application/single_app/functions_settings.py | 4 + application/single_app/route_backend_chats.py | 486 ++++++++++++++++- .../single_app/route_backend_documents.py | 54 +- .../single_app/route_backend_plugins.py | 8 + .../single_app/route_enhanced_citations.py | 186 ++++++- .../route_frontend_admin_settings.py | 2 + .../single_app/route_frontend_chats.py | 86 ++- .../single_app/semantic_kernel_loader.py | 26 +- .../tabular_processing_plugin.py | 515 ++++++++++++++++++ .../static/js/admin/admin_settings.js | 14 +- .../static/js/chat/chat-enhanced-citations.js | 153 +++++- .../static/js/chat/chat-input-actions.js | 17 +- .../single_app/templates/admin_settings.html | 6 + application/single_app/tmp2xmw9c75.xlsx | Bin 0 -> 1428472 bytes .../v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md | 67 +++ docs/explanation/release_notes.md | 41 ++ .../test_chat_tabular_sk_trigger.py | 319 +++++++++++ 21 files changed, 2350 insertions(+), 133 deletions(-) create mode 100644 application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py create mode 100644 application/single_app/tmp2xmw9c75.xlsx create mode 100644 docs/explanation/fixes/v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md create mode 100644 functional_tests/test_chat_tabular_sk_trigger.py diff --git a/CLAUDE.md b/CLAUDE.md index 4e9c2830..2ad07a8a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -58,6 +58,7 @@ return render_template('page.html', settings=public_settings) ## Version Management +- Its important to update the version at the end of every plan - Version is stored in `config.py`: `VERSION = "X.XXX.XXX"` - When incrementing, only change the third segment (e.g., `0.238.024` -> `0.238.025`) - Include the current version in functional test file headers and documentation files @@ -83,7 +84,7 @@ return render_template('page.html', settings=public_settings) ## Release Notes -After completing code changes, offer to update `docs/explanation/release_notes.md`. +After completing plans and code changes, offer to update `docs/explanation/release_notes.md`. - Add entries under the current version from `config.py` - If the version was bumped, create a new section at the top: `### **(vX.XXX.XXX)**` diff --git a/application/single_app/config.py b/application/single_app/config.py index 1e83e045..bec5513b 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -257,6 +257,8 @@ def get_redis_cache_infrastructure_endpoint(redis_hostname: str) -> str: storage_account_user_documents_container_name = "user-documents" storage_account_group_documents_container_name = "group-documents" storage_account_public_documents_container_name = "public-documents" +storage_account_personal_chat_container_name = "personal-chat" +storage_account_group_chat_container_name = "group-chat" # Initialize Azure Cosmos DB client cosmos_endpoint = os.getenv("AZURE_COSMOS_ENDPOINT") @@ -757,9 +759,11 @@ def initialize_clients(settings): # This addresses the issue where the application assumes containers exist if blob_service_client: for container_name in [ - storage_account_user_documents_container_name, - storage_account_group_documents_container_name, - storage_account_public_documents_container_name + storage_account_user_documents_container_name, + storage_account_group_documents_container_name, + storage_account_public_documents_container_name, + storage_account_personal_chat_container_name, + storage_account_group_chat_container_name ]: try: container_client = blob_service_client.get_container_client(container_name) diff --git a/application/single_app/functions_content.py b/application/single_app/functions_content.py index 376d23f4..3116ed82 100644 --- a/application/single_app/functions_content.py +++ b/application/single_app/functions_content.py @@ -352,7 +352,7 @@ def generate_embedding( embedding_model = selected_embedding_model['deploymentName'] while True: - random_delay = random.uniform(0.5, 2.0) + random_delay = random.uniform(0.05, 0.2) time.sleep(random_delay) try: @@ -385,3 +385,102 @@ def generate_embedding( except Exception as e: raise + +def generate_embeddings_batch( + texts, + batch_size=16, + max_retries=5, + initial_delay=1.0, + delay_multiplier=2.0 +): + """Generate embeddings for multiple texts in batches. + + Azure OpenAI embeddings API accepts a list of strings as input. + This reduces per-call overhead and delay significantly. + + Args: + texts: List of text strings to embed. + batch_size: Number of texts per API call (default 16). + max_retries: Max retries on rate limit errors. + initial_delay: Initial retry delay in seconds. + delay_multiplier: Multiplier for exponential backoff. + + Returns: + list of (embedding, token_usage) tuples, one per input text. + """ + settings = get_settings() + + enable_embedding_apim = settings.get('enable_embedding_apim', False) + + if enable_embedding_apim: + embedding_model = settings.get('azure_apim_embedding_deployment') + embedding_client = AzureOpenAI( + api_version=settings.get('azure_apim_embedding_api_version'), + azure_endpoint=settings.get('azure_apim_embedding_endpoint'), + api_key=settings.get('azure_apim_embedding_subscription_key')) + else: + if (settings.get('azure_openai_embedding_authentication_type') == 'managed_identity'): + token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope) + + embedding_client = AzureOpenAI( + api_version=settings.get('azure_openai_embedding_api_version'), + azure_endpoint=settings.get('azure_openai_embedding_endpoint'), + azure_ad_token_provider=token_provider + ) + + embedding_model_obj = settings.get('embedding_model', {}) + if embedding_model_obj and embedding_model_obj.get('selected'): + selected_embedding_model = embedding_model_obj['selected'][0] + embedding_model = selected_embedding_model['deploymentName'] + else: + embedding_client = AzureOpenAI( + api_version=settings.get('azure_openai_embedding_api_version'), + azure_endpoint=settings.get('azure_openai_embedding_endpoint'), + api_key=settings.get('azure_openai_embedding_key') + ) + + embedding_model_obj = settings.get('embedding_model', {}) + if embedding_model_obj and embedding_model_obj.get('selected'): + selected_embedding_model = embedding_model_obj['selected'][0] + embedding_model = selected_embedding_model['deploymentName'] + + results = [] + for i in range(0, len(texts), batch_size): + batch = texts[i:i + batch_size] + retries = 0 + current_delay = initial_delay + + while True: + random_delay = random.uniform(0.05, 0.2) + time.sleep(random_delay) + + try: + response = embedding_client.embeddings.create( + model=embedding_model, + input=batch + ) + + for item in response.data: + token_usage = None + if hasattr(response, 'usage') and response.usage: + token_usage = { + 'prompt_tokens': response.usage.prompt_tokens // len(batch), + 'total_tokens': response.usage.total_tokens // len(batch), + 'model_deployment_name': embedding_model + } + results.append((item.embedding, token_usage)) + break + + except RateLimitError as e: + retries += 1 + if retries > max_retries: + raise + + wait_time = current_delay * random.uniform(1.0, 1.5) + time.sleep(wait_time) + current_delay *= delay_multiplier + + except Exception as e: + raise + + return results diff --git a/application/single_app/functions_documents.py b/application/single_app/functions_documents.py index ce08066d..110afbd2 100644 --- a/application/single_app/functions_documents.py +++ b/application/single_app/functions_documents.py @@ -1646,6 +1646,191 @@ def save_chunks(page_text_content, page_number, file_name, user_id, document_id, # Return token usage information for accumulation return token_usage +def save_chunks_batch(chunks_data, user_id, document_id, group_id=None, public_workspace_id=None): + """ + Save multiple chunks at once using batch embedding and batch AI Search upload. + Significantly faster than calling save_chunks() per chunk. + + Args: + chunks_data: list of dicts with keys: page_text_content, page_number, file_name + user_id: The user ID + document_id: The document ID + group_id: Optional group ID for group documents + public_workspace_id: Optional public workspace ID for public documents + + Returns: + dict with 'total_tokens', 'prompt_tokens', 'model_deployment_name' + """ + from functions_content import generate_embeddings_batch + + current_time = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ') + is_group = group_id is not None + is_public_workspace = public_workspace_id is not None + + # Retrieve metadata once for all chunks + try: + if is_public_workspace: + metadata = get_document_metadata( + document_id=document_id, + user_id=user_id, + public_workspace_id=public_workspace_id + ) + elif is_group: + metadata = get_document_metadata( + document_id=document_id, + user_id=user_id, + group_id=group_id + ) + else: + metadata = get_document_metadata( + document_id=document_id, + user_id=user_id + ) + + if not metadata: + raise ValueError(f"No metadata found for document {document_id}") + + version = metadata.get("version") if metadata.get("version") else 1 + except Exception as e: + log_event(f"[save_chunks_batch] Error retrieving metadata for document {document_id}: {repr(e)}", level=logging.ERROR) + raise + + # Generate all embeddings in batches + texts = [c['page_text_content'] for c in chunks_data] + try: + embedding_results = generate_embeddings_batch(texts) + except Exception as e: + log_event(f"[save_chunks_batch] Error generating batch embeddings for document {document_id}: {e}", level=logging.ERROR) + raise + + # Check for vision analysis once + vision_analysis = metadata.get('vision_analysis') + vision_text = "" + if vision_analysis: + vision_text_parts = [] + vision_text_parts.append("\n\n=== AI Vision Analysis ===") + vision_text_parts.append(f"Model: {vision_analysis.get('model', 'unknown')}") + if vision_analysis.get('description'): + vision_text_parts.append(f"\nDescription: {vision_analysis['description']}") + if vision_analysis.get('objects'): + objects_list = vision_analysis['objects'] + if isinstance(objects_list, list): + vision_text_parts.append(f"\nObjects Detected: {', '.join(objects_list)}") + else: + vision_text_parts.append(f"\nObjects Detected: {objects_list}") + if vision_analysis.get('text'): + vision_text_parts.append(f"\nVisible Text: {vision_analysis['text']}") + if vision_analysis.get('analysis'): + vision_text_parts.append(f"\nContextual Analysis: {vision_analysis['analysis']}") + vision_text = "\n".join(vision_text_parts) + + # Build all chunk documents + chunk_documents = [] + total_token_usage = {'total_tokens': 0, 'prompt_tokens': 0, 'model_deployment_name': None} + + for idx, chunk_info in enumerate(chunks_data): + embedding, token_usage = embedding_results[idx] + page_number = chunk_info['page_number'] + file_name = chunk_info['file_name'] + page_text_content = chunk_info['page_text_content'] + + if token_usage: + total_token_usage['total_tokens'] += token_usage.get('total_tokens', 0) + total_token_usage['prompt_tokens'] += token_usage.get('prompt_tokens', 0) + if not total_token_usage['model_deployment_name']: + total_token_usage['model_deployment_name'] = token_usage.get('model_deployment_name') + + chunk_id = f"{document_id}_{page_number}" + enhanced_chunk_text = page_text_content + vision_text if vision_text else page_text_content + + if is_public_workspace: + chunk_document = { + "id": chunk_id, + "document_id": document_id, + "chunk_id": str(page_number), + "chunk_text": enhanced_chunk_text, + "embedding": embedding, + "file_name": file_name, + "chunk_keywords": [], + "chunk_summary": "", + "page_number": page_number, + "author": [], + "title": "", + "document_classification": "None", + "document_tags": metadata.get('tags', []), + "chunk_sequence": page_number, + "upload_date": current_time, + "version": version, + "public_workspace_id": public_workspace_id + } + elif is_group: + shared_group_ids = metadata.get('shared_group_ids', []) if metadata else [] + chunk_document = { + "id": chunk_id, + "document_id": document_id, + "chunk_id": str(page_number), + "chunk_text": enhanced_chunk_text, + "embedding": embedding, + "file_name": file_name, + "chunk_keywords": [], + "chunk_summary": "", + "page_number": page_number, + "author": [], + "title": "", + "document_classification": "None", + "document_tags": metadata.get('tags', []), + "chunk_sequence": page_number, + "upload_date": current_time, + "version": version, + "group_id": group_id, + "shared_group_ids": shared_group_ids + } + else: + shared_user_ids = metadata.get('shared_user_ids', []) if metadata else [] + chunk_document = { + "id": chunk_id, + "document_id": document_id, + "chunk_id": str(page_number), + "chunk_text": enhanced_chunk_text, + "embedding": embedding, + "file_name": file_name, + "chunk_keywords": [], + "chunk_summary": "", + "page_number": page_number, + "author": [], + "title": "", + "document_classification": "None", + "document_tags": metadata.get('tags', []), + "chunk_sequence": page_number, + "upload_date": current_time, + "version": version, + "user_id": user_id, + "shared_user_ids": shared_user_ids + } + + chunk_documents.append(chunk_document) + + # Batch upload to AI Search + try: + if is_public_workspace: + search_client = CLIENTS["search_client_public"] + elif is_group: + search_client = CLIENTS["search_client_group"] + else: + search_client = CLIENTS["search_client_user"] + + # Upload in sub-batches of 32 to avoid request size limits + upload_batch_size = 32 + for i in range(0, len(chunk_documents), upload_batch_size): + sub_batch = chunk_documents[i:i + upload_batch_size] + search_client.upload_documents(documents=sub_batch) + + except Exception as e: + log_event(f"[save_chunks_batch] Error uploading batch to AI Search for document {document_id}: {e}", level=logging.ERROR) + raise + + return total_token_usage + def get_document_metadata_for_citations(document_id, user_id=None, group_id=None, public_workspace_id=None): """ Retrieve keywords and abstract from a document for creating metadata citations. @@ -4669,37 +4854,30 @@ def process_single_tabular_sheet(df, document_id, user_id, file_name, update_cal # Consider accumulating page count in the caller if needed. update_callback(number_of_pages=num_chunks_final) - # Save chunks, prepending the header to each + # Save chunks, prepending the header to each โ€” use batch processing for speed + all_chunks = [] for idx, chunk_rows_content in enumerate(final_chunks_content, start=1): - # Prepend header - header length does not count towards chunk size limit chunk_with_header = header_string + chunk_rows_content - - update_callback( - current_file_chunk=idx, - status=f"Saving chunk {idx}/{num_chunks_final} from {file_name}..." - ) - - args = { + all_chunks.append({ "page_text_content": chunk_with_header, "page_number": idx, - "file_name": file_name, - "user_id": user_id, - "document_id": document_id - } + "file_name": file_name + }) - if is_public_workspace: - args["public_workspace_id"] = public_workspace_id - elif is_group: - args["group_id"] = group_id + if all_chunks: + update_callback( + current_file_chunk=1, + status=f"Batch processing {num_chunks_final} chunks from {file_name}..." + ) - token_usage = save_chunks(**args) - total_chunks_saved += 1 - - # Accumulate embedding tokens - if token_usage: - total_embedding_tokens += token_usage.get('total_tokens', 0) - if not embedding_model_name: - embedding_model_name = token_usage.get('model_deployment_name') + batch_token_usage = save_chunks_batch( + all_chunks, user_id, document_id, + group_id=group_id, public_workspace_id=public_workspace_id + ) + total_chunks_saved = len(all_chunks) + if batch_token_usage: + total_embedding_tokens = batch_token_usage.get('total_tokens', 0) + embedding_model_name = batch_token_usage.get('model_deployment_name') return total_chunks_saved, total_embedding_tokens, embedding_model_name @@ -4729,63 +4907,75 @@ def process_tabular(document_id, user_id, temp_file_path, original_filename, fil args["group_id"] = group_id upload_to_blob(**args) + update_callback(enhanced_citations=True, status=f"Enhanced citations enabled for {file_ext}") - try: - if file_ext == '.csv': - # Process CSV - # Read CSV, attempt to infer header, keep data as string initially - df = pandas.read_csv( - temp_file_path, - keep_default_na=False, - dtype=str + # When enhanced citations is on, index a single schema summary chunk + # instead of row-by-row chunking. The tabular processing plugin handles analysis. + if enable_enhanced_citations: + try: + if file_ext == '.csv': + df_preview = pandas.read_csv(temp_file_path, keep_default_na=False, dtype=str, nrows=5) + full_df = pandas.read_csv(temp_file_path, keep_default_na=False, dtype=str) + elif file_ext in ('.xlsx', '.xls', '.xlsm'): + engine = 'openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd' + df_preview = pandas.read_excel(temp_file_path, engine=engine, keep_default_na=False, dtype=str, nrows=5) + full_df = pandas.read_excel(temp_file_path, engine=engine, keep_default_na=False, dtype=str) + else: + raise ValueError(f"Unsupported tabular file type: {file_ext}") + + row_count = len(full_df) + columns = list(df_preview.columns) + preview_rows = df_preview.head(5).to_string(index=False) + + schema_summary = ( + f"Tabular data file: {original_filename}\n" + f"Columns ({len(columns)}): {', '.join(columns)}\n" + f"Total rows: {row_count}\n" + f"Preview (first 5 rows):\n{preview_rows}\n\n" + f"This file is available for detailed analysis via the Tabular Processing plugin." ) - args = { - "df": df, - "document_id": document_id, - "user_id": user_id, + + update_callback(number_of_pages=1, status=f"Indexing schema summary for {original_filename}...") + + save_args = { + "page_text_content": schema_summary, + "page_number": 1, "file_name": original_filename, - "update_callback": update_callback + "user_id": user_id, + "document_id": document_id } - if is_public_workspace: - args["public_workspace_id"] = public_workspace_id + save_args["public_workspace_id"] = public_workspace_id elif is_group: - args["group_id"] = group_id + save_args["group_id"] = group_id - result = process_single_tabular_sheet(**args) - if isinstance(result, tuple) and len(result) == 3: - chunks, tokens, model = result - total_chunks_saved = chunks - total_embedding_tokens += tokens - if not embedding_model_name: - embedding_model_name = model - else: - total_chunks_saved = result - - elif file_ext in ('.xlsx', '.xls', '.xlsm'): - # Process Excel (potentially multiple sheets) - excel_file = pandas.ExcelFile( - temp_file_path, - engine='openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd' - ) - sheet_names = excel_file.sheet_names - base_name, ext = os.path.splitext(original_filename) - - accumulated_total_chunks = 0 - for sheet_name in sheet_names: - update_callback(status=f"Processing sheet '{sheet_name}'...") - # Read specific sheet, get values (not formulas), keep data as string - # Note: pandas typically reads values, not formulas by default. - df = excel_file.parse(sheet_name, keep_default_na=False, dtype=str) + token_usage = save_chunks(**save_args) + total_chunks_saved = 1 + if token_usage: + total_embedding_tokens = token_usage.get('total_tokens', 0) + embedding_model_name = token_usage.get('model_deployment_name') - # Create effective filename for this sheet - effective_filename = f"{base_name}-{sheet_name}{ext}" if len(sheet_names) > 1 else original_filename + # Don't return here โ€” fall through to metadata extraction below + except Exception as e: + log_event(f"[process_tabular] Error creating schema summary, falling back to row-by-row: {e}", level=logging.WARNING) + # Fall through to existing row-by-row processing + # Only do row-by-row chunking if schema-only didn't produce chunks + if total_chunks_saved == 0: + try: + if file_ext == '.csv': + # Process CSV + # Read CSV, attempt to infer header, keep data as string initially + df = pandas.read_csv( + temp_file_path, + keep_default_na=False, + dtype=str + ) args = { "df": df, "document_id": document_id, "user_id": user_id, - "file_name": effective_filename, + "file_name": original_filename, "update_callback": update_callback } @@ -4797,21 +4987,62 @@ def process_tabular(document_id, user_id, temp_file_path, original_filename, fil result = process_single_tabular_sheet(**args) if isinstance(result, tuple) and len(result) == 3: chunks, tokens, model = result - accumulated_total_chunks += chunks + total_chunks_saved = chunks total_embedding_tokens += tokens if not embedding_model_name: embedding_model_name = model else: - accumulated_total_chunks += result + total_chunks_saved = result - total_chunks_saved = accumulated_total_chunks # Total across all sheets + elif file_ext in ('.xlsx', '.xls', '.xlsm'): + # Process Excel (potentially multiple sheets) + excel_file = pandas.ExcelFile( + temp_file_path, + engine='openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd' + ) + sheet_names = excel_file.sheet_names + base_name, ext = os.path.splitext(original_filename) + accumulated_total_chunks = 0 + for sheet_name in sheet_names: + update_callback(status=f"Processing sheet '{sheet_name}'...") + # Read specific sheet, get values (not formulas), keep data as string + # Note: pandas typically reads values, not formulas by default. + df = excel_file.parse(sheet_name, keep_default_na=False, dtype=str) - except pandas.errors.EmptyDataError: - print(f"Warning: Tabular file or sheet is empty: {original_filename}") - update_callback(status=f"Warning: File/sheet is empty - {original_filename}", number_of_pages=0) - except Exception as e: - raise Exception(f"Failed processing Tabular file {original_filename}: {e}") + # Create effective filename for this sheet + effective_filename = f"{base_name}-{sheet_name}{ext}" if len(sheet_names) > 1 else original_filename + + args = { + "df": df, + "document_id": document_id, + "user_id": user_id, + "file_name": effective_filename, + "update_callback": update_callback + } + + if is_public_workspace: + args["public_workspace_id"] = public_workspace_id + elif is_group: + args["group_id"] = group_id + + result = process_single_tabular_sheet(**args) + if isinstance(result, tuple) and len(result) == 3: + chunks, tokens, model = result + accumulated_total_chunks += chunks + total_embedding_tokens += tokens + if not embedding_model_name: + embedding_model_name = model + else: + accumulated_total_chunks += result + + total_chunks_saved = accumulated_total_chunks # Total across all sheets + + except pandas.errors.EmptyDataError: + log_event(f"[process_tabular] Warning: Tabular file or sheet is empty: {original_filename}", level=logging.WARNING) + update_callback(status=f"Warning: File/sheet is empty - {original_filename}", number_of_pages=0) + except Exception as e: + raise Exception(f"Failed processing Tabular file {original_filename}: {e}") # Extract metadata if enabled and chunks were processed settings = get_settings() diff --git a/application/single_app/functions_settings.py b/application/single_app/functions_settings.py index 4164fab4..f3dc59de 100644 --- a/application/single_app/functions_settings.py +++ b/application/single_app/functions_settings.py @@ -25,6 +25,7 @@ def get_settings(use_cosmos=False): 'enable_text_plugin': True, 'enable_default_embedding_model_plugin': False, 'enable_fact_memory_plugin': True, + 'enable_tabular_processing_plugin': False, 'enable_multi_agent_orchestration': False, 'max_rounds_per_agent': 1, 'enable_semantic_kernel': False, @@ -394,6 +395,9 @@ def update_settings(new_settings): # always fetch the latest settings doc, which includes your merges settings_item = get_settings() settings_item.update(new_settings) + # Dependency enforcement: tabular processing requires enhanced citations + if not settings_item.get('enable_enhanced_citations', False): + settings_item['enable_tabular_processing_plugin'] = False cosmos_settings_container.upsert_item(settings_item) cache_updater = getattr(app_settings_cache, "update_settings_cache", None) if callable(cache_updater): diff --git a/application/single_app/route_backend_chats.py b/application/single_app/route_backend_chats.py index abd7a9f4..8154b7d4 100644 --- a/application/single_app/route_backend_chats.py +++ b/application/single_app/route_backend_chats.py @@ -40,6 +40,185 @@ def get_kernel_agents(): log_event(f"[SKChat] get_kernel_agents - g.kernel_agents: {type(g_agents)} ({len(g_agents) if g_agents else 0} agents), builtins.kernel_agents: {type(builtins_agents)} ({len(builtins_agents) if builtins_agents else 0} agents)", level=logging.INFO) return g_agents or builtins_agents +async def run_tabular_sk_analysis(user_question, tabular_filenames, user_id, + conversation_id, gpt_model, settings, + source_hint="workspace", group_id=None, + public_workspace_id=None): + """Run lightweight SK with TabularProcessingPlugin to analyze tabular data. + + Creates a temporary Kernel with only the TabularProcessingPlugin, uses the + same chat model as the user's session, and returns computed analysis results. + Returns None on failure for graceful degradation. + """ + from semantic_kernel import Kernel as SKKernel + from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion + from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior + from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import AzureChatPromptExecutionSettings + from semantic_kernel.contents.chat_history import ChatHistory as SKChatHistory + from semantic_kernel_plugins.tabular_processing_plugin import TabularProcessingPlugin + + try: + log_event(f"[Tabular SK Analysis] Starting analysis for files: {tabular_filenames}", level=logging.INFO) + + # 1. Create lightweight kernel with only tabular plugin + kernel = SKKernel() + tabular_plugin = TabularProcessingPlugin() + kernel.add_plugin(tabular_plugin, plugin_name="tabular_processing") + + # 2. Create chat service using same config as main chat + enable_gpt_apim = settings.get('enable_gpt_apim', False) + if enable_gpt_apim: + chat_service = AzureChatCompletion( + service_id="tabular-analysis", + deployment_name=gpt_model, + endpoint=settings.get('azure_apim_gpt_endpoint'), + api_key=settings.get('azure_apim_gpt_subscription_key'), + api_version=settings.get('azure_apim_gpt_api_version'), + ) + else: + auth_type = settings.get('azure_openai_gpt_authentication_type') + if auth_type == 'managed_identity': + token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope) + chat_service = AzureChatCompletion( + service_id="tabular-analysis", + deployment_name=gpt_model, + endpoint=settings.get('azure_openai_gpt_endpoint'), + api_version=settings.get('azure_openai_gpt_api_version'), + ad_token_provider=token_provider, + ) + else: + chat_service = AzureChatCompletion( + service_id="tabular-analysis", + deployment_name=gpt_model, + endpoint=settings.get('azure_openai_gpt_endpoint'), + api_key=settings.get('azure_openai_gpt_key'), + api_version=settings.get('azure_openai_gpt_api_version'), + ) + kernel.add_service(chat_service) + + # 3. Pre-dispatch: load file schemas to eliminate discovery LLM rounds + source_context = f"source='{source_hint}'" + if group_id: + source_context += f", group_id='{group_id}'" + if public_workspace_id: + source_context += f", public_workspace_id='{public_workspace_id}'" + + schema_parts = [] + for fname in tabular_filenames: + try: + container, blob_path = tabular_plugin._resolve_blob_location_with_fallback( + user_id, conversation_id, fname, source_hint, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = tabular_plugin._read_tabular_blob_to_dataframe(container, blob_path) + df_numeric = tabular_plugin._try_numeric_conversion(df.copy()) + schema_info = { + "filename": fname, + "row_count": len(df), + "columns": list(df.columns), + "dtypes": {col: str(dtype) for col, dtype in df_numeric.dtypes.items()}, + "preview": df.head(3).to_dict(orient='records') + } + schema_parts.append(json.dumps(schema_info, indent=2, default=str)) + log_event(f"[Tabular SK Analysis] Pre-loaded schema for {fname} ({len(df)} rows)", level=logging.DEBUG) + except Exception as e: + log_event(f"[Tabular SK Analysis] Failed to pre-load schema for {fname}: {e}", level=logging.WARNING) + schema_parts.append(json.dumps({"filename": fname, "error": f"Could not pre-load: {str(e)}"})) + + schema_context = "\n".join(schema_parts) + + # 4. Build chat history with pre-loaded schemas + chat_history = SKChatHistory() + chat_history.add_system_message( + "You are a data analyst. Use the tabular_processing plugin functions to " + "analyze the data and answer the user's question.\n\n" + f"FILE SCHEMAS (pre-loaded โ€” do NOT call list_tabular_files or describe_tabular_file):\n" + f"{schema_context}\n\n" + "IMPORTANT: Batch multiple independent function calls in a SINGLE response. " + "For example, call multiple aggregate_column or group_by_aggregate functions " + "at once rather than one at a time.\n\n" + "Return the computed results clearly." + ) + + chat_history.add_user_message( + f"Analyze the tabular data to answer: {user_question}\n" + f"Use user_id='{user_id}', conversation_id='{conversation_id}', {source_context}." + ) + + # 5. Execute with auto function calling + execution_settings = AzureChatPromptExecutionSettings( + service_id="tabular-analysis", + function_choice_behavior=FunctionChoiceBehavior.Auto( + maximum_auto_invoke_attempts=5 + ), + ) + + result = await chat_service.get_chat_message_contents( + chat_history, execution_settings, kernel=kernel + ) + + if result and result[0].content: + analysis = result[0].content + # Cap at 20k characters to stay within token budget + if len(analysis) > 20000: + analysis = analysis[:20000] + "\n[Analysis truncated]" + log_event(f"[Tabular SK Analysis] Analysis complete, {len(analysis)} chars", level=logging.INFO) + return analysis + log_event("[Tabular SK Analysis] No content in SK response", level=logging.WARNING) + return None + + except Exception as e: + log_event(f"[Tabular SK Analysis] Error: {e}", level=logging.WARNING, exceptionTraceback=True) + return None + +def collect_tabular_sk_citations(user_id, conversation_id): + """Collect plugin invocations from the tabular SK analysis and convert to citation format.""" + from semantic_kernel_plugins.plugin_invocation_logger import get_plugin_logger + + plugin_logger = get_plugin_logger() + plugin_invocations = plugin_logger.get_invocations_for_conversation(user_id, conversation_id) + + if not plugin_invocations: + return [] + + def make_json_serializable(obj): + if obj is None: + return None + elif isinstance(obj, (str, int, float, bool)): + return obj + elif isinstance(obj, dict): + return {str(k): make_json_serializable(v) for k, v in obj.items()} + elif isinstance(obj, (list, tuple)): + return [make_json_serializable(item) for item in obj] + else: + return str(obj) + + citations = [] + for inv in plugin_invocations: + timestamp_str = None + if inv.timestamp: + if hasattr(inv.timestamp, 'isoformat'): + timestamp_str = inv.timestamp.isoformat() + else: + timestamp_str = str(inv.timestamp) + + citation = { + 'tool_name': f"{inv.plugin_name}.{inv.function_name}", + 'function_name': inv.function_name, + 'plugin_name': inv.plugin_name, + 'function_arguments': make_json_serializable(inv.parameters), + 'function_result': make_json_serializable(inv.result), + 'duration_ms': inv.duration_ms, + 'timestamp': timestamp_str, + 'success': inv.success, + 'error_message': make_json_serializable(inv.error_message), + 'user_id': inv.user_id + } + citations.append(citation) + + log_event(f"[Tabular SK Citations] Collected {len(citations)} tool execution citations", level=logging.INFO) + return citations + def register_route_backend_chats(app): @app.route('/api/chat', methods=['POST']) @swagger_route(security=get_auth_security()) @@ -970,6 +1149,70 @@ def result_requires_message_reload(result: Any) -> bool: 'documents': combined_documents # Keep track of docs used }) + # Auto-detect tabular files in search results and prompt the LLM to use the plugin + if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + tabular_files_in_results = set() + for source_doc in combined_documents: + fname = source_doc.get('file_name', '') + if fname and any(fname.lower().endswith(ext) for ext in TABULAR_EXTENSIONS): + tabular_files_in_results.add(fname) + + if tabular_files_in_results: + # Determine source based on document_scope, not just active IDs + if document_scope == 'group' and active_group_id: + tabular_source_hint = "group" + elif document_scope == 'public' and active_public_workspace_id: + tabular_source_hint = "public" + else: + tabular_source_hint = "workspace" + + tabular_filenames_str = ", ".join(tabular_files_in_results) + + # Run SK tabular analysis to pre-compute results + tabular_analysis = asyncio.run(run_tabular_sk_analysis( + user_question=user_message, + tabular_filenames=tabular_files_in_results, + user_id=user_id, + conversation_id=conversation_id, + gpt_model=gpt_model, + settings=settings, + source_hint=tabular_source_hint, + group_id=active_group_id if tabular_source_hint == "group" else None, + public_workspace_id=active_public_workspace_id if tabular_source_hint == "public" else None, + )) + + if tabular_analysis: + # Inject pre-computed analysis results as context + tabular_system_msg = ( + f"The following analysis was computed from the tabular file(s) " + f"{tabular_filenames_str} using data analysis functions:\n\n" + f"{tabular_analysis}\n\n" + f"Use these computed results to answer the user's question accurately." + ) + else: + # Fallback: instruct LLM to use plugin functions (for agent mode) + tabular_system_msg = ( + f"IMPORTANT: The search results include data from tabular file(s): {tabular_filenames_str}. " + f"The search results contain only a schema summary (column names and a few sample rows), NOT the full data. " + f"You MUST use the tabular_processing plugin functions to answer ANY question about these files. " + f"Do NOT attempt to answer using the schema summary alone โ€” it is incomplete. " + f"Available functions: describe_tabular_file, aggregate_column, filter_rows, query_tabular_data, group_by_aggregate. " + f"Use source='{tabular_source_hint}'" + + (f" and group_id='{active_group_id}'" if tabular_source_hint == "group" else "") + + (f" and public_workspace_id='{active_public_workspace_id}'" if tabular_source_hint == "public" else "") + + "." + ) + system_messages_for_augmentation.append({ + 'role': 'system', + 'content': tabular_system_msg + }) + + # Collect tool execution citations from SK tabular analysis + if tabular_analysis: + tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id) + if tabular_sk_citations: + agent_citations_list.extend(tabular_sk_citations) + # Loop through each source document/chunk used for this message for source_doc in combined_documents: # 4. Create a citation dictionary, selecting the desired fields @@ -1155,8 +1398,8 @@ def result_requires_message_reload(result: Any) -> bool: """ # Update the system message with enhanced content and updated documents array if system_messages_for_augmentation: - system_messages_for_augmentation[-1]['content'] = system_prompt_search - system_messages_for_augmentation[-1]['documents'] = combined_documents + system_messages_for_augmentation[0]['content'] = system_prompt_search + system_messages_for_augmentation[0]['documents'] = combined_documents # --- END NEW METADATA CITATIONS --- # Update conversation classifications if new ones were found @@ -1670,6 +1913,7 @@ def result_requires_message_reload(result: Any) -> bool: allowed_roles_in_history = ['user', 'assistant'] # Add 'system' if you PERSIST general system messages not related to augmentation max_file_content_length_in_history = 50000 # Increased limit for all file content in history max_tabular_content_length_in_history = 50000 # Same limit for tabular data consistency + chat_tabular_files = set() # Track tabular files uploaded directly to chat for message in recent_messages: role = message.get('role') @@ -1705,25 +1949,38 @@ def result_requires_message_reload(result: Any) -> bool: filename = message.get('filename', 'uploaded_file') file_content = message.get('file_content', '') # Assuming file content is stored is_table = message.get('is_table', False) - - # Use higher limit for tabular data that needs complete analysis - content_limit = max_tabular_content_length_in_history if is_table else max_file_content_length_in_history - - display_content = file_content[:content_limit] - if len(file_content) > content_limit: - display_content += "..." - - # Enhanced message for tabular data - if is_table: + file_content_source = message.get('file_content_source', '') + + # Tabular files stored in blob (enhanced citations enabled) - reference plugin + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) # Track for mini SK analysis conversation_history_for_api.append({ - 'role': 'system', # Represent file as system info - 'content': f"[User uploaded a tabular data file named '{filename}'. This is CSV format data for analysis:\n{display_content}]\nThis is complete tabular data in CSV format. You can perform calculations, analysis, and data operations on this dataset." + 'role': 'system', + 'content': f"[User uploaded a tabular data file named '{filename}'. " + f"The file is stored in blob storage and available for analysis. " + f"Use the tabular_processing plugin functions (list_tabular_files, describe_tabular_file, " + f"aggregate_column, filter_rows, query_tabular_data, group_by_aggregate) to analyze this data. " + f"The file source is 'chat'.]" }) else: - conversation_history_for_api.append({ - 'role': 'system', # Represent file as system info - 'content': f"[User uploaded a file named '{filename}'. Content preview:\n{display_content}]\nUse this file context if relevant." - }) + # Use higher limit for tabular data that needs complete analysis + content_limit = max_tabular_content_length_in_history if is_table else max_file_content_length_in_history + + display_content = file_content[:content_limit] + if len(file_content) > content_limit: + display_content += "..." + + # Enhanced message for tabular data + if is_table: + conversation_history_for_api.append({ + 'role': 'system', # Represent file as system info + 'content': f"[User uploaded a tabular data file named '{filename}'. This is CSV format data for analysis:\n{display_content}]\nThis is complete tabular data in CSV format. You can perform calculations, analysis, and data operations on this dataset." + }) + else: + conversation_history_for_api.append({ + 'role': 'system', # Represent file as system info + 'content': f"[User uploaded a file named '{filename}'. Content preview:\n{display_content}]\nUse this file context if relevant." + }) elif role == 'image': # Handle image uploads with extracted text and vision analysis filename = message.get('filename', 'uploaded_image') is_user_upload = message.get('metadata', {}).get('is_user_upload', False) @@ -1787,6 +2044,45 @@ def result_requires_message_reload(result: Any) -> bool: # Ignored roles: 'safety', 'blocked', 'system' (if they are only for augmentation/summary) + # --- Mini SK analysis for tabular files uploaded directly to chat --- + if chat_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + chat_tabular_filenames_str = ", ".join(chat_tabular_files) + log_event( + f"[Chat Tabular SK] Detected {len(chat_tabular_files)} tabular file(s) uploaded to chat: {chat_tabular_filenames_str}", + level=logging.INFO + ) + + chat_tabular_analysis = asyncio.run(run_tabular_sk_analysis( + user_question=user_message, + tabular_filenames=chat_tabular_files, + user_id=user_id, + conversation_id=conversation_id, + gpt_model=gpt_model, + settings=settings, + source_hint="chat", + )) + + if chat_tabular_analysis: + # Inject pre-computed analysis results as context + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"The following analysis was computed from the chat-uploaded tabular file(s) " + f"{chat_tabular_filenames_str} using data analysis functions:\n\n" + f"{chat_tabular_analysis}\n\n" + f"Use these computed results to answer the user's question accurately." + ) + }) + + # Collect tool execution citations from SK tabular analysis + chat_tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id) + if chat_tabular_sk_citations: + agent_citations_list.extend(chat_tabular_sk_citations) + + debug_print(f"[Chat Tabular SK] Analysis injected, {len(chat_tabular_analysis)} chars") + else: + debug_print("[Chat Tabular SK] Analysis returned None, relying on existing file context messages") + # Ensure the very last message is the current user's message (it should be if fetched correctly) if not conversation_history_for_api or conversation_history_for_api[-1]['role'] != 'user': debug_print("Warning: Last message in history is not the user's current message. Appending.") @@ -3390,7 +3686,55 @@ def emit_thought(step_type, content, detail=None): 'content': system_prompt_search, 'documents': combined_documents }) - + + # Auto-detect tabular files in search results and run SK analysis + if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + tabular_files_in_results = set() + for source_doc in combined_documents: + fname = source_doc.get('file_name', '') + if fname and any(fname.lower().endswith(ext) for ext in TABULAR_EXTENSIONS): + tabular_files_in_results.add(fname) + + if tabular_files_in_results: + # Determine source based on document_scope, not just active IDs + if document_scope == 'group' and active_group_id: + tabular_source_hint = "group" + elif document_scope == 'public' and active_public_workspace_id: + tabular_source_hint = "public" + else: + tabular_source_hint = "workspace" + + tabular_filenames_str = ", ".join(tabular_files_in_results) + + # Run SK tabular analysis to pre-compute results + tabular_analysis = asyncio.run(run_tabular_sk_analysis( + user_question=user_message, + tabular_filenames=tabular_files_in_results, + user_id=user_id, + conversation_id=conversation_id, + gpt_model=gpt_model, + settings=settings, + source_hint=tabular_source_hint, + group_id=active_group_id if tabular_source_hint == "group" else None, + public_workspace_id=active_public_workspace_id if tabular_source_hint == "public" else None, + )) + + if tabular_analysis: + system_messages_for_augmentation.append({ + 'role': 'system', + 'content': ( + f"The following analysis was computed from the tabular file(s) " + f"{tabular_filenames_str} using data analysis functions:\n\n" + f"{tabular_analysis}\n\n" + f"Use these computed results to answer the user's question accurately." + ) + }) + + # Collect tool execution citations from SK tabular analysis + tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id) + if tabular_sk_citations: + agent_citations_list.extend(tabular_sk_citations) + # Reorder hybrid citations list in descending order based on page_number hybrid_citations_list.sort(key=lambda x: x.get('page_number', 0), reverse=True) @@ -3455,15 +3799,108 @@ def emit_thought(step_type, content, detail=None): 'content': aug_msg['content'] }) - # Add recent messages + # Add recent messages (with file role handling) allowed_roles_in_history = ['user', 'assistant'] + max_file_content_length_in_history = 50000 + max_tabular_content_length_in_history = 50000 + chat_tabular_files = set() # Track tabular files uploaded directly to chat + for message in recent_messages: - if message.get('role') in allowed_roles_in_history: + role = message.get('role') + content = message.get('content', '') + + if role in allowed_roles_in_history: conversation_history_for_api.append({ - 'role': message['role'], - 'content': message.get('content', '') + 'role': role, + 'content': content }) - + elif role == 'file': + filename = message.get('filename', 'uploaded_file') + file_content = message.get('file_content', '') + is_table = message.get('is_table', False) + file_content_source = message.get('file_content_source', '') + + # Tabular files stored in blob - track for mini SK analysis + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"[User uploaded a tabular data file named '{filename}'. " + f"The file is stored in blob storage and available for analysis. " + f"Use the tabular_processing plugin functions (list_tabular_files, " + f"describe_tabular_file, aggregate_column, filter_rows, " + f"query_tabular_data, group_by_aggregate) to analyze this data. " + f"The file source is 'chat'.]" + ) + }) + else: + content_limit = ( + max_tabular_content_length_in_history if is_table + else max_file_content_length_in_history + ) + display_content = file_content[:content_limit] + if len(file_content) > content_limit: + display_content += "..." + + if is_table: + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"[User uploaded a tabular data file named '{filename}'. " + f"This is CSV format data for analysis:\n{display_content}]\n" + f"This is complete tabular data in CSV format. You can perform " + f"calculations, analysis, and data operations on this dataset." + ) + }) + else: + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"[User uploaded a file named '{filename}'. " + f"Content preview:\n{display_content}]\n" + f"Use this file context if relevant." + ) + }) + + # --- Mini SK analysis for tabular files uploaded directly to chat --- + if chat_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + chat_tabular_filenames_str = ", ".join(chat_tabular_files) + log_event( + f"[Chat Tabular SK] Streaming: Detected {len(chat_tabular_files)} tabular file(s) uploaded to chat: {chat_tabular_filenames_str}", + level=logging.INFO + ) + + chat_tabular_analysis = asyncio.run(run_tabular_sk_analysis( + user_question=user_message, + tabular_filenames=chat_tabular_files, + user_id=user_id, + conversation_id=conversation_id, + gpt_model=gpt_model, + settings=settings, + source_hint="chat", + )) + + if chat_tabular_analysis: + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"The following analysis was computed from the chat-uploaded tabular file(s) " + f"{chat_tabular_filenames_str} using data analysis functions:\n\n" + f"{chat_tabular_analysis}\n\n" + f"Use these computed results to answer the user's question accurately." + ) + }) + + # Collect tool execution citations + chat_tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id) + if chat_tabular_sk_citations: + agent_citations_list.extend(chat_tabular_sk_citations) + + debug_print(f"[Chat Tabular SK] Streaming: Analysis injected, {len(chat_tabular_analysis)} chars") + else: + debug_print("[Chat Tabular SK] Streaming: Analysis returned None, relying on existing file context") + except Exception as e: yield f"data: {json.dumps({'error': f'History error: {str(e)}'})}\n\n" return @@ -3613,7 +4050,6 @@ async def stream_agent_async(): return chunks, usage_data # Execute async streaming - import asyncio try: # Try to get existing event loop loop = asyncio.get_event_loop() diff --git a/application/single_app/route_backend_documents.py b/application/single_app/route_backend_documents.py index 28d4ef69..0e9d490b 100644 --- a/application/single_app/route_backend_documents.py +++ b/application/single_app/route_backend_documents.py @@ -7,6 +7,7 @@ from utils_cache import invalidate_personal_search_cache from functions_debug import * from functions_activity_logging import log_document_upload, log_document_metadata_update_transaction +import io import os import requests from flask import current_app @@ -72,7 +73,58 @@ def get_file_content(): filename = items_sorted[0].get('filename', 'Untitled') is_table = items_sorted[0].get('is_table', False) - debug_print(f"[GET_FILE_CONTENT] Filename: {filename}, is_table: {is_table}") + file_content_source = items_sorted[0].get('file_content_source', '') + debug_print(f"[GET_FILE_CONTENT] Filename: {filename}, is_table: {is_table}, source: {file_content_source}") + + # Handle blob-stored tabular files (enhanced citations enabled) + if file_content_source == 'blob': + blob_container = items_sorted[0].get('blob_container', '') + blob_path = items_sorted[0].get('blob_path', '') + debug_print(f"[GET_FILE_CONTENT] Blob-stored file: container={blob_container}, path={blob_path}") + + if not blob_container or not blob_path: + return jsonify({'error': 'Blob storage reference is incomplete'}), 500 + + try: + blob_service_client = CLIENTS.get("storage_account_office_docs_client") + if not blob_service_client: + return jsonify({'error': 'Blob storage client not available'}), 500 + + blob_client = blob_service_client.get_blob_client( + container=blob_container, + blob=blob_path + ) + stream = blob_client.download_blob() + blob_data = stream.readall() + + # Convert to CSV using pandas for display + file_ext = os.path.splitext(filename)[1].lower() + if file_ext == '.csv': + import pandas + df = pandas.read_csv(io.BytesIO(blob_data)) + combined_content = df.to_csv(index=False) + elif file_ext in ['.xlsx', '.xlsm']: + import pandas + df = pandas.read_excel(io.BytesIO(blob_data), engine='openpyxl') + combined_content = df.to_csv(index=False) + elif file_ext == '.xls': + import pandas + df = pandas.read_excel(io.BytesIO(blob_data), engine='xlrd') + combined_content = df.to_csv(index=False) + else: + combined_content = blob_data.decode('utf-8', errors='replace') + + debug_print(f"[GET_FILE_CONTENT] Successfully read blob content, length: {len(combined_content)}") + return jsonify({ + 'file_content': combined_content, + 'filename': filename, + 'is_table': is_table, + 'file_content_source': 'blob' + }), 200 + + except Exception as blob_err: + debug_print(f"[GET_FILE_CONTENT] Error reading from blob: {blob_err}") + return jsonify({'error': f'Error reading file from storage: {str(blob_err)}'}), 500 add_file_task_to_file_processing_log(document_id=file_id, user_id=user_id, content="Combining file content from chunks, filename: " + filename + ", is_table: " + str(is_table)) combined_parts = [] diff --git a/application/single_app/route_backend_plugins.py b/application/single_app/route_backend_plugins.py index 63c7854e..f4d4dca0 100644 --- a/application/single_app/route_backend_plugins.py +++ b/application/single_app/route_backend_plugins.py @@ -624,6 +624,8 @@ def get_core_plugin_settings(): 'enable_text_plugin': bool(settings.get('enable_text_plugin', True)), 'enable_default_embedding_model_plugin': bool(settings.get('enable_default_embedding_model_plugin', True)), 'enable_fact_memory_plugin': bool(settings.get('enable_fact_memory_plugin', True)), + 'enable_tabular_processing_plugin': bool(settings.get('enable_tabular_processing_plugin', False)), + 'enable_enhanced_citations': bool(settings.get('enable_enhanced_citations', False)), 'enable_semantic_kernel': bool(settings.get('enable_semantic_kernel', False)), 'allow_user_plugins': bool(settings.get('allow_user_plugins', True)), 'allow_group_plugins': bool(settings.get('allow_group_plugins', True)), @@ -646,6 +648,7 @@ def update_core_plugin_settings(): 'enable_text_plugin', 'enable_default_embedding_model_plugin', 'enable_fact_memory_plugin', + 'enable_tabular_processing_plugin', 'allow_user_plugins', 'allow_group_plugins' ] @@ -663,6 +666,11 @@ def update_core_plugin_settings(): return jsonify({'error': f"Field '{key}' must be a boolean."}), 400 updates[key] = data[key] logging.info("Validated plugin settings: %s", updates) + # Dependency: tabular processing requires enhanced citations + if updates.get('enable_tabular_processing_plugin', False): + full_settings = get_settings() + if not full_settings.get('enable_enhanced_citations', False): + return jsonify({'error': 'Tabular Processing requires Enhanced Citations to be enabled.'}), 400 # Update settings success = update_settings(updates) if success: diff --git a/application/single_app/route_enhanced_citations.py b/application/single_app/route_enhanced_citations.py index c81ef225..44a35223 100644 --- a/application/single_app/route_enhanced_citations.py +++ b/application/single_app/route_enhanced_citations.py @@ -8,6 +8,7 @@ import requests import mimetypes import io +import pandas from functions_authentication import login_required, user_required, get_current_user_id from functions_settings import get_settings, enabled_required @@ -15,7 +16,7 @@ from functions_group import get_user_groups from functions_public_workspaces import get_user_visible_public_workspace_ids_from_settings from swagger_wrapper import swagger_route, get_auth_security -from config import CLIENTS, storage_account_user_documents_container_name, storage_account_group_documents_container_name, storage_account_public_documents_container_name, IMAGE_EXTENSIONS, VIDEO_EXTENSIONS, AUDIO_EXTENSIONS +from config import CLIENTS, storage_account_user_documents_container_name, storage_account_group_documents_container_name, storage_account_public_documents_container_name, storage_account_personal_chat_container_name, IMAGE_EXTENSIONS, VIDEO_EXTENSIONS, AUDIO_EXTENSIONS, TABULAR_EXTENSIONS, cosmos_messages_container from functions_debug import debug_print def register_enhanced_citations_routes(app): @@ -183,6 +184,189 @@ def get_enhanced_citation_pdf(): except Exception as e: return jsonify({"error": str(e)}), 500 + @app.route("/api/enhanced_citations/tabular", methods=["GET"]) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + @enabled_required("enable_enhanced_citations") + def get_enhanced_citation_tabular(): + """ + Serve original tabular file (CSV, XLSX, etc.) from blob storage for download. + Used for chat-uploaded tabular files stored in blob storage. + """ + conversation_id = request.args.get("conversation_id") + file_id = request.args.get("file_id") + + if not conversation_id or not file_id: + return jsonify({"error": "conversation_id and file_id are required"}), 400 + + user_id = get_current_user_id() + if not user_id: + return jsonify({"error": "User not authenticated"}), 401 + + try: + # Look up the file message in Cosmos to get blob reference + query_str = """ + SELECT * FROM c + WHERE c.conversation_id = @conversation_id + AND c.id = @file_id + """ + items = list(cosmos_messages_container.query_items( + query=query_str, + parameters=[ + {'name': '@conversation_id', 'value': conversation_id}, + {'name': '@file_id', 'value': file_id} + ], + partition_key=conversation_id + )) + + if not items: + return jsonify({"error": "File not found"}), 404 + + file_msg = items[0] + file_content_source = file_msg.get('file_content_source', '') + + if file_content_source != 'blob': + return jsonify({"error": "File is not stored in blob storage"}), 400 + + blob_container = file_msg.get('blob_container', '') + blob_path = file_msg.get('blob_path', '') + filename = file_msg.get('filename', 'download') + + if not blob_container or not blob_path: + return jsonify({"error": "Blob reference is incomplete"}), 500 + + blob_service_client = CLIENTS.get("storage_account_office_docs_client") + if not blob_service_client: + return jsonify({"error": "Storage not available"}), 500 + + blob_client = blob_service_client.get_blob_client( + container=blob_container, + blob=blob_path + ) + stream = blob_client.download_blob() + content = stream.readall() + + # Determine content type + content_type, _ = mimetypes.guess_type(filename) + if not content_type: + content_type = 'application/octet-stream' + + return Response( + content, + content_type=content_type, + headers={ + 'Content-Length': str(len(content)), + 'Content-Disposition': f'attachment; filename="{filename}"', + 'Cache-Control': 'private, max-age=300', + } + ) + + except Exception as e: + debug_print(f"Error serving tabular citation: {e}") + return jsonify({"error": str(e)}), 500 + + @app.route("/api/enhanced_citations/tabular_workspace", methods=["GET"]) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + @enabled_required("enable_enhanced_citations") + def get_enhanced_citation_tabular_workspace(): + """ + Serve tabular file (CSV, XLSX, etc.) from blob storage for workspace documents. + Uses doc_id to look up the document across personal, group, and public workspaces. + """ + doc_id = request.args.get("doc_id") + if not doc_id: + return jsonify({"error": "doc_id is required"}), 400 + + user_id = get_current_user_id() + if not user_id: + return jsonify({"error": "User not authenticated"}), 401 + + try: + doc_response, status_code = get_document(user_id, doc_id) + if status_code != 200: + return doc_response, status_code + + raw_doc = doc_response.get_json() + file_name = raw_doc.get('file_name', '') + ext = file_name.lower().split('.')[-1] if '.' in file_name else '' + + if ext not in ('csv', 'xlsx', 'xls', 'xlsm'): + return jsonify({"error": "File is not a tabular file"}), 400 + + return serve_enhanced_citation_content(raw_doc, force_download=True) + + except Exception as e: + debug_print(f"Error serving tabular workspace citation: {e}") + return jsonify({"error": str(e)}), 500 + + @app.route("/api/enhanced_citations/tabular_preview", methods=["GET"]) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + @enabled_required("enable_enhanced_citations") + def get_enhanced_citation_tabular_preview(): + """ + Return JSON preview of a tabular file for rendering as an HTML table. + Reads the file into a pandas DataFrame and returns columns + rows as JSON. + """ + doc_id = request.args.get("doc_id") + max_rows = min(int(request.args.get("max_rows", 200)), 500) + if not doc_id: + return jsonify({"error": "doc_id is required"}), 400 + + user_id = get_current_user_id() + if not user_id: + return jsonify({"error": "User not authenticated"}), 401 + + try: + doc_response, status_code = get_document(user_id, doc_id) + if status_code != 200: + return doc_response, status_code + + raw_doc = doc_response.get_json() + file_name = raw_doc.get('file_name', '') + ext = file_name.lower().rsplit('.', 1)[-1] if '.' in file_name else '' + if ext not in ('csv', 'xlsx', 'xls', 'xlsm'): + return jsonify({"error": "File is not a tabular file"}), 400 + + # Download blob + workspace_type, container_name = determine_workspace_type_and_container(raw_doc) + blob_name = get_blob_name(raw_doc, workspace_type) + blob_service_client = CLIENTS.get("storage_account_office_docs_client") + if not blob_service_client: + return jsonify({"error": "Blob storage client not available"}), 500 + blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name) + data = blob_client.download_blob().readall() + + # Read into DataFrame + if ext == 'csv': + df = pandas.read_csv(io.BytesIO(data), keep_default_na=False, dtype=str) + elif ext in ('xlsx', 'xlsm'): + df = pandas.read_excel(io.BytesIO(data), engine='openpyxl', keep_default_na=False, dtype=str) + elif ext == 'xls': + df = pandas.read_excel(io.BytesIO(data), engine='xlrd', keep_default_na=False, dtype=str) + else: + return jsonify({"error": f"Unsupported file type: {ext}"}), 400 + + total_rows = len(df) + preview = df.head(max_rows) + + return jsonify({ + "filename": file_name, + "total_rows": total_rows, + "total_columns": len(df.columns), + "columns": list(df.columns), + "rows": preview.values.tolist(), + "truncated": total_rows > max_rows + }) + + except Exception as e: + debug_print(f"Error generating tabular preview: {e}") + return jsonify({"error": str(e)}), 500 + def get_document(user_id, doc_id): """ Get document metadata - searches across all enabled workspace types diff --git a/application/single_app/route_frontend_admin_settings.py b/application/single_app/route_frontend_admin_settings.py index 8c49522b..fdd77ce5 100644 --- a/application/single_app/route_frontend_admin_settings.py +++ b/application/single_app/route_frontend_admin_settings.py @@ -98,6 +98,8 @@ def admin_settings(): settings['enable_text_plugin'] = False if 'enable_fact_memory_plugin' not in settings: settings['enable_fact_memory_plugin'] = False + if 'enable_tabular_processing_plugin' not in settings: + settings['enable_tabular_processing_plugin'] = False if 'enable_default_embedding_model_plugin' not in settings: settings['enable_default_embedding_model_plugin'] = False if 'enable_multi_agent_orchestration' not in settings: diff --git a/application/single_app/route_frontend_chats.py b/application/single_app/route_frontend_chats.py index ca0feb1a..67a41879 100644 --- a/application/single_app/route_frontend_chats.py +++ b/application/single_app/route_frontend_chats.py @@ -237,8 +237,33 @@ def upload_file(): # Handle XML, YAML, and LOG files as text for inline chat extracted_content = extract_text_file(temp_file_path) elif file_ext_nodot in TABULAR_EXTENSIONS: - extracted_content = extract_table_file(temp_file_path, file_ext) is_table = True + + # Upload tabular file to blob storage for tabular processing plugin access + if settings.get('enable_enhanced_citations', False): + try: + blob_service_client = CLIENTS.get("storage_account_office_docs_client") + if blob_service_client: + blob_path = f"{user_id}/{conversation_id}/{filename}" + blob_client = blob_service_client.get_blob_client( + container=storage_account_personal_chat_container_name, + blob=blob_path + ) + metadata = { + "conversation_id": str(conversation_id), + "user_id": str(user_id) + } + with open(temp_file_path, "rb") as blob_f: + blob_client.upload_blob(blob_f, overwrite=True, metadata=metadata) + log_event(f"Uploaded chat tabular file to blob storage: {blob_path}") + except Exception as blob_err: + log_event( + f"Warning: Failed to upload chat tabular file to blob storage: {blob_err}", + level=logging.WARNING + ) + else: + # Only extract content for Cosmos storage when enhanced citations is disabled + extracted_content = extract_table_file(temp_file_path, file_ext) else: return jsonify({'error': 'Unsupported file type'}), 400 @@ -395,25 +420,50 @@ def upload_file(): current_thread_id = str(uuid.uuid4()) - file_message = { - 'id': file_message_id, - 'conversation_id': conversation_id, - 'role': 'file', - 'filename': filename, - 'file_content': extracted_content, - 'is_table': is_table, - 'timestamp': datetime.utcnow().isoformat(), - 'model_deployment_name': None, - 'metadata': { - 'thread_info': { - 'thread_id': current_thread_id, - 'previous_thread_id': previous_thread_id, - 'active_thread': True, - 'thread_attempt': 1 + # When enhanced citations is enabled and file is tabular, store a lightweight + # reference without file_content to avoid Cosmos DB size limits. + # The tabular data lives in blob storage and is served from there. + if is_table and settings.get('enable_enhanced_citations', False): + file_message = { + 'id': file_message_id, + 'conversation_id': conversation_id, + 'role': 'file', + 'filename': filename, + 'is_table': is_table, + 'file_content_source': 'blob', + 'blob_container': storage_account_personal_chat_container_name, + 'blob_path': f"{user_id}/{conversation_id}/{filename}", + 'timestamp': datetime.utcnow().isoformat(), + 'model_deployment_name': None, + 'metadata': { + 'thread_info': { + 'thread_id': current_thread_id, + 'previous_thread_id': previous_thread_id, + 'active_thread': True, + 'thread_attempt': 1 + } } } - } - + else: + file_message = { + 'id': file_message_id, + 'conversation_id': conversation_id, + 'role': 'file', + 'filename': filename, + 'file_content': extracted_content, + 'is_table': is_table, + 'timestamp': datetime.utcnow().isoformat(), + 'model_deployment_name': None, + 'metadata': { + 'thread_info': { + 'thread_id': current_thread_id, + 'previous_thread_id': previous_thread_id, + 'active_thread': True, + 'thread_attempt': 1 + } + } + } + # Add vision analysis if available if vision_analysis: file_message['vision_analysis'] = vision_analysis diff --git a/application/single_app/semantic_kernel_loader.py b/application/single_app/semantic_kernel_loader.py index 78f54203..eb9685bc 100644 --- a/application/single_app/semantic_kernel_loader.py +++ b/application/single_app/semantic_kernel_loader.py @@ -19,6 +19,7 @@ from semantic_kernel.functions.kernel_plugin import KernelPlugin from semantic_kernel_plugins.embedding_model_plugin import EmbeddingModelPlugin from semantic_kernel_plugins.fact_memory_plugin import FactMemoryPlugin +from semantic_kernel_plugins.tabular_processing_plugin import TabularProcessingPlugin from functions_settings import get_settings, get_user_settings from foundry_agent_runtime import AzureAIFoundryChatCompletionAgent from functions_appinsights import log_event, get_appinsights_logger @@ -408,6 +409,13 @@ def load_embedding_model_plugin(kernel: Kernel, settings): description="Provides text embedding functions using the configured embedding model." ) +def load_tabular_processing_plugin(kernel: Kernel): + kernel.add_plugin( + TabularProcessingPlugin(), + plugin_name="tabular_processing", + description="Provides data analysis on tabular files (CSV, XLSX) stored in blob storage. Can list files, describe schemas, aggregate columns, filter rows, run queries, and perform group-by operations." + ) + def load_core_plugins_only(kernel: Kernel, settings): """Load only core plugins for model-only conversations without agents.""" debug_print(f"[SK Loader] Loading core plugins only for model-only mode...") @@ -429,6 +437,10 @@ def load_core_plugins_only(kernel: Kernel, settings): load_text_plugin(kernel) log_event("[SK Loader] Loaded Text plugin.", level=logging.INFO) + if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + load_tabular_processing_plugin(kernel) + log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO) + # =================== Semantic Kernel Initialization =================== def initialize_semantic_kernel(user_id: str=None, redis_client=None): debug_print(f"[SK Loader] Initializing Semantic Kernel and plugins...") @@ -1013,6 +1025,14 @@ def load_plugins_for_kernel(kernel, plugin_manifests, settings, mode_label="glob except Exception as e: log_event(f"[SK Loader] Failed to load Fact Memory Plugin: {e}", level=logging.WARNING) + # Register Tabular Processing Plugin if enabled (requires enhanced citations) + if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + try: + load_tabular_processing_plugin(kernel) + log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO) + except Exception as e: + log_event(f"[SK Loader] Failed to load Tabular Processing plugin: {e}", level=logging.WARNING) + # Conditionally load static embedding model plugin if settings.get('enable_default_embedding_model_plugin', True): try: @@ -1357,7 +1377,11 @@ def load_user_semantic_kernel(kernel: Kernel, settings, user_id: str, redis_clie load_embedding_model_plugin(kernel, settings) print(f"[SK Loader] Loaded Default Embedding Model plugin.") log_event("[SK Loader] Loaded Default Embedding Model plugin.", level=logging.INFO) - + + if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False): + load_tabular_processing_plugin(kernel) + log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO) + # Get selected agent from user settings (this still needs to be in user settings for UI state) user_settings = get_user_settings(user_id).get('settings', {}) selected_agent = user_settings.get('selected_agent') diff --git a/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py b/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py new file mode 100644 index 00000000..a525250b --- /dev/null +++ b/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py @@ -0,0 +1,515 @@ +# tabular_processing_plugin.py +""" +TabularProcessingPlugin for Semantic Kernel: provides data analysis operations +on tabular files (CSV, XLSX, XLS, XLSM) stored in Azure Blob Storage. + +Works with workspace documents (user-documents, group-documents, public-documents) +and chat-uploaded documents (personal-chat container). +""" +import asyncio +import io +import json +import logging +import pandas +from typing import Annotated, Optional, List +from semantic_kernel.functions import kernel_function +from semantic_kernel_plugins.plugin_invocation_logger import plugin_function_logger +from functions_appinsights import log_event +from config import ( + CLIENTS, + TABULAR_EXTENSIONS, + storage_account_user_documents_container_name, + storage_account_personal_chat_container_name, + storage_account_group_documents_container_name, + storage_account_public_documents_container_name, +) + + +class TabularProcessingPlugin: + """Provides data analysis functions on tabular files stored in blob storage.""" + + SUPPORTED_EXTENSIONS = {'.csv', '.xlsx', '.xls', '.xlsm'} + + def __init__(self): + self._df_cache = {} # Per-instance cache: (container, blob_name) -> DataFrame + + def _get_blob_service_client(self): + """Get the blob service client from CLIENTS cache.""" + client = CLIENTS.get("storage_account_office_docs_client") + if not client: + raise RuntimeError("Blob storage client not available. Enhanced citations must be enabled.") + return client + + def _list_tabular_blobs(self, container_name: str, prefix: str) -> List[str]: + """List all tabular file blobs under a given prefix.""" + client = self._get_blob_service_client() + container_client = client.get_container_client(container_name) + blobs = [] + for blob in container_client.list_blobs(name_starts_with=prefix): + name_lower = blob['name'].lower() + if any(name_lower.endswith(ext) for ext in self.SUPPORTED_EXTENSIONS): + blobs.append(blob['name']) + return blobs + + def _read_tabular_blob_to_dataframe(self, container_name: str, blob_name: str) -> pandas.DataFrame: + """Download a blob and read it into a pandas DataFrame. Uses per-instance cache.""" + cache_key = (container_name, blob_name) + if cache_key in self._df_cache: + log_event(f"[TabularProcessingPlugin] Cache hit for {blob_name}", level=logging.DEBUG) + return self._df_cache[cache_key].copy() + + client = self._get_blob_service_client() + blob_client = client.get_blob_client(container=container_name, blob=blob_name) + stream = blob_client.download_blob() + data = stream.readall() + + name_lower = blob_name.lower() + if name_lower.endswith('.csv'): + df = pandas.read_csv(io.BytesIO(data), keep_default_na=False, dtype=str) + elif name_lower.endswith('.xlsx') or name_lower.endswith('.xlsm'): + df = pandas.read_excel(io.BytesIO(data), engine='openpyxl', keep_default_na=False, dtype=str) + elif name_lower.endswith('.xls'): + df = pandas.read_excel(io.BytesIO(data), engine='xlrd', keep_default_na=False, dtype=str) + else: + raise ValueError(f"Unsupported tabular file type: {blob_name}") + + self._df_cache[cache_key] = df + log_event(f"[TabularProcessingPlugin] Cached DataFrame for {blob_name} ({len(df)} rows)", level=logging.DEBUG) + return df.copy() + + def _try_numeric_conversion(self, df: pandas.DataFrame) -> pandas.DataFrame: + """Attempt to convert string columns to numeric where possible.""" + for col in df.columns: + try: + df[col] = pandas.to_numeric(df[col]) + except (ValueError, TypeError): + pass + return df + + def _resolve_blob_location(self, user_id: str, conversation_id: str, filename: str, source: str, + group_id: str = None, public_workspace_id: str = None) -> tuple: + """Resolve container name and blob path from source type.""" + source = source.lower().strip() + if source == 'chat': + container = storage_account_personal_chat_container_name + blob_path = f"{user_id}/{conversation_id}/{filename}" + elif source == 'workspace': + container = storage_account_user_documents_container_name + blob_path = f"{user_id}/{filename}" + elif source == 'group': + if not group_id: + raise ValueError("group_id is required for source='group'") + container = storage_account_group_documents_container_name + blob_path = f"{group_id}/{filename}" + elif source == 'public': + if not public_workspace_id: + raise ValueError("public_workspace_id is required for source='public'") + container = storage_account_public_documents_container_name + blob_path = f"{public_workspace_id}/{filename}" + else: + raise ValueError(f"Unknown source '{source}'. Use 'workspace', 'chat', 'group', or 'public'.") + return container, blob_path + + def _resolve_blob_location_with_fallback(self, user_id: str, conversation_id: str, filename: str, source: str, + group_id: str = None, public_workspace_id: str = None) -> tuple: + """Try primary source first, then fall back to other containers if blob not found.""" + source = source.lower().strip() + attempts = [] + + # Primary attempt based on specified source + try: + primary = self._resolve_blob_location(user_id, conversation_id, filename, source, group_id, public_workspace_id) + attempts.append(primary) + except ValueError: + pass + + # Fallback attempts in priority order (skip the primary source) + if source != 'workspace': + attempts.append((storage_account_user_documents_container_name, f"{user_id}/{filename}")) + if source != 'group' and group_id: + attempts.append((storage_account_group_documents_container_name, f"{group_id}/{filename}")) + if source != 'public' and public_workspace_id: + attempts.append((storage_account_public_documents_container_name, f"{public_workspace_id}/{filename}")) + if source != 'chat': + attempts.append((storage_account_personal_chat_container_name, f"{user_id}/{conversation_id}/{filename}")) + + client = self._get_blob_service_client() + for container, blob_path in attempts: + try: + blob_client = client.get_blob_client(container=container, blob=blob_path) + if blob_client.exists(): + log_event(f"[TabularProcessingPlugin] Found blob at {container}/{blob_path}", level=logging.DEBUG) + return container, blob_path + except Exception: + continue + + # If nothing found, return primary for the original error message + if attempts: + return attempts[0] + raise ValueError(f"Could not resolve blob location for {filename}") + + @kernel_function( + description=( + "List all tabular data files available for a user. Checks workspace documents " + "(user-documents container), chat-uploaded documents (personal-chat container), " + "and optionally group or public workspace documents. " + "Returns a JSON list of available files with their source." + ), + name="list_tabular_files" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def list_tabular_files( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON list of available tabular files"]: + """List all tabular files available for the user across all accessible containers.""" + def _sync_work(): + results = [] + try: + workspace_prefix = f"{user_id}/" + workspace_blobs = self._list_tabular_blobs( + storage_account_user_documents_container_name, workspace_prefix + ) + for blob in workspace_blobs: + filename = blob.split('/')[-1] + results.append({ + "filename": filename, + "blob_path": blob, + "source": "workspace", + "container": storage_account_user_documents_container_name + }) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error listing workspace blobs: {e}", level=logging.WARNING) + + try: + chat_prefix = f"{user_id}/{conversation_id}/" + chat_blobs = self._list_tabular_blobs( + storage_account_personal_chat_container_name, chat_prefix + ) + for blob in chat_blobs: + filename = blob.split('/')[-1] + results.append({ + "filename": filename, + "blob_path": blob, + "source": "chat", + "container": storage_account_personal_chat_container_name + }) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error listing chat blobs: {e}", level=logging.WARNING) + + if group_id: + try: + group_prefix = f"{group_id}/" + group_blobs = self._list_tabular_blobs( + storage_account_group_documents_container_name, group_prefix + ) + for blob in group_blobs: + filename = blob.split('/')[-1] + results.append({ + "filename": filename, + "blob_path": blob, + "source": "group", + "container": storage_account_group_documents_container_name + }) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error listing group blobs: {e}", level=logging.WARNING) + + if public_workspace_id: + try: + public_prefix = f"{public_workspace_id}/" + public_blobs = self._list_tabular_blobs( + storage_account_public_documents_container_name, public_prefix + ) + for blob in public_blobs: + filename = blob.split('/')[-1] + results.append({ + "filename": filename, + "blob_path": blob, + "source": "public", + "container": storage_account_public_documents_container_name + }) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error listing public blobs: {e}", level=logging.WARNING) + + return json.dumps(results, indent=2) + return await asyncio.to_thread(_sync_work) + + @kernel_function( + description=( + "Get a summary of a tabular file including column names, row count, data types, " + "and a preview of the first few rows." + ), + name="describe_tabular_file" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def describe_tabular_file( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + filename: Annotated[str, "The filename of the tabular file"], + source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat", + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON summary of the tabular file"]: + """Get schema and preview of a tabular file.""" + def _sync_work(): + try: + container, blob_path = self._resolve_blob_location( + user_id, conversation_id, filename, source, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = self._read_tabular_blob_to_dataframe(container, blob_path) + df_numeric = self._try_numeric_conversion(df.copy()) + + summary = { + "filename": filename, + "row_count": len(df), + "column_count": len(df.columns), + "columns": list(df.columns), + "dtypes": {col: str(dtype) for col, dtype in df_numeric.dtypes.items()}, + "preview": df.head(5).to_dict(orient='records'), + "null_counts": df.isnull().sum().to_dict() + } + return json.dumps(summary, indent=2, default=str) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error describing file: {e}", level=logging.WARNING) + return json.dumps({"error": str(e)}) + return await asyncio.to_thread(_sync_work) + + @kernel_function( + description=( + "Execute an aggregation operation on a column of a tabular file. " + "Supported operations: sum, mean, count, min, max, median, std, nunique, value_counts." + ), + name="aggregate_column" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def aggregate_column( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + filename: Annotated[str, "The filename of the tabular file"], + column: Annotated[str, "The column name to aggregate"], + operation: Annotated[str, "Aggregation: sum, mean, count, min, max, median, std, nunique, value_counts"], + source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat", + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON result of the aggregation"]: + """Execute an aggregation operation on a column.""" + def _sync_work(): + try: + container, blob_path = self._resolve_blob_location( + user_id, conversation_id, filename, source, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = self._read_tabular_blob_to_dataframe(container, blob_path) + df = self._try_numeric_conversion(df) + + if column not in df.columns: + return json.dumps({"error": f"Column '{column}' not found. Available: {list(df.columns)}"}) + + series = df[column] + op = operation.lower().strip() + + if op == 'sum': + result = series.sum() + elif op == 'mean': + result = series.mean() + elif op == 'count': + result = series.count() + elif op == 'min': + result = series.min() + elif op == 'max': + result = series.max() + elif op == 'median': + result = series.median() + elif op == 'std': + result = series.std() + elif op == 'nunique': + result = series.nunique() + elif op == 'value_counts': + result = series.value_counts().to_dict() + else: + return json.dumps({"error": f"Unsupported operation: {operation}. Use sum, mean, count, min, max, median, std, nunique, value_counts."}) + + return json.dumps({"column": column, "operation": op, "result": result}, indent=2, default=str) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error aggregating column: {e}", level=logging.WARNING) + return json.dumps({"error": str(e)}) + return await asyncio.to_thread(_sync_work) + + @kernel_function( + description=( + "Filter rows in a tabular file based on conditions and return matching rows. " + "Supports operators: ==, !=, >, <, >=, <=, contains, startswith, endswith." + ), + name="filter_rows" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def filter_rows( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + filename: Annotated[str, "The filename of the tabular file"], + column: Annotated[str, "The column to filter on"], + operator: Annotated[str, "Operator: ==, !=, >, <, >=, <=, contains, startswith, endswith"], + value: Annotated[str, "The value to compare against"], + source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat", + max_rows: Annotated[str, "Maximum rows to return"] = "100", + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON list of matching rows"]: + """Filter rows based on a condition.""" + def _sync_work(): + try: + container, blob_path = self._resolve_blob_location( + user_id, conversation_id, filename, source, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = self._read_tabular_blob_to_dataframe(container, blob_path) + df = self._try_numeric_conversion(df) + + if column not in df.columns: + return json.dumps({"error": f"Column '{column}' not found. Available: {list(df.columns)}"}) + + series = df[column] + op = operator.strip().lower() + + numeric_value = None + try: + numeric_value = float(value) + except (ValueError, TypeError): + pass + + if op == '==' or op == 'equals': + if numeric_value is not None and pandas.api.types.is_numeric_dtype(series): + mask = series == numeric_value + else: + mask = series.astype(str).str.lower() == value.lower() + elif op == '!=': + if numeric_value is not None and pandas.api.types.is_numeric_dtype(series): + mask = series != numeric_value + else: + mask = series.astype(str).str.lower() != value.lower() + elif op == '>': + mask = series > numeric_value + elif op == '<': + mask = series < numeric_value + elif op == '>=': + mask = series >= numeric_value + elif op == '<=': + mask = series <= numeric_value + elif op == 'contains': + mask = series.astype(str).str.contains(value, case=False, na=False) + elif op == 'startswith': + mask = series.astype(str).str.lower().str.startswith(value.lower()) + elif op == 'endswith': + mask = series.astype(str).str.lower().str.endswith(value.lower()) + else: + return json.dumps({"error": f"Unsupported operator: {operator}"}) + + limit = int(max_rows) + filtered = df[mask].head(limit) + return json.dumps({ + "total_matches": int(mask.sum()), + "returned_rows": len(filtered), + "data": filtered.to_dict(orient='records') + }, indent=2, default=str) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error filtering rows: {e}", level=logging.WARNING) + return json.dumps({"error": str(e)}) + return await asyncio.to_thread(_sync_work) + + @kernel_function( + description=( + "Execute a pandas query expression against a tabular file for advanced analysis. " + "The query string uses pandas DataFrame.query() syntax. " + "Examples: 'Age > 30 and State == \"CA\"', 'Price < 100'" + ), + name="query_tabular_data" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def query_tabular_data( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + filename: Annotated[str, "The filename of the tabular file"], + query_expression: Annotated[str, "Pandas query expression (e.g. 'Age > 30 and State == \"CA\"')"], + source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat", + max_rows: Annotated[str, "Maximum rows to return"] = "100", + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON result of the query"]: + """Execute a pandas query expression against a tabular file.""" + def _sync_work(): + try: + container, blob_path = self._resolve_blob_location( + user_id, conversation_id, filename, source, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = self._read_tabular_blob_to_dataframe(container, blob_path) + df = self._try_numeric_conversion(df) + + result_df = df.query(query_expression) + limit = int(max_rows) + return json.dumps({ + "total_matches": len(result_df), + "returned_rows": min(len(result_df), limit), + "data": result_df.head(limit).to_dict(orient='records') + }, indent=2, default=str) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error querying data: {e}", level=logging.WARNING) + return json.dumps({"error": f"Query error: {str(e)}. Ensure column names and values are correct."}) + return await asyncio.to_thread(_sync_work) + + @kernel_function( + description=( + "Perform a group-by aggregation on a tabular file. " + "Groups data by one column and aggregates another column. " + "Supported operations: sum, mean, count, min, max." + ), + name="group_by_aggregate" + ) + @plugin_function_logger("TabularProcessingPlugin") + async def group_by_aggregate( + self, + user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"], + conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"], + filename: Annotated[str, "The filename of the tabular file"], + group_by_column: Annotated[str, "The column to group by"], + aggregate_column: Annotated[str, "The column to aggregate"], + operation: Annotated[str, "Aggregation operation: sum, mean, count, min, max"], + source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat", + group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None, + public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None, + ) -> Annotated[str, "JSON result of the group-by aggregation"]: + """Group by one column and aggregate another.""" + def _sync_work(): + try: + container, blob_path = self._resolve_blob_location( + user_id, conversation_id, filename, source, + group_id=group_id, public_workspace_id=public_workspace_id + ) + df = self._read_tabular_blob_to_dataframe(container, blob_path) + df = self._try_numeric_conversion(df) + + for col in [group_by_column, aggregate_column]: + if col not in df.columns: + return json.dumps({"error": f"Column '{col}' not found. Available: {list(df.columns)}"}) + + op = operation.lower().strip() + grouped = df.groupby(group_by_column)[aggregate_column].agg(op) + return json.dumps({ + "group_by": group_by_column, + "aggregate_column": aggregate_column, + "operation": op, + "groups": len(grouped), + "result": grouped.to_dict() + }, indent=2, default=str) + except Exception as e: + log_event(f"[TabularProcessingPlugin] Error in group-by: {e}", level=logging.WARNING) + return json.dumps({"error": str(e)}) + return await asyncio.to_thread(_sync_work) diff --git a/application/single_app/static/js/admin/admin_settings.js b/application/single_app/static/js/admin/admin_settings.js index 87478db0..9cf4580d 100644 --- a/application/single_app/static/js/admin/admin_settings.js +++ b/application/single_app/static/js/admin/admin_settings.js @@ -1237,10 +1237,11 @@ function setupToggles() { const mathToggle = document.getElementById('toggle-math-plugin'); const textToggle = document.getElementById('toggle-text-plugin'); const factMemoryToggle = document.getElementById('toggle-fact-memory-plugin'); + const tabularProcessingToggle = document.getElementById('toggle-tabular-processing-plugin'); const embeddingToggle = document.getElementById('toggle-default-embedding-model-plugin'); const allowUserPluginsToggle = document.getElementById('toggle-allow-user-plugins'); const allowGroupPluginsToggle = document.getElementById('toggle-allow-group-plugins'); - const toggles = [timeToggle, httpToggle, waitToggle, mathToggle, textToggle, factMemoryToggle, embeddingToggle, allowUserPluginsToggle, allowGroupPluginsToggle]; + const toggles = [timeToggle, httpToggle, waitToggle, mathToggle, textToggle, factMemoryToggle, tabularProcessingToggle, embeddingToggle, allowUserPluginsToggle, allowGroupPluginsToggle]; // Feedback area let feedbackDiv = document.getElementById('core-plugin-toggles-feedback'); if (!feedbackDiv) { @@ -1270,6 +1271,16 @@ function setupToggles() { if (textToggle) textToggle.checked = !!settings.enable_text_plugin; if (embeddingToggle) embeddingToggle.checked = !!settings.enable_default_embedding_model_plugin; if (factMemoryToggle) factMemoryToggle.checked = !!settings.enable_fact_memory_plugin; + if (tabularProcessingToggle) { + tabularProcessingToggle.checked = !!settings.enable_tabular_processing_plugin; + const ecEnabled = !!settings.enable_enhanced_citations; + tabularProcessingToggle.disabled = !ecEnabled; + const depNote = document.getElementById('tabular-processing-dependency-note'); + if (depNote) { + depNote.textContent = ecEnabled ? 'Requires Enhanced Citations' : 'Requires Enhanced Citations (currently disabled)'; + depNote.className = ecEnabled ? 'text-muted d-block ms-4' : 'text-danger d-block ms-4'; + } + } if (allowUserPluginsToggle) allowUserPluginsToggle.checked = !!settings.allow_user_plugins; if (allowGroupPluginsToggle) allowGroupPluginsToggle.checked = !!settings.allow_group_plugins; } catch (err) { @@ -1291,6 +1302,7 @@ function setupToggles() { enable_text_plugin: textToggle ? textToggle.checked : false, enable_default_embedding_model_plugin: embeddingToggle ? embeddingToggle.checked : false, enable_fact_memory_plugin: factMemoryToggle ? factMemoryToggle.checked : false, + enable_tabular_processing_plugin: tabularProcessingToggle ? tabularProcessingToggle.checked : false, allow_user_plugins: allowUserPluginsToggle ? allowUserPluginsToggle.checked : false, allow_group_plugins: allowGroupPluginsToggle ? allowGroupPluginsToggle.checked : false }; diff --git a/application/single_app/static/js/chat/chat-enhanced-citations.js b/application/single_app/static/js/chat/chat-enhanced-citations.js index dcda708b..9d4344bb 100644 --- a/application/single_app/static/js/chat/chat-enhanced-citations.js +++ b/application/single_app/static/js/chat/chat-enhanced-citations.js @@ -18,11 +18,13 @@ export function getFileType(fileName) { const imageExtensions = ['jpg', 'jpeg', 'png', 'bmp', 'tiff', 'tif']; const videoExtensions = ['mp4', 'mov', 'avi', 'mkv', 'flv', 'webm', 'wmv', 'm4v', '3gp']; const audioExtensions = ['mp3', 'wav', 'ogg', 'aac', 'flac', 'm4a']; - + const tabularExtensions = ['csv', 'xlsx', 'xls', 'xlsm']; + if (imageExtensions.includes(ext)) return 'image'; if (ext === 'pdf') return 'pdf'; if (videoExtensions.includes(ext)) return 'video'; if (audioExtensions.includes(ext)) return 'audio'; + if (tabularExtensions.includes(ext)) return 'tabular'; return 'other'; } @@ -66,6 +68,9 @@ export function showEnhancedCitationModal(docId, pageNumberOrTimestamp, citation const audioTimestamp = convertTimestampToSeconds(pageNumberOrTimestamp); showAudioModal(docId, audioTimestamp, docMetadata.file_name); break; + case 'tabular': + showTabularDownloadModal(docId, docMetadata.file_name); + break; default: // Fall back to text citation for unsupported types import('./chat-citations.js').then(module => { @@ -291,6 +296,119 @@ export function showAudioModal(docId, timestamp, fileName) { modalInstance.show(); } +/** + * Show tabular file preview modal with data table + * @param {string} docId - Document ID + * @param {string} fileName - File name + */ +export function showTabularDownloadModal(docId, fileName) { + console.log(`Showing tabular preview modal for docId: ${docId}, fileName: ${fileName}`); + showLoadingIndicator(); + + // Create or get tabular modal + let tabularModal = document.getElementById("enhanced-tabular-modal"); + if (!tabularModal) { + tabularModal = createTabularModal(); + } + + const title = tabularModal.querySelector(".modal-title"); + const tableContainer = tabularModal.querySelector("#enhanced-tabular-table-container"); + const rowInfo = tabularModal.querySelector("#enhanced-tabular-row-info"); + const downloadBtn = tabularModal.querySelector("#enhanced-tabular-download"); + const errorContainer = tabularModal.querySelector("#enhanced-tabular-error"); + + title.textContent = `Tabular Data: ${fileName}`; + tableContainer.innerHTML = '

    Loading data preview...

    '; + rowInfo.textContent = ''; + errorContainer.classList.add('d-none'); + + const downloadUrl = `/api/enhanced_citations/tabular_workspace?doc_id=${encodeURIComponent(docId)}`; + downloadBtn.href = downloadUrl; + downloadBtn.download = fileName; + + // Show modal immediately with loading state + const modalInstance = new bootstrap.Modal(tabularModal); + modalInstance.show(); + + // Fetch preview data + const previewUrl = `/api/enhanced_citations/tabular_preview?doc_id=${encodeURIComponent(docId)}`; + fetch(previewUrl) + .then(response => { + if (!response.ok) throw new Error(`HTTP ${response.status}`); + return response.json(); + }) + .then(data => { + hideLoadingIndicator(); + if (data.error) { + showTabularError(tableContainer, errorContainer, data.error); + return; + } + renderTabularPreview(tableContainer, rowInfo, data); + }) + .catch(error => { + hideLoadingIndicator(); + console.error('Error loading tabular preview:', error); + showTabularError(tableContainer, errorContainer, 'Could not load data preview.'); + }); +} + +/** + * Render tabular data as an HTML table + * @param {HTMLElement} container - Table container element + * @param {HTMLElement} rowInfo - Row info display element + * @param {Object} data - Preview data from API + */ +function renderTabularPreview(container, rowInfo, data) { + const { columns, rows, total_rows, truncated } = data; + + // Build table HTML + let html = ''; + + // Header + html += ''; + for (const col of columns) { + const escaped = col.replace(/&/g, '&').replace(//g, '>'); + html += ``; + } + html += ''; + + // Body + html += ''; + for (const row of rows) { + html += ''; + for (const cell of row) { + const val = cell === null || cell === undefined ? '' : String(cell); + const escaped = val.replace(/&/g, '&').replace(//g, '>'); + html += ``; + } + html += ''; + } + html += '
    ${escaped}
    ${escaped}
    '; + + container.innerHTML = html; + + // Row info + const displayedRows = rows.length; + const totalFormatted = total_rows.toLocaleString(); + if (truncated) { + rowInfo.textContent = `Showing ${displayedRows.toLocaleString()} of ${totalFormatted} rows`; + } else { + rowInfo.textContent = `${totalFormatted} rows, ${columns.length} columns`; + } +} + +/** + * Show error state in tabular modal with download fallback + * @param {HTMLElement} tableContainer - Table container element + * @param {HTMLElement} errorContainer - Error display element + * @param {string} message - Error message + */ +function showTabularError(tableContainer, errorContainer, message) { + tableContainer.innerHTML = '
    '; + errorContainer.textContent = message + ' You can still download the file below.'; + errorContainer.classList.remove('d-none'); +} + /** * Convert timestamp string to seconds * @param {string|number} timestamp - Timestamp in various formats @@ -445,3 +563,36 @@ function createPdfModal() { document.body.appendChild(modal); return modal; } + +/** + * Create tabular file preview modal HTML structure + * @returns {HTMLElement} - Modal element + */ +function createTabularModal() { + const modal = document.createElement("div"); + modal.id = "enhanced-tabular-modal"; + modal.classList.add("modal", "fade"); + modal.tabIndex = -1; + modal.innerHTML = ` +
    +
    +
    +
    Tabular Data Citation
    + +
    +
    +
    +
    +
    +
    + + + Download File + +
    +
    +
    + `; + document.body.appendChild(modal); + return modal; +} diff --git a/application/single_app/static/js/chat/chat-input-actions.js b/application/single_app/static/js/chat/chat-input-actions.js index 77851319..c0c7832b 100644 --- a/application/single_app/static/js/chat/chat-input-actions.js +++ b/application/single_app/static/js/chat/chat-input-actions.js @@ -127,11 +127,11 @@ export function fetchFileContent(conversationId, fileId) { hideLoadingIndicator(); if (data.file_content && data.filename) { - showFileContentPopup(data.file_content, data.filename, data.is_table); + showFileContentPopup(data.file_content, data.filename, data.is_table, data.file_content_source, conversationId, fileId); } else if (data.error) { showToast(data.error, "danger"); } else { - ashowToastlert("Unexpected response from server.", "danger"); + showToast("Unexpected response from server.", "danger"); } }) .catch((error) => { @@ -141,7 +141,7 @@ export function fetchFileContent(conversationId, fileId) { }); } -export function showFileContentPopup(fileContent, filename, isTable) { +export function showFileContentPopup(fileContent, filename, isTable, fileContentSource, conversationId, fileId) { let modalContainer = document.getElementById("file-modal"); if (!modalContainer) { modalContainer = document.createElement("div"); @@ -155,6 +155,7 @@ export function showFileContentPopup(fileContent, filename, isTable) {
    Uploaded File: ${filename}
    +
    +
    + + + + Requires Enhanced Citations +
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autonomously. + * **Pre-Computed Results Injected as Context**: The mini-agent's computed analysis (exact numerical results, aggregations, filtered data) is injected into the main LLM's system context so it can present accurate, citation-backed answers without hallucinating numbers. + * **Graceful Degradation**: If the mini-agent analysis fails for any reason, the system falls back to instructing the main LLM to use the tabular processing plugin functions directly, preserving full functionality. + * **Non-Streaming and Streaming Support**: Both chat modes are supported. The mini-agent runs synchronously before the main LLM call in both paths. + * **Requires Enhanced Citations**: The tabular processing plugin depends on the blob storage client initialized by the enhanced citations system. The `enable_enhanced_citations` admin setting must be enabled for tabular data analysis to activate. + * **Files Modified**: `route_backend_chats.py`, `semantic_kernel_plugins/tabular_processing_plugin.py`, `config.py`. + * (Ref: `run_tabular_sk_analysis()`, `TabularProcessingPlugin`, `collect_tabular_sk_citations()`, `TABULAR_EXTENSIONS`) + +* **Tabular Tool Execution Citations** + * Every tool call made by the SK mini-agent during tabular analysis is captured and surfaced as an agent citation, providing full transparency into the data analysis pipeline. + * **Automatic Capture**: The existing `@plugin_function_logger` decorator on all `TabularProcessingPlugin` functions records each invocation including function name, input parameters, returned results, execution duration, and success/failure status. + * **Citation Format**: Tool execution citations appear in the same "Agent Tool Execution" modal used by full agent mode, showing `tool_name` (e.g., `TabularProcessingPlugin.aggregate_column`), `function_arguments` (the exact parameters passed), and `function_result` (the computed data returned). + * **End-to-End Auditability**: Users can verify exactly which aggregations, filters, or queries were run against their data, what parameters were used, and what raw results were returned โ€” before the LLM summarized them into the final response. + * **Files Modified**: `route_backend_chats.py`. + * (Ref: `collect_tabular_sk_citations()`, `plugin_invocation_logger.py`) + +* **SK Mini-Agent Performance Optimization** + * Reduced typical tabular analysis time from ~74 seconds to an estimated ~30-33 seconds (55-60% reduction) through three complementary optimizations. + * **DataFrame Caching**: Per-request in-memory cache eliminates redundant blob downloads. Previously, each of the ~8 tool calls in a typical analysis downloaded and parsed the same file independently. Now the file is downloaded once and subsequent calls read from cache. Cache is automatically scoped to the request (new plugin instance per analysis) and garbage-collected afterward. + * **Pre-Dispatch Schema Injection**: File schemas (columns, data types, row counts, and a 3-row preview) are pre-loaded and injected into the SK mini-agent's system prompt before execution begins. This eliminates 2 LLM round-trips that were previously spent on file discovery (`list_tabular_files`) and schema inspection (`describe_tabular_file`), allowing the model to jump directly to analysis tool calls. + * **Async Plugin Functions**: All six `@kernel_function` methods converted to `async def` using `asyncio.to_thread()`. This enables Semantic Kernel's built-in `asyncio.gather()` to truly parallelize batched tool calls (e.g., 3 simultaneous `aggregate_column` calls) instead of executing them serially on the event loop. + * **Batching Instructions**: The system prompt now instructs the model to batch multiple independent function calls in a single response, reducing LLM round-trips further. + * **Files Modified**: `tabular_processing_plugin.py`, `route_backend_chats.py`, `config.py`. + * (Ref: `_df_cache`, `asyncio.to_thread`, pre-dispatch schema injection in `run_tabular_sk_analysis()`) + +--- * **Chat with Agent Button for Group Agents** * Added a "Chat" button to each group agent row, allowing users to quickly select a group agent and navigate to the chat page. * (Ref: `group_agents.js`, `group_workspaces.html`) diff --git a/functional_tests/test_chat_tabular_sk_trigger.py b/functional_tests/test_chat_tabular_sk_trigger.py new file mode 100644 index 00000000..9de91b39 --- /dev/null +++ b/functional_tests/test_chat_tabular_sk_trigger.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python3 +# test_chat_tabular_sk_trigger.py +""" +Functional test for chat-uploaded tabular file SK mini-agent trigger fix. +Version: 0.239.008 +Implemented in: 0.239.008 + +This test ensures that when tabular data files (CSV, XLSX) are uploaded directly +to a chat conversation, the SK mini-agent (`run_tabular_sk_analysis`) is properly +triggered in model-only mode (no agent selected) to pre-compute analysis results. + +Previously, the mini SK agent only triggered from search results, missing +chat-uploaded files entirely. The streaming path also ignored file role messages. + +This test validates: +1. Chat-uploaded tabular files are detected in conversation history (file role) +2. Filenames are collected into chat_tabular_files set for both blob and inline cases +3. The streaming path properly handles file role messages (not just user/assistant) +4. The mini SK trigger block activates when chat_tabular_files is non-empty +""" + +import sys +import os + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'application', 'single_app')) + + +def test_chat_tabular_file_detection(): + """Test that chat-uploaded tabular files are detected from file role messages.""" + print("Testing chat-uploaded tabular file detection...") + + try: + # Simulate conversation messages as they would appear from Cosmos DB + recent_messages = [ + { + 'role': 'file', + 'filename': 'Sample_-_Superstore_1.xlsx', + 'file_content': '', + 'is_table': True, + 'file_content_source': 'blob', + 'metadata': {}, + }, + { + 'role': 'user', + 'content': 'analyze sales/profit', + 'metadata': {}, + }, + ] + + # Simulate the conversation history building logic (streaming path) + conversation_history_for_api = [] + allowed_roles_in_history = ['user', 'assistant'] + max_file_content_length_in_history = 50000 + max_tabular_content_length_in_history = 50000 + chat_tabular_files = set() + + for message in recent_messages: + role = message.get('role') + content = message.get('content', '') + + if role in allowed_roles_in_history: + conversation_history_for_api.append({ + 'role': role, + 'content': content + }) + elif role == 'file': + filename = message.get('filename', 'uploaded_file') + file_content = message.get('file_content', '') + is_table = message.get('is_table', False) + file_content_source = message.get('file_content_source', '') + + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"[User uploaded a tabular data file named '{filename}'. " + f"The file is stored in blob storage and available for analysis. " + f"The file source is 'chat'.]" + ) + }) + + # Verify file was detected + assert 'Sample_-_Superstore_1.xlsx' in chat_tabular_files, \ + f"Expected xlsx file in chat_tabular_files, got: {chat_tabular_files}" + + # Verify system message was added for the file + system_msgs = [m for m in conversation_history_for_api if m['role'] == 'system'] + assert len(system_msgs) == 1, f"Expected 1 system message, got {len(system_msgs)}" + assert 'Sample_-_Superstore_1.xlsx' in system_msgs[0]['content'], \ + "System message should reference the uploaded file" + + # Verify user message was included + user_msgs = [m for m in conversation_history_for_api if m['role'] == 'user'] + assert len(user_msgs) == 1, f"Expected 1 user message, got {len(user_msgs)}" + assert user_msgs[0]['content'] == 'analyze sales/profit', \ + "User message content should be preserved" + + print("Test passed!") + return True + + except Exception as e: + print(f"Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_non_tabular_file_handling(): + """Test that non-tabular files are handled with content preview.""" + print("Testing non-tabular file handling in streaming path...") + + try: + recent_messages = [ + { + 'role': 'file', + 'filename': 'report.txt', + 'file_content': 'This is the report content.', + 'is_table': False, + 'file_content_source': 'inline', + 'metadata': {}, + }, + { + 'role': 'user', + 'content': 'summarize the report', + 'metadata': {}, + }, + ] + + conversation_history_for_api = [] + allowed_roles_in_history = ['user', 'assistant'] + max_file_content_length_in_history = 50000 + max_tabular_content_length_in_history = 50000 + chat_tabular_files = set() + + for message in recent_messages: + role = message.get('role') + content = message.get('content', '') + + if role in allowed_roles_in_history: + conversation_history_for_api.append({ + 'role': role, + 'content': content + }) + elif role == 'file': + filename = message.get('filename', 'uploaded_file') + file_content = message.get('file_content', '') + is_table = message.get('is_table', False) + file_content_source = message.get('file_content_source', '') + + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) + conversation_history_for_api.append({ + 'role': 'system', + 'content': f"[User uploaded a tabular data file named '{filename}'.]" + }) + else: + content_limit = ( + max_tabular_content_length_in_history if is_table + else max_file_content_length_in_history + ) + display_content = file_content[:content_limit] + if len(file_content) > content_limit: + display_content += "..." + + conversation_history_for_api.append({ + 'role': 'system', + 'content': ( + f"[User uploaded a file named '{filename}'. " + f"Content preview:\n{display_content}]\n" + f"Use this file context if relevant." + ) + }) + + # Non-tabular files should NOT be in chat_tabular_files + assert len(chat_tabular_files) == 0, \ + f"Non-tabular files should not be tracked, got: {chat_tabular_files}" + + # System message should contain file content preview + system_msgs = [m for m in conversation_history_for_api if m['role'] == 'system'] + assert len(system_msgs) == 1, f"Expected 1 system message, got {len(system_msgs)}" + assert 'report.txt' in system_msgs[0]['content'], \ + "System message should reference the file" + assert 'This is the report content.' in system_msgs[0]['content'], \ + "System message should include file content preview" + + print("Test passed!") + return True + + except Exception as e: + print(f"Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_multiple_tabular_files(): + """Test detection of multiple tabular files uploaded to chat.""" + print("Testing multiple tabular file detection...") + + try: + recent_messages = [ + { + 'role': 'file', + 'filename': 'sales_2024.xlsx', + 'file_content': '', + 'is_table': True, + 'file_content_source': 'blob', + 'metadata': {}, + }, + { + 'role': 'file', + 'filename': 'inventory.csv', + 'file_content': 'item,count\nwidget,100', + 'is_table': True, + 'file_content_source': 'blob', + 'metadata': {}, + }, + { + 'role': 'user', + 'content': 'compare sales to inventory', + 'metadata': {}, + }, + ] + + chat_tabular_files = set() + conversation_history_for_api = [] + allowed_roles_in_history = ['user', 'assistant'] + + for message in recent_messages: + role = message.get('role') + content = message.get('content', '') + + if role in allowed_roles_in_history: + conversation_history_for_api.append({'role': role, 'content': content}) + elif role == 'file': + filename = message.get('filename', 'uploaded_file') + is_table = message.get('is_table', False) + file_content_source = message.get('file_content_source', '') + + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) + conversation_history_for_api.append({ + 'role': 'system', + 'content': f"[Tabular file '{filename}' available for analysis.]" + }) + + assert len(chat_tabular_files) == 2, \ + f"Expected 2 tabular files, got {len(chat_tabular_files)}" + assert 'sales_2024.xlsx' in chat_tabular_files, "Missing sales_2024.xlsx" + assert 'inventory.csv' in chat_tabular_files, "Missing inventory.csv" + + print("Test passed!") + return True + + except Exception as e: + print(f"Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_inline_tabular_not_tracked_for_sk(): + """Test that inline tabular files (not blob) are NOT tracked for SK analysis.""" + print("Testing inline tabular files are not tracked for SK analysis...") + + try: + recent_messages = [ + { + 'role': 'file', + 'filename': 'data.csv', + 'file_content': 'col1,col2\n1,2\n3,4', + 'is_table': True, + 'file_content_source': 'inline', # Not blob + 'metadata': {}, + }, + ] + + chat_tabular_files = set() + + for message in recent_messages: + role = message.get('role') + if role == 'file': + is_table = message.get('is_table', False) + file_content_source = message.get('file_content_source', '') + filename = message.get('filename', '') + + if is_table and file_content_source == 'blob': + chat_tabular_files.add(filename) + + assert len(chat_tabular_files) == 0, \ + f"Inline tabular files should not be tracked for SK, got: {chat_tabular_files}" + + print("Test passed!") + return True + + except Exception as e: + print(f"Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +if __name__ == "__main__": + results = [] + results.append(test_chat_tabular_file_detection()) + results.append(test_non_tabular_file_handling()) + results.append(test_multiple_tabular_files()) + results.append(test_inline_tabular_not_tracked_for_sk()) + + print(f"\n{'='*50}") + print(f"Results: {sum(results)}/{len(results)} tests passed") + if all(results): + print("All tests passed!") + sys.exit(0) + else: + print("Some tests failed!") + sys.exit(1) From 36d79d2f817649ba89de1e62c610fb867a38bbee Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Fri, 6 Mar 2026 11:41:48 -0500 Subject: [PATCH 4/8] Fix bugs 2026 03 05 (#781) * Improve SQL action ui and config * adding user_id and created by modified by to group and global agents/actions and activity logs for all agent/action CRUD tasks * schema is injected directly into the agent's brain at load time, and descriptions explicitly instruct the LLM on the correct tool-calling workflow. --- application/single_app/config.py | 2 +- .../single_app/semantic_kernel_loader.py | 119 +++++++++ .../logged_plugin_loader.py | 4 +- .../sql_query_plugin.py | 103 +++++++- .../sql_schema_plugin.py | 18 +- .../SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md | 79 ++++++ docs/explanation/release_notes.md | 13 + .../test_sql_query_plugin_schema_awareness.py | 248 ++++++++++++++++++ 8 files changed, 565 insertions(+), 21 deletions(-) create mode 100644 docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md create mode 100644 functional_tests/test_sql_query_plugin_schema_awareness.py diff --git a/application/single_app/config.py b/application/single_app/config.py index bec5513b..cc7f5778 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -94,7 +94,7 @@ EXECUTOR_TYPE = 'thread' EXECUTOR_MAX_WORKERS = 30 SESSION_TYPE = 'filesystem' -VERSION = "0.239.013" +VERSION = "0.239.014" SECRET_KEY = os.getenv('SECRET_KEY', 'dev-secret-key-change-in-production') diff --git a/application/single_app/semantic_kernel_loader.py b/application/single_app/semantic_kernel_loader.py index eb9685bc..22735c56 100644 --- a/application/single_app/semantic_kernel_loader.py +++ b/application/single_app/semantic_kernel_loader.py @@ -733,6 +733,104 @@ def normalize(s): print(f"[SK Loader] Error loading agent-specific plugins: {e}") log_event(f"[SK Loader] Error loading agent-specific plugins: {e}", level=logging.ERROR, exceptionTraceback=True) + +def _extract_sql_schema_for_instructions(kernel) -> str: + """ + Check if any SQL Schema plugins are loaded in the kernel and extract their schema + information to inject into agent instructions. + + Returns a formatted schema summary string, or empty string if no SQL schema plugins found. + """ + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + schema_parts = [] + + try: + # Iterate through all registered plugins in the kernel + for plugin_name, plugin in kernel.plugins.items(): + # Check if the underlying plugin object is a SQLSchemaPlugin + # Kernel plugins wrap the original object - we need to check the underlying instance + plugin_obj = None + + # Try to access the underlying plugin instance + if isinstance(plugin, SQLSchemaPlugin): + plugin_obj = plugin + elif hasattr(plugin, '_plugin_instance'): + if isinstance(plugin._plugin_instance, SQLSchemaPlugin): + plugin_obj = plugin._plugin_instance + else: + # Check if any function in this plugin belongs to a SQLSchemaPlugin + for func_name, func in plugin.functions.items(): + if hasattr(func, 'method') and hasattr(func.method, '__self__'): + if isinstance(func.method.__self__, SQLSchemaPlugin): + plugin_obj = func.method.__self__ + break + + if plugin_obj is not None: + print(f"[SK Loader] Found SQL Schema plugin: {plugin_name}, fetching schema...") + try: + schema_result = plugin_obj.get_database_schema() + if schema_result and hasattr(schema_result, 'data'): + schema_data = schema_result.data + else: + schema_data = schema_result + + if isinstance(schema_data, dict) and "tables" in schema_data: + db_name = schema_data.get("database_name", "Unknown") + db_type = schema_data.get("database_type", "Unknown") + + schema_text = f"### Database: {db_name} ({db_type})\n\n" + + for table_name, table_info in schema_data["tables"].items(): + schema_name = table_info.get("schema_name", "dbo") + qualified_name = f"{schema_name}.{table_name}" if schema_name else table_name + schema_text += f"**Table: {qualified_name}**\n" + + columns = table_info.get("columns", []) + if columns: + schema_text += "| Column | Type | Nullable |\n|--------|------|----------|\n" + for col in columns: + col_name = col.get("column_name", "?") + col_type = col.get("data_type", "?") + nullable = "Yes" if col.get("is_nullable", True) else "No" + schema_text += f"| {col_name} | {col_type} | {nullable} |\n" + + pks = table_info.get("primary_keys", []) + if pks: + schema_text += f"Primary Key(s): {', '.join(pks)}\n" + + schema_text += "\n" + + # Add relationships + relationships = schema_data.get("relationships", []) + if relationships: + schema_text += "**Relationships (Foreign Keys):**\n" + for rel in relationships: + parent = rel.get("parent_table", "?") + parent_col = rel.get("parent_column", "?") + ref = rel.get("referenced_table", "?") + ref_col = rel.get("referenced_column", "?") + schema_text += f"- {parent}.{parent_col} โ†’ {ref}.{ref_col}\n" + schema_text += "\n" + + schema_parts.append(schema_text) + print(f"[SK Loader] Successfully extracted schema for {db_name}: {len(schema_data['tables'])} tables") + else: + print(f"[SK Loader] Schema data for {plugin_name} was empty or had unexpected format") + + except Exception as e: + print(f"[SK Loader] Warning: Failed to fetch schema from {plugin_name}: {e}") + log_event(f"[SK Loader] Failed to fetch SQL schema for injection: {e}", + extra={"plugin_name": plugin_name, "error": str(e)}, + level=logging.WARNING) + except Exception as e: + print(f"[SK Loader] Warning: Error iterating kernel plugins for SQL schema: {e}") + log_event(f"[SK Loader] Error iterating kernel plugins for SQL schema: {e}", + extra={"error": str(e)}, level=logging.WARNING) + + return "\n".join(schema_parts) + + def load_single_agent_for_kernel(kernel, agent_cfg, settings, context_obj, redis_client=None, mode_label="global"): """ DRY helper to load a single agent (default agent) for the kernel. @@ -871,6 +969,27 @@ def load_single_agent_for_kernel(kernel, agent_cfg, settings, context_obj, redis group_id=group_id, ) + # Auto-inject SQL database schema into agent instructions if SQL plugins are loaded + try: + sql_schema_summary = _extract_sql_schema_for_instructions(kernel) + if sql_schema_summary: + agent_config["instructions"] = ( + agent_config.get("instructions", "") + + "\n\n## Available Database Schema\n" + "The following database tables and columns are available for SQL queries. " + "ALWAYS use these exact table and column names when writing SQL queries.\n\n" + + sql_schema_summary + + "\n\nWhen a user asks a question about data, use the schema above to construct " + "the appropriate SQL query and execute it using the SQL Query plugin functions. " + "Do NOT ask the user for table or column names โ€” use the schema provided above." + ) + print(f"[SK Loader] Injected SQL schema into agent instructions for {agent_config['name']}") + except Exception as e: + print(f"[SK Loader] Warning: Failed to inject SQL schema into instructions: {e}") + log_event(f"[SK Loader] Failed to inject SQL schema into agent instructions: {e}", + extra={"agent_name": agent_config["name"], "error": str(e)}, + level=logging.WARNING) + try: kwargs = { "name": agent_config["name"], diff --git a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py index 64443633..f1633dbb 100644 --- a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py +++ b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py @@ -117,8 +117,8 @@ def _create_plugin_instance(self, manifest: Dict[str, Any]): return self._create_openapi_plugin(manifest) elif plugin_type == 'python': return self._create_python_plugin(manifest) - #elif plugin_type in ['sql_schema', 'sql_query']: - # return self._create_sql_plugin(manifest) + elif plugin_type in ['sql_schema', 'sql_query']: + return self._create_sql_plugin(manifest) else: try: debug_print(f"[Logged Plugin Loader] Attempting to discover plugin type: {plugin_type}") diff --git a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py index ccad030f..82d63f15 100644 --- a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py +++ b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py @@ -176,11 +176,12 @@ def metadata(self) -> Dict[str, Any]: user_desc = self._metadata.get("description", f"SQL Query plugin for {self.database_type} database") api_desc = ( "This plugin executes SQL queries against databases and returns structured results. " - "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. The plugin includes " - "query sanitization, validation, and security features including parameterized queries, " - "read-only mode, result limiting, and timeout protection. It automatically cleans queries " - "from unnecessary characters and formats results for easy consumption by AI agents. " - "The plugin handles database-specific SQL variations and connection management." + "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. " + "WORKFLOW: Before executing any query, you MUST first use the SQL Schema plugin to discover " + "available tables, column names, data types, and relationships. Then construct valid SQL queries " + "using the discovered schema with correct fully-qualified table names (e.g., dbo.TableName). " + "The plugin includes query sanitization, validation, and security features including " + "parameterized queries, read-only mode, result limiting, and timeout protection." ) full_desc = f"{user_desc}\n\n{api_desc}" @@ -215,14 +216,24 @@ def metadata(self) -> Dict[str, Any]: {"name": "query", "type": "str", "description": "The SQL query to validate", "required": True} ], "returns": {"type": "ResultWithMetadata", "description": "Validation result with any issues found"} + }, + { + "name": "query_database", + "description": "Execute a SQL query to answer a question about the database", + "parameters": [ + {"name": "question", "type": "str", "description": "The natural language question being answered", "required": True}, + {"name": "query", "type": "str", "description": "The SQL query to execute", "required": True}, + {"name": "max_rows", "type": "int", "description": "Maximum number of rows to return (overrides default)", "required": False} + ], + "returns": {"type": "ResultWithMetadata", "description": "Query results with columns, data, and original question context"} } ] } def get_functions(self) -> List[str]: - return ["execute_query", "execute_scalar", "validate_query"] + return ["execute_query", "execute_scalar", "validate_query", "query_database"] - @kernel_function(description="Execute a SQL query and return results") + @kernel_function(description="Execute a SQL query against the database and return results as structured data with columns and rows. IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list from the SQL Schema plugin to discover available tables, column names, data types, and relationships. Use the discovered schema to construct valid SQL queries with correct table and column names. Always use fully qualified table names (e.g., dbo.TableName) when available. Results are limited by max_rows to prevent excessive data transfer.") @plugin_function_logger("SQLQueryPlugin") def execute_query( self, @@ -301,7 +312,7 @@ def execute_query( } return ResultWithMetadata(error_result, self.metadata) - @kernel_function(description="Execute a query that returns a single value") + @kernel_function(description="Execute a query that returns a single scalar value (e.g., COUNT, SUM, MAX, MIN). You MUST first discover the database schema using get_database_schema or get_table_list before calling this function to ensure correct table and column references.") @plugin_function_logger("SQLQueryPlugin") def execute_scalar( self, @@ -360,7 +371,7 @@ def execute_scalar( } return ResultWithMetadata(error_result, self.metadata) - @kernel_function(description="Validate a SQL query without executing it") + @kernel_function(description="Validate a SQL query for syntax correctness and safety without executing it. Use this to pre-check complex queries before execution, especially when constructing multi-table JOINs or complex WHERE clauses.") @plugin_function_logger("SQLQueryPlugin") def validate_query(self, query: str) -> ResultWithMetadata: """Validate a SQL query without executing it""" @@ -380,6 +391,80 @@ def validate_query(self, query: str) -> ResultWithMetadata: } return ResultWithMetadata(error_result, self.metadata) + @kernel_function(description="Execute a SQL query to answer a question about the database. This is a convenience function that executes a SQL query and returns results along with the original question for context. IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list from the SQL Schema plugin to discover available tables, column names, data types, and relationships. Then construct the appropriate SQL query using the discovered schema and provide it along with the original question.") + @plugin_function_logger("SQLQueryPlugin") + def query_database( + self, + question: str, + query: str, + max_rows: Optional[int] = None + ) -> ResultWithMetadata: + """Execute a SQL query to answer a specific question about the database""" + try: + # Clean and validate the query + cleaned_query = self._clean_query(query) + validation_result = self._validate_query(cleaned_query) + + if not validation_result["is_valid"]: + raise ValueError(f"Invalid query: {validation_result['issues']}") + + conn = self._get_connection() + cursor = conn.cursor() + + # Set query timeout + if hasattr(cursor, 'settimeout'): + cursor.settimeout(self.timeout) + + cursor.execute(cleaned_query) + + # Get column names + if hasattr(cursor, 'description') and cursor.description: + columns = [desc[0] for desc in cursor.description] + else: + columns = [] + + # Fetch results with row limit + effective_max_rows = max_rows or self.max_rows + + if self.database_type == 'sqlite': + rows = cursor.fetchall() + if len(rows) > effective_max_rows: + rows = rows[:effective_max_rows] + results = [dict(row) for row in rows] + else: + rows = cursor.fetchmany(effective_max_rows) + results = [] + for row in rows: + if isinstance(row, (list, tuple)): + results.append(dict(zip(columns, row))) + else: + results.append(row) + + # Prepare result data with question context + result_data = { + "question": question, + "columns": columns, + "data": results, + "row_count": len(results), + "is_truncated": len(results) >= effective_max_rows, + "query": cleaned_query + } + + log_event(f"[SQLQueryPlugin] query_database executed successfully, returned {len(results)} rows", extra={"question": question}) + return ResultWithMetadata(result_data, self.metadata) + + except Exception as e: + log_event(f"[SQLQueryPlugin] Error in query_database: {e}", extra={"question": question}) + error_result = { + "error": str(e), + "question": question, + "query": query, + "columns": [], + "data": [], + "row_count": 0 + } + return ResultWithMetadata(error_result, self.metadata) + def _clean_query(self, query: str) -> str: """Clean query from unnecessary characters and formatting""" if not query: diff --git a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py index 01d89aa2..a5529805 100644 --- a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py +++ b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py @@ -165,11 +165,11 @@ def metadata(self) -> Dict[str, Any]: user_desc = self._metadata.get("description", f"SQL Schema plugin for {self.database_type} database") api_desc = ( "This plugin connects to SQL databases and extracts schema information including tables, columns, " - "data types, primary keys, foreign keys, and relationships. It supports SQL Server, PostgreSQL, " - "MySQL, and SQLite databases. The plugin provides structured schema data that can be used by " - "AI agents to understand database structure and generate appropriate SQL queries. " - "Authentication supports connection strings, username/password, and integrated authentication. " - "The plugin handles database-specific SQL variations for schema extraction." + "data types, primary keys, foreign keys, and relationships. WORKFLOW: ALWAYS call get_database_schema " + "or get_table_list FIRST before executing any SQL queries via the SQL Query plugin. This ensures " + "you have accurate table names, column names, and relationship information to construct valid queries. " + "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. " + "Authentication supports connection strings, username/password, and integrated authentication." ) full_desc = f"{user_desc}\n\n{api_desc}" @@ -219,7 +219,7 @@ def get_functions(self) -> List[str]: return ["get_database_schema", "get_table_schema", "get_table_list", "get_relationships"] @plugin_function_logger("SQLSchemaPlugin") - @kernel_function(description="Get complete database schema including all tables, columns, and relationships") + @kernel_function(description="Get complete database schema including all tables, columns, data types, primary keys, foreign keys, and relationships. ALWAYS call this function FIRST before executing any SQL queries to understand the database structure. This ensures you generate correct table names, column names, and JOIN conditions. The returned schema should be used to construct valid SQL queries with the correct fully-qualified table names (e.g., dbo.TableName) and column references.") def get_database_schema( self, include_system_tables: bool = False, @@ -333,7 +333,7 @@ def get_database_schema( log_event(f"[SQLSchemaPlugin] Error getting database schema: {e}") raise - @kernel_function(description="Get detailed schema for a specific table") + @kernel_function(description="Get the detailed schema (column names, data types, constraints) for a specific table. Call this after discovering tables via get_database_schema or get_table_list to verify exact column names and data types before constructing queries, ensuring that all column names used in queries are correct and exist in the table.") @plugin_function_logger("SQLSchemaPlugin") def get_table_schema(self, table_name: str) -> ResultWithMetadata: """Get detailed schema for a specific table""" @@ -350,7 +350,7 @@ def get_table_schema(self, table_name: str) -> ResultWithMetadata: log_event(f"[SQLSchemaPlugin] Error getting table schema for {table_name}: {e}") raise - @kernel_function(description="Get list of all tables in the database") + @kernel_function(description="Return the names of all tables in the database. Use this function first to discover which tables are available before attempting to query or get schemas for specific tables. This is a lightweight alternative to get_database_schema when you only need table names.") @plugin_function_logger("SQLSchemaPlugin") def get_table_list( self, @@ -385,7 +385,7 @@ def get_table_list( log_event(f"[SQLSchemaPlugin] Error getting table list: {e}") raise - @kernel_function(description="Get foreign key relationships between tables") + @kernel_function(description="Get foreign key relationships between tables. Use this to understand how tables connect via JOIN conditions before writing multi-table queries. This returns parent/child table and column pairs that define the relationships.") def get_relationships(self, table_name: Optional[str] = None) -> ResultWithMetadata: """Get foreign key relationships between tables""" try: diff --git a/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md new file mode 100644 index 00000000..ee0c22ed --- /dev/null +++ b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md @@ -0,0 +1,79 @@ +# SQL Query Plugin Schema Awareness Fix + +## Fix Title +SQL Query Plugin - Schema Awareness and Workflow Guidance + +## Issue Description +When users asked database-related questions (e.g., "what is user1 licensed to use?"), agents connected to SQL databases would ask for clarification about table/column names instead of querying the database directly. The agent had no citations, meaning it never actually called any database tools. + +## Root Cause Analysis +Three interconnected issues caused this failure: + +1. **Generic `@kernel_function` descriptions**: The SQL Query and SQL Schema plugin function descriptions were terse and generic (e.g., "Execute a SQL query and return results"). They provided no workflow guidance telling the LLM to discover the schema first before writing queries. In contrast, the working LogAnalyticsPlugin uses highly prescriptive descriptions like "Use this function FIRST to discover which tables are available BEFORE attempting to query." + +2. **No schema context in agent instructions**: Agent instructions were passed through verbatim from configuration with no automatic injection of database schema information. Without knowing what tables (e.g., `dbo.Licenses`, `dbo.Usage`, `dbo.Procurements`) and columns existed, the LLM couldn't generate valid SQL queries. + +3. **Independent, disconnected plugins**: The SQL Schema Plugin and SQL Query Plugin operated as completely independent plugins with no linkage. There was no mechanism to ensure schema discovery happened before query execution. + +## Version Implemented +**Fixed in version: 0.239.014** + +## Files Modified + +| File | Change | +|------|--------| +| `application/single_app/semantic_kernel_plugins/sql_schema_plugin.py` | Rewrote all `@kernel_function` descriptions with prescriptive workflow guidance; updated `metadata` property description | +| `application/single_app/semantic_kernel_plugins/sql_query_plugin.py` | Rewrote all `@kernel_function` descriptions with schema-first workflow guidance; added `query_database` convenience function; updated `metadata` property description | +| `application/single_app/semantic_kernel_loader.py` | Added `_extract_sql_schema_for_instructions()` helper function; auto-injects database schema into agent instructions at load time when SQL plugins are detected | +| `application/single_app/semantic_kernel_plugins/logged_plugin_loader.py` | Uncommented the SQL-specific plugin creation path (`_create_sql_plugin`) for explicit SQL plugin handling | +| `application/single_app/config.py` | Version bump to 0.239.014 | + +## Code Changes Summary + +### 1. Prescriptive Function Descriptions (sql_schema_plugin.py, sql_query_plugin.py) +- `get_database_schema`: Now says "ALWAYS call this function FIRST before executing any SQL queries" +- `get_table_list`: Now says "Use this function first to discover which tables are available" +- `get_table_schema`: Now says "Call this after discovering tables via get_database_schema or get_table_list" +- `get_relationships`: Now says "Use this to understand how tables connect via JOIN conditions" +- `execute_query`: Now says "IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list" +- `execute_scalar`: Now says "You MUST first discover the database schema" +- Pattern modeled after the successfully working LogAnalyticsPlugin + +### 2. New `query_database` Convenience Function (sql_query_plugin.py) +- Accepts `question` (natural language) and `query` (SQL) parameters +- Returns results with the original question context for better LLM response formatting +- Gives the LLM an intent-aligned tool option + +### 3. Auto Schema Injection (semantic_kernel_loader.py) +- New `_extract_sql_schema_for_instructions()` function detects SQL Schema plugins in the kernel +- Calls `get_database_schema()` at agent load time to fetch full schema +- Formats schema as markdown tables (table names, columns, types, relationships) +- Appends schema to agent instructions with directive: "Do NOT ask the user for table or column names" +- This ensures the LLM ALWAYS has schema context even if it doesn't call the schema plugin + +### 4. Enabled SQL Plugin Creation Path (logged_plugin_loader.py) +- Uncommented the `elif plugin_type in ['sql_schema', 'sql_query']` branch +- SQL plugins now use the explicit `_create_sql_plugin()` method instead of generic discovery fallback + +## Testing Approach +- Functional test: `functional_tests/test_sql_query_plugin_schema_awareness.py` +- Validates all `@kernel_function` descriptions contain workflow guidance keywords +- Verifies `query_database` function exists with correct parameters +- Confirms SQL plugin creation path is enabled (not commented out) +- Checks schema injection function exists and is integrated into agent loading + +## Impact Analysis +- **SQL-connected agents**: Will now automatically receive database schema in their instructions, enabling proper query generation without user clarification +- **Non-SQL agents**: Completely unaffected (schema injection only triggers when SQL Schema plugins are detected) +- **LogAnalytics agents**: Unaffected (different plugin type) +- **Performance**: One-time schema fetch at agent load time adds minimal latency; schema is cached in instructions for the session + +## Before/After Comparison + +### Before +- User: "What is user1 licensed to use?" +- Agent: "I need the exact user identifier... Please provide the identifier..." (no database call, no citations) + +### After (Expected) +- User: "What is user1 licensed to use?" +- Agent: Calls `get_database_schema` โ†’ sees Licenses, Usage, Procurements tables โ†’ generates JOIN query โ†’ returns actual license data with citations diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md index bba394db..9af582c1 100644 --- a/docs/explanation/release_notes.md +++ b/docs/explanation/release_notes.md @@ -2,6 +2,19 @@ # Feature Release +### **(v0.239.014)** + +#### Bug Fixes + +* **SQL Query Plugin Schema Awareness** + * Fixed agents connected to SQL databases asking users for clarification about table/column names instead of querying the database directly. + * Root cause: SQL Query and SQL Schema plugin `@kernel_function` descriptions were generic with no workflow guidance, agent instructions had no database schema context, and the two plugins operated independently with no linkage. + * Rewrote all `@kernel_function` descriptions in both SQL plugins to be prescriptive workflow guides (modeled after the working LogAnalyticsPlugin), explicitly instructing the LLM to discover schema first before generating queries. + * Added auto-injection of database schema into agent instructions at load time โ€” when SQL Schema plugins are detected, the full schema (tables, columns, types, relationships) is fetched and appended to the agent's system prompt. + * Added new `query_database(question, query)` convenience function to `SQLQueryPlugin` for intent-aligned tool calling. + * Enabled the SQL-specific plugin creation path in `logged_plugin_loader.py` (was previously commented out). + * (Ref: `sql_query_plugin.py`, `sql_schema_plugin.py`, `semantic_kernel_loader.py`, `logged_plugin_loader.py`) + ### **(v0.239.008)** #### Bug Fixes diff --git a/functional_tests/test_sql_query_plugin_schema_awareness.py b/functional_tests/test_sql_query_plugin_schema_awareness.py new file mode 100644 index 00000000..b16f73bd --- /dev/null +++ b/functional_tests/test_sql_query_plugin_schema_awareness.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 +""" +Functional test for SQL Query Plugin Schema Awareness improvements. +Version: 0.239.014 +Implemented in: 0.239.014 + +This test ensures that the SQL Query and SQL Schema plugins have proper +workflow-guiding descriptions in their @kernel_function decorators, metadata +properties, and that the new query_database convenience function exists. +These improvements ensure agents discover database schema before attempting +to generate and execute SQL queries. +""" + +import sys +import os + +# Add the application directory to the path for imports +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app')) + + +def test_sql_schema_plugin_descriptions(): + """Test that SQL Schema Plugin has prescriptive workflow descriptions.""" + print("๐Ÿ” Testing SQL Schema Plugin @kernel_function descriptions...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + import inspect + + source = inspect.getsource(SQLSchemaPlugin) + + # Check get_database_schema has workflow guidance + assert "ALWAYS call this function FIRST" in source, \ + "get_database_schema description should contain 'ALWAYS call this function FIRST'" + + assert "before executing any SQL queries" in source, \ + "get_database_schema description should guide calling before queries" + + # Check get_table_list has workflow guidance + assert "Use this function first to discover which tables are available" in source, \ + "get_table_list description should guide discovery workflow" + + # Check get_table_schema has workflow guidance + assert "Call this after discovering tables" in source, \ + "get_table_schema description should reference discovery step" + + # Check get_relationships has workflow guidance + assert "JOIN conditions" in source, \ + "get_relationships description should mention JOIN conditions" + + print("โœ… SQL Schema Plugin descriptions contain workflow guidance!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_sql_query_plugin_descriptions(): + """Test that SQL Query Plugin has prescriptive workflow descriptions.""" + print("๐Ÿ” Testing SQL Query Plugin @kernel_function descriptions...") + + try: + from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin + import inspect + + source = inspect.getsource(SQLQueryPlugin) + + # Check execute_query has schema requirement + assert "you MUST first call get_database_schema or get_table_list" in source, \ + "execute_query description should require schema discovery first" + + assert "fully qualified table names" in source, \ + "execute_query description should mention fully qualified table names" + + # Check execute_scalar has schema requirement + assert "MUST first discover the database schema" in source, \ + "execute_scalar description should require schema discovery" + + # Check validate_query has useful guidance + assert "pre-check complex queries" in source, \ + "validate_query description should guide pre-checking" + + print("โœ… SQL Query Plugin descriptions contain workflow guidance!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_query_database_function_exists(): + """Test that the new query_database convenience function exists.""" + print("๐Ÿ” Testing query_database function existence...") + + try: + from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin + + # Check the function exists on the class + assert hasattr(SQLQueryPlugin, 'query_database'), \ + "SQLQueryPlugin should have a query_database method" + + # Check it's in get_functions list + # We need to create a minimal instance to test, but we can check the class method + import inspect + source = inspect.getsource(SQLQueryPlugin.query_database) + + assert "question" in source, \ + "query_database should accept a 'question' parameter" + + assert "query" in source, \ + "query_database should accept a 'query' parameter" + + assert "@kernel_function" in inspect.getsource(SQLQueryPlugin), \ + "query_database should be decorated with @kernel_function" + + print("โœ… query_database function exists with correct parameters!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_metadata_descriptions_updated(): + """Test that metadata property descriptions include workflow guidance.""" + print("๐Ÿ” Testing metadata property descriptions...") + + try: + from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + import inspect + + # Check SQL Query Plugin metadata + query_source = inspect.getsource(SQLQueryPlugin) + assert "WORKFLOW: Before executing any query" in query_source, \ + "SQL Query Plugin metadata should contain workflow guidance" + + # Check SQL Schema Plugin metadata + schema_source = inspect.getsource(SQLSchemaPlugin) + assert "ALWAYS call get_database_schema" in schema_source, \ + "SQL Schema Plugin metadata should contain workflow guidance" + + print("โœ… Metadata descriptions include workflow guidance!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_sql_plugin_creation_path_enabled(): + """Test that the SQL plugin creation path is enabled in logged_plugin_loader.""" + print("๐Ÿ” Testing SQL plugin creation path is enabled...") + + try: + from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader + import inspect + + source = inspect.getsource(LoggedPluginLoader._create_plugin_instance) + + # Check the SQL path is NOT commented out + assert "elif plugin_type in ['sql_schema', 'sql_query']:" in source, \ + "SQL plugin creation path should be enabled (not commented out)" + + assert "return self._create_sql_plugin(manifest)" in source, \ + "SQL plugin creation should call _create_sql_plugin" + + # Make sure it's not still commented + lines = source.split('\n') + for line in lines: + stripped = line.strip() + if "sql_schema" in stripped and "sql_query" in stripped and "elif" in stripped: + assert not stripped.startswith('#'), \ + "SQL plugin creation path should NOT be commented out" + break + + print("โœ… SQL plugin creation path is enabled!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_schema_injection_function_exists(): + """Test that the schema injection helper function exists in semantic_kernel_loader.""" + print("๐Ÿ” Testing schema injection function exists...") + + try: + # Read the file and check for the function + loader_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + '..', 'application', 'single_app', 'semantic_kernel_loader.py' + ) + + with open(loader_path, 'r', encoding='utf-8') as f: + content = f.read() + + assert "def _extract_sql_schema_for_instructions(kernel)" in content, \ + "_extract_sql_schema_for_instructions function should exist in semantic_kernel_loader.py" + + assert "sql_schema_summary = _extract_sql_schema_for_instructions(kernel)" in content, \ + "Schema injection should be called in the agent loading flow" + + assert "Available Database Schema" in content, \ + "Schema injection should add 'Available Database Schema' header to instructions" + + assert "Do NOT ask the user for table or column names" in content, \ + "Schema injection should instruct agent not to ask user for schema info" + + print("โœ… Schema injection function exists and is integrated!") + return True + + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +if __name__ == "__main__": + tests = [ + test_sql_schema_plugin_descriptions, + test_sql_query_plugin_descriptions, + test_query_database_function_exists, + test_metadata_descriptions_updated, + test_sql_plugin_creation_path_enabled, + test_schema_injection_function_exists, + ] + results = [] + + for test in tests: + print(f"\n๐Ÿงช Running {test.__name__}...") + results.append(test()) + + success = all(results) + print(f"\n๐Ÿ“Š Results: {sum(results)}/{len(results)} tests passed") + sys.exit(0 if success else 1) From a23023eaae85ca0bfade7db1c66dfa6256a75bb4 Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Fri, 6 Mar 2026 14:35:08 -0500 Subject: [PATCH 5/8] Updated all 4 @kernel_function descriptions in sql_schema_plugin.py to use the resilient pattern: --- README.md | 1 + application/single_app/config.py | 2 +- .../single_app/semantic_kernel_loader.py | 91 +++++++ .../logged_plugin_loader.py | 58 +++++ .../sql_query_plugin.py | 6 +- .../sql_schema_plugin.py | 101 ++++---- .../SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md | 139 ++++++++--- docs/explanation/release_notes.md | 35 +++ .../test_sql_auto_schema_companion.py | 222 +++++++++++++++++ .../test_sql_schema_sys_catalog_views.py | 231 ++++++++++++++++++ 10 files changed, 810 insertions(+), 76 deletions(-) create mode 100644 functional_tests/test_sql_auto_schema_companion.py create mode 100644 functional_tests/test_sql_schema_sys_catalog_views.py diff --git a/README.md b/README.md index 31ea020b..cffbeedc 100644 --- a/README.md +++ b/README.md @@ -144,6 +144,7 @@ azd up - **Metadata Extraction (Optional)**: Apply an AI model (configurable GPT model via Admin Settings) to automatically generate keywords, two-sentence summaries, and infer author/date for uploaded documents. Allows manual override for richer search context. - **File Processing Logs (Optional)**: Enable verbose logging for all ingestion pipelines (workspaces and ephemeral chat uploads) to aid in debugging, monitoring, and auditing file processing steps. - **Redis Cache (Optional)**: Integrate Azure Cache for Redis to provide a distributed, high-performance session store. This enables true horizontal scaling and high availability by decoupling user sessions from individual app instances. +- **SQL Database Agents (Optional)**: Connect agents to Azure SQL or other SQL databases through configurable SQL Query and SQL Schema plugins. Database schema is automatically discovered and injected into agent instructions at load time, enabling agents to answer natural language questions by generating and executing SQL queries without requiring users to know table or column names. - **Authentication & RBAC**: Secure access via Azure Active Directory (Entra ID) using MSAL. Supports Managed Identities for Azure service authentication, group-based controls, and custom application roles (`Admin`, `User`, `CreateGroup`, `SafetyAdmin`, `FeedbackAdmin`). - **Supported File Types**: diff --git a/application/single_app/config.py b/application/single_app/config.py index cc7f5778..772802c1 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -94,7 +94,7 @@ EXECUTOR_TYPE = 'thread' EXECUTOR_MAX_WORKERS = 30 SESSION_TYPE = 'filesystem' -VERSION = "0.239.014" +VERSION = "0.239.017" SECRET_KEY = os.getenv('SECRET_KEY', 'dev-secret-key-change-in-production') diff --git a/application/single_app/semantic_kernel_loader.py b/application/single_app/semantic_kernel_loader.py index 22735c56..c2a7cc1e 100644 --- a/application/single_app/semantic_kernel_loader.py +++ b/application/single_app/semantic_kernel_loader.py @@ -828,6 +828,97 @@ def _extract_sql_schema_for_instructions(kernel) -> str: log_event(f"[SK Loader] Error iterating kernel plugins for SQL schema: {e}", extra={"error": str(e)}, level=logging.WARNING) + # Fallback: If no SQLSchemaPlugin was found, check for SQLQueryPlugin instances + # and create a temporary SQLSchemaPlugin from their connection config to extract schema + if not schema_parts: + from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin as _SQLQueryPlugin + + try: + for plugin_name, plugin in kernel.plugins.items(): + query_obj = None + + if isinstance(plugin, _SQLQueryPlugin): + query_obj = plugin + elif hasattr(plugin, '_plugin_instance'): + if isinstance(plugin._plugin_instance, _SQLQueryPlugin): + query_obj = plugin._plugin_instance + else: + for func_name, func in plugin.functions.items(): + if hasattr(func, 'method') and hasattr(func.method, '__self__'): + if isinstance(func.method.__self__, _SQLQueryPlugin): + query_obj = func.method.__self__ + break + + if query_obj is not None: + print(f"[SK Loader] Fallback: Found SQLQueryPlugin '{plugin_name}', creating temporary schema extractor...") + try: + temp_manifest = { + 'type': 'sql_schema', + 'name': f'{plugin_name}_temp_schema', + 'database_type': getattr(query_obj, 'database_type', 'azure_sql'), + 'server': getattr(query_obj, 'server', ''), + 'database': getattr(query_obj, 'database', ''), + 'username': getattr(query_obj, 'username', ''), + 'password': getattr(query_obj, 'password', ''), + 'driver': getattr(query_obj, 'driver', ''), + 'connection_string': getattr(query_obj, 'connection_string', ''), + } + temp_schema = SQLSchemaPlugin(temp_manifest) + schema_result = temp_schema.get_database_schema() + if schema_result and hasattr(schema_result, 'data'): + schema_data = schema_result.data + else: + schema_data = schema_result + + if isinstance(schema_data, dict) and "tables" in schema_data: + db_name = schema_data.get("database_name", "Unknown") + db_type = schema_data.get("database_type", "Unknown") + + schema_text = f"### Database: {db_name} ({db_type})\n\n" + + for table_name, table_info in schema_data["tables"].items(): + schema_name = table_info.get("schema_name", "dbo") + qualified_name = f"{schema_name}.{table_name}" if schema_name else table_name + schema_text += f"**Table: {qualified_name}**\n" + + columns = table_info.get("columns", []) + if columns: + schema_text += "| Column | Type | Nullable |\n|--------|------|----------|\n" + for col in columns: + col_name = col.get("column_name", "?") + col_type = col.get("data_type", "?") + nullable = "Yes" if col.get("is_nullable", True) else "No" + schema_text += f"| {col_name} | {col_type} | {nullable} |\n" + + pks = table_info.get("primary_keys", []) + if pks: + schema_text += f"Primary Key(s): {', '.join(pks)}\n" + + schema_text += "\n" + + relationships = schema_data.get("relationships", []) + if relationships: + schema_text += "**Relationships (Foreign Keys):**\n" + for rel in relationships: + parent = rel.get("parent_table", "?") + parent_col = rel.get("parent_column", "?") + ref = rel.get("referenced_table", "?") + ref_col = rel.get("referenced_column", "?") + schema_text += f"- {parent}.{parent_col} โ†’ {ref}.{ref_col}\n" + schema_text += "\n" + + schema_parts.append(schema_text) + print(f"[SK Loader] Fallback: Successfully extracted schema from SQLQueryPlugin '{plugin_name}': {len(schema_data['tables'])} tables") + except Exception as e: + print(f"[SK Loader] Fallback: Failed to extract schema from SQLQueryPlugin '{plugin_name}': {e}") + log_event(f"[SK Loader] Fallback schema extraction failed", + extra={"plugin_name": plugin_name, "error": str(e)}, + level=logging.WARNING) + except Exception as e: + print(f"[SK Loader] Warning: Error in fallback SQL schema extraction: {e}") + log_event(f"[SK Loader] Error in fallback SQL schema extraction: {e}", + extra={"error": str(e)}, level=logging.WARNING) + return "\n".join(schema_parts) diff --git a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py index f1633dbb..f7c9e38c 100644 --- a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py +++ b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py @@ -80,6 +80,10 @@ def load_plugin_from_manifest(self, manifest: Dict[str, Any], # Register the plugin with the kernel self._register_plugin_with_kernel(plugin_instance, plugin_name) + # Auto-create companion SQL Schema plugin when loading a SQL Query plugin + if plugin_type == 'sql_query': + self._auto_create_companion_schema_plugin(manifest, plugin_name) + log_event( f"[Logged Plugin Loader] Successfully loaded plugin: {plugin_name}", extra={ @@ -221,6 +225,60 @@ def _create_sql_plugin(self, manifest: Dict[str, Any]): self.logger.error(f"Failed to import SQL plugin class for {plugin_type}: {e}") return None + def _auto_create_companion_schema_plugin(self, query_manifest: Dict[str, Any], query_plugin_name: str): + """ + Auto-create a companion SQLSchemaPlugin when a SQLQueryPlugin is loaded. + This ensures the agent has access to database schema information for constructing queries. + The schema plugin uses the same connection details as the query plugin. + """ + try: + # Derive schema plugin name from query plugin name + if query_plugin_name.endswith('_query'): + schema_plugin_name = query_plugin_name[:-6] + '_schema' + else: + schema_plugin_name = query_plugin_name + '_schema' + + # Check if schema plugin already exists in kernel + if schema_plugin_name in self.kernel.plugins: + log_event( + f"[Logged Plugin Loader] Companion schema plugin already exists: {schema_plugin_name}", + level=logging.DEBUG + ) + return + + # Create schema manifest from query manifest (same connection details) + schema_manifest = dict(query_manifest) + schema_manifest['type'] = 'sql_schema' + schema_manifest['name'] = schema_plugin_name + + # Create the schema plugin instance + schema_instance = SQLSchemaPlugin(schema_manifest) + + # Enable logging if supported + if hasattr(schema_instance, 'enable_invocation_logging'): + schema_instance.enable_invocation_logging(True) + + # Wrap functions if it's a BasePlugin + if isinstance(schema_instance, BasePlugin): + self._wrap_plugin_functions(schema_instance, schema_plugin_name) + + # Register with kernel + self._register_plugin_with_kernel(schema_instance, schema_plugin_name) + + log_event( + f"[Logged Plugin Loader] Auto-created companion SQL Schema plugin: {schema_plugin_name}", + extra={"query_plugin": query_plugin_name, "schema_plugin": schema_plugin_name}, + level=logging.INFO + ) + + except Exception as e: + log_event( + f"[Logged Plugin Loader] Warning: Failed to auto-create companion schema plugin", + extra={"query_plugin": query_plugin_name, "error": str(e)}, + level=logging.WARNING, + exceptionTraceback=True + ) + def _wrap_plugin_functions(self, plugin_instance, plugin_name: str): """Wrap all kernel functions in a plugin with logging.""" log_event(f"[Logged Plugin Loader] Checking logging status for plugin", diff --git a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py index 82d63f15..084c4c9b 100644 --- a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py +++ b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py @@ -233,7 +233,7 @@ def metadata(self) -> Dict[str, Any]: def get_functions(self) -> List[str]: return ["execute_query", "execute_scalar", "validate_query", "query_database"] - @kernel_function(description="Execute a SQL query against the database and return results as structured data with columns and rows. IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list from the SQL Schema plugin to discover available tables, column names, data types, and relationships. Use the discovered schema to construct valid SQL queries with correct table and column names. Always use fully qualified table names (e.g., dbo.TableName) when available. Results are limited by max_rows to prevent excessive data transfer.") + @kernel_function(description="Execute a SQL query against the database and return results as structured data with columns and rows. If the database schema is provided in your instructions, use those exact table and column names to construct valid SQL queries. If no schema is available in your instructions, call get_database_schema or get_table_list from the SQL Schema plugin to discover tables first. Always use fully qualified table names (e.g., dbo.TableName) when available. Results are limited by max_rows to prevent excessive data transfer.") @plugin_function_logger("SQLQueryPlugin") def execute_query( self, @@ -312,7 +312,7 @@ def execute_query( } return ResultWithMetadata(error_result, self.metadata) - @kernel_function(description="Execute a query that returns a single scalar value (e.g., COUNT, SUM, MAX, MIN). You MUST first discover the database schema using get_database_schema or get_table_list before calling this function to ensure correct table and column references.") + @kernel_function(description="Execute a query that returns a single scalar value (e.g., COUNT, SUM, MAX, MIN). If the database schema is provided in your instructions, use it directly to construct the query. Otherwise, call get_database_schema from the SQL Schema plugin first to discover table and column names.") @plugin_function_logger("SQLQueryPlugin") def execute_scalar( self, @@ -391,7 +391,7 @@ def validate_query(self, query: str) -> ResultWithMetadata: } return ResultWithMetadata(error_result, self.metadata) - @kernel_function(description="Execute a SQL query to answer a question about the database. This is a convenience function that executes a SQL query and returns results along with the original question for context. IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list from the SQL Schema plugin to discover available tables, column names, data types, and relationships. Then construct the appropriate SQL query using the discovered schema and provide it along with the original question.") + @kernel_function(description="Execute a SQL query to answer a question about the database. This is a convenience function that executes a SQL query and returns results along with the original question for context. If the database schema is provided in your instructions, use those table and column names directly to construct the query. Otherwise, first call get_database_schema from the SQL Schema plugin to discover the schema. Then construct the appropriate SQL query and provide it along with the original question.") @plugin_function_logger("SQLQueryPlugin") def query_database( self, diff --git a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py index a5529805..7d5737f3 100644 --- a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py +++ b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py @@ -219,7 +219,7 @@ def get_functions(self) -> List[str]: return ["get_database_schema", "get_table_schema", "get_table_list", "get_relationships"] @plugin_function_logger("SQLSchemaPlugin") - @kernel_function(description="Get complete database schema including all tables, columns, data types, primary keys, foreign keys, and relationships. ALWAYS call this function FIRST before executing any SQL queries to understand the database structure. This ensures you generate correct table names, column names, and JOIN conditions. The returned schema should be used to construct valid SQL queries with the correct fully-qualified table names (e.g., dbo.TableName) and column references.") + @kernel_function(description="Get complete database schema including all tables, columns, data types, primary keys, foreign keys, and relationships. If the database schema is already provided in your instructions, use that directly and do NOT call this function. Only call this function if you need to discover the schema and it was not already provided. The returned schema should be used to construct valid SQL queries with the correct fully-qualified table names (e.g., dbo.TableName) and column references.") def get_database_schema( self, include_system_tables: bool = False, @@ -255,24 +255,26 @@ def get_database_schema( # Get schema for each table for table in tables: - if isinstance(table, tuple) and len(table) >= 2: + try: + # Robust row parsing โ€” works with pyodbc.Row, tuple, list, etc. table_name = table[0] - schema_name = table[1] - qualified_table_name = f"{schema_name}.{table_name}" - else: - table_name = table[0] if isinstance(table, tuple) else table + schema_name = table[1] if len(table) >= 2 else None + qualified_table_name = f"{schema_name}.{table_name}" if schema_name else str(table_name) + except (TypeError, IndexError): + table_name = str(table) schema_name = None qualified_table_name = table_name try: - table_schema = self._get_table_schema_data(cursor, table_name, schema_name) - schema_data["tables"][table_name] = table_schema - print(f"[SQLSchemaPlugin] Got schema for table: {qualified_table_name}") + table_schema = self._get_table_schema_data(cursor, str(table_name), str(schema_name) if schema_name else None) + schema_data["tables"][str(table_name)] = table_schema + print(f"[SQLSchemaPlugin] Got schema for table: {qualified_table_name} ({len(table_schema.get('columns', []))} columns)") except Exception as e: print(f"[SQLSchemaPlugin] Error getting schema for table {qualified_table_name}: {e}") log_event(f"[SQLSchemaPlugin] Error getting table schema", extra={ "table_name": qualified_table_name, - "error": str(e) + "error": str(e), + "raw_row": repr(table) }) # Get relationships @@ -333,7 +335,7 @@ def get_database_schema( log_event(f"[SQLSchemaPlugin] Error getting database schema: {e}") raise - @kernel_function(description="Get the detailed schema (column names, data types, constraints) for a specific table. Call this after discovering tables via get_database_schema or get_table_list to verify exact column names and data types before constructing queries, ensuring that all column names used in queries are correct and exist in the table.") + @kernel_function(description="Get the detailed schema (column names, data types, constraints) for a specific table. If the database schema is already provided in your instructions, use that directly instead of calling this function. Only call this if you need details for a specific table not already in your instructions.") @plugin_function_logger("SQLSchemaPlugin") def get_table_schema(self, table_name: str) -> ResultWithMetadata: """Get detailed schema for a specific table""" @@ -350,7 +352,7 @@ def get_table_schema(self, table_name: str) -> ResultWithMetadata: log_event(f"[SQLSchemaPlugin] Error getting table schema for {table_name}: {e}") raise - @kernel_function(description="Return the names of all tables in the database. Use this function first to discover which tables are available before attempting to query or get schemas for specific tables. This is a lightweight alternative to get_database_schema when you only need table names.") + @kernel_function(description="Return the names of all tables in the database. If the database schema is already provided in your instructions, use that directly instead of calling this function. Only call this if you need to discover available tables and they are not already listed in your instructions.") @plugin_function_logger("SQLSchemaPlugin") def get_table_list( self, @@ -368,14 +370,14 @@ def get_table_list( table_list = [] for table_row in tables: - if isinstance(table_row, (list, tuple)): + try: table_info = { - "table_name": table_row[0], - "schema": table_row[1] if len(table_row) > 1 else None, - "table_type": table_row[2] if len(table_row) > 2 else "TABLE" + "table_name": str(table_row[0]), + "schema": str(table_row[1]) if len(table_row) > 1 else None, + "table_type": str(table_row[2]) if len(table_row) > 2 else "TABLE" } - else: - table_info = {"table_name": table_row, "schema": None, "table_type": "TABLE"} + except (TypeError, IndexError): + table_info = {"table_name": str(table_row), "schema": None, "table_type": "TABLE"} table_list.append(table_info) log_event(f"[SQLSchemaPlugin] Retrieved {len(table_list)} tables") @@ -385,7 +387,7 @@ def get_table_list( log_event(f"[SQLSchemaPlugin] Error getting table list: {e}") raise - @kernel_function(description="Get foreign key relationships between tables. Use this to understand how tables connect via JOIN conditions before writing multi-table queries. This returns parent/child table and column pairs that define the relationships.") + @kernel_function(description="Get foreign key relationships between tables. If the database schema and relationships are already provided in your instructions, use those directly instead of calling this function. Only call this if you need relationship details not already in your instructions.") def get_relationships(self, table_name: Optional[str] = None) -> ResultWithMetadata: """Get foreign key relationships between tables""" try: @@ -402,17 +404,20 @@ def get_relationships(self, table_name: Optional[str] = None) -> ResultWithMetad raise def _get_tables_query(self, include_system_tables: bool, table_filter: Optional[str]) -> str: - """Get database-specific query for listing tables""" + """Get database-specific query for listing tables. + Uses sys.tables/sys.schemas for SQL Server (more reliable than INFORMATION_SCHEMA + in Azure SQL environments with restricted permissions).""" if self.database_type == 'sqlserver': base_query = """ - SELECT TABLE_NAME, TABLE_SCHEMA, TABLE_TYPE - FROM INFORMATION_SCHEMA.TABLES - WHERE TABLE_TYPE = 'BASE TABLE' + SELECT t.name AS TABLE_NAME, s.name AS TABLE_SCHEMA, 'BASE TABLE' AS TABLE_TYPE + FROM sys.tables t + INNER JOIN sys.schemas s ON t.schema_id = s.schema_id + WHERE t.type = 'U' """ if not include_system_tables: - base_query += " AND TABLE_SCHEMA NOT IN ('sys', 'information_schema')" + base_query += " AND s.name NOT IN ('sys', 'information_schema')" if table_filter: - base_query += f" AND TABLE_NAME LIKE '{table_filter.replace('*', '%')}'" + base_query += f" AND t.name LIKE '{table_filter.replace('*', '%')}'" return base_query elif self.database_type == 'postgresql': @@ -467,22 +472,30 @@ def _get_table_schema_data(self, cursor, table_name: str, schema_name: str = Non if pk_query: cursor.execute(pk_query) pks = cursor.fetchall() - schema_data["primary_keys"] = [pk[0] if isinstance(pk, (list, tuple)) else pk for pk in pks] + schema_data["primary_keys"] = [str(pk[0]) for pk in pks] return schema_data def _get_columns_query(self, table_name: str, schema_name: str = None) -> str: - """Get database-specific query for table columns""" + """Get database-specific query for table columns. + Uses sys.columns/sys.types for SQL Server (consistent with sys.tables used for enumeration).""" if self.database_type == 'sqlserver': - where_clause = f"WHERE TABLE_NAME = '{table_name}'" - if schema_name: - where_clause += f" AND TABLE_SCHEMA = '{schema_name}'" + schema_filter = f"AND s.name = '{schema_name}'" if schema_name else "" return f""" - SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE, COLUMN_DEFAULT, - CHARACTER_MAXIMUM_LENGTH, NUMERIC_PRECISION, NUMERIC_SCALE - FROM INFORMATION_SCHEMA.COLUMNS - {where_clause} - ORDER BY ORDINAL_POSITION + SELECT + c.name AS COLUMN_NAME, + TYPE_NAME(c.user_type_id) AS DATA_TYPE, + CASE WHEN c.is_nullable = 1 THEN 'YES' ELSE 'NO' END AS IS_NULLABLE, + dc.definition AS COLUMN_DEFAULT, + c.max_length AS CHARACTER_MAXIMUM_LENGTH, + c.precision AS NUMERIC_PRECISION, + c.scale AS NUMERIC_SCALE + FROM sys.columns c + INNER JOIN sys.tables t ON c.object_id = t.object_id + INNER JOIN sys.schemas s ON t.schema_id = s.schema_id + LEFT JOIN sys.default_constraints dc ON c.default_object_id = dc.object_id + WHERE t.name = '{table_name}' {schema_filter} + ORDER BY c.column_id """ elif self.database_type == 'postgresql': return f""" @@ -498,16 +511,18 @@ def _get_columns_query(self, table_name: str, schema_name: str = None) -> str: return f"PRAGMA table_info({table_name})" def _get_primary_keys_query(self, table_name: str, schema_name: str = None) -> Optional[str]: - """Get database-specific query for primary keys""" + """Get database-specific query for primary keys. + Uses sys.indexes/sys.index_columns for SQL Server (consistent with sys.tables).""" if self.database_type == 'sqlserver': - where_clause = f"WHERE TABLE_NAME = '{table_name}'" - if schema_name: - where_clause += f" AND TABLE_SCHEMA = '{schema_name}'" + schema_filter = f"AND s.name = '{schema_name}'" if schema_name else "" return f""" - SELECT COLUMN_NAME - FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE - {where_clause} - AND CONSTRAINT_NAME LIKE 'PK_%' + SELECT c.name AS COLUMN_NAME + FROM sys.index_columns ic + INNER JOIN sys.columns c ON ic.object_id = c.object_id AND ic.column_id = c.column_id + INNER JOIN sys.indexes i ON ic.object_id = i.object_id AND ic.index_id = i.index_id + INNER JOIN sys.tables t ON i.object_id = t.object_id + INNER JOIN sys.schemas s ON t.schema_id = s.schema_id + WHERE i.is_primary_key = 1 AND t.name = '{table_name}' {schema_filter} """ elif self.database_type == 'postgresql': return f""" diff --git a/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md index ee0c22ed..9713a62c 100644 --- a/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md +++ b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md @@ -1,79 +1,160 @@ # SQL Query Plugin Schema Awareness Fix ## Fix Title -SQL Query Plugin - Schema Awareness and Workflow Guidance +SQL Query Plugin - Schema Awareness, Companion Plugin Auto-Creation, and Workflow Guidance ## Issue Description When users asked database-related questions (e.g., "what is user1 licensed to use?"), agents connected to SQL databases would ask for clarification about table/column names instead of querying the database directly. The agent had no citations, meaning it never actually called any database tools. ## Root Cause Analysis -Three interconnected issues caused this failure: -1. **Generic `@kernel_function` descriptions**: The SQL Query and SQL Schema plugin function descriptions were terse and generic (e.g., "Execute a SQL query and return results"). They provided no workflow guidance telling the LLM to discover the schema first before writing queries. In contrast, the working LogAnalyticsPlugin uses highly prescriptive descriptions like "Use this function FIRST to discover which tables are available BEFORE attempting to query." +### Original Root Causes (v0.239.014) +Three interconnected issues caused the initial failure: -2. **No schema context in agent instructions**: Agent instructions were passed through verbatim from configuration with no automatic injection of database schema information. Without knowing what tables (e.g., `dbo.Licenses`, `dbo.Usage`, `dbo.Procurements`) and columns existed, the LLM couldn't generate valid SQL queries. +1. **Generic `@kernel_function` descriptions**: The SQL Query and SQL Schema plugin function descriptions were terse and generic (e.g., "Execute a SQL query and return results"). They provided no workflow guidance telling the LLM to discover the schema first before writing queries. -3. **Independent, disconnected plugins**: The SQL Schema Plugin and SQL Query Plugin operated as completely independent plugins with no linkage. There was no mechanism to ensure schema discovery happened before query execution. +2. **No schema context in agent instructions**: Agent instructions were passed through verbatim from configuration with no automatic injection of database schema information. + +3. **Independent, disconnected plugins**: The SQL Schema Plugin and SQL Query Plugin operated as completely independent plugins with no linkage. + +### Deeper Root Causes Discovered (v0.239.015) +The v0.239.014 fix improved descriptions but actually made things **worse** because: + +4. **No companion schema plugin was ever loaded**: The ESAM agent only had ONE action configured (`sql_query` type). No `sql_schema` action existed in the agent's actions. The `_create_sql_plugin()` method creates exactly what the manifest requests โ€” so only `SQLQueryPlugin` was loaded, never `SQLSchemaPlugin`. + +5. **Descriptions demanded non-existent functions**: The v0.239.014 descriptions said "you MUST first call get_database_schema or get_table_list from the SQL Schema plugin" โ€” but those functions didn't exist in the kernel since no schema plugin was loaded. This created an **impossible dependency** that made the LLM ask for clarification instead. + +6. **Schema extraction found nothing**: `_extract_sql_schema_for_instructions()` only searched for `SQLSchemaPlugin` instances. Since none existed in the kernel, it returned an empty string, so no schema was injected into agent instructions. + +7. **SQLPluginFactory was disconnected**: The `SQLPluginFactory` class was designed to create `(SQLSchemaPlugin, SQLQueryPlugin)` pairs, but was never called by the `LoggedPluginLoader` pipeline. + +### Empty Schema Tables from INFORMATION_SCHEMA (v0.239.016) +After the v0.239.015 fix, the agent could answer simple queries (e.g., "what is user1 licensed to use?" correctly returned Office 365 license data). However, complex multi-table JOIN queries (e.g., "which department is spending the most on licensing?") still failed because: + +8. **INFORMATION_SCHEMA views returned empty results on Azure SQL**: The `_get_tables_query()`, `_get_columns_query()`, and `_get_primary_keys_query()` methods used `INFORMATION_SCHEMA.TABLES`, `INFORMATION_SCHEMA.COLUMNS`, and `INFORMATION_SCHEMA.KEY_COLUMN_USAGE` respectively. These views returned **zero rows** in this Azure SQL environment, even though the database contained 5 user tables. + +9. **sys.\* catalog views worked correctly**: The `_get_relationships_data()` method used `sys.foreign_keys`, `sys.tables`, and `sys.columns` โ€” and successfully returned 4 foreign key relationships. This proved the database connection and permissions were fine, but `INFORMATION_SCHEMA` access was restricted or misconfigured. + +10. **pyodbc.Row type mismatch**: The table iteration code used `isinstance(table, tuple)` to check row types, but `pyodbc.Row` objects may not pass this check depending on the pyodbc version. When `isinstance` returned `False`, the code fell into an `else` branch that assigned the entire Row object as the table name, causing subsequent SQL queries to fail silently in the exception handler. + +11. **Result**: `get_database_schema` returned `{'tables': {}, 'relationships': [4 items]}` โ€” the agent had foreign key metadata but no table/column definitions, making it impossible to construct multi-table JOINs. ## Version Implemented -**Fixed in version: 0.239.014** +**Initial fix in version: 0.239.014** +**Companion plugin fix in version: 0.239.015** +**Schema catalog views fix in version: 0.239.016** ## Files Modified | File | Change | |------|--------| -| `application/single_app/semantic_kernel_plugins/sql_schema_plugin.py` | Rewrote all `@kernel_function` descriptions with prescriptive workflow guidance; updated `metadata` property description | -| `application/single_app/semantic_kernel_plugins/sql_query_plugin.py` | Rewrote all `@kernel_function` descriptions with schema-first workflow guidance; added `query_database` convenience function; updated `metadata` property description | -| `application/single_app/semantic_kernel_loader.py` | Added `_extract_sql_schema_for_instructions()` helper function; auto-injects database schema into agent instructions at load time when SQL plugins are detected | -| `application/single_app/semantic_kernel_plugins/logged_plugin_loader.py` | Uncommented the SQL-specific plugin creation path (`_create_sql_plugin`) for explicit SQL plugin handling | -| `application/single_app/config.py` | Version bump to 0.239.014 | +| `application/single_app/semantic_kernel_plugins/sql_schema_plugin.py` | Rewrote all `@kernel_function` descriptions with prescriptive workflow guidance (v0.239.014); migrated all SQL Server queries from INFORMATION_SCHEMA to sys.\* catalog views and fixed pyodbc.Row handling (v0.239.016) | +| `application/single_app/semantic_kernel_plugins/sql_query_plugin.py` | Rewrote all `@kernel_function` descriptions with resilient conditional guidance (v0.239.015); added `query_database` convenience function (v0.239.014); updated `metadata` property description | +| `application/single_app/semantic_kernel_loader.py` | Added `_extract_sql_schema_for_instructions()` helper function; auto-injects database schema into agent instructions; added SQLQueryPlugin fallback detection (v0.239.015) | +| `application/single_app/semantic_kernel_plugins/logged_plugin_loader.py` | Enabled SQL plugin creation path (v0.239.014); added `_auto_create_companion_schema_plugin()` method that auto-creates a SQLSchemaPlugin whenever a SQLQueryPlugin is loaded (v0.239.015) | +| `application/single_app/config.py` | Version bump to 0.239.016 | ## Code Changes Summary -### 1. Prescriptive Function Descriptions (sql_schema_plugin.py, sql_query_plugin.py) +### v0.239.014 Changes + +#### 1. Prescriptive Function Descriptions (sql_schema_plugin.py) - `get_database_schema`: Now says "ALWAYS call this function FIRST before executing any SQL queries" - `get_table_list`: Now says "Use this function first to discover which tables are available" - `get_table_schema`: Now says "Call this after discovering tables via get_database_schema or get_table_list" - `get_relationships`: Now says "Use this to understand how tables connect via JOIN conditions" -- `execute_query`: Now says "IMPORTANT: Before calling this function, you MUST first call get_database_schema or get_table_list" -- `execute_scalar`: Now says "You MUST first discover the database schema" -- Pattern modeled after the successfully working LogAnalyticsPlugin -### 2. New `query_database` Convenience Function (sql_query_plugin.py) +#### 2. New `query_database` Convenience Function (sql_query_plugin.py) - Accepts `question` (natural language) and `query` (SQL) parameters - Returns results with the original question context for better LLM response formatting -- Gives the LLM an intent-aligned tool option -### 3. Auto Schema Injection (semantic_kernel_loader.py) +#### 3. Auto Schema Injection (semantic_kernel_loader.py) - New `_extract_sql_schema_for_instructions()` function detects SQL Schema plugins in the kernel - Calls `get_database_schema()` at agent load time to fetch full schema - Formats schema as markdown tables (table names, columns, types, relationships) - Appends schema to agent instructions with directive: "Do NOT ask the user for table or column names" + +#### 4. Enabled SQL Plugin Creation Path (logged_plugin_loader.py) +- Uncommented the `elif plugin_type in ['sql_schema', 'sql_query']` branch + +### v0.239.015 Changes (Complete Fix) + +#### 5. Auto-Create Companion Schema Plugin (logged_plugin_loader.py) +- New `_auto_create_companion_schema_plugin()` method +- When a `sql_query` plugin is loaded, automatically creates a companion `SQLSchemaPlugin` using the same connection details +- Derives schema plugin name: `enterprise_software_asset_management_query` โ†’ `enterprise_software_asset_management_schema` +- Checks if the companion already exists (idempotent) +- Enables logging, wraps functions, registers with kernel +- This is the **critical fix** โ€” ensures schema discovery is always available even when only `sql_query` is configured + +#### 6. Resilient Function Descriptions (sql_query_plugin.py) +- Changed from "you MUST first call get_database_schema" to "If the database schema is provided in your instructions, use those exact table and column names. If no schema is available, call get_database_schema" +- This dual-path approach works whether schema is injected in instructions OR available via schema plugin functions + +#### 7. SQLQueryPlugin Fallback in Schema Extraction (semantic_kernel_loader.py) +- Added fallback in `_extract_sql_schema_for_instructions()` that also detects `SQLQueryPlugin` instances +- If no `SQLSchemaPlugin` is found, creates a temporary `SQLSchemaPlugin` from the query plugin's connection config +- Belt-and-suspenders safety net in case companion auto-creation fails +- Appends schema to agent instructions with directive: "Do NOT ask the user for table or column names" - This ensures the LLM ALWAYS has schema context even if it doesn't call the schema plugin ### 4. Enabled SQL Plugin Creation Path (logged_plugin_loader.py) - Uncommented the `elif plugin_type in ['sql_schema', 'sql_query']` branch - SQL plugins now use the explicit `_create_sql_plugin()` method instead of generic discovery fallback +### v0.239.016 Changes (Schema Catalog Views Fix) + +#### 8. Migrated SQL Server Queries to sys.\* Catalog Views (sql_schema_plugin.py) +- **`_get_tables_query()`**: Replaced `INFORMATION_SCHEMA.TABLES` with `sys.tables t INNER JOIN sys.schemas s ON t.schema_id = s.schema_id WHERE t.type = 'U'` +- **`_get_columns_query()`**: Replaced `INFORMATION_SCHEMA.COLUMNS` with `sys.columns c INNER JOIN sys.tables t ... LEFT JOIN sys.default_constraints dc ...` using `TYPE_NAME(c.user_type_id)` for data type resolution +- **`_get_primary_keys_query()`**: Replaced `INFORMATION_SCHEMA.KEY_COLUMN_USAGE` with `sys.index_columns ic INNER JOIN sys.indexes i ... WHERE i.is_primary_key = 1` +- This makes all SQL Server schema queries consistent with `_get_relationships_data()`, which already used sys.\* views successfully + +#### 9. Robust pyodbc.Row Handling (sql_schema_plugin.py) +- **`get_database_schema()`**: Replaced `isinstance(table, tuple)` checks with try/except indexing; all row field values cast to `str()` before use as dict keys +- **`get_table_list()`**: Same robust Row handling pattern applied to table row iteration +- **`_get_table_schema_data()`**: Primary key list comprehension updated to use `str(pk[0])` without isinstance checks +- This ensures the code works correctly regardless of whether `pyodbc.Row` inherits from `tuple` in the installed pyodbc version + ## Testing Approach -- Functional test: `functional_tests/test_sql_query_plugin_schema_awareness.py` -- Validates all `@kernel_function` descriptions contain workflow guidance keywords -- Verifies `query_database` function exists with correct parameters -- Confirms SQL plugin creation path is enabled (not commented out) -- Checks schema injection function exists and is integrated into agent loading +- Functional test (v0.239.014): `functional_tests/test_sql_query_plugin_schema_awareness.py` +- Functional test (v0.239.015): `functional_tests/test_sql_auto_schema_companion.py` +- Validates `_auto_create_companion_schema_plugin` method exists with correct signature +- Confirms companion creation is triggered in `load_plugin_from_manifest` for `sql_query` type +- Verifies schema plugin name derivation logic (`_query` โ†’ `_schema` suffix swap) +- Checks `@kernel_function` descriptions are resilient (no hard dependency on non-existent functions) +- Validates `_extract_sql_schema_for_instructions` has SQLQueryPlugin fallback +- Confirms version updated to 0.239.015 +- Functional test (v0.239.016): `functional_tests/test_sql_schema_sys_catalog_views.py` +- Validates all SQL Server queries use `sys.tables`, `sys.columns`, `sys.indexes` instead of `INFORMATION_SCHEMA` +- Confirms pyodbc.Row-safe iteration (no `isinstance(table, tuple)` checks) +- Verifies primary key query uses `sys.index_columns` with `is_primary_key = 1` +- Checks PostgreSQL/MySQL/SQLite queries remain unchanged +- Confirms version updated to 0.239.016 ## Impact Analysis -- **SQL-connected agents**: Will now automatically receive database schema in their instructions, enabling proper query generation without user clarification -- **Non-SQL agents**: Completely unaffected (schema injection only triggers when SQL Schema plugins are detected) +- **SQL-connected agents**: Will now automatically have BOTH a query plugin AND a companion schema plugin, even when only `sql_query` is configured. Schema is injected into agent instructions at load time. +- **Non-SQL agents**: Completely unaffected (companion creation only triggers for `sql_query` type) - **LogAnalytics agents**: Unaffected (different plugin type) - **Performance**: One-time schema fetch at agent load time adds minimal latency; schema is cached in instructions for the session +- **Backwards compatible**: If both `sql_query` and `sql_schema` actions are explicitly configured, the companion auto-creation is skipped (checks for existing plugin) ## Before/After Comparison -### Before +### Before (v0.239.014) +- User: "What is user1 licensed to use?" +- Agent: "I need the exact user identifier... Please provide the identifier..." (no database call, no citations) +- Root cause: Descriptions demanded calling schema functions that didn't exist in the kernel + +### After v0.239.015 - User: "What is user1 licensed to use?" -- Agent: "I need the exact user identifier... Please provide the identifier..." (no database call, no citations) +- Agent: Correctly returns Office 365 license data with LicenseID 1, TotalQuantity 52 (simple single-table queries work) +- User: "Which department is spending the most on licensing?" +- Agent: Fails โ€” says "I don't see a department dimension in the current schema" and calls `get_database_schema` which returns `{'tables': {}}` (empty) +- Root cause: INFORMATION_SCHEMA views returned no results on Azure SQL -### After (Expected) +### After v0.239.016 - User: "What is user1 licensed to use?" -- Agent: Calls `get_database_schema` โ†’ sees Licenses, Usage, Procurements tables โ†’ generates JOIN query โ†’ returns actual license data with citations +- Agent: Correctly returns license data (still works) +- User: "Which department is spending the most on licensing?" +- Agent: Has full schema with all 5 tables and their columns โ†’ can construct multi-table JOINs (Licenses โ†’ Procurements for cost, Usage for department) โ†’ returns department-level spending analysis diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md index 9af582c1..f705655f 100644 --- a/docs/explanation/release_notes.md +++ b/docs/explanation/release_notes.md @@ -2,6 +2,41 @@ # Feature Release +### **(v0.239.017)** + +#### Bug Fixes + +* **SQL Schema Plugin โ€” Eliminate Redundant Schema Calls** + * Fixed agent calling `get_database_schema` twice per query even though the full schema was already injected into the agent's instructions at load time. + * Root cause: The `@kernel_function` descriptions in `sql_schema_plugin.py` said "ALWAYS call this function FIRST," which overrode the schema context already available in the instructions. + * Updated all four function descriptions (`get_database_schema`, `get_table_schema`, `get_table_list`, `get_relationships`) to use the resilient pattern: "If the database schema is already provided in your instructions, use that directly and do NOT call this function." + * This eliminates ~400ms+ of unnecessary database round trips per query and aligns with the same pattern already used in `sql_query_plugin.py` since v0.239.015. + * (Ref: `sql_schema_plugin.py`, `@kernel_function` descriptions, schema injection) + +### **(v0.239.016)** + +#### Bug Fixes + +* **SQL Schema Plugin โ€” Empty Tables from INFORMATION_SCHEMA** + * Fixed `get_database_schema` returning `'tables': {}` (empty) despite the database having tables, while relationships were returned correctly. + * Root cause: SQL Server table/column enumeration used `INFORMATION_SCHEMA.TABLES` and `INFORMATION_SCHEMA.COLUMNS` views, which returned empty results in the Azure SQL environment. Meanwhile, the relationships query used `sys.foreign_keys`/`sys.tables`/`sys.columns` catalog views which worked perfectly. + * Migrated all SQL Server schema queries to use `sys.*` catalog views consistently: `sys.tables`/`sys.schemas` for table enumeration, `sys.columns` with `TYPE_NAME()` for column details, and `sys.indexes`/`sys.index_columns` for primary key detection. + * Fixed `pyodbc.Row` handling throughout the plugin โ€” removed all `isinstance(table, tuple)` checks that could fail with pyodbc Row objects, replaced with robust try/except indexing. + * This enables the full schema (tables, columns, types, PKs, FKs) to be injected into agent instructions, allowing agents to construct complex multi-table JOINs for analytical queries. + * (Ref: `sql_schema_plugin.py`, `sys.tables`, `sys.columns`, `sys.indexes`, pyodbc.Row handling) + +### **(v0.239.015)** + +#### Bug Fixes + +* **SQL Query Plugin โ€” Auto-Create Companion Schema Plugin** + * Fixed the remaining issue where SQL-connected agents still asked for clarification instead of querying the database, even after v0.239.014 description improvements. + * Root cause: Agents configured with only a `sql_query` action never had a `SQLSchemaPlugin` loaded in the kernel. The v0.239.014 descriptions demanded calling `get_database_schema` โ€” a function that didn't exist โ€” creating an impossible dependency that caused the LLM to ask for clarification. + * `LoggedPluginLoader` now automatically creates a companion `SQLSchemaPlugin` whenever a `SQLQueryPlugin` is loaded, using the same connection details. This ensures schema discovery is always available. + * Updated `@kernel_function` descriptions to be resilient: "If the database schema is provided in your instructions, use it directly. Otherwise, call get_database_schema." This dual-path approach works whether schema is injected via instructions or available via plugin functions. + * Added fallback in `_extract_sql_schema_for_instructions()` to also detect `SQLQueryPlugin` instances and create a temporary schema extractor if no `SQLSchemaPlugin` is found. + * (Ref: `logged_plugin_loader.py`, `sql_query_plugin.py`, `semantic_kernel_loader.py`) + ### **(v0.239.014)** #### Bug Fixes diff --git a/functional_tests/test_sql_auto_schema_companion.py b/functional_tests/test_sql_auto_schema_companion.py new file mode 100644 index 00000000..2f488c97 --- /dev/null +++ b/functional_tests/test_sql_auto_schema_companion.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +""" +Functional test for SQL Auto Schema Companion Plugin creation. +Version: 0.239.015 +Implemented in: 0.239.015 + +This test ensures that when a SQL Query plugin is loaded via LoggedPluginLoader, +a companion SQL Schema plugin is automatically created and registered in the kernel. +This fixes the issue where agents with only a sql_query action configured would have +no schema awareness, causing the LLM to ask for clarification instead of querying. + +Additionally validates that: +- SQLQueryPlugin @kernel_function descriptions are resilient (don't demand non-existent functions) +- The _extract_sql_schema_for_instructions fallback detects SQLQueryPlugin instances +""" + +import sys +import os +import inspect + +# Add the application directory to the path for imports +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app')) + + +def test_companion_schema_plugin_method_exists(): + """Test that LoggedPluginLoader has the _auto_create_companion_schema_plugin method.""" + print("๐Ÿ” Testing _auto_create_companion_schema_plugin method exists...") + + try: + from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader + + assert hasattr(LoggedPluginLoader, '_auto_create_companion_schema_plugin'), \ + "LoggedPluginLoader must have _auto_create_companion_schema_plugin method" + + method = getattr(LoggedPluginLoader, '_auto_create_companion_schema_plugin') + assert callable(method), "_auto_create_companion_schema_plugin must be callable" + + # Verify method signature includes query_manifest and query_plugin_name + sig = inspect.signature(method) + param_names = list(sig.parameters.keys()) + assert 'query_manifest' in param_names, "Method must accept query_manifest parameter" + assert 'query_plugin_name' in param_names, "Method must accept query_plugin_name parameter" + + print("โœ… _auto_create_companion_schema_plugin method exists with correct signature!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_companion_creation_triggered_for_sql_query(): + """Test that load_plugin_from_manifest triggers companion creation for sql_query type.""" + print("๐Ÿ” Testing companion creation trigger in load_plugin_from_manifest...") + + try: + from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader + + source = inspect.getsource(LoggedPluginLoader.load_plugin_from_manifest) + + # The method should check for sql_query type and call companion creation + assert "plugin_type == 'sql_query'" in source, \ + "load_plugin_from_manifest must check for sql_query plugin type" + assert "_auto_create_companion_schema_plugin" in source, \ + "load_plugin_from_manifest must call _auto_create_companion_schema_plugin" + + print("โœ… load_plugin_from_manifest triggers companion creation for sql_query!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_schema_plugin_name_derivation(): + """Test that the companion schema plugin name is correctly derived.""" + print("๐Ÿ” Testing schema plugin name derivation logic...") + + try: + from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader + + source = inspect.getsource(LoggedPluginLoader._auto_create_companion_schema_plugin) + + # Should handle _query suffix replacement + assert "endswith('_query')" in source, \ + "Method should check for _query suffix" + assert "'_schema'" in source, \ + "Method should derive _schema suffix" + + # Should check if schema plugin already exists + assert "self.kernel.plugins" in source, \ + "Method should check kernel.plugins for existing schema plugin" + + # Should create SQLSchemaPlugin + assert "SQLSchemaPlugin" in source, \ + "Method should create a SQLSchemaPlugin instance" + + # Should register with kernel + assert "_register_plugin_with_kernel" in source, \ + "Method should register the companion plugin with the kernel" + + print("โœ… Schema plugin name derivation and creation logic is correct!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_resilient_query_plugin_descriptions(): + """Test that SQLQueryPlugin descriptions don't demand non-existent schema functions.""" + print("๐Ÿ” Testing SQLQueryPlugin @kernel_function descriptions are resilient...") + + try: + from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin + + source = inspect.getsource(SQLQueryPlugin) + + # Descriptions should NOT contain "you MUST first call" which creates a hard dependency + assert "you MUST first call" not in source, \ + "Descriptions should not contain 'you MUST first call' (creates hard dependency on non-existent functions)" + assert "You MUST first discover" not in source, \ + "Descriptions should not contain 'You MUST first discover' (creates hard dependency)" + + # Descriptions SHOULD contain resilient language + assert "If the database schema is provided in your instructions" in source, \ + "Descriptions should reference schema from instructions as primary source" + + # Should still mention the schema plugin as a fallback + assert "get_database_schema" in source, \ + "Descriptions should still reference get_database_schema as a fallback option" + + print("โœ… SQLQueryPlugin descriptions are resilient and don't demand non-existent functions!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_schema_extraction_fallback(): + """Test that _extract_sql_schema_for_instructions has a fallback for SQLQueryPlugin.""" + print("๐Ÿ” Testing _extract_sql_schema_for_instructions fallback...") + + try: + from semantic_kernel_loader import _extract_sql_schema_for_instructions + + source = inspect.getsource(_extract_sql_schema_for_instructions) + + # Should have fallback logic + assert "Fallback" in source or "fallback" in source, \ + "Function should contain fallback logic" + + # Should check for SQLQueryPlugin + assert "SQLQueryPlugin" in source, \ + "Fallback should check for SQLQueryPlugin instances" + + # Should create temporary schema extractor + assert "temp_manifest" in source or "temp_schema" in source, \ + "Fallback should create a temporary schema extractor" + + print("โœ… _extract_sql_schema_for_instructions has SQLQueryPlugin fallback!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_version_updated(): + """Test that the version has been updated for this fix.""" + print("๐Ÿ” Testing version update...") + + try: + # Read config.py directly to check version + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), + '..', 'application', 'single_app', 'config.py') + with open(config_path, 'r') as f: + config_content = f.read() + + assert 'VERSION = "0.239.015"' in config_content, \ + f"config.py should contain VERSION = \"0.239.015\"" + + print("โœ… Version correctly updated to 0.239.015!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +if __name__ == "__main__": + tests = [ + test_companion_schema_plugin_method_exists, + test_companion_creation_triggered_for_sql_query, + test_schema_plugin_name_derivation, + test_resilient_query_plugin_descriptions, + test_schema_extraction_fallback, + test_version_updated, + ] + results = [] + + for test in tests: + print(f"\n๐Ÿงช Running {test.__name__}...") + results.append(test()) + + success = all(results) + passed = sum(results) + total = len(results) + print(f"\n๐Ÿ“Š Results: {passed}/{total} tests passed") + + if not success: + failed_tests = [t.__name__ for t, r in zip(tests, results) if not r] + print(f"โŒ Failed tests: {', '.join(failed_tests)}") + + sys.exit(0 if success else 1) diff --git a/functional_tests/test_sql_schema_sys_catalog_views.py b/functional_tests/test_sql_schema_sys_catalog_views.py new file mode 100644 index 00000000..dd1c5f05 --- /dev/null +++ b/functional_tests/test_sql_schema_sys_catalog_views.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +""" +Functional test for SQL Schema Plugin sys.* catalog view migration. +Version: 0.239.016 +Implemented in: 0.239.016 + +This test ensures that the SQL Schema Plugin uses sys.tables, sys.columns, +sys.indexes (catalog views) instead of INFORMATION_SCHEMA views for SQL Server, +fixing the issue where get_database_schema returned empty tables dict ('tables': {}) +despite the database having tables. The sys.* views are proven to work since the +relationships query (which uses sys.foreign_keys) always returned valid data. + +Also validates robust pyodbc.Row handling (no isinstance(tuple) checks). +""" + +import sys +import os +import inspect + +# Add the application directory to the path for imports +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app')) + + +def test_tables_query_uses_sys_views(): + """Test that SQL Server tables query uses sys.tables instead of INFORMATION_SCHEMA.""" + print("๐Ÿ” Testing SQL Server tables query uses sys.tables...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + source = inspect.getsource(SQLSchemaPlugin._get_tables_query) + + # Should use sys.tables + assert "sys.tables" in source, \ + "_get_tables_query should reference sys.tables" + assert "sys.schemas" in source, \ + "_get_tables_query should reference sys.schemas" + + # Should NOT use INFORMATION_SCHEMA.TABLES for sqlserver + # (other DB types still use their own patterns) + assert "INFORMATION_SCHEMA.TABLES" not in source, \ + "_get_tables_query should NOT use INFORMATION_SCHEMA.TABLES (use sys.tables instead)" + + print("โœ… Tables query uses sys.tables!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_columns_query_uses_sys_views(): + """Test that SQL Server columns query uses sys.columns instead of INFORMATION_SCHEMA.""" + print("๐Ÿ” Testing SQL Server columns query uses sys.columns...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + source = inspect.getsource(SQLSchemaPlugin._get_columns_query) + + # Should use sys.columns + assert "sys.columns" in source, \ + "_get_columns_query should reference sys.columns" + assert "TYPE_NAME" in source, \ + "_get_columns_query should use TYPE_NAME() for data type resolution" + + # Should NOT use INFORMATION_SCHEMA.COLUMNS for sqlserver + assert "INFORMATION_SCHEMA.COLUMNS" not in source, \ + "_get_columns_query should NOT use INFORMATION_SCHEMA.COLUMNS (use sys.columns instead)" + + print("โœ… Columns query uses sys.columns!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_primary_keys_query_uses_sys_views(): + """Test that SQL Server PK query uses sys.indexes instead of INFORMATION_SCHEMA.""" + print("๐Ÿ” Testing SQL Server primary keys query uses sys.indexes...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + source = inspect.getsource(SQLSchemaPlugin._get_primary_keys_query) + + # Should use sys.indexes with is_primary_key + assert "sys.indexes" in source, \ + "_get_primary_keys_query should reference sys.indexes" + assert "is_primary_key" in source, \ + "_get_primary_keys_query should filter on is_primary_key" + assert "sys.index_columns" in source, \ + "_get_primary_keys_query should reference sys.index_columns" + + # Should NOT use INFORMATION_SCHEMA.KEY_COLUMN_USAGE for sqlserver section + # Note: PostgreSQL/MySQL sections may still use INFORMATION_SCHEMA (that's fine) + # We check that the sqlserver branch uses sys.* by verifying sys.indexes is present + assert "sys.index_columns" in source, \ + "_get_primary_keys_query should reference sys.index_columns for SQL Server" + + print("โœ… Primary keys query uses sys.indexes!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_robust_row_handling_no_isinstance_tuple(): + """Test that table row parsing doesn't rely on isinstance(tuple) checks.""" + print("๐Ÿ” Testing robust pyodbc.Row handling...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + # Check get_database_schema table iteration + schema_source = inspect.getsource(SQLSchemaPlugin.get_database_schema) + + # Should NOT use isinstance(table, tuple) pattern + assert "isinstance(table, tuple)" not in schema_source, \ + "get_database_schema should not use isinstance(table, tuple) โ€” pyodbc.Row may not be a tuple" + + # Should use try/except for robust indexing + assert "except (TypeError, IndexError)" in schema_source, \ + "get_database_schema should handle TypeError/IndexError for row parsing" + + # Check get_table_list too + list_source = inspect.getsource(SQLSchemaPlugin.get_table_list) + assert "isinstance(table_row, (list, tuple))" not in list_source, \ + "get_table_list should not use isinstance tuple check" + + # Check primary keys list comprehension + schema_data_source = inspect.getsource(SQLSchemaPlugin._get_table_schema_data) + assert "isinstance(pk, (list, tuple))" not in schema_data_source, \ + "Primary key parsing should not use isinstance tuple check" + + print("โœ… Robust Row handling โ€” no isinstance(tuple) dependencies!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_consistency_with_relationships_query(): + """Test that all SQL Server queries use sys.* views consistently (like relationships).""" + print("๐Ÿ” Testing consistency: all SQL Server queries use sys.* views...") + + try: + from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin + + # The relationships query already used sys.* โ€” verify it still does + rel_source = inspect.getsource(SQLSchemaPlugin._get_relationships_data) + assert "sys.foreign_keys" in rel_source, \ + "Relationships query should use sys.foreign_keys" + assert "sys.tables" in rel_source, \ + "Relationships query should reference sys.tables" + + # Tables query should also use sys.* + tables_source = inspect.getsource(SQLSchemaPlugin._get_tables_query) + assert "sys.tables" in tables_source + + # Columns query should also use sys.* + cols_source = inspect.getsource(SQLSchemaPlugin._get_columns_query) + assert "sys.columns" in cols_source + + # PK query should also use sys.* + pk_source = inspect.getsource(SQLSchemaPlugin._get_primary_keys_query) + assert "sys.indexes" in pk_source + + print("โœ… All SQL Server queries consistently use sys.* catalog views!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +def test_version_updated(): + """Test that the version has been updated for this fix.""" + print("๐Ÿ” Testing version update...") + + try: + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), + '..', 'application', 'single_app', 'config.py') + with open(config_path, 'r') as f: + config_content = f.read() + + assert 'VERSION = "0.239.016"' in config_content, \ + f"config.py should contain VERSION = \"0.239.016\"" + + print("โœ… Version correctly updated to 0.239.016!") + return True + except Exception as e: + print(f"โŒ Test failed: {e}") + import traceback + traceback.print_exc() + return False + + +if __name__ == "__main__": + tests = [ + test_tables_query_uses_sys_views, + test_columns_query_uses_sys_views, + test_primary_keys_query_uses_sys_views, + test_robust_row_handling_no_isinstance_tuple, + test_consistency_with_relationships_query, + test_version_updated, + ] + results = [] + + for test in tests: + print(f"\n๐Ÿงช Running {test.__name__}...") + results.append(test()) + + success = all(results) + passed = sum(results) + total = len(results) + print(f"\n๐Ÿ“Š Results: {passed}/{total} tests passed") + + if not success: + failed_tests = [t.__name__ for t, r in zip(tests, results) if not r] + print(f"โŒ Failed tests: {', '.join(failed_tests)}") + + sys.exit(0 if success else 1) From 09d33e3094def13fb26d68c3cfbee147864c1cd5 Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Fri, 6 Mar 2026 15:50:32 -0500 Subject: [PATCH 6/8] improve thought timeline --- application/single_app/config.py | 2 +- application/single_app/route_backend_chats.py | 118 +++++++++++ docs/explanation/release_notes.md | 193 ++++++++---------- 3 files changed, 202 insertions(+), 111 deletions(-) diff --git a/application/single_app/config.py b/application/single_app/config.py index 772802c1..a7f90bfc 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -94,7 +94,7 @@ EXECUTOR_TYPE = 'thread' EXECUTOR_MAX_WORKERS = 30 SESSION_TYPE = 'filesystem' -VERSION = "0.239.017" +VERSION = "0.239.020" SECRET_KEY = os.getenv('SECRET_KEY', 'dev-secret-key-change-in-production') diff --git a/application/single_app/route_backend_chats.py b/application/single_app/route_backend_chats.py index 8154b7d4..293ec929 100644 --- a/application/single_app/route_backend_chats.py +++ b/application/single_app/route_backend_chats.py @@ -226,6 +226,7 @@ def register_route_backend_chats(app): @user_required def chat_api(): try: + request_start_time = time.time() settings = get_settings() data = request.get_json() user_id = get_current_user_id() @@ -2426,7 +2427,9 @@ def orchestrator_error(e): }) if selected_agent: + agent_deployment_name = getattr(selected_agent, 'deployment_name', None) or gpt_model thought_tracker.add_thought('agent_tool_call', f"Sending to agent '{getattr(selected_agent, 'display_name', getattr(selected_agent, 'name', 'unknown'))}'") + thought_tracker.add_thought('generation', f"Sending to '{agent_deployment_name}'") # Register callback to write plugin thoughts to Cosmos in real-time callback_key = f"{user_id}:{conversation_id}" @@ -2443,6 +2446,9 @@ def on_plugin_invocation(inv): plugin_logger.register_callback(callback_key, on_plugin_invocation) + thought_tracker.add_thought('generation', 'Generating response...') + agent_invoke_start_time = time.time() + def invoke_selected_agent(): return asyncio.run(run_sk_call( selected_agent.invoke, @@ -2454,6 +2460,10 @@ def agent_success(result): notice = None agent_used = getattr(selected_agent, 'name', 'All Plugins') + # Emit responded thought with total duration from user message + agent_total_duration_s = round(time.time() - request_start_time, 1) + thought_tracker.add_thought('generation', f"'{agent_deployment_name}' responded ({agent_total_duration_s}s from initial message)") + # Deregister real-time thought callback plugin_logger.deregister_callbacks(callback_key) @@ -2580,6 +2590,10 @@ def foundry_agent_success(result): or agent_used ) + # Emit responded thought with total duration from user message + foundry_total_duration_s = round(time.time() - request_start_time, 1) + thought_tracker.add_thought('generation', f"'{actual_model_deployment}' responded ({foundry_total_duration_s}s from initial message)") + # Deregister real-time thought callback plugin_logger.deregister_callbacks(callback_key) @@ -3061,6 +3075,7 @@ def chat_stream_api(): data = request.get_json() user_id = get_current_user_id() settings = get_settings() + request_start_time = time.time() except Exception as e: return jsonify({'error': f'Failed to parse request: {str(e)}'}), 400 @@ -3476,6 +3491,101 @@ def emit_thought(step_type, content, detail=None): thought_tracker.add_thought(step_type, content, detail) return f"data: {json.dumps({'type': 'thought', 'step_index': thought_tracker.current_index - 1, 'step_type': step_type, 'content': content})}\n\n" + # Content Safety check (matching non-streaming path) + blocked = False + if settings.get('enable_content_safety') and "content_safety_client" in CLIENTS: + yield emit_thought('content_safety', 'Checking content safety...') + try: + content_safety_client = CLIENTS["content_safety_client"] + request_obj = AnalyzeTextOptions(text=user_message) + cs_response = content_safety_client.analyze_text(request_obj) + + max_severity = 0 + triggered_categories = [] + blocklist_matches = [] + block_reasons = [] + + for cat_result in cs_response.categories_analysis: + triggered_categories.append({ + "category": cat_result.category, + "severity": cat_result.severity + }) + if cat_result.severity > max_severity: + max_severity = cat_result.severity + + if cs_response.blocklists_match: + for match in cs_response.blocklists_match: + blocklist_matches.append({ + "blocklistName": match.blocklist_name, + "blocklistItemId": match.blocklist_item_id, + "blocklistItemText": match.blocklist_item_text + }) + + if max_severity >= 4: + blocked = True + block_reasons.append("Max severity >= 4") + if len(blocklist_matches) > 0: + blocked = True + block_reasons.append("Blocklist match") + + if blocked: + # Upsert to safety container + safety_item = { + 'id': str(uuid.uuid4()), + 'user_id': user_id, + 'conversation_id': conversation_id, + 'message': user_message, + 'triggered_categories': triggered_categories, + 'blocklist_matches': blocklist_matches, + 'timestamp': datetime.utcnow().isoformat(), + 'reason': "; ".join(block_reasons), + 'metadata': {} + } + cosmos_safety_container.upsert_item(safety_item) + + # Build blocked message + blocked_msg_content = ( + "Your message was blocked by Content Safety.\n\n" + f"**Reason**: {', '.join(block_reasons)}\n" + "Triggered categories:\n" + ) + for cat in triggered_categories: + blocked_msg_content += ( + f" - {cat['category']} (severity={cat['severity']})\n" + ) + if blocklist_matches: + blocked_msg_content += ( + "\nBlocklist Matches:\n" + + "\n".join([f" - {m['blocklistItemText']} (in {m['blocklistName']})" + for m in blocklist_matches]) + ) + + # Insert safety message + safety_message_id = f"{conversation_id}_safety_{int(time.time())}_{random.randint(1000,9999)}" + safety_doc = { + 'id': safety_message_id, + 'conversation_id': conversation_id, + 'role': 'safety', + 'content': blocked_msg_content.strip(), + 'timestamp': datetime.utcnow().isoformat(), + 'model_deployment_name': None, + 'metadata': {}, + } + cosmos_messages_container.upsert_item(safety_doc) + + conversation_item['last_updated'] = datetime.utcnow().isoformat() + cosmos_conversations_container.upsert_item(conversation_item) + + # Stream the blocked response and stop + yield f"data: {json.dumps({'content': blocked_msg_content.strip(), 'blocked': True})}\n\n" + yield "data: [DONE]\n\n" + return + + except HttpResponseError as e: + debug_print(f"[Content Safety Error - Streaming] {e}") + except Exception as ex: + debug_print(f"[Content Safety - Streaming] Unexpected error: {ex}") + # Hybrid search (if enabled) combined_documents = [] if hybrid_search_enabled: @@ -3994,6 +4104,7 @@ def emit_thought(step_type, content, detail=None): if use_agent_streaming and selected_agent: # Stream from agent using invoke_stream yield emit_thought('agent_tool_call', f"Sending to agent '{agent_display_name_used or agent_name_used}'") + yield emit_thought('generation', f"Sending to '{actual_model_used}'") debug_print(f"--- Streaming from Agent: {agent_name_used} ---") # Register callback to persist plugin thoughts to Cosmos in real-time @@ -4023,6 +4134,9 @@ def on_plugin_invocation_streaming(inv): for msg in conversation_history_for_api ] + yield emit_thought('generation', 'Generating response...') + agent_stream_start_time = time.time() + # Stream agent responses - collect chunks first then yield async def stream_agent_async(): """Collect all streaming chunks from agent""" @@ -4072,6 +4186,10 @@ async def stream_agent_async(): yield f"data: {json.dumps({'error': f'Agent streaming failed: {str(stream_error)}'})}\n\n" return + # Emit responded thought with total duration from user message + agent_stream_total_duration_s = round(time.time() - request_start_time, 1) + yield emit_thought('generation', f"'{actual_model_used}' responded ({agent_stream_total_duration_s}s from initial message)") + # Deregister callback (agent completed successfully) plugin_logger_cb.deregister_callbacks(callback_key) diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md index f705655f..0053ffad 100644 --- a/docs/explanation/release_notes.md +++ b/docs/explanation/release_notes.md @@ -2,66 +2,17 @@ # Feature Release -### **(v0.239.017)** +### **(v0.239.020)** -#### Bug Fixes - -* **SQL Schema Plugin โ€” Eliminate Redundant Schema Calls** - * Fixed agent calling `get_database_schema` twice per query even though the full schema was already injected into the agent's instructions at load time. - * Root cause: The `@kernel_function` descriptions in `sql_schema_plugin.py` said "ALWAYS call this function FIRST," which overrode the schema context already available in the instructions. - * Updated all four function descriptions (`get_database_schema`, `get_table_schema`, `get_table_list`, `get_relationships`) to use the resilient pattern: "If the database schema is already provided in your instructions, use that directly and do NOT call this function." - * This eliminates ~400ms+ of unnecessary database round trips per query and aligns with the same pattern already used in `sql_query_plugin.py` since v0.239.015. - * (Ref: `sql_schema_plugin.py`, `@kernel_function` descriptions, schema injection) - -### **(v0.239.016)** - -#### Bug Fixes - -* **SQL Schema Plugin โ€” Empty Tables from INFORMATION_SCHEMA** - * Fixed `get_database_schema` returning `'tables': {}` (empty) despite the database having tables, while relationships were returned correctly. - * Root cause: SQL Server table/column enumeration used `INFORMATION_SCHEMA.TABLES` and `INFORMATION_SCHEMA.COLUMNS` views, which returned empty results in the Azure SQL environment. Meanwhile, the relationships query used `sys.foreign_keys`/`sys.tables`/`sys.columns` catalog views which worked perfectly. - * Migrated all SQL Server schema queries to use `sys.*` catalog views consistently: `sys.tables`/`sys.schemas` for table enumeration, `sys.columns` with `TYPE_NAME()` for column details, and `sys.indexes`/`sys.index_columns` for primary key detection. - * Fixed `pyodbc.Row` handling throughout the plugin โ€” removed all `isinstance(table, tuple)` checks that could fail with pyodbc Row objects, replaced with robust try/except indexing. - * This enables the full schema (tables, columns, types, PKs, FKs) to be injected into agent instructions, allowing agents to construct complex multi-table JOINs for analytical queries. - * (Ref: `sql_schema_plugin.py`, `sys.tables`, `sys.columns`, `sys.indexes`, pyodbc.Row handling) - -### **(v0.239.015)** - -#### Bug Fixes - -* **SQL Query Plugin โ€” Auto-Create Companion Schema Plugin** - * Fixed the remaining issue where SQL-connected agents still asked for clarification instead of querying the database, even after v0.239.014 description improvements. - * Root cause: Agents configured with only a `sql_query` action never had a `SQLSchemaPlugin` loaded in the kernel. The v0.239.014 descriptions demanded calling `get_database_schema` โ€” a function that didn't exist โ€” creating an impossible dependency that caused the LLM to ask for clarification. - * `LoggedPluginLoader` now automatically creates a companion `SQLSchemaPlugin` whenever a `SQLQueryPlugin` is loaded, using the same connection details. This ensures schema discovery is always available. - * Updated `@kernel_function` descriptions to be resilient: "If the database schema is provided in your instructions, use it directly. Otherwise, call get_database_schema." This dual-path approach works whether schema is injected via instructions or available via plugin functions. - * Added fallback in `_extract_sql_schema_for_instructions()` to also detect `SQLQueryPlugin` instances and create a temporary schema extractor if no `SQLSchemaPlugin` is found. - * (Ref: `logged_plugin_loader.py`, `sql_query_plugin.py`, `semantic_kernel_loader.py`) - -### **(v0.239.014)** - -#### Bug Fixes - -* **SQL Query Plugin Schema Awareness** - * Fixed agents connected to SQL databases asking users for clarification about table/column names instead of querying the database directly. - * Root cause: SQL Query and SQL Schema plugin `@kernel_function` descriptions were generic with no workflow guidance, agent instructions had no database schema context, and the two plugins operated independently with no linkage. - * Rewrote all `@kernel_function` descriptions in both SQL plugins to be prescriptive workflow guides (modeled after the working LogAnalyticsPlugin), explicitly instructing the LLM to discover schema first before generating queries. - * Added auto-injection of database schema into agent instructions at load time โ€” when SQL Schema plugins are detected, the full schema (tables, columns, types, relationships) is fetched and appended to the agent's system prompt. - * Added new `query_database(question, query)` convenience function to `SQLQueryPlugin` for intent-aligned tool calling. - * Enabled the SQL-specific plugin creation path in `logged_plugin_loader.py` (was previously commented out). - * (Ref: `sql_query_plugin.py`, `sql_schema_plugin.py`, `semantic_kernel_loader.py`, `logged_plugin_loader.py`) - -### **(v0.239.008)** - -#### Bug Fixes +#### User Interface Enhancements -* **Chat-Uploaded Tabular Files Now Trigger SK Mini-Agent in Model-Only Mode** - * Fixed an issue where tabular files (CSV, XLSX, XLS, XLSM) uploaded directly to a chat conversation were not analyzed by the SK mini-agent when no agent was selected. The model would describe what analysis it would perform instead of returning actual computed results. - * **Root Cause**: The mini SK agent only triggered from search results, but chat-uploaded files are stored in blob storage and not indexed in Azure AI Search. Additionally, the streaming path completely ignored `file` role messages in conversation history. - * **Fix**: Both streaming and non-streaming chat paths now detect chat-uploaded tabular files during conversation history building and trigger `run_tabular_sk_analysis(source_hint="chat")` to pre-compute results. The streaming path also now properly handles `file` role messages (tabular and non-tabular) matching the non-streaming path's behavior. - * (Ref: `route_backend_chats.py`, `run_tabular_sk_analysis()`, `collect_tabular_sk_citations()`) +* **Agent Responded Thought โ€” Seconds & Total Duration** + * The "responded" thought now shows time in **seconds** instead of milliseconds, and clarifies it is the total time from the initial user message (e.g., `'gpt-5-nano' responded (16.3s from initial message)`). + * A `request_start_time` is now captured at the top of both the non-streaming and streaming chat handlers, so the duration reflects the full request lifecycle โ€” including content safety, hybrid search, and agent invocation โ€” not just the model response time. + * Applies to all three agent paths: local SK agents (non-streaming), Azure AI Foundry agents, and streaming SK agents. + * (Ref: `route_backend_chats.py`, `request_start_time`, agent responded thoughts) ---- -### **(v0.239.013)** +### **(v0.239.019)** #### New Features @@ -79,24 +30,6 @@ * All personal, group, and admin routes for both agents and actions are wired up. * (Ref: `functions_activity_logging.py`, `route_backend_agents.py`, `route_backend_plugins.py`) -### **(v0.239.008)** - -#### User Interface Enhancements - -* **List/Grid View Toggle for Agents and Actions** - * Added a list/grid view toggle to all four workspace areas: personal agents, personal actions, group agents, and group actions. - * **Grid View**: Large cards with type icon, humanized name, truncated description, and action buttons (Chat, View, Edit, Delete as applicable). - * **List View**: Improved table layout with fixed column widths (28%/47%/25%), humanized display names, and truncated descriptions with hover tooltips for full text. - * **View Button**: New eye-icon button on every agent and action that opens a read-only detail modal with gradient-header summary cards (Basic Information, Model Configuration, Instructions for agents; Basic Information, Configuration for actions). - * **Name Humanization**: Display names are now automatically parsed โ€” underscores and camelCase/PascalCase boundaries are converted to properly spaced, title-cased words (e.g., `myCustomAgent` โ†’ `My Custom Agent`). - * **Persistent Preference**: View mode selection (list/grid) is saved per area in localStorage and restored on page load. - * New shared utility module `view-utils.js` provides reusable functions for all four workspace areas. - * (Ref: `view-utils.js`, `workspace_agents.js`, `workspace_plugins.js`, `plugin_common.js`, `group_agents.js`, `group_plugins.js`, `workspace.html`, `group_workspaces.html`, `styles.css`) - -### **(v0.239.007)** - -#### New Features - * **Tabular Data Analysis โ€” SK Mini-Agent for Normal Chat** * Tabular files (CSV, XLSX, XLS, XLSM) detected in search results now trigger a lightweight Semantic Kernel mini-agent that pre-computes data analysis before the main LLM response. This brings the same analytical depth previously only available in full agent mode to every normal chat conversation. * **Automatic Detection**: When AI Search results include tabular files from any workspace (personal, group, or public) or chat-uploaded documents, the system automatically identifies them via the `TABULAR_EXTENSIONS` configuration and routes the query through the SK mini-agent pipeline. @@ -106,7 +39,6 @@ * **Graceful Degradation**: If the mini-agent analysis fails for any reason, the system falls back to instructing the main LLM to use the tabular processing plugin functions directly, preserving full functionality. * **Non-Streaming and Streaming Support**: Both chat modes are supported. The mini-agent runs synchronously before the main LLM call in both paths. * **Requires Enhanced Citations**: The tabular processing plugin depends on the blob storage client initialized by the enhanced citations system. The `enable_enhanced_citations` admin setting must be enabled for tabular data analysis to activate. - * **Files Modified**: `route_backend_chats.py`, `semantic_kernel_plugins/tabular_processing_plugin.py`, `config.py`. * (Ref: `run_tabular_sk_analysis()`, `TabularProcessingPlugin`, `collect_tabular_sk_citations()`, `TABULAR_EXTENSIONS`) * **Tabular Tool Execution Citations** @@ -114,7 +46,6 @@ * **Automatic Capture**: The existing `@plugin_function_logger` decorator on all `TabularProcessingPlugin` functions records each invocation including function name, input parameters, returned results, execution duration, and success/failure status. * **Citation Format**: Tool execution citations appear in the same "Agent Tool Execution" modal used by full agent mode, showing `tool_name` (e.g., `TabularProcessingPlugin.aggregate_column`), `function_arguments` (the exact parameters passed), and `function_result` (the computed data returned). * **End-to-End Auditability**: Users can verify exactly which aggregations, filters, or queries were run against their data, what parameters were used, and what raw results were returned โ€” before the LLM summarized them into the final response. - * **Files Modified**: `route_backend_chats.py`. * (Ref: `collect_tabular_sk_citations()`, `plugin_invocation_logger.py`) * **SK Mini-Agent Performance Optimization** @@ -123,25 +54,62 @@ * **Pre-Dispatch Schema Injection**: File schemas (columns, data types, row counts, and a 3-row preview) are pre-loaded and injected into the SK mini-agent's system prompt before execution begins. This eliminates 2 LLM round-trips that were previously spent on file discovery (`list_tabular_files`) and schema inspection (`describe_tabular_file`), allowing the model to jump directly to analysis tool calls. * **Async Plugin Functions**: All six `@kernel_function` methods converted to `async def` using `asyncio.to_thread()`. This enables Semantic Kernel's built-in `asyncio.gather()` to truly parallelize batched tool calls (e.g., 3 simultaneous `aggregate_column` calls) instead of executing them serially on the event loop. * **Batching Instructions**: The system prompt now instructs the model to batch multiple independent function calls in a single response, reducing LLM round-trips further. - * **Files Modified**: `tabular_processing_plugin.py`, `route_backend_chats.py`, `config.py`. * (Ref: `_df_cache`, `asyncio.to_thread`, pre-dispatch schema injection in `run_tabular_sk_analysis()`) ---- -* **Chat with Agent Button for Group Agents** - * Added a "Chat" button to each group agent row, allowing users to quickly select a group agent and navigate to the chat page. - * (Ref: `group_agents.js`, `group_workspaces.html`) +* **SQL Test Connection Button** + * Added a "Test Connection" button to the SQL Database Configuration section (Step 3) of the action wizard, allowing users to validate database connectivity before saving. + * Supports all database types: SQL Server, Azure SQL (with managed identity), PostgreSQL, MySQL, and SQLite. + * Shows inline success/failure alerts with a 15-second timeout cap and sanitized error messages. + * New backend endpoint: `POST /api/plugins/test-sql-connection`. + * (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `_plugin_modal.html`) -### **(v0.239.006)** +#### Bug Fixes -#### User Interface Enhancements +* **Content Safety for Streaming Chat Path** + * Added full Azure AI Content Safety checking to the streaming (`/api/chat/stream`) SSE path, matching the existing non-streaming (`/api/chat`) implementation. + * Previously, only the non-streaming path performed content safety analysis; streaming conversations bypassed safety checks entirely. + * Implementation includes: `AnalyzeTextOptions` analysis, severity threshold checking (severity โ‰ฅ 4 blocks the message), blocklist matching, persistence of blocked messages to `cosmos_safety_container`, creation of safety-role message documents, and proper SSE event delivery of blocked status to the client. + * On block, the streaming generator yields the safety message and `[DONE]` event, then stops โ€” preventing any further LLM invocation. + * Errors in the content safety call are caught and logged without breaking the chat flow, consistent with the non-streaming behavior. + * (Ref: `route_backend_chats.py`, streaming SSE generator, `AnalyzeTextOptions`, `cosmos_safety_container`) -* **Hidden Deprecated Action Types** - * Deprecated action types (`sql_schema`, `ui_test`, `queue_storage`, `blob_storage`, `embedding_model`) are now hidden from the action creation wizard type selector. Existing actions of these types remain functional. - * (Ref: `plugin_modal_stepper.js`) +* **SQL Schema Plugin โ€” Eliminate Redundant Schema Calls** + * Fixed agent calling `get_database_schema` twice per query even though the full schema was already injected into the agent's instructions at load time. + * Root cause: The `@kernel_function` descriptions in `sql_schema_plugin.py` said "ALWAYS call this function FIRST," which overrode the schema context already available in the instructions. + * Updated all four function descriptions (`get_database_schema`, `get_table_schema`, `get_table_list`, `get_relationships`) to use the resilient pattern: "If the database schema is already provided in your instructions, use that directly and do NOT call this function." + * This eliminates ~400ms+ of unnecessary database round trips per query and aligns with the same pattern already used in `sql_query_plugin.py`. + * (Ref: `sql_schema_plugin.py`, `@kernel_function` descriptions, schema injection) -### **(v0.239.005)** +* **SQL Schema Plugin โ€” Empty Tables from INFORMATION_SCHEMA** + * Fixed `get_database_schema` returning `'tables': {}` (empty) despite the database having tables, while relationships were returned correctly. + * Root cause: SQL Server table/column enumeration used `INFORMATION_SCHEMA.TABLES` and `INFORMATION_SCHEMA.COLUMNS` views, which returned empty results in the Azure SQL environment. Meanwhile, the relationships query used `sys.foreign_keys`/`sys.tables`/`sys.columns` catalog views which worked perfectly. + * Migrated all SQL Server schema queries to use `sys.*` catalog views consistently: `sys.tables`/`sys.schemas` for table enumeration, `sys.columns` with `TYPE_NAME()` for column details, and `sys.indexes`/`sys.index_columns` for primary key detection. + * Fixed `pyodbc.Row` handling throughout the plugin โ€” removed all `isinstance(table, tuple)` checks that could fail with pyodbc Row objects, replaced with robust try/except indexing. + * This enables the full schema (tables, columns, types, PKs, FKs) to be injected into agent instructions, allowing agents to construct complex multi-table JOINs for analytical queries. + * (Ref: `sql_schema_plugin.py`, `sys.tables`, `sys.columns`, `sys.indexes`, pyodbc.Row handling) -#### Bug Fixes +* **SQL Query Plugin โ€” Auto-Create Companion Schema Plugin** + * Fixed the remaining issue where SQL-connected agents still asked for clarification instead of querying the database, even after description improvements. + * Root cause: Agents configured with only a `sql_query` action never had a `SQLSchemaPlugin` loaded in the kernel. The descriptions demanded calling `get_database_schema` โ€” a function that didn't exist โ€” creating an impossible dependency that caused the LLM to ask for clarification. + * `LoggedPluginLoader` now automatically creates a companion `SQLSchemaPlugin` whenever a `SQLQueryPlugin` is loaded, using the same connection details. This ensures schema discovery is always available. + * Updated `@kernel_function` descriptions to be resilient: "If the database schema is provided in your instructions, use it directly. Otherwise, call get_database_schema." This dual-path approach works whether schema is injected via instructions or available via plugin functions. + * Added fallback in `_extract_sql_schema_for_instructions()` to also detect `SQLQueryPlugin` instances and create a temporary schema extractor if no `SQLSchemaPlugin` is found. + * (Ref: `logged_plugin_loader.py`, `sql_query_plugin.py`, `semantic_kernel_loader.py`) + +* **SQL Query Plugin Schema Awareness** + * Fixed agents connected to SQL databases asking users for clarification about table/column names instead of querying the database directly. + * Root cause: SQL Query and SQL Schema plugin `@kernel_function` descriptions were generic with no workflow guidance, agent instructions had no database schema context, and the two plugins operated independently with no linkage. + * Rewrote all `@kernel_function` descriptions in both SQL plugins to be prescriptive workflow guides (modeled after the working LogAnalyticsPlugin), explicitly instructing the LLM to discover schema first before generating queries. + * Added auto-injection of database schema into agent instructions at load time โ€” when SQL Schema plugins are detected, the full schema (tables, columns, types, relationships) is fetched and appended to the agent's system prompt. + * Added new `query_database(question, query)` convenience function to `SQLQueryPlugin` for intent-aligned tool calling. + * Enabled the SQL-specific plugin creation path in `logged_plugin_loader.py` (was previously commented out). + * (Ref: `sql_query_plugin.py`, `sql_schema_plugin.py`, `semantic_kernel_loader.py`, `logged_plugin_loader.py`) + +* **Chat-Uploaded Tabular Files Now Trigger SK Mini-Agent in Model-Only Mode** + * Fixed an issue where tabular files (CSV, XLSX, XLS, XLSM) uploaded directly to a chat conversation were not analyzed by the SK mini-agent when no agent was selected. The model would describe what analysis it would perform instead of returning actual computed results. + * **Root Cause**: The mini SK agent only triggered from search results, but chat-uploaded files are stored in blob storage and not indexed in Azure AI Search. Additionally, the streaming path completely ignored `file` role messages in conversation history. + * **Fix**: Both streaming and non-streaming chat paths now detect chat-uploaded tabular files during conversation history building and trigger `run_tabular_sk_analysis(source_hint="chat")` to pre-compute results. The streaming path also now properly handles `file` role messages (tabular and non-tabular) matching the non-streaming path's behavior. + * (Ref: `route_backend_chats.py`, `run_tabular_sk_analysis()`, `collect_tabular_sk_citations()`) * **Group SQL Action/Plugin Save Failure** * Fixed group SQL actions (sql_query and sql_schema types) failing to save correctly due to missing endpoint placeholder. Group routes now apply the same `sql://sql_query` / `sql://sql_schema` endpoint logic as personal action routes. @@ -150,16 +118,34 @@ * Fixed `__Secret` key suffix mismatch in additional settings schemas where `connection_string__Secret` and `password__Secret` didn't match the runtime's expected `connection_string` and `password` field names. Also removed duplicate `azuresql` enum value. * (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `sql_query.definition.json`, `sql_schema.definition.json`, `sql_query_plugin.additional_settings.schema.json`, `sql_schema_plugin.additional_settings.schema.json`) -#### New Features +#### User Interface Enhancements -* **SQL Test Connection Button** - * Added a "Test Connection" button to the SQL Database Configuration section (Step 3) of the action wizard, allowing users to validate database connectivity before saving. - * Supports all database types: SQL Server, Azure SQL (with managed identity), PostgreSQL, MySQL, and SQLite. - * Shows inline success/failure alerts with a 15-second timeout cap and sanitized error messages. - * New backend endpoint: `POST /api/plugins/test-sql-connection`. - * (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `_plugin_modal.html`) +* **Enhanced Agent Execution Thoughts** + * Added detailed model-level status messages during agent execution, giving users full visibility into each stage of the AI pipeline. + * **Model Identification**: A new "Sending to '{deployment_name}'" thought appears immediately after "Sending to agent", showing the exact model deployment being used (e.g., `gpt-5-nano`). + * **Generating Response**: A "Generating response..." thought now appears before the agent begins its invocation loop, matching the existing behavior for non-agent GPT calls. + * **Model Responded with Duration**: A "'{deployment_name}' responded ({duration}ms)" thought appears after the agent completes, showing total wall-clock execution time. + * Applies to all three agent paths: local SK agents (streaming and non-streaming) and Azure AI Foundry agents. + * Uses the existing `generation` step type (lightning bolt icon) โ€” no frontend changes required. + * (Ref: `route_backend_chats.py`, `ThoughtTracker`, agent execution pipeline) -#### User Interface Enhancements +* **List/Grid View Toggle for Agents and Actions** + * Added a list/grid view toggle to all four workspace areas: personal agents, personal actions, group agents, and group actions. + * **Grid View**: Large cards with type icon, humanized name, truncated description, and action buttons (Chat, View, Edit, Delete as applicable). + * **List View**: Improved table layout with fixed column widths (28%/47%/25%), humanized display names, and truncated descriptions with hover tooltips for full text. + * **View Button**: New eye-icon button on every agent and action that opens a read-only detail modal with gradient-header summary cards (Basic Information, Model Configuration, Instructions for agents; Basic Information, Configuration for actions). + * **Name Humanization**: Display names are now automatically parsed โ€” underscores and camelCase/PascalCase boundaries are converted to properly spaced, title-cased words (e.g., `myCustomAgent` โ†’ `My Custom Agent`). + * **Persistent Preference**: View mode selection (list/grid) is saved per area in localStorage and restored on page load. + * New shared utility module `view-utils.js` provides reusable functions for all four workspace areas. + * (Ref: `view-utils.js`, `workspace_agents.js`, `workspace_plugins.js`, `plugin_common.js`, `group_agents.js`, `group_plugins.js`, `workspace.html`, `group_workspaces.html`, `styles.css`) + +* **Chat with Agent Button for Group Agents** + * Added a "Chat" button to each group agent row, allowing users to quickly select a group agent and navigate to the chat page. + * (Ref: `group_agents.js`, `group_workspaces.html`) + +* **Hidden Deprecated Action Types** + * Deprecated action types (`sql_schema`, `ui_test`, `queue_storage`, `blob_storage`, `embedding_model`) are now hidden from the action creation wizard type selector. Existing actions of these types remain functional. + * (Ref: `plugin_modal_stepper.js`) * **Advanced Settings Collapse Toggle** * Step 4 (Advanced) content is now hidden behind a collapsible toggle button ("Show Advanced Settings") instead of being displayed by default. Reduces visual noise for most users. @@ -257,19 +243,6 @@ * **Files Modified**: `chat-documents.js`, `chat-messages.js`, `functions_search.py`, `route_backend_chats.py`, `chats.html`. * (Ref: Multi-document selection, tag filtering, OData search integration, `CHAT_DOCUMENT_AND_TAG_FILTERING.md`) -#### New Features - -* **Conversation Export** - * Export one or multiple conversations from the Chat page in JSON or Markdown format. - * **Single Export**: Use the ellipsis menu on any conversation to quickly export it. - * **Multi-Export**: Enter selection mode, check the conversations you want, and click the export button. - * A guided 4-step wizard walks you through selection review, format choice, packaging options (single file or ZIP archive), and download. - * Sensitive internal metadata is automatically stripped from exported data for security. - -* **Retention Policy UI for Groups and Public Workspaces** - * Can now configure conversation and document retention periods directly from the workspace and group management page. - * Choose from preset retention periods ranging from 7 days to 10 years, use the organization default, or disable automatic deletion entirely. - #### Bug Fixes * **Citation Parsing Bug Fix** From 8be4f31371b004081b4368c9298809374ca4e089 Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Mon, 9 Mar 2026 09:19:43 -0400 Subject: [PATCH 7/8] added export pdf and conversation summary --- application/single_app/config.py | 2 +- application/single_app/route_backend_chats.py | 16 +- .../route_backend_conversation_export.py | 1770 +++++++++++++++-- .../single_app/route_backend_conversations.py | 94 +- .../js/chat/chat-conversation-details.js | 165 +- .../single_app/static/js/chat/chat-export.js | 118 +- .../features/CONVERSATION_EXPORT.md | 263 ++- .../v0.239.022/CONVERSATION_EXPORT.md | 58 + docs/explanation/release_notes.md | 122 +- functional_tests/test_conversation_export.py | 754 ++++--- .../test_persistent_conversation_summary.py | 279 +++ 11 files changed, 3028 insertions(+), 613 deletions(-) create mode 100644 docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md create mode 100644 functional_tests/test_persistent_conversation_summary.py diff --git a/application/single_app/config.py b/application/single_app/config.py index a7f90bfc..741d8b55 100644 --- a/application/single_app/config.py +++ b/application/single_app/config.py @@ -94,7 +94,7 @@ EXECUTOR_TYPE = 'thread' EXECUTOR_MAX_WORKERS = 30 SESSION_TYPE = 'filesystem' -VERSION = "0.239.020" +VERSION = "0.239.031" SECRET_KEY = os.getenv('SECRET_KEY', 'dev-secret-key-change-in-production') diff --git a/application/single_app/route_backend_chats.py b/application/single_app/route_backend_chats.py index 293ec929..f923dd1b 100644 --- a/application/single_app/route_backend_chats.py +++ b/application/single_app/route_backend_chats.py @@ -2446,7 +2446,6 @@ def on_plugin_invocation(inv): plugin_logger.register_callback(callback_key, on_plugin_invocation) - thought_tracker.add_thought('generation', 'Generating response...') agent_invoke_start_time = time.time() def invoke_selected_agent(): @@ -2720,7 +2719,7 @@ def kernel_error(e): 'on_error': kernel_error }) - thought_tracker.add_thought('generation', 'Generating response...') + thought_tracker.add_thought('generation', f"Sending to '{gpt_model}'") def invoke_gpt_fallback(): if not conversation_history_for_api: raise Exception('Cannot generate response: No conversation history available.') @@ -2804,12 +2803,18 @@ def gpt_error(e): }) fallback_result = try_fallback_chain(fallback_steps) + # Unpack result - handle both 4-tuple (SK) and 5-tuple (GPT with tokens) if len(fallback_result) == 5: ai_message, final_model_used, chat_mode, kernel_fallback_notice, token_usage_data = fallback_result else: ai_message, final_model_used, chat_mode, kernel_fallback_notice = fallback_result token_usage_data = None + + # Emit responded thought for non-agent paths (agent paths emit their own inside callbacks) + if not selected_agent: + gpt_total_duration_s = round(time.time() - request_start_time, 1) + thought_tracker.add_thought('generation', f"'{final_model_used}' responded ({gpt_total_duration_s}s from initial message)") # Collect token usage from Semantic Kernel services if available if kernel and not token_usage_data: @@ -4134,7 +4139,6 @@ def on_plugin_invocation_streaming(inv): for msg in conversation_history_for_api ] - yield emit_thought('generation', 'Generating response...') agent_stream_start_time = time.time() # Stream agent responses - collect chunks first then yield @@ -4306,7 +4310,7 @@ def make_json_serializable(obj): else: # Stream from regular GPT model (non-agent) - yield emit_thought('generation', 'Generating response...') + yield emit_thought('generation', f"Sending to '{gpt_model}'") debug_print(f"--- Streaming from GPT ({gpt_model}) ---") # Prepare stream parameters @@ -4357,6 +4361,10 @@ def make_json_serializable(obj): 'captured_at': datetime.utcnow().isoformat() } debug_print(f"[Streaming Tokens] Captured usage - prompt: {chunk.usage.prompt_tokens}, completion: {chunk.usage.completion_tokens}, total: {chunk.usage.total_tokens}") + + # Emit responded thought for regular LLM streaming + gpt_stream_total_duration_s = round(time.time() - request_start_time, 1) + yield emit_thought('generation', f"'{gpt_model}' responded ({gpt_stream_total_duration_s}s from initial message)") # Stream complete - save message and send final metadata # Get user thread info to maintain thread consistency diff --git a/application/single_app/route_backend_conversation_export.py b/application/single_app/route_backend_conversation_export.py index aad750e4..abd6490f 100644 --- a/application/single_app/route_backend_conversation_export.py +++ b/application/single_app/route_backend_conversation_export.py @@ -2,17 +2,31 @@ import io import json +import markdown2 +import re +import tempfile import zipfile +from collections import Counter, defaultdict from datetime import datetime +from html import escape as _escape_html +from typing import Any, Dict, List, Optional from config import * +from flask import jsonify, make_response, request +from functions_appinsights import log_event from functions_authentication import * -from functions_settings import * -from flask import Response, jsonify, request, make_response +from functions_chat import sort_messages_by_thread +from functions_conversation_metadata import update_conversation_with_metadata from functions_debug import debug_print +from functions_settings import * +from functions_thoughts import get_thoughts_for_conversation from swagger_wrapper import swagger_route, get_auth_security +TRANSCRIPT_ROLES = {'user', 'assistant'} +SUMMARY_SOURCE_CHAR_LIMIT = 60000 + + def register_route_backend_conversation_export(app): """Register conversation export API routes.""" @@ -29,32 +43,36 @@ def api_export_conversations(): conversation_ids (list): List of conversation IDs to export. format (str): Export format โ€” "json" or "markdown". packaging (str): Output packaging โ€” "single" or "zip". + include_summary_intro (bool): Whether to generate a per-conversation intro. + summary_model_deployment (str): Optional model deployment for summary generation. """ user_id = get_current_user_id() if not user_id: return jsonify({'error': 'User not authenticated'}), 401 - data = request.get_json() + data = request.get_json(silent=True) if not data: return jsonify({'error': 'Request body is required'}), 400 conversation_ids = data.get('conversation_ids', []) - export_format = data.get('format', 'json').lower() - packaging = data.get('packaging', 'single').lower() + export_format = str(data.get('format', 'json')).lower() + packaging = str(data.get('packaging', 'single')).lower() + include_summary_intro = bool(data.get('include_summary_intro', False)) + summary_model_deployment = str(data.get('summary_model_deployment', '') or '').strip() if not conversation_ids or not isinstance(conversation_ids, list): return jsonify({'error': 'At least one conversation_id is required'}), 400 - if export_format not in ('json', 'markdown'): - return jsonify({'error': 'Format must be "json" or "markdown"'}), 400 + if export_format not in ('json', 'markdown', 'pdf'): + return jsonify({'error': 'Format must be "json", "markdown", or "pdf"'}), 400 if packaging not in ('single', 'zip'): return jsonify({'error': 'Packaging must be "single" or "zip"'}), 400 try: + settings = get_settings() exported = [] for conv_id in conversation_ids: - # Verify ownership and fetch conversation try: conversation = cosmos_conversations_container.read_item( item=conv_id, @@ -64,225 +82,1597 @@ def api_export_conversations(): debug_print(f"Export: conversation {conv_id} not found or access denied") continue - # Verify user owns this conversation if conversation.get('user_id') != user_id: debug_print(f"Export: user {user_id} does not own conversation {conv_id}") continue - # Fetch messages ordered by timestamp - message_query = f""" + message_query = """ SELECT * FROM c - WHERE c.conversation_id = '{conv_id}' + WHERE c.conversation_id = @conversation_id ORDER BY c.timestamp ASC """ messages = list(cosmos_messages_container.query_items( query=message_query, + parameters=[{'name': '@conversation_id', 'value': conv_id}], partition_key=conv_id )) - # Filter for active thread messages only - filtered_messages = [] - for msg in messages: - thread_info = msg.get('metadata', {}).get('thread_info', {}) - active = thread_info.get('active_thread') - if active is True or active is None or 'active_thread' not in thread_info: - filtered_messages.append(msg) - - exported.append({ - 'conversation': _sanitize_conversation(conversation), - 'messages': [_sanitize_message(m) for m in filtered_messages] - }) + exported.append( + _build_export_entry( + conversation=conversation, + raw_messages=messages, + user_id=user_id, + settings=settings, + include_summary_intro=include_summary_intro, + summary_model_deployment=summary_model_deployment + ) + ) if not exported: return jsonify({'error': 'No accessible conversations found'}), 404 - # Generate export content timestamp_str = datetime.utcnow().strftime('%Y%m%d_%H%M%S') if packaging == 'zip': return _build_zip_response(exported, export_format, timestamp_str) - else: - return _build_single_file_response(exported, export_format, timestamp_str) - - except Exception as e: - debug_print(f"Export error: {str(e)}") - return jsonify({'error': f'Export failed: {str(e)}'}), 500 - - def _sanitize_conversation(conv): - """Return only user-facing conversation fields.""" - return { - 'id': conv.get('id'), - 'title': conv.get('title', 'Untitled'), - 'last_updated': conv.get('last_updated', ''), - 'chat_type': conv.get('chat_type', 'personal'), - 'tags': conv.get('tags', []), - 'is_pinned': conv.get('is_pinned', False), - 'context': conv.get('context', []) - } - - def _sanitize_message(msg): - """Return only user-facing message fields.""" - result = { - 'role': msg.get('role', ''), - 'content': msg.get('content', ''), - 'timestamp': msg.get('timestamp', ''), - } - # Include citations if present - if msg.get('citations'): - result['citations'] = msg['citations'] - # Include context/tool info if present - if msg.get('context'): - result['context'] = msg['context'] - return result - - def _build_single_file_response(exported, export_format, timestamp_str): - """Build a single-file download response.""" - if export_format == 'json': - content = json.dumps(exported, indent=2, ensure_ascii=False, default=str) - filename = f"conversations_export_{timestamp_str}.json" - content_type = 'application/json; charset=utf-8' + + return _build_single_file_response(exported, export_format, timestamp_str) + + except Exception as exc: + debug_print(f"Export error: {str(exc)}") + log_event(f"Conversation export failed: {exc}", level="WARNING") + return jsonify({'error': f'Export failed: {str(exc)}'}), 500 + + +def _build_export_entry( + conversation: Dict[str, Any], + raw_messages: List[Dict[str, Any]], + user_id: str, + settings: Dict[str, Any], + include_summary_intro: bool = False, + summary_model_deployment: str = '' +) -> Dict[str, Any]: + filtered_messages = _filter_messages_for_export(raw_messages) + ordered_messages = sort_messages_by_thread(filtered_messages) + + raw_thoughts = get_thoughts_for_conversation(conversation.get('id'), user_id) + thoughts_by_message = defaultdict(list) + for thought in raw_thoughts: + thoughts_by_message[thought.get('message_id')].append(_sanitize_thought(thought)) + + exported_messages = [] + role_counts = Counter() + total_citation_counts = Counter({'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}) + transcript_index = 0 + total_thoughts = 0 + + for sequence_index, message in enumerate(ordered_messages, start=1): + role = message.get('role', 'unknown') + role_counts[role] += 1 + + message_transcript_index = None + if role in TRANSCRIPT_ROLES: + transcript_index += 1 + message_transcript_index = transcript_index + + thoughts = thoughts_by_message.get(message.get('id'), []) + exported_message = _sanitize_message( + message, + sequence_index=sequence_index, + transcript_index=message_transcript_index, + thoughts=thoughts + ) + exported_messages.append(exported_message) + + counts = exported_message.get('citation_counts', {}) + for key in total_citation_counts: + total_citation_counts[key] += counts.get(key, 0) + total_thoughts += len(thoughts) + + # Compute message time range for summary caching + message_time_start = None + message_time_end = None + if ordered_messages: + message_time_start = ordered_messages[0].get('timestamp') + message_time_end = ordered_messages[-1].get('timestamp') + + sanitized_conversation = _sanitize_conversation( + conversation, + messages=exported_messages, + role_counts=role_counts, + citation_counts=total_citation_counts, + thought_count=total_thoughts + ) + summary_intro = _build_summary_intro( + messages=exported_messages, + conversation=conversation, + sanitized_conversation=sanitized_conversation, + settings=settings, + enabled=include_summary_intro, + summary_model_deployment=summary_model_deployment, + message_time_start=message_time_start, + message_time_end=message_time_end + ) + + return { + 'conversation': sanitized_conversation, + 'summary_intro': summary_intro, + 'messages': exported_messages + } + + +def _filter_messages_for_export(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + filtered_messages = [] + for message in messages: + metadata = message.get('metadata', {}) or {} + if metadata.get('is_deleted') is True: + continue + + thread_info = metadata.get('thread_info', {}) or {} + active = thread_info.get('active_thread') + if active is True or active is None or 'active_thread' not in thread_info: + filtered_messages.append(message) + + return filtered_messages + + +def _sanitize_conversation( + conversation: Dict[str, Any], + messages: List[Dict[str, Any]], + role_counts: Counter, + citation_counts: Counter, + thought_count: int +) -> Dict[str, Any]: + transcript_count = sum(1 for message in messages if message.get('is_transcript_message')) + return { + 'id': conversation.get('id'), + 'title': conversation.get('title', 'Untitled'), + 'last_updated': conversation.get('last_updated', ''), + 'chat_type': conversation.get('chat_type', 'personal'), + 'tags': conversation.get('tags', []), + 'context': conversation.get('context', []), + 'classification': conversation.get('classification', []), + 'strict': conversation.get('strict', False), + 'is_pinned': conversation.get('is_pinned', False), + 'scope_locked': conversation.get('scope_locked'), + 'locked_contexts': conversation.get('locked_contexts', []), + 'message_count': len(messages), + 'transcript_message_count': transcript_count, + 'message_counts_by_role': dict(role_counts), + 'citation_counts': dict(citation_counts), + 'thought_count': thought_count + } + + +def _sanitize_message( + message: Dict[str, Any], + sequence_index: int, + transcript_index: Optional[int], + thoughts: List[Dict[str, Any]] +) -> Dict[str, Any]: + role = message.get('role', '') + content = message.get('content', '') + raw_citation_buckets = _collect_raw_citation_buckets(message) + normalized_citations = _normalize_citations(raw_citation_buckets) + citation_counts = _build_citation_counts(normalized_citations) + details = _curate_message_details(message, citation_counts, len(thoughts)) + + return { + 'id': message.get('id'), + 'role': role, + 'speaker_label': _role_to_label(role), + 'sequence_index': sequence_index, + 'transcript_index': transcript_index, + 'label': f"Turn {transcript_index}" if transcript_index else f"Message {sequence_index}", + 'is_transcript_message': role in TRANSCRIPT_ROLES, + 'timestamp': message.get('timestamp', ''), + 'content': content, + 'content_text': _normalize_content(content), + 'details': details, + 'citations': normalized_citations, + 'citation_counts': citation_counts, + 'thoughts': thoughts, + 'legacy_citations': raw_citation_buckets['legacy'], + 'hybrid_citations': raw_citation_buckets['hybrid'], + 'web_search_citations': raw_citation_buckets['web'], + 'agent_citations': raw_citation_buckets['agent'] + } + + +def _sanitize_thought(thought: Dict[str, Any]) -> Dict[str, Any]: + return { + 'step_index': thought.get('step_index'), + 'step_type': thought.get('step_type'), + 'content': thought.get('content'), + 'detail': thought.get('detail'), + 'duration_ms': thought.get('duration_ms'), + 'timestamp': thought.get('timestamp') + } + + +def _collect_raw_citation_buckets(message: Dict[str, Any]) -> Dict[str, List[Any]]: + def ensure_list(value: Any) -> List[Any]: + if not value: + return [] + return value if isinstance(value, list) else [value] + + return { + 'legacy': ensure_list(message.get('citations')), + 'hybrid': ensure_list(message.get('hybrid_citations')), + 'web': ensure_list(message.get('web_search_citations')), + 'agent': ensure_list(message.get('agent_citations')) + } + + +def _normalize_citations(raw_citation_buckets: Dict[str, List[Any]]) -> List[Dict[str, Any]]: + normalized = [] + + for citation in raw_citation_buckets.get('hybrid', []): + if isinstance(citation, dict): + normalized.append({ + 'citation_type': 'document', + 'label': _build_document_citation_label(citation), + 'file_name': citation.get('file_name'), + 'title': citation.get('title') or citation.get('file_name'), + 'page_number': citation.get('page_number'), + 'citation_id': citation.get('citation_id'), + 'chunk_id': citation.get('chunk_id'), + 'metadata_type': citation.get('metadata_type'), + 'metadata_content': citation.get('metadata_content'), + 'score': citation.get('score'), + 'classification': citation.get('classification'), + 'url': citation.get('url') + }) else: - parts = [] - for entry in exported: - parts.append(_conversation_to_markdown(entry)) - content = '\n\n---\n\n'.join(parts) - filename = f"conversations_export_{timestamp_str}.md" - content_type = 'text/markdown; charset=utf-8' - - response = make_response(content) - response.headers['Content-Type'] = content_type - response.headers['Content-Disposition'] = f'attachment; filename="{filename}"' - return response - - def _build_zip_response(exported, export_format, timestamp_str): - """Build a ZIP archive containing one file per conversation.""" - buffer = io.BytesIO() - with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf: - for entry in exported: - conv = entry['conversation'] - safe_title = _safe_filename(conv.get('title', 'Untitled')) - conv_id_short = conv.get('id', 'unknown')[:8] - - if export_format == 'json': - file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str) - ext = 'json' - else: - file_content = _conversation_to_markdown(entry) - ext = 'md' + normalized.append({ + 'citation_type': 'document', + 'label': str(citation), + 'value': str(citation) + }) + + for citation in raw_citation_buckets.get('web', []): + if isinstance(citation, dict): + title = citation.get('title') or citation.get('url') or 'Web source' + normalized.append({ + 'citation_type': 'web', + 'label': title, + 'title': title, + 'url': citation.get('url') + }) + else: + normalized.append({ + 'citation_type': 'web', + 'label': str(citation), + 'value': str(citation) + }) + + for citation in raw_citation_buckets.get('agent', []): + if isinstance(citation, dict): + tool_name = citation.get('tool_name') or citation.get('function_name') or 'Tool invocation' + normalized.append({ + 'citation_type': 'agent_tool', + 'label': tool_name, + 'tool_name': citation.get('tool_name'), + 'function_name': citation.get('function_name'), + 'plugin_name': citation.get('plugin_name'), + 'success': citation.get('success'), + 'timestamp': citation.get('timestamp') + }) + else: + normalized.append({ + 'citation_type': 'agent_tool', + 'label': str(citation), + 'value': str(citation) + }) + + for citation in raw_citation_buckets.get('legacy', []): + if isinstance(citation, dict): + title = citation.get('title') or citation.get('filepath') or citation.get('url') or 'Legacy citation' + normalized.append({ + 'citation_type': 'legacy', + 'label': title, + 'title': title, + 'url': citation.get('url'), + 'filepath': citation.get('filepath') + }) + else: + normalized.append({ + 'citation_type': 'legacy', + 'label': str(citation), + 'value': str(citation) + }) + + return normalized + + +def _build_document_citation_label(citation: Dict[str, Any]) -> str: + file_name = citation.get('file_name') or citation.get('title') or 'Document source' + metadata_type = citation.get('metadata_type') + page_number = citation.get('page_number') + + if metadata_type: + return f"{file_name} โ€” {metadata_type.replace('_', ' ').title()}" + if page_number not in (None, ''): + return f"{file_name} โ€” Page {page_number}" + return file_name + + +def _build_citation_counts(citations: List[Dict[str, Any]]) -> Dict[str, int]: + counts = { + 'document': 0, + 'web': 0, + 'agent_tool': 0, + 'legacy': 0, + 'total': len(citations) + } + for citation in citations: + citation_type = citation.get('citation_type') + if citation_type in counts: + counts[citation_type] += 1 + return counts + + +def _curate_message_details( + message: Dict[str, Any], + citation_counts: Dict[str, int], + thought_count: int +) -> Dict[str, Any]: + role = message.get('role', '') + metadata = message.get('metadata', {}) or {} + details: Dict[str, Any] = {} + + if role == 'user': + details['interaction_mode'] = _remove_empty_values({ + 'button_states': metadata.get('button_states'), + 'workspace_search': _curate_workspace_search(metadata.get('workspace_search')), + 'prompt_selection': _curate_prompt_selection(metadata.get('prompt_selection')), + 'agent_selection': _curate_agent_selection(metadata.get('agent_selection')), + 'model_selection': _curate_model_selection(metadata.get('model_selection')) + }) + elif role == 'assistant': + details['generation'] = _remove_empty_values({ + 'augmented': message.get('augmented'), + 'model_deployment': message.get('model_deployment_name'), + 'agent_name': message.get('agent_name'), + 'agent_display_name': message.get('agent_display_name'), + 'reasoning_effort': metadata.get('reasoning_effort'), + 'hybrid_search_query': message.get('hybridsearch_query'), + 'token_usage': _curate_token_usage(metadata.get('token_usage')), + 'citation_counts': citation_counts, + 'thought_count': thought_count + }) + else: + details['message_context'] = _remove_empty_values({ + 'filename': message.get('filename'), + 'prompt': message.get('prompt'), + 'is_table': message.get('is_table'), + 'model_deployment': message.get('model_deployment_name') + }) + + return _remove_empty_values(details) + + +def _curate_workspace_search(workspace_search: Optional[Dict[str, Any]]) -> Dict[str, Any]: + if not isinstance(workspace_search, dict): + return {} + return _remove_empty_values({ + 'search_enabled': workspace_search.get('search_enabled'), + 'document_scope': workspace_search.get('document_scope'), + 'document_name': workspace_search.get('document_name'), + 'document_filename': workspace_search.get('document_filename'), + 'group_name': workspace_search.get('group_name'), + 'classification': workspace_search.get('classification'), + 'public_workspace_id': workspace_search.get('active_public_workspace_id') + }) - file_name = f"{safe_title}_{conv_id_short}.{ext}" - zf.writestr(file_name, file_content) - buffer.seek(0) - filename = f"conversations_export_{timestamp_str}.zip" +def _curate_prompt_selection(prompt_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]: + if not isinstance(prompt_selection, dict): + return {} + return _remove_empty_values({ + 'prompt_name': prompt_selection.get('prompt_name'), + 'selected_prompt_index': prompt_selection.get('selected_prompt_index'), + 'selected_prompt_text': prompt_selection.get('selected_prompt_text') + }) - response = make_response(buffer.read()) - response.headers['Content-Type'] = 'application/zip' - response.headers['Content-Disposition'] = f'attachment; filename="{filename}"' - return response - def _conversation_to_markdown(entry): - """Convert a conversation + messages entry to Markdown format.""" - conv = entry['conversation'] - messages = entry['messages'] +def _curate_agent_selection(agent_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]: + if not isinstance(agent_selection, dict): + return {} + return _remove_empty_values({ + 'selected_agent': agent_selection.get('selected_agent'), + 'agent_display_name': agent_selection.get('agent_display_name'), + 'is_global': agent_selection.get('is_global'), + 'is_group': agent_selection.get('is_group'), + 'group_name': agent_selection.get('group_name') + }) + + +def _curate_model_selection(model_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]: + if not isinstance(model_selection, dict): + return {} + return _remove_empty_values({ + 'selected_model': model_selection.get('selected_model'), + 'frontend_requested_model': model_selection.get('frontend_requested_model'), + 'reasoning_effort': model_selection.get('reasoning_effort'), + 'streaming': model_selection.get('streaming') + }) + + +def _curate_token_usage(token_usage: Any) -> Dict[str, Any]: + if not isinstance(token_usage, dict): + return {} + return _remove_empty_values({ + 'prompt_tokens': token_usage.get('prompt_tokens'), + 'completion_tokens': token_usage.get('completion_tokens'), + 'total_tokens': token_usage.get('total_tokens') + }) + + +def _remove_empty_values(value: Any) -> Any: + if isinstance(value, dict): + cleaned = {} + for key, item in value.items(): + cleaned_item = _remove_empty_values(item) + if cleaned_item in (None, '', [], {}): + continue + cleaned[key] = cleaned_item + return cleaned + + if isinstance(value, list): + cleaned_list = [] + for item in value: + cleaned_item = _remove_empty_values(item) + if cleaned_item in (None, '', [], {}): + continue + cleaned_list.append(cleaned_item) + return cleaned_list + + return value + + +def generate_conversation_summary( + messages: List[Dict[str, Any]], + conversation_title: str, + settings: Dict[str, Any], + model_deployment: str, + message_time_start: str = None, + message_time_end: str = None, + conversation_id: str = None +) -> Dict[str, Any]: + """Generate a conversation summary using the LLM and optionally persist it. + + This is the shared helper used by both the export pipeline and the + on-demand summary API endpoint. Returns a summary dict suitable for + storage in conversation metadata. + + Raises ValueError when there is no content to summarise and + RuntimeError on model errors. + """ + transcript_lines = [] + for message in messages: + content_text = message.get('content_text', '') + if not content_text: + continue + role = message.get('role', 'unknown') + speaker = message.get('speaker_label', role).upper() + transcript_lines.append(f"{speaker}: {content_text}") + + transcript_text = '\n\n'.join(transcript_lines).strip() + if not transcript_text: + raise ValueError('No message content was available to summarize.') + + transcript_text = _truncate_for_summary(transcript_text) + + gpt_client, gpt_model = _initialize_gpt_client(settings, model_deployment) + summary_prompt = ( + "You are summarizing a conversation for an export document. " + "Read the full conversation below and write a concise summary. " + "Use your judgement on length: for short conversations write one brief paragraph, " + "for longer or more detailed conversations write two paragraphs. " + "If you need refer to the user, use their name, but do not refer to the user too often." + "Cover the goals, the key topics discussed, any data or tools referenced, " + "and the main outcomes or answers provided. " + "Be factual and neutral. Return plain text only โ€” no headings, no bullet points, no markdown formatting." + ) + + model_lower = gpt_model.lower() + is_reasoning_model = ( + 'o1' in model_lower or 'o3' in model_lower or 'gpt-5' in model_lower + ) + instruction_role = 'developer' if is_reasoning_model else 'system' + + debug_print(f"Summary generation: sending {len(transcript_lines)} messages " + f"({len(transcript_text)} chars) to {gpt_model} (role={instruction_role})") + + summary_response = gpt_client.chat.completions.create( + model=gpt_model, + messages=[ + { + 'role': instruction_role, + 'content': summary_prompt + }, + { + 'role': 'user', + 'content': ( + f"Conversation Title: {conversation_title}\n\n" + f"{transcript_text}" + ) + } + ] + ) + + debug_print(f"Summary generation: response choices=" + f"{len(summary_response.choices) if summary_response.choices else 0}, " + f"finish_reason={summary_response.choices[0].finish_reason if summary_response.choices else 'N/A'}") + + summary_text = (summary_response.choices[0].message.content or '').strip() if summary_response.choices else '' + if not summary_text: + debug_print('Summary generation: model returned an empty response') + log_event('Conversation summary generation returned empty response', level='WARNING') + raise RuntimeError('Summary model returned an empty response.') + + summary_data = { + 'content': summary_text, + 'model_deployment': gpt_model, + 'generated_at': datetime.utcnow().isoformat(), + 'message_time_start': message_time_start, + 'message_time_end': message_time_end + } + + # Persist to Cosmos when a conversation_id is available + if conversation_id: + try: + update_conversation_with_metadata(conversation_id, {'summary': summary_data}) + debug_print(f"Summary persisted to conversation {conversation_id}") + except Exception as persist_exc: + debug_print(f"Failed to persist summary to Cosmos: {persist_exc}") + log_event(f"Failed to persist conversation summary: {persist_exc}", level="WARNING") + + return summary_data + + +def _build_summary_intro( + messages: List[Dict[str, Any]], + conversation: Dict[str, Any], + sanitized_conversation: Dict[str, Any], + settings: Dict[str, Any], + enabled: bool, + summary_model_deployment: str, + message_time_start: str = None, + message_time_end: str = None +) -> Dict[str, Any]: + """Build the summary_intro block for the export payload. + + Uses cached summary from conversation metadata when present and + still current (no newer messages). Otherwise generates a fresh + summary via ``generate_conversation_summary`` and persists it. + """ + summary_intro = { + 'enabled': enabled, + 'generated': False, + 'model_deployment': summary_model_deployment or None, + 'generated_at': None, + 'content': '', + 'error': None + } + + if not enabled: + return summary_intro + + # Check for a cached summary stored in the conversation document + existing_summary = conversation.get('summary') + if existing_summary and isinstance(existing_summary, dict): + cached_end = existing_summary.get('message_time_end') + if cached_end and message_time_end and cached_end >= message_time_end: + debug_print('Export summary: using cached summary from conversation metadata') + summary_intro.update({ + 'generated': True, + 'model_deployment': existing_summary.get('model_deployment'), + 'generated_at': existing_summary.get('generated_at'), + 'content': existing_summary.get('content', ''), + 'error': None + }) + return summary_intro + debug_print('Export summary: cached summary is stale, regenerating') + + try: + conversation_id = conversation.get('id') + conversation_title = sanitized_conversation.get('title', 'Untitled') + + summary_data = generate_conversation_summary( + messages=messages, + conversation_title=conversation_title, + settings=settings, + model_deployment=summary_model_deployment, + message_time_start=message_time_start, + message_time_end=message_time_end, + conversation_id=conversation_id + ) + + summary_intro.update({ + 'generated': True, + 'model_deployment': summary_data.get('model_deployment'), + 'generated_at': summary_data.get('generated_at'), + 'content': summary_data.get('content', ''), + 'error': None + }) + return summary_intro + + except (ValueError, RuntimeError) as known_exc: + debug_print(f"Export summary generation issue: {known_exc}") + summary_intro['error'] = str(known_exc) + if hasattr(known_exc, 'model_deployment'): + summary_intro['model_deployment'] = known_exc.model_deployment + return summary_intro + + except Exception as exc: + debug_print(f"Export summary generation failed: {exc}") + log_event(f"Conversation export summary generation failed: {exc}", level="WARNING") + summary_intro['error'] = str(exc) + return summary_intro + + +def _truncate_for_summary(transcript_text: str) -> str: + if len(transcript_text) <= SUMMARY_SOURCE_CHAR_LIMIT: + return transcript_text + + head_chars = SUMMARY_SOURCE_CHAR_LIMIT // 2 + tail_chars = SUMMARY_SOURCE_CHAR_LIMIT - head_chars + return ( + transcript_text[:head_chars] + + "\n\n[... transcript truncated for export summary generation ...]\n\n" + + transcript_text[-tail_chars:] + ) + + +def _initialize_gpt_client(settings: Dict[str, Any], requested_model: str = ''): + enable_gpt_apim = settings.get('enable_gpt_apim', False) + + if enable_gpt_apim: + raw_models = settings.get('azure_apim_gpt_deployment', '') or '' + apim_models = [model.strip() for model in raw_models.split(',') if model.strip()] + if not apim_models: + raise ValueError('APIM GPT deployment name is not configured.') + + if requested_model and requested_model not in apim_models: + raise ValueError(f"Requested summary model '{requested_model}' is not configured for APIM.") + + gpt_model = requested_model or apim_models[0] + gpt_client = AzureOpenAI( + api_version=settings.get('azure_apim_gpt_api_version'), + azure_endpoint=settings.get('azure_apim_gpt_endpoint'), + api_key=settings.get('azure_apim_gpt_subscription_key') + ) + return gpt_client, gpt_model + + auth_type = settings.get('azure_openai_gpt_authentication_type') + endpoint = settings.get('azure_openai_gpt_endpoint') + api_version = settings.get('azure_openai_gpt_api_version') + gpt_model_obj = settings.get('gpt_model', {}) or {} + + if requested_model: + gpt_model = requested_model + elif gpt_model_obj.get('selected'): + gpt_model = gpt_model_obj['selected'][0]['deploymentName'] + else: + raise ValueError('No GPT model selected or configured for export summary generation.') + + if auth_type == 'managed_identity': + token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope) + gpt_client = AzureOpenAI( + api_version=api_version, + azure_endpoint=endpoint, + azure_ad_token_provider=token_provider + ) + else: + api_key = settings.get('azure_openai_gpt_key') + if not api_key: + raise ValueError('Azure OpenAI API Key not configured.') + gpt_client = AzureOpenAI( + api_version=api_version, + azure_endpoint=endpoint, + api_key=api_key + ) + + return gpt_client, gpt_model + + +def _build_single_file_response(exported: List[Dict[str, Any]], export_format: str, timestamp_str: str): + """Build a single-file download response.""" + if export_format == 'json': + content = json.dumps(exported, indent=2, ensure_ascii=False, default=str) + filename = f"conversations_export_{timestamp_str}.json" + content_type = 'application/json; charset=utf-8' + elif export_format == 'pdf': + if len(exported) == 1: + content = _conversation_to_pdf_bytes(exported[0]) + else: + combined_parts = [] + for idx, entry in enumerate(exported): + if idx > 0: + combined_parts.append( + '

    ' + ) + combined_parts.append(_build_pdf_html_body(entry)) + content = _html_body_to_pdf_bytes('\n'.join(combined_parts)) + filename = f"conversations_export_{timestamp_str}.pdf" + content_type = 'application/pdf' + else: + parts = [] + for entry in exported: + parts.append(_conversation_to_markdown(entry)) + content = '\n\n---\n\n'.join(parts) + filename = f"conversations_export_{timestamp_str}.md" + content_type = 'text/markdown; charset=utf-8' + + response = make_response(content) + response.headers['Content-Type'] = content_type + response.headers['Content-Disposition'] = f'attachment; filename="{filename}"' + return response + + +def _build_zip_response(exported: List[Dict[str, Any]], export_format: str, timestamp_str: str): + """Build a ZIP archive containing one file per conversation.""" + buffer = io.BytesIO() + with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf: + for entry in exported: + conversation = entry['conversation'] + safe_title = _safe_filename(conversation.get('title', 'Untitled')) + conversation_id_short = conversation.get('id', 'unknown')[:8] + + if export_format == 'json': + file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str) + ext = 'json' + elif export_format == 'pdf': + file_content = _conversation_to_pdf_bytes(entry) + ext = 'pdf' + else: + file_content = _conversation_to_markdown(entry) + ext = 'md' - lines = [] - title = conv.get('title', 'Untitled') - lines.append(f"# {title}") + file_name = f"{safe_title}_{conversation_id_short}.{ext}" + zf.writestr(file_name, file_content) + + buffer.seek(0) + filename = f"conversations_export_{timestamp_str}.zip" + + response = make_response(buffer.read()) + response.headers['Content-Type'] = 'application/zip' + response.headers['Content-Disposition'] = f'attachment; filename="{filename}"' + return response + + +def _conversation_to_markdown(entry: Dict[str, Any]) -> str: + """Convert a conversation + messages entry to Markdown format.""" + conversation = entry['conversation'] + messages = entry['messages'] + summary_intro = entry.get('summary_intro', {}) or {} + + transcript_messages = [message for message in messages if message.get('is_transcript_message')] + detail_messages = [message for message in messages if message.get('details')] + reference_messages = [message for message in messages if message.get('citations')] + thought_messages = [message for message in messages if message.get('thoughts')] + supplemental_messages = [message for message in messages if not message.get('is_transcript_message')] + + lines: List[str] = [] + lines.append(f"# {conversation.get('title', 'Untitled')}") + lines.append('') + lines.append(f"**Last Updated:** {conversation.get('last_updated', '')} ") + lines.append(f"**Chat Type:** {conversation.get('chat_type', 'personal')} ") + lines.append(f"**Messages:** {conversation.get('message_count', len(messages))} ") + if conversation.get('tags'): + lines.append(f"**Tags:** {', '.join(_format_tag(tag) for tag in conversation.get('tags', []))} ") + if conversation.get('classification'): + lines.append(f"**Classification:** {', '.join(_format_tag(item) for item in conversation.get('classification', []))} ") + lines.append('') + + if summary_intro.get('enabled') and summary_intro.get('generated') and summary_intro.get('content'): + lines.append('## Abstract') lines.append('') + lines.append(summary_intro.get('content', '')) + lines.append('') + lines.append(f"_Generated with {summary_intro.get('model_deployment') or 'configured model'} on {summary_intro.get('generated_at')}_") + lines.append('') + elif summary_intro.get('enabled') and summary_intro.get('error'): + lines.append('> _A summary intro was requested, but it could not be generated for this export._') + lines.append(f"> _Error: {summary_intro.get('error')}_") + lines.append('') + + lines.append('## Transcript') + lines.append('') + if not transcript_messages: + lines.append('_No user or assistant transcript messages were available for export._') + lines.append('') + else: + for message in transcript_messages: + lines.append(f"### {message.get('label')} โ€” {message.get('speaker_label')}") + if message.get('timestamp'): + lines.append(f"*{message.get('timestamp')}*") + lines.append('') + lines.append(message.get('content_text') or '_No content recorded._') + lines.append('') - # Metadata - last_updated = conv.get('last_updated', '') - chat_type = conv.get('chat_type', 'personal') - tags = conv.get('tags', []) - - lines.append(f"**Last Updated:** {last_updated} ") - lines.append(f"**Chat Type:** {chat_type} ") - if tags: - tag_strs = [str(t) for t in tags] - lines.append(f"**Tags:** {', '.join(tag_strs)} ") - lines.append(f"**Messages:** {len(messages)} ") + lines.append('## Appendix A โ€” Conversation Metadata') + lines.append('') + metadata_to_render = _remove_empty_values({ + 'context': conversation.get('context'), + 'classification': conversation.get('classification'), + 'strict': conversation.get('strict'), + 'is_pinned': conversation.get('is_pinned'), + 'scope_locked': conversation.get('scope_locked'), + 'locked_contexts': conversation.get('locked_contexts'), + 'message_counts_by_role': conversation.get('message_counts_by_role'), + 'citation_counts': conversation.get('citation_counts'), + 'thought_count': conversation.get('thought_count') + }) + _append_markdown_mapping(lines, metadata_to_render) + lines.append('') + + if detail_messages: + lines.append('## Appendix B โ€” Message Details') lines.append('') - lines.append('---') + for message in detail_messages: + lines.append(f"### {message.get('label')} โ€” {message.get('speaker_label')}") + if message.get('timestamp'): + lines.append(f"*{message.get('timestamp')}*") + lines.append('') + _append_markdown_mapping(lines, message.get('details', {})) + lines.append('') + + if reference_messages: + lines.append('## Appendix C โ€” References') lines.append('') + for message in reference_messages: + lines.append(f"### {message.get('label')} โ€” {message.get('speaker_label')}") + if message.get('timestamp'): + lines.append(f"*{message.get('timestamp')}*") + lines.append('') + _append_citations_markdown(lines, message) + lines.append('') - # Messages - for msg in messages: - role = msg.get('role', 'unknown') - timestamp = msg.get('timestamp', '') - raw_content = msg.get('content', '') - content = _normalize_content(raw_content) - - role_label = role.capitalize() - if role == 'assistant': - role_label = 'Assistant' - elif role == 'user': - role_label = 'User' - elif role == 'system': - role_label = 'System' - elif role == 'tool': - role_label = 'Tool' - - lines.append(f"### {role_label}") - if timestamp: - lines.append(f"*{timestamp}*") + if thought_messages: + lines.append('## Appendix D โ€” Processing Thoughts') + lines.append('') + for message in thought_messages: + lines.append(f"### {message.get('label')} โ€” {message.get('speaker_label')}") + if message.get('timestamp'): + lines.append(f"*{message.get('timestamp')}*") lines.append('') - lines.append(content) + for thought in message.get('thoughts', []): + thought_label = thought.get('step_type', 'step').replace('_', ' ').title() + lines.append(f"1. **{thought_label}:** {thought.get('content') or 'No content recorded.'}") + if thought.get('duration_ms') is not None: + lines.append(f" - **Duration:** {thought.get('duration_ms')} ms") + if thought.get('timestamp'): + lines.append(f" - **Timestamp:** {thought.get('timestamp')}") + if thought.get('detail'): + lines.append(' - **Detail:**') + _append_code_block(lines, thought.get('detail'), indent=' ') lines.append('') - # Citations - citations = msg.get('citations') - if citations: - lines.append('**Citations:**') - if isinstance(citations, list): - for cit in citations: - if isinstance(cit, dict): - source = cit.get('title') or cit.get('filepath') or cit.get('url', 'Unknown') - lines.append(f"- {source}") - else: - lines.append(f"- {cit}") - lines.append('') - - lines.append('---') + if supplemental_messages: + lines.append('## Appendix E โ€” Supplemental Messages') + lines.append('') + for message in supplemental_messages: + lines.append(f"### {message.get('label')} โ€” {message.get('speaker_label')}") + if message.get('timestamp'): + lines.append(f"*{message.get('timestamp')}*") + lines.append('') + lines.append(message.get('content_text') or '_No content recorded._') lines.append('') - return '\n'.join(lines) + return '\n'.join(lines).strip() - def _normalize_content(content): - """Normalize message content to a plain string. - - Content may be a string, a list of content-part dicts - (e.g. [{"type": "text", "text": "..."}, ...]), or a dict. - """ - if isinstance(content, str): - return content - if isinstance(content, list): - parts = [] - for item in content: - if isinstance(item, dict): - if item.get('type') == 'text': - parts.append(item.get('text', '')) - elif item.get('type') == 'image_url': - parts.append('[Image]') + +def _append_citations_markdown(lines: List[str], message: Dict[str, Any]): + document_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'document'] + web_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'web'] + agent_citations = message.get('agent_citations', []) or [] + legacy_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'legacy'] + + if not any([document_citations, web_citations, agent_citations, legacy_citations]): + lines.append('_No citations were recorded for this message._') + return + + if document_citations: + lines.append('#### Document Sources') + lines.append('') + for index, citation in enumerate(document_citations, start=1): + lines.append(f"{index}. **{citation.get('label', 'Document source')}**") + detail_mapping = _remove_empty_values({ + 'citation_id': citation.get('citation_id'), + 'page_number': citation.get('page_number'), + 'classification': citation.get('classification'), + 'score': citation.get('score'), + 'metadata_type': citation.get('metadata_type') + }) + _append_markdown_mapping(lines, detail_mapping, indent=1) + if citation.get('metadata_content'): + lines.append(' - **Metadata Content:**') + _append_code_block(lines, citation.get('metadata_content'), indent=' ') + lines.append('') + + if web_citations: + lines.append('#### Web Sources') + lines.append('') + for index, citation in enumerate(web_citations, start=1): + title = citation.get('title') or citation.get('label') or 'Web source' + url = citation.get('url') + if url: + lines.append(f"{index}. [{title}]({url})") + else: + lines.append(f"{index}. {title}") + lines.append('') + + if agent_citations: + lines.append('#### Tool Invocations') + lines.append('') + for index, citation in enumerate(agent_citations, start=1): + label = citation.get('tool_name') or citation.get('function_name') or f"Tool {index}" + lines.append(f"{index}. **{label}**") + detail_mapping = _remove_empty_values({ + 'function_name': citation.get('function_name'), + 'plugin_name': citation.get('plugin_name'), + 'success': citation.get('success'), + 'timestamp': citation.get('timestamp') + }) + _append_markdown_mapping(lines, detail_mapping, indent=1) + if citation.get('function_arguments') not in (None, '', [], {}): + lines.append(' - **Arguments:**') + _append_code_block(lines, citation.get('function_arguments'), indent=' ') + if citation.get('function_result') not in (None, '', [], {}): + lines.append(' - **Result:**') + _append_code_block(lines, citation.get('function_result'), indent=' ') + lines.append('') + + if legacy_citations: + lines.append('#### Legacy Citation Records') + lines.append('') + for index, citation in enumerate(legacy_citations, start=1): + lines.append(f"{index}. {citation.get('label', 'Legacy citation')}") + lines.append('') + + +def _append_markdown_mapping(lines: List[str], mapping: Dict[str, Any], indent: int = 0): + if not isinstance(mapping, dict) or not mapping: + return + + prefix = ' ' * indent + for key, value in mapping.items(): + label = _format_markdown_key(key) + if isinstance(value, dict): + lines.append(f"{prefix}- **{label}:**") + _append_markdown_mapping(lines, value, indent + 1) + elif isinstance(value, list): + if not value: + continue + if all(not isinstance(item, (dict, list)) for item in value): + lines.append(f"{prefix}- **{label}:** {', '.join(_stringify_markdown_value(item) for item in value)}") + else: + lines.append(f"{prefix}- **{label}:**") + for item in value: + if isinstance(item, dict): + lines.append(f"{prefix} -") + _append_markdown_mapping(lines, item, indent + 2) else: - parts.append(str(item)) + lines.append(f"{prefix} - {_stringify_markdown_value(item)}") + else: + lines.append(f"{prefix}- **{label}:** {_stringify_markdown_value(value)}") + + +def _append_code_block(lines: List[str], value: Any, indent: str = ''): + if isinstance(value, (dict, list)): + code_block = json.dumps(value, indent=2, ensure_ascii=False, default=str) + language = 'json' + else: + code_block = str(value) + language = 'text' + + lines.append(f"{indent}```{language}") + for line in code_block.splitlines() or ['']: + lines.append(f"{indent}{line}") + lines.append(f"{indent}```") + + +def _format_markdown_key(key: str) -> str: + return str(key).replace('_', ' ').title() + + +def _stringify_markdown_value(value: Any) -> str: + if isinstance(value, bool): + return 'Yes' if value else 'No' + return str(value) + + +def _format_tag(tag: Any) -> str: + """Format a tag or classification entry for display. + + Tags in Cosmos are stored as dicts such as + ``{'category': 'model', 'value': 'gpt-5'}`` or + ``{'category': 'participant', 'name': 'Alice', 'user_id': '...'}`` + but they can also be plain strings in older data. + """ + if isinstance(tag, dict): + category = tag.get('category', '') + # Participant tags carry a readable name / email + name = tag.get('name') or tag.get('email') or tag.get('display_name') + if name: + return f"{category}: {name}" if category else str(name) + # Document tags carry a title + title = tag.get('title') or tag.get('document_id') + if title: + return f"{category}: {title}" if category else str(title) + # Generic category/value tags + value = tag.get('value') + if value: + return f"{category}: {value}" if category else str(value) + return category or str(tag) + return str(tag) + + +def _role_to_label(role: str) -> str: + role_map = { + 'assistant': 'Assistant', + 'user': 'User', + 'system': 'System', + 'tool': 'Tool', + 'file': 'File', + 'image': 'Image', + 'safety': 'Safety', + 'blocked': 'Blocked' + } + return role_map.get(role, str(role).capitalize() or 'Message') + + +def _normalize_content(content: Any) -> str: + """Normalize message content to a plain string.""" + if isinstance(content, str): + return content + if isinstance(content, list): + parts = [] + for item in content: + if isinstance(item, dict): + if item.get('type') == 'text': + parts.append(item.get('text', '')) + elif item.get('type') == 'image_url': + parts.append('[Image]') else: parts.append(str(item)) - return '\n'.join(parts) - if isinstance(content, dict): - if content.get('type') == 'text': - return content.get('text', '') - return str(content) - return str(content) if content else '' - - def _safe_filename(title): - """Create a filesystem-safe filename from a conversation title.""" - import re - # Remove or replace unsafe characters - safe = re.sub(r'[<>:"/\\|?*]', '_', title) - safe = re.sub(r'\s+', '_', safe) - safe = safe.strip('_. ') - # Truncate to reasonable length - if len(safe) > 50: - safe = safe[:50] - return safe or 'Untitled' + else: + parts.append(str(item)) + return '\n'.join(parts) + if isinstance(content, dict): + if content.get('type') == 'text': + return content.get('text', '') + return str(content) + return str(content) if content else '' + + +def _safe_filename(title: str) -> str: + """Create a filesystem-safe filename from a conversation title.""" + safe = re.sub(r'[<>:"/\\|?*]', '_', title) + safe = re.sub(r'\s+', '_', safe) + safe = safe.strip('_. ') + if len(safe) > 50: + safe = safe[:50] + return safe or 'Untitled' + + +# --------------------------------------------------------------------------- +# PDF Export โ€” HTML generation and PyMuPDF Story rendering +# --------------------------------------------------------------------------- + +_PDF_CSS = """ +body { + font-family: sans-serif; + font-size: 10pt; + color: #222; + line-height: 1.4; +} +h1 { + font-size: 16pt; + color: #1a1a2e; + margin-bottom: 2pt; +} +h2 { + font-size: 13pt; + color: #16213e; + margin-top: 16pt; + margin-bottom: 6pt; + border-bottom: 1px solid #ccc; + padding-bottom: 4pt; +} +h3 { + font-size: 11pt; + color: #0f3460; + margin-top: 10pt; + margin-bottom: 4pt; +} +h4 { + font-size: 10pt; + color: #333; + margin-top: 8pt; + margin-bottom: 4pt; +} +p { + margin-top: 2pt; + margin-bottom: 4pt; +} +.metadata { + font-size: 8pt; + color: #666; +} +.abstract { + background-color: #f8f9fa; + padding: 8pt; + margin-bottom: 8pt; +} +.note { + font-size: 9pt; + color: #856404; + background-color: #fff3cd; + padding: 6pt; +} +.bubble { + padding: 8pt 12pt; + margin-bottom: 8pt; +} +.bubble-header { + font-size: 8pt; + color: #444; + margin-bottom: 2pt; +} +.ts { + font-weight: normal; + color: #888; +} +.user-bubble { + background-color: #c8e0fa; + margin-left: 60pt; +} +.assistant-bubble { + background-color: #f1f0f0; + margin-right: 60pt; +} +.system-bubble { + background-color: #fff3cd; + margin-left: 30pt; + margin-right: 30pt; + font-size: 9pt; +} +.file-bubble { + background-color: #e8f5e9; + margin-right: 60pt; + font-size: 9pt; +} +.other-bubble { + background-color: #f5f5f5; + margin-left: 30pt; + margin-right: 30pt; + font-size: 9pt; +} +table { + border-collapse: collapse; + width: 100%; + font-size: 9pt; + margin-bottom: 8pt; +} +th, td { + border: 1px solid #ddd; + padding: 4pt 6pt; + text-align: left; +} +th { + background-color: #f5f5f5; + font-weight: bold; +} +pre { + background-color: #f5f5f5; + padding: 6pt; + font-size: 8pt; + font-family: monospace; +} +code { + font-family: monospace; + font-size: 9pt; + background-color: #f0f0f0; + padding: 1pt 3pt; +} +ol, ul { + margin-top: 4pt; + margin-bottom: 8pt; +} +li { + margin-bottom: 4pt; +} +small { + font-size: 8pt; + color: #666; +} +a { + color: #0066cc; +} +""" + + +def _pdf_bubble_class(role: str) -> str: + """Return the CSS class for a chat bubble based on message role.""" + role_classes = { + 'user': 'user-bubble', + 'assistant': 'assistant-bubble', + 'system': 'system-bubble', + 'file': 'file-bubble', + 'image': 'file-bubble' + } + return role_classes.get(role, 'other-bubble') + + +def _build_pdf_html_body(entry: Dict[str, Any]) -> str: + """Build the HTML body content for a single conversation PDF.""" + conversation = entry['conversation'] + messages = entry['messages'] + summary_intro = entry.get('summary_intro', {}) or {} + + transcript_messages = [m for m in messages if m.get('is_transcript_message')] + detail_messages = [m for m in messages if m.get('details')] + reference_messages = [m for m in messages if m.get('citations')] + thought_messages = [m for m in messages if m.get('thoughts')] + supplemental_messages = [m for m in messages if not m.get('is_transcript_message')] + + parts: List[str] = [] + + # --- Title and metadata --- + parts.append(f'

    {_escape_html(conversation.get("title", "Untitled"))}

    ') + meta_items = [ + f'Last Updated: {_escape_html(str(conversation.get("last_updated", "")))}', + f'Chat Type: {_escape_html(str(conversation.get("chat_type", "personal")))}', + f'Messages: {conversation.get("message_count", len(messages))}' + ] + tags = conversation.get('tags') + if tags: + meta_items.append(f'Tags: {_escape_html(", ".join(_format_tag(t) for t in tags))}') + classification = conversation.get('classification') + if classification: + meta_items.append( + f'Classification: {_escape_html(", ".join(_format_tag(c) for c in classification))}' + ) + parts.append(f'

    {"  |  ".join(meta_items)}

    ') + + # --- Abstract --- + if summary_intro.get('enabled') and summary_intro.get('generated') and summary_intro.get('content'): + parts.append('

    Abstract

    ') + abstract_html = markdown2.markdown( + summary_intro.get('content', ''), + extras=['fenced-code-blocks', 'tables'] + ) + parts.append(f'
    {abstract_html}
    ') + parts.append( + f'

    Generated with ' + f'{_escape_html(str(summary_intro.get("model_deployment") or "configured model"))} on ' + f'{_escape_html(str(summary_intro.get("generated_at", "")))}

    ' + ) + elif summary_intro.get('enabled') and summary_intro.get('error'): + error_text = _escape_html(str(summary_intro.get('error', ''))) + parts.append( + '

    A summary intro was requested, ' + 'but could not be generated for this export.
    ' + f'Error: {error_text}

    ' + ) + + # --- Transcript with chat bubbles --- + parts.append('

    Transcript

    ') + if not transcript_messages: + parts.append( + '

    No user or assistant transcript messages were available for export.

    ' + ) + else: + for message in transcript_messages: + role = message.get('role', '') + bubble_class = _pdf_bubble_class(role) + label = message.get('label', '') + speaker = message.get('speaker_label', '') + timestamp = message.get('timestamp', '') + content = message.get('content_text', '') or 'No content recorded.' + + parts.append(f'
    ') + ts_str = ( + f'  |  {_escape_html(str(timestamp))}' + if timestamp else '' + ) + parts.append( + f'

    {_escape_html(label)} โ€” ' + f'{_escape_html(speaker)}{ts_str}

    ' + ) + content_html = markdown2.markdown( + content, + extras=['fenced-code-blocks', 'tables', 'break-on-newline'] + ) + parts.append(content_html) + parts.append('
    ') + + # --- Appendix A: Conversation Metadata --- + parts.append('

    Appendix A โ€” Conversation Metadata

    ') + metadata_to_render = _remove_empty_values({ + 'context': conversation.get('context'), + 'classification': conversation.get('classification'), + 'strict': conversation.get('strict'), + 'is_pinned': conversation.get('is_pinned'), + 'scope_locked': conversation.get('scope_locked'), + 'locked_contexts': conversation.get('locked_contexts'), + 'message_counts_by_role': conversation.get('message_counts_by_role'), + 'citation_counts': conversation.get('citation_counts'), + 'thought_count': conversation.get('thought_count') + }) + _append_html_table(parts, metadata_to_render) + + # --- Appendix B: Message Details --- + if detail_messages: + parts.append('

    Appendix B โ€” Message Details

    ') + for message in detail_messages: + parts.append( + f'

    {_escape_html(message.get("label", ""))} โ€” ' + f'{_escape_html(message.get("speaker_label", ""))}

    ' + ) + if message.get('timestamp'): + parts.append( + f'

    {_escape_html(str(message.get("timestamp")))}

    ' + ) + _append_html_table(parts, message.get('details', {})) + + # --- Appendix C: References --- + if reference_messages: + parts.append('

    Appendix C โ€” References

    ') + for message in reference_messages: + parts.append( + f'

    {_escape_html(message.get("label", ""))} โ€” ' + f'{_escape_html(message.get("speaker_label", ""))}

    ' + ) + if message.get('timestamp'): + parts.append( + f'

    {_escape_html(str(message.get("timestamp")))}

    ' + ) + _append_html_citations(parts, message) + + # --- Appendix D: Processing Thoughts --- + if thought_messages: + parts.append('

    Appendix D โ€” Processing Thoughts

    ') + for message in thought_messages: + parts.append( + f'

    {_escape_html(message.get("label", ""))} โ€” ' + f'{_escape_html(message.get("speaker_label", ""))}

    ' + ) + if message.get('timestamp'): + parts.append( + f'

    {_escape_html(str(message.get("timestamp")))}

    ' + ) + parts.append('
      ') + for thought in message.get('thoughts', []): + thought_label = (thought.get('step_type') or 'step').replace('_', ' ').title() + parts.append( + f'
    1. {_escape_html(thought_label)}: ' + f'{_escape_html(str(thought.get("content") or "No content recorded."))}' + ) + if thought.get('duration_ms') is not None: + parts.append( + f'
      Duration: {thought.get("duration_ms")} ms' + ) + if thought.get('timestamp'): + parts.append( + f'
      Timestamp: ' + f'{_escape_html(str(thought.get("timestamp")))}' + ) + if thought.get('detail'): + parts.append('
      Detail:') + _append_html_code_block(parts, thought.get('detail')) + parts.append('
      2. ') + parts.append('
    ') + + # --- Appendix E: Supplemental Messages --- + if supplemental_messages: + parts.append('

    Appendix E โ€” Supplemental Messages

    ') + for message in supplemental_messages: + parts.append( + f'

    {_escape_html(message.get("label", ""))} โ€” ' + f'{_escape_html(message.get("speaker_label", ""))}

    ' + ) + if message.get('timestamp'): + parts.append( + f'

    {_escape_html(str(message.get("timestamp")))}

    ' + ) + content = message.get('content_text', '') or 'No content recorded.' + content_html = markdown2.markdown( + content, + extras=['fenced-code-blocks', 'tables', 'break-on-newline'] + ) + parts.append(content_html) + + return '\n'.join(parts) + + +def _render_pdf_bytes(body_html: str) -> bytes: + """Render HTML body content to PDF bytes using PyMuPDF Story API.""" + MEDIABOX = fitz.paper_rect("letter") + WHERE = MEDIABOX + (36, 36, -36, -36) + + story = fitz.Story(html=body_html, user_css=_PDF_CSS) + + tmp_path = None + try: + with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as tmp: + tmp_path = tmp.name + + writer = fitz.DocumentWriter(tmp_path) + more = True + while more: + device = writer.begin_page(MEDIABOX) + more, _ = story.place(WHERE) + story.draw(device) + writer.end_page() + writer.close() + del story + del writer + + with open(tmp_path, 'rb') as f: + return f.read() + finally: + if tmp_path: + try: + os.unlink(tmp_path) + except OSError: + pass + + +def _conversation_to_pdf_bytes(entry: Dict[str, Any]) -> bytes: + """Convert a conversation export entry to PDF bytes.""" + body_html = _build_pdf_html_body(entry) + return _render_pdf_bytes(body_html) + + +def _html_body_to_pdf_bytes(body_html: str) -> bytes: + """Convert raw HTML body content to PDF bytes.""" + return _render_pdf_bytes(body_html) + + +def _append_html_table(parts: List[str], mapping: Dict[str, Any]): + """Append a key-value mapping as an HTML table.""" + if not isinstance(mapping, dict) or not mapping: + parts.append('

    No data available.

    ') + return + + parts.append('

    ') + parts.append('') + for key, value in mapping.items(): + label = _format_markdown_key(key) + if isinstance(value, dict): + formatted = _format_nested_html_value(value) + elif isinstance(value, list): + formatted = ( + ', '.join(_escape_html(str(item)) for item in value) + if value else 'None' + ) + elif isinstance(value, bool): + formatted = 'Yes' if value else 'No' + else: + formatted = _escape_html(str(value)) + parts.append(f'') + parts.append('
    PropertyValue
    {_escape_html(label)}{formatted}
    ') + + +def _format_nested_html_value(mapping: Dict[str, Any], depth: int = 0) -> str: + """Format a nested dict as an HTML string for table cells.""" + if not mapping: + return 'None' + + items = [] + for key, value in mapping.items(): + label = _format_markdown_key(key) + if isinstance(value, dict): + nested = _format_nested_html_value(value, depth + 1) + items.append(f'{_escape_html(label)}:
    {nested}') + elif isinstance(value, list): + list_str = ( + ', '.join(_escape_html(str(v)) for v in value) + if value else 'None' + ) + items.append(f'{_escape_html(label)}: {list_str}') + elif isinstance(value, bool): + items.append(f'{_escape_html(label)}: {"Yes" if value else "No"}') + else: + items.append(f'{_escape_html(label)}: {_escape_html(str(value))}') + return '
    '.join(items) + + +def _append_html_citations(parts: List[str], message: Dict[str, Any]): + """Append citation data as HTML.""" + citations = message.get('citations', []) + if not citations: + parts.append('

    No citations were recorded for this message.

    ') + return + + doc_citations = [c for c in citations if c.get('citation_type') == 'document'] + web_citations = [c for c in citations if c.get('citation_type') == 'web'] + agent_citations = [c for c in citations if c.get('citation_type') == 'agent_tool'] + legacy_citations = [c for c in citations if c.get('citation_type') == 'legacy'] + + if doc_citations: + parts.append('

    Document Sources

    ') + parts.append('
      ') + for citation in doc_citations: + parts.append( + f'
    1. {_escape_html(str(citation.get("label", "Document source")))}' + ) + detail_items = _remove_empty_values({ + 'citation_id': citation.get('citation_id'), + 'page_number': citation.get('page_number'), + 'classification': citation.get('classification'), + 'score': citation.get('score'), + 'metadata_type': citation.get('metadata_type') + }) + if detail_items: + detail_str = '; '.join( + f'{_format_markdown_key(k)}: {_escape_html(str(v))}' + for k, v in detail_items.items() + ) + parts.append(f'
      {detail_str}') + if citation.get('metadata_content'): + parts.append('
      Metadata Content:') + _append_html_code_block(parts, citation.get('metadata_content')) + parts.append('
      2. ') + parts.append('
    ') + + if web_citations: + parts.append('

    Web Sources

    ') + parts.append('
      ') + for citation in web_citations: + title = _escape_html( + str(citation.get('title') or citation.get('label') or 'Web source') + ) + url = citation.get('url') + if url: + parts.append(f'
    1. {title}
      2. ') + else: + parts.append(f'
    2. {title}
      3. ') + parts.append('
    ') + + if agent_citations: + parts.append('

    Tool Invocations

    ') + parts.append('
      ') + for citation in agent_citations: + label = _escape_html( + str(citation.get('tool_name') or citation.get('function_name') or 'Tool') + ) + parts.append(f'
    1. {label}') + detail_items = _remove_empty_values({ + 'function_name': citation.get('function_name'), + 'plugin_name': citation.get('plugin_name'), + 'success': citation.get('success'), + 'timestamp': citation.get('timestamp') + }) + if detail_items: + detail_str = '; '.join( + f'{_format_markdown_key(k)}: {_escape_html(str(v))}' + for k, v in detail_items.items() + ) + parts.append(f'
      {detail_str}') + parts.append('
      2. ') + parts.append('
    ') + + if legacy_citations: + parts.append('

    Legacy Citation Records

    ') + parts.append('
      ') + for citation in legacy_citations: + parts.append( + f'
    1. {_escape_html(str(citation.get("label", "Legacy citation")))}
      2. ' + ) + parts.append('
    ') + + +def _append_html_code_block(parts: List[str], value: Any): + """Append a code block in HTML format.""" + if isinstance(value, (dict, list)): + code_text = json.dumps(value, indent=2, ensure_ascii=False, default=str) + else: + code_text = str(value) + parts.append(f'
    {_escape_html(code_text)}
    ') diff --git a/application/single_app/route_backend_conversations.py b/application/single_app/route_backend_conversations.py index ac25bd8c..d90d7e4a 100644 --- a/application/single_app/route_backend_conversations.py +++ b/application/single_app/route_backend_conversations.py @@ -3,7 +3,7 @@ from config import * from functions_authentication import * from functions_settings import * -from functions_conversation_metadata import get_conversation_metadata +from functions_conversation_metadata import get_conversation_metadata, update_conversation_with_metadata from flask import Response, request from functions_debug import debug_print from swagger_wrapper import swagger_route, get_auth_security @@ -812,7 +812,8 @@ def get_conversation_metadata_api(conversation_id): "is_hidden": conversation_item.get('is_hidden', False), "scope_locked": conversation_item.get('scope_locked'), "locked_contexts": conversation_item.get('locked_contexts', []), - "chat_type": conversation_item.get('chat_type') + "chat_type": conversation_item.get('chat_type'), + "summary": conversation_item.get('summary') }), 200 except CosmosResourceNotFoundError: @@ -821,6 +822,95 @@ def get_conversation_metadata_api(conversation_id): print(f"Error retrieving conversation metadata: {e}") return jsonify({'error': 'Failed to retrieve conversation metadata'}), 500 + @app.route('/api/conversations//summary', methods=['POST']) + @swagger_route(security=get_auth_security()) + @login_required + @user_required + def generate_conversation_summary_api(conversation_id): + """ + Generate (or regenerate) a summary for a conversation and persist it. + + Request body (optional): + { "model_deployment": "gpt-4o" } + + Returns the generated summary dict on success. + """ + from route_backend_conversation_export import generate_conversation_summary, _normalize_content + from functions_chat import sort_messages_by_thread + + user_id = get_current_user_id() + if not user_id: + return jsonify({'error': 'User not authenticated'}), 401 + + try: + conversation_item = cosmos_conversations_container.read_item( + item=conversation_id, + partition_key=conversation_id + ) + if conversation_item.get('user_id') != user_id: + return jsonify({'error': 'Forbidden'}), 403 + except CosmosResourceNotFoundError: + return jsonify({'error': 'Conversation not found'}), 404 + except Exception as e: + debug_print(f"Error reading conversation for summary: {e}") + return jsonify({'error': 'Failed to read conversation'}), 500 + + body = request.get_json(silent=True) or {} + model_deployment = body.get('model_deployment', '') + + # Query messages for this conversation + try: + query = "SELECT * FROM c WHERE c.conversation_id = @cid ORDER BY c.timestamp ASC" + params = [{"name": "@cid", "value": conversation_id}] + raw_messages = list(cosmos_messages_container.query_items( + query=query, + parameters=params, + enable_cross_partition_query=True + )) + except Exception as e: + debug_print(f"Error querying messages for summary: {e}") + return jsonify({'error': 'Failed to query messages'}), 500 + + if not raw_messages: + return jsonify({'error': 'No messages in this conversation'}), 400 + + # Build lightweight export-style message list for the summary helper + ordered_messages = sort_messages_by_thread(raw_messages) + export_messages = [] + for msg in ordered_messages: + role = msg.get('role', 'unknown') + # Content may be a string OR a list of content parts โ€” normalise it + content = _normalize_content(msg.get('content', '')) + speaker = 'USER' if role == 'user' else 'ASSISTANT' if role == 'assistant' else role.upper() + export_messages.append({ + 'role': role, + 'content_text': content, + 'speaker_label': speaker + }) + + message_time_start = ordered_messages[0].get('timestamp') if ordered_messages else None + message_time_end = ordered_messages[-1].get('timestamp') if ordered_messages else None + + settings = get_settings() + + try: + summary_data = generate_conversation_summary( + messages=export_messages, + conversation_title=conversation_item.get('title', 'Untitled'), + settings=settings, + model_deployment=model_deployment, + message_time_start=message_time_start, + message_time_end=message_time_end, + conversation_id=conversation_id + ) + return jsonify({'success': True, 'summary': summary_data}), 200 + + except (ValueError, RuntimeError) as known_exc: + return jsonify({'error': str(known_exc)}), 400 + except Exception as exc: + debug_print(f"Summary generation API error: {exc}") + return jsonify({'error': 'Summary generation failed'}), 500 + @app.route('/api/conversations//scope_lock', methods=['PATCH']) @swagger_route(security=get_auth_security()) @login_required diff --git a/application/single_app/static/js/chat/chat-conversation-details.js b/application/single_app/static/js/chat/chat-conversation-details.js index 19851bae..484128af 100644 --- a/application/single_app/static/js/chat/chat-conversation-details.js +++ b/application/single_app/static/js/chat/chat-conversation-details.js @@ -75,7 +75,7 @@ export async function showConversationDetails(conversationId) { * @returns {string} HTML string */ function renderConversationMetadata(metadata, conversationId) { - const { context = [], tags = [], strict = false, classification = [], last_updated, chat_type = 'personal', is_pinned = false, is_hidden = false, scope_locked, locked_contexts = [] } = metadata; + const { context = [], tags = [], strict = false, classification = [], last_updated, chat_type = 'personal', is_pinned = false, is_hidden = false, scope_locked, locked_contexts = [], summary = null } = metadata; // Organize tags by category const tagsByCategory = { @@ -97,6 +97,18 @@ function renderConversationMetadata(metadata, conversationId) { // Build HTML sections let html = `
    + +
    +
    +
    +
    Summary
    + ${summary ? `Generated ${formatDate(summary.generated_at)}${summary.model_deployment ? ` ยท ${summary.model_deployment}` : ''}` : ''} +
    +
    + ${renderSummaryContent(summary, conversationId)} +
    +
    +
    @@ -570,8 +582,159 @@ function extractPageNumbers(chunkIds) { return pages.sort((a, b) => parseInt(a) - parseInt(b)); } +/** + * Render the summary card body content + * @param {Object|null} summary - Existing summary data or null + * @param {string} conversationId - The conversation ID + * @returns {string} HTML string + */ +function renderSummaryContent(summary, conversationId) { + if (summary && summary.content) { + return ` +

    ${escapeHtml(summary.content)}

    +
    + +
    + `; + } + + // Build model options from the global model-select dropdown + const modelOptions = getAvailableModelOptions(); + return ` +

    No summary has been generated for this conversation yet.

    +
    + + +
    + `; +} + +/** + * Get available model options from the global #model-select dropdown + * @returns {string} HTML option elements + */ +function getAvailableModelOptions() { + const globalSelect = document.getElementById('model-select'); + if (!globalSelect) { + return ''; + } + let options = ''; + for (const opt of globalSelect.options) { + options += ``; + } + return options || ''; +} + +/** + * Handle summary generation (generate or regenerate) + * @param {string} conversationId - The conversation ID + * @param {string} modelDeployment - Selected model deployment + */ +async function handleGenerateSummary(conversationId, modelDeployment) { + const cardBody = document.getElementById('summary-card-body'); + if (!cardBody) { + return; + } + + cardBody.innerHTML = ` +
    + + Generating summary... +
    + `; + + try { + const response = await fetch(`/api/conversations/${conversationId}/summary`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ model_deployment: modelDeployment }) + }); + + if (!response.ok) { + const errData = await response.json().catch(() => ({})); + throw new Error(errData.error || `HTTP ${response.status}`); + } + + const data = await response.json(); + const summary = data.summary; + cardBody.innerHTML = renderSummaryContent(summary, conversationId); + + // Update card header with generation info + const cardHeader = cardBody.closest('.card').querySelector('.card-header'); + if (cardHeader && summary) { + const smallEl = cardHeader.querySelector('small'); + const infoText = `Generated ${formatDate(summary.generated_at)}${summary.model_deployment ? ` ยท ${summary.model_deployment}` : ''}`; + if (smallEl) { + smallEl.textContent = infoText; + } else { + const small = document.createElement('small'); + small.className = 'opacity-75'; + small.textContent = infoText; + cardHeader.appendChild(small); + } + } + + } catch (error) { + console.error('Error generating summary:', error); + cardBody.innerHTML = ` +
    + + Failed to generate summary: ${escapeHtml(error.message)} +
    + ${renderSummaryContent(null, conversationId)} + `; + } +} + +/** + * Simple HTML escapefor display + * @param {string} str - String to escape + * @returns {string} Escaped string + */ +function escapeHtml(str) { + if (!str) { + return ''; + } + const div = document.createElement('div'); + div.textContent = str; + return div.innerHTML; +} + // Event listeners for details buttons document.addEventListener('click', function(e) { + // Generate summary button + if (e.target.closest('#generate-summary-btn')) { + e.preventDefault(); + const btn = e.target.closest('#generate-summary-btn'); + const cid = btn.getAttribute('data-conversation-id'); + const modelSelect = document.getElementById('summary-model-select'); + const model = modelSelect ? modelSelect.value : ''; + handleGenerateSummary(cid, model); + return; + } + + // Regenerate summary button + if (e.target.closest('#regenerate-summary-btn')) { + e.preventDefault(); + const btn = e.target.closest('#regenerate-summary-btn'); + const cid = btn.getAttribute('data-conversation-id'); + // Use the currently selected global model for regeneration + const globalSelect = document.getElementById('model-select'); + const model = globalSelect ? globalSelect.value : ''; + handleGenerateSummary(cid, model); + return; + } + if (e.target.closest('.details-btn')) { e.preventDefault(); diff --git a/application/single_app/static/js/chat/chat-export.js b/application/single_app/static/js/chat/chat-export.js index 269cbfe0..fc53d2b6 100644 --- a/application/single_app/static/js/chat/chat-export.js +++ b/application/single_app/static/js/chat/chat-export.js @@ -15,6 +15,8 @@ let exportConversationIds = []; let exportConversationTitles = {}; let exportFormat = 'json'; let exportPackaging = 'single'; +let includeSummaryIntro = false; +let summaryModelDeployment = ''; let currentStep = 1; let totalSteps = 3; let skipSelectionStep = false; @@ -53,14 +55,16 @@ function openExportWizard(conversationIds, skipSelection) { exportConversationTitles = {}; exportFormat = 'json'; exportPackaging = conversationIds.length > 1 ? 'zip' : 'single'; + includeSummaryIntro = false; + summaryModelDeployment = _getDefaultSummaryModel(); skipSelectionStep = !!skipSelection; // Determine step configuration if (skipSelectionStep) { - totalSteps = 3; + totalSteps = 4; currentStep = 1; // Format step (mapped to visual step) } else { - totalSteps = 4; + totalSteps = 5; currentStep = 1; // Selection review step } @@ -142,19 +146,21 @@ function _renderCurrentStep() { if (!stepBody) return; if (skipSelectionStep) { - // Steps: 1=Format, 2=Packaging, 3=Download + // Steps: 1=Format, 2=Packaging, 3=Summary, 4=Download switch (currentStep) { case 1: _renderFormatStep(stepBody); break; case 2: _renderPackagingStep(stepBody); break; - case 3: _renderDownloadStep(stepBody); break; + case 3: _renderSummaryStep(stepBody); break; + case 4: _renderDownloadStep(stepBody); break; } } else { - // Steps: 1=Selection, 2=Format, 3=Packaging, 4=Download + // Steps: 1=Selection, 2=Format, 3=Packaging, 4=Summary, 5=Download switch (currentStep) { case 1: _renderSelectionStep(stepBody); break; case 2: _renderFormatStep(stepBody); break; case 3: _renderPackagingStep(stepBody); break; - case 4: _renderDownloadStep(stepBody); break; + case 4: _renderSummaryStep(stepBody); break; + case 5: _renderDownloadStep(stepBody); break; } } } @@ -210,7 +216,7 @@ function _renderFormatStep(container) {

    Select the format for your exported conversations.

    -
    +
    @@ -219,7 +225,7 @@ function _renderFormatStep(container) {
    -
    +
    @@ -228,6 +234,15 @@ function _renderFormatStep(container) {
    +
    +
    +
    + +
    PDF
    +

    Print-ready format with chat bubbles. Ideal for archiving and printing.

    +
    +
    +
    `; // Wire card clicks @@ -297,11 +312,68 @@ function _renderPackagingStep(container) { }); } +function _renderSummaryStep(container) { + const mainModelSelect = getEl('model-select'); + const hasModelOptions = Boolean(mainModelSelect && mainModelSelect.options.length > 0); + const defaultSummaryModel = summaryModelDeployment || _getDefaultSummaryModel(); + const perConversationText = exportConversationIds.length > 1 + ? 'An intro will be generated for each exported conversation.' + : 'An intro will be generated for this conversation.'; + + container.innerHTML = ` +
    +
    Optional Intro Summary
    +

    Add a short abstract before the exported transcript. ${perConversationText}

    +
    +
    + + +
    +
    +
    + + +
    Uses the same model list as the chat composer.
    +
    +
    `; + + const toggle = getEl('export-summary-toggle'); + const modelContainer = getEl('export-summary-model-container'); + const summaryModelSelect = getEl('export-summary-model'); + + if (summaryModelSelect && hasModelOptions) { + summaryModelSelect.value = defaultSummaryModel || summaryModelSelect.value; + summaryModelDeployment = summaryModelSelect.value; + summaryModelSelect.addEventListener('change', () => { + summaryModelDeployment = summaryModelSelect.value; + }); + } + + if (toggle) { + toggle.addEventListener('change', () => { + includeSummaryIntro = toggle.checked; + if (modelContainer) { + modelContainer.classList.toggle('d-none', !includeSummaryIntro); + } + if (includeSummaryIntro && summaryModelSelect && !summaryModelSelect.value) { + summaryModelSelect.value = _getDefaultSummaryModel(); + summaryModelDeployment = summaryModelSelect.value; + } + }); + } +} + function _renderDownloadStep(container) { const count = exportConversationIds.length; - const formatLabel = exportFormat === 'json' ? 'JSON' : 'Markdown'; + const formatLabels = { json: 'JSON', markdown: 'Markdown', pdf: 'PDF' }; + const formatLabel = formatLabels[exportFormat] || exportFormat.toUpperCase(); const packagingLabel = exportPackaging === 'zip' ? 'ZIP Archive' : 'Single File'; - const ext = exportPackaging === 'zip' ? '.zip' : (exportFormat === 'json' ? '.json' : '.md'); + const extMap = { json: '.json', markdown: '.md', pdf: '.pdf' }; + const ext = exportPackaging === 'zip' ? '.zip' : (extMap[exportFormat] || '.bin'); + const summaryLabel = includeSummaryIntro ? 'Enabled' : 'Disabled'; + const summaryModelLabel = includeSummaryIntro ? (summaryModelDeployment || 'Configured default') : 'โ€”'; let conversationsList = ''; exportConversationIds.forEach(id => { @@ -328,6 +400,14 @@ function _renderDownloadStep(container) {
    Packaging:
    ${packagingLabel}
    +
    +
    Intro summary:
    +
    ${summaryLabel}
    +
    +
    +
    Summary model:
    +
    ${_escapeHtml(summaryModelLabel)}
    +
    File type:
    ${ext}
    @@ -364,6 +444,7 @@ function _updateStepIndicators() { steps = [ { label: 'Format', icon: 'bi-filetype-json' }, { label: 'Packaging', icon: 'bi-box' }, + { label: 'Summary', icon: 'bi-card-text' }, { label: 'Download', icon: 'bi-download' } ]; } else { @@ -371,6 +452,7 @@ function _updateStepIndicators() { { label: 'Select', icon: 'bi-list-check' }, { label: 'Format', icon: 'bi-filetype-json' }, { label: 'Packaging', icon: 'bi-box' }, + { label: 'Summary', icon: 'bi-card-text' }, { label: 'Download', icon: 'bi-download' } ]; } @@ -448,7 +530,9 @@ async function _executeExport() { body: JSON.stringify({ conversation_ids: exportConversationIds, format: exportFormat, - packaging: exportPackaging + packaging: exportPackaging, + include_summary_intro: includeSummaryIntro, + summary_model_deployment: includeSummaryIntro ? summaryModelDeployment : null }) }); @@ -460,7 +544,8 @@ async function _executeExport() { // Get filename from Content-Disposition header const disposition = response.headers.get('Content-Disposition') || ''; const filenameMatch = disposition.match(/filename="?([^"]+)"?/); - const filename = filenameMatch ? filenameMatch[1] : `conversations_export.${exportPackaging === 'zip' ? 'zip' : (exportFormat === 'json' ? 'json' : 'md')}`; + const fallbackExtMap = { json: 'json', markdown: 'md', pdf: 'pdf' }; + const filename = filenameMatch ? filenameMatch[1] : `conversations_export.${exportPackaging === 'zip' ? 'zip' : (fallbackExtMap[exportFormat] || 'bin')}`; // Download the blob const blob = await response.blob(); @@ -511,6 +596,15 @@ function _escapeHtml(text) { return div.innerHTML; } +function _getDefaultSummaryModel() { + const mainModelSelect = getEl('model-select'); + if (!mainModelSelect) { + return ''; + } + + return mainModelSelect.value || (mainModelSelect.options[0] ? mainModelSelect.options[0].value : ''); +} + // --- Expose Globally --- window.chatExport = { openExportWizard diff --git a/docs/explanation/features/CONVERSATION_EXPORT.md b/docs/explanation/features/CONVERSATION_EXPORT.md index c56d261a..a674dca4 100644 --- a/docs/explanation/features/CONVERSATION_EXPORT.md +++ b/docs/explanation/features/CONVERSATION_EXPORT.md @@ -1,139 +1,186 @@ # Conversation Export ## Overview -The Conversation Export feature allows users to export one or multiple conversations directly from the Chats experience. A multi-step wizard modal guides users through format selection, output packaging, and downloading the final file. +The Conversation Export feature lets users export one or more chats from the Chats experience as JSON, Markdown, or PDF. The export now mirrors the live conversation view more closely by excluding deleted and inactive-thread messages, including processing thoughts, and preserving the modern citation buckets used by the chat UI. -**Version Implemented:** 0.237.050 +**Version Implemented:** 0.239.022 (base), 0.239.023 (PDF export), 0.239.030 (persistent summaries) ## Dependencies -- Flask (backend route) -- Azure Cosmos DB (conversation and message storage) -- Bootstrap 5 (modal, step indicators, cards) -- ES modules (chat-export.js) +- Flask backend route and file responses +- Azure Cosmos DB conversation, message, and thought containers +- Bootstrap 5 modal workflow +- ES modules in `static/js/chat/chat-export.js` +- Azure OpenAI / APIM-backed chat model access for optional intro summaries +- PyMuPDF (fitz) for HTML-to-PDF rendering via the Story API + +## Implemented in version: **0.239.030** ## Architecture Overview ### Backend -- **Route file:** `route_backend_conversation_export.py` +- **Route file:** `application/single_app/route_backend_conversation_export.py` - **Endpoint:** `POST /api/conversations/export` -- **Registration:** Called via `register_route_backend_conversation_export(app)` in `app.py` +- **Registration:** `register_route_backend_conversation_export(app)` + +The export route now: +- verifies the current user owns each requested conversation +- loads messages ordered by timestamp, then reapplies thread ordering to match the chat UI +- removes soft-deleted messages and inactive-thread retries +- joins processing thoughts from the thoughts container by `message_id` +- builds both normalized citation summaries and raw citation buckets +- optionally generates a per-conversation intro summary using the selected chat model + +### Frontend +- **JS module:** `application/single_app/static/js/chat/chat-export.js` +- **Modal host:** `application/single_app/templates/chats.html` +- **Entry point:** `window.chatExport.openExportWizard(conversationIds, skipSelection)` + +The wizard now has up to 5 steps: +1. **Selection** โ€” Review selected conversations (multi-select exports only) +2. **Format** โ€” Choose JSON, Markdown, or PDF +3. **Packaging** โ€” Choose a single file or ZIP archive +4. **Summary** โ€” Optionally generate a per-conversation intro summary and choose the summary model from the same model list used in the chat composer +5. **Download** โ€” Review settings and download the export + +## Request Payload -The endpoint accepts a JSON body with: | Field | Type | Description | |---|---|---| -| `conversation_ids` | list[str] | IDs of conversations to export | -| `format` | string | `"json"` or `"markdown"` | +| `conversation_ids` | list[str] | Conversation IDs to export | +| `format` | string | `"json"`, `"markdown"`, or `"pdf"` | | `packaging` | string | `"single"` or `"zip"` | - -The server verifies user ownership of each conversation, fetches messages from Cosmos DB, filters for active thread messages, sanitizes internal fields, and returns either a single file or ZIP archive as a binary download. - -### Frontend -- **JS module:** `static/js/chat/chat-export.js` -- **Modal HTML:** Embedded in `templates/chats.html` (`#export-wizard-modal`) -- **Global API:** `window.chatExport.openExportWizard(conversationIds, skipSelection)` - -The wizard has up to 4 steps: -1. **Selection Review** โ€” Shows selected conversations with titles (skipped for single-conversation export) -2. **Format** โ€” Choose between JSON and Markdown via action-type cards -3. **Packaging** โ€” Choose between single file and ZIP archive -4. **Download** โ€” Summary and download button - -## Entry Points - -### Single Conversation Export -- **Sidebar ellipsis menu** โ†’ "Export" item (in `chat-sidebar-conversations.js`) -- **Left-pane ellipsis menu** โ†’ "Export" item (in `chat-conversations.js`) -- Both call `window.chatExport.openExportWizard([conversationId], true)` โ€” skips the selection step - -### Multi-Conversation Export -- Enter selection mode by clicking "Select" on any conversation -- Select multiple conversations via checkboxes -- Click the export button in: - - **Left-pane header** โ€” `#export-selected-btn` (btn-info, download icon) - - **Sidebar actions bar** โ€” `#sidebar-export-selected-btn` -- These call `window.chatExport.openExportWizard(selectedIds, false)` โ€” shows all 4 steps +| `include_summary_intro` | boolean | Whether to generate a per-conversation intro summary | +| `summary_model_deployment` | string | Optional selected model deployment for the summary intro | ## Export Formats ### JSON -Produces a JSON array where each entry contains: -```json -{ - "conversation": { - "id": "...", - "title": "...", - "last_updated": "...", - "chat_type": "...", - "tags": [], - "is_pinned": false, - "context": [] - }, - "messages": [ - { - "role": "user", - "content": "...", - "timestamp": "...", - "citations": [] - } - ] -} -``` +Each exported conversation entry contains: +- `conversation` metadata and aggregate counts +- `summary_intro` status and content (when enabled) +- `messages` with: + - raw `content` + - normalized `content_text` + - curated `details` + - normalized `citations` + - raw `legacy_citations`, `hybrid_citations`, `web_search_citations`, and `agent_citations` + - nested `thoughts` + +This makes the JSON export useful both for readable inspection and downstream processing. ### Markdown -Produces a Markdown document with: -- `# Title` heading -- Metadata block (last updated, chat type, tags, message count) -- `### Role` sections per message with timestamps -- Citation lists where applicable -- `---` separators between messages and conversations +Markdown exports are now organized like a lightweight report: +- conversation title and metadata header +- optional **Abstract** section generated by the selected model +- **Transcript** section with clean user/assistant turns only +- appendix sections for: + - conversation metadata + - curated message details + - references/citations + - processing thoughts + - supplemental non-transcript messages such as file or system records + +This keeps the main body readable while moving verbose reference material to the end of the document. + +### PDF +PDF exports render the conversation as a print-ready document with chat-bubble styling that visually resembles the live chat interface: +- **User messages** are displayed in blue bubbles (#c8e0fa) aligned to the right +- **Assistant messages** are displayed in gray bubbles (#f1f0f0) aligned to the left +- **System and file messages** use distinct background colors +- Message content is converted from Markdown to HTML for rich formatting +- The same appendix structure as Markdown is included (metadata, details, references, thoughts, supplemental messages) +- PDF rendering uses PyMuPDF's Story API on US Letter paper (612 ร— 792pt) with 0.5-inch margins +- Multi-page content is handled automatically with page overflow + +## Citation Handling + +The export supports the same citation categories used in the live chat UI: +- **Document citations** from `hybrid_citations` +- **Web citations** from `web_search_citations` +- **Agent/tool citations** from `agent_citations` +- **Legacy citations** for backwards-compatible older messages + +Normalized citation summaries provide a single export-friendly list, while the raw buckets preserve the original structured data. + +## Thoughts Handling + +If processing thoughts are enabled and available, they are exported with the assistant message they belong to. Each thought includes: +- `step_index` +- `step_type` +- `content` +- `detail` +- `duration_ms` +- `timestamp` + +Markdown exports place these in the **Processing Thoughts** appendix. ## Output Packaging ### Single File -- One file containing all selected conversations -- JSON: `.json` file -- Markdown: `.md` file with `---` separators between conversations +- JSON exports combine all selected conversations into one `.json` file +- Markdown exports combine all selected conversations into one `.md` file separated by `---` +- PDF exports combine all selected conversations into one `.pdf` file with visual separators between conversations ### ZIP Archive -- One file per conversation inside a `.zip` -- Filenames: `{sanitized_title}_{id_prefix}.{ext}` -- Titles are sanitized for filesystem safety (special chars replaced, truncated to 50 chars) - -## File Structure -``` -application/single_app/ -โ”œโ”€โ”€ route_backend_conversation_export.py # Backend API endpoint -โ”œโ”€โ”€ app.py # Route registration -โ”œโ”€โ”€ static/js/chat/ -โ”‚ โ”œโ”€โ”€ chat-export.js # Export wizard module -โ”‚ โ”œโ”€โ”€ chat-conversations.js # Left-pane wiring -โ”‚ โ””โ”€โ”€ chat-sidebar-conversations.js # Sidebar wiring -โ”œโ”€โ”€ templates/ -โ”‚ โ”œโ”€โ”€ chats.html # Modal HTML + button + script -โ”‚ โ”œโ”€โ”€ _sidebar_nav.html # Sidebar export button -โ”‚ โ””โ”€โ”€ _sidebar_short_nav.html # Short sidebar export button -functional_tests/ -โ””โ”€โ”€ test_conversation_export.py # Functional tests -``` - -## Security -- Endpoint requires `@login_required` and `@user_required` decorators -- Each conversation is verified for user ownership before export -- Internal Cosmos DB fields (`_rid`, `_self`, `_etag`, `user_id`, etc.) are stripped from output -- No sensitive data is included in the export +- Each conversation is written to its own file in the archive +- Filenames use `{sanitized_title}_{conversation_id_prefix}.{ext}` +- Summary intros remain per conversation in both JSON and Markdown archive entries + +## Persistent Conversation Summaries (v0.239.030) + +Summaries generated during export or on demand are now persisted to the conversation document in Cosmos DB and reused automatically. + +### How Caching Works +- Each summary stores `message_time_start` and `message_time_end` from the messages it was built from. +- On subsequent exports, `_build_summary_intro` compares the cached `message_time_end` against the latest message timestamp. If no new messages exist, the cached summary is returned instantly. +- If new messages exist beyond the cached range, a fresh summary is generated, saved, and returned. + +### On-Demand Generation +- The conversation details modal (opened from the sidebar) now includes a **Summary** card. +- If no summary exists, a **Generate Summary** button with a model selector lets users create one without exporting. +- If a summary exists, the content, generation date, and model are displayed with a **Regenerate** button. +- New API endpoint: `POST /api/conversations//summary` with optional `{ "model_deployment": "..." }` body. + +### Reusable Helper +- `generate_conversation_summary()` (in `route_backend_conversation_export.py`) is the shared LLM call function used by both the export pipeline and the API endpoint. +- It handles transcript assembly, truncation, model role selection (developer vs system), and Cosmos persistence. + +## Security and Data-Shaping Rules +- Route uses authenticated user checks before export +- Only user-owned personal conversations are exported +- Internal Cosmos metadata is not passed through +- Deleted messages and inactive-thread retries are excluded +- Exported message details are curated rather than raw metadata dumps +- Raw settings are not exposed to the browser; the export wizard reuses the existing chat model selector already rendered from sanitized settings + +## Files Updated +- `application/single_app/route_backend_conversation_export.py` +- `application/single_app/route_backend_conversations.py` +- `application/single_app/static/js/chat/chat-export.js` +- `application/single_app/static/js/chat/chat-conversation-details.js` +- `application/single_app/config.py` +- `functional_tests/test_conversation_export.py` +- `functional_tests/test_persistent_conversation_summary.py` ## Testing and Validation -- **Functional test:** `functional_tests/test_conversation_export.py` -- Tests cover: - - Conversation sanitization (internal field stripping) - - Message sanitization - - Markdown generation (headings, metadata, citations) - - JSON structure validation - - ZIP packaging (correct entries, valid content) - - Filename sanitization (special chars, truncation, empty input) - - Active thread message filtering +- **Functional tests:** + - `functional_tests/test_conversation_export.py` โ€” export pipeline (8 tests) + - `functional_tests/test_persistent_conversation_summary.py` โ€” summary caching (8 tests) + +Coverage includes: +- deleted/inactive message filtering +- normalized and raw citation export +- thoughts attached to assistant messages +- transcript-style Markdown appendices +- summary-intro metadata shape +- filename sanitization and ZIP naming +- content normalization and citation-count helpers +- PDF HTML body generation with chat-bubble classes +- PDF byte output validation (%PDF- header) ## Known Limitations -- Export is limited to conversations the authenticated user owns -- Very large conversations (thousands of messages) may take longer to process -- The wizard fetches conversation titles client-side; if a title lookup fails, it shows the conversation ID instead +- Export remains limited to user-owned personal conversations +- Optional intro summaries increase export time because each selected conversation is summarized individually +- Very large conversations may be truncated for summary generation, though the full export content is still preserved in the output +- Summary generation is best-effort: if model initialization or generation fails, the export still completes and records the summary error state +- PDF rendering relies on PyMuPDFโ€™s CSS subset which does not support flexbox, float, or full border-radius; bubble alignment is approximated using margin offsets diff --git a/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md b/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md new file mode 100644 index 00000000..edb69c2e --- /dev/null +++ b/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md @@ -0,0 +1,58 @@ +# Conversation Export + +## Overview +Snapshot of the Conversation Export feature as implemented in version **0.239.022**. + +This version updates export generation so JSON includes modern citation buckets, normalized citation summaries, and processing thoughts, while Markdown becomes a transcript-first report with appendix sections and optional AI-generated intro summaries. + +**Version Implemented:** 0.239.022 +**Dependencies:** Flask export route, Azure Cosmos DB conversations/messages/thoughts, Bootstrap modal workflow, chat-export.js, Azure OpenAI/APIM chat models + +## Technical Summary + +### Backend +- Filters out deleted messages and inactive-thread retries +- Reapplies thread-aware ordering to align with the live chat view +- Includes both normalized and raw citations per message +- Joins persisted processing thoughts by `message_id` +- Supports optional per-conversation `summary_intro` generation using a selected model + +### Frontend +- Adds a summary step to the export wizard +- Lets users enable or disable intro summaries +- Reuses the existing chat model selector options for summary model choice + +## Export Shape + +### JSON +Each conversation entry contains: +- `conversation` +- `summary_intro` +- `messages` + +Each message can include: +- `content` +- `content_text` +- `details` +- `citations` +- `legacy_citations` +- `hybrid_citations` +- `web_search_citations` +- `agent_citations` +- `thoughts` + +### Markdown +Markdown exports contain: +- metadata header +- optional abstract +- transcript body +- appendices for metadata, message details, references, thoughts, and supplemental messages + +## Files Updated +- `application/single_app/route_backend_conversation_export.py` +- `application/single_app/static/js/chat/chat-export.js` +- `application/single_app/config.py` +- `functional_tests/test_conversation_export.py` + +## Testing +Validated by `functional_tests/test_conversation_export.py`. diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md index 0053ffad..444c8c87 100644 --- a/docs/explanation/release_notes.md +++ b/docs/explanation/release_notes.md @@ -2,17 +2,117 @@ # Feature Release -### **(v0.239.020)** +### **(v0.239.031)** -#### User Interface Enhancements +#### Bug Fixes -* **Agent Responded Thought โ€” Seconds & Total Duration** - * The "responded" thought now shows time in **seconds** instead of milliseconds, and clarifies it is the total time from the initial user message (e.g., `'gpt-5-nano' responded (16.3s from initial message)`). - * A `request_start_time` is now captured at the top of both the non-streaming and streaming chat handlers, so the duration reflects the full request lifecycle โ€” including content safety, hybrid search, and agent invocation โ€” not just the model response time. - * Applies to all three agent paths: local SK agents (non-streaming), Azure AI Foundry agents, and streaming SK agents. - * (Ref: `route_backend_chats.py`, `request_start_time`, agent responded thoughts) +* **On-Demand Summary Generation โ€” Content Normalization Fix** + * Fixed the `POST /api/conversations//summary` endpoint failing with an error when generating summaries from the conversation details modal. + * Root cause: message `content` in Cosmos DB can be a list of content parts (e.g., `[{type: "text", text: "..."}]`) rather than a plain string. The endpoint was passing the raw list as `content_text`, which either stringified incorrectly or produced empty transcript text. + * Now uses `_normalize_content()` to properly flatten list/dict content into plain text, matching the export pipeline's behavior. + * (Ref: `route_backend_conversations.py`, `_normalize_content`, `generate_conversation_summary`) -### **(v0.239.019)** +### **(v0.239.030)** + +#### New Features + +* **Persistent Conversation Summaries** + * Summaries generated during conversation export are now saved to the conversation document in Cosmos DB for future reuse. + * Cached summaries include `message_time_start` and `message_time_end` โ€” when a conversation has new messages beyond the cached range, a fresh summary is generated automatically. + * The conversation details modal now shows a **Summary** card at the top. If a summary exists it displays the content, generation date, and model used. If no summary exists a **Generate Summary** button with model selector lets users create one on demand. + * A **Regenerate** button is available on existing summaries to force a refresh with the currently selected model. + * New `POST /api/conversations//summary` endpoint accepts an optional `model_deployment` and returns the generated summary. + * The `GET /api/conversations//metadata` response now includes a `summary` field. + * Extracted `generate_conversation_summary()` as a shared helper used by both the export pipeline and the new API endpoint. + * (Ref: `route_backend_conversation_export.py`, `route_backend_conversations.py`, `chat-conversation-details.js`, `functions_conversation_metadata.py`) + +### **(v0.239.029)** + +#### Bug Fixes + +* **Export Summary โ€” Remove Token Constraints** + * Removed all `max_tokens` / `max_completion_tokens` caps from the export summary API call. The model now decides output length naturally based on the prompt, matching how chat calls work in `route_backend_chats.py`. + * Rewrote the system prompt to instruct the model to judge length itself: one paragraph for short conversations, two for longer ones. + * This fixes reasoning models (gpt-5, o1, o3) exhausting their token budget on internal thinking with nothing left for visible output. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`) + +### **(v0.239.028)** + +#### Bug Fixes + +* **Export Summary Token Budget for Reasoning Models** + * Fixed export summary returning empty content with reasoning models (gpt-5, o1, o3) that was caused by internal thinking tokens consuming the entire `max_completion_tokens` budget of 512, leaving nothing for output (`finish_reason=length` with empty content). + * Increased `max_completion_tokens` to 16384 for reasoning models to accommodate thinking tokens; standard models use `max_tokens=1024`. + * Added null-safe content extraction to prevent crashes when reasoning models return `None` content. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, reasoning model token budgets) + +### **(v0.239.027)** + +#### Bug Fixes + +* **Export Summary Generation Rewrite** + * Fixed export summary returning empty responses with reasoning models (gpt-5, o1, o3) by using the `developer` role instead of `system` for instruction messages. + * Summary now includes ALL messages (user, assistant, system, file, image analysis) instead of only user/assistant transcript messages, giving the model full conversation context. + * Simplified the summary prompt to produce 1-2 factual paragraphs instead of bullet points, with increased token limit (512). + * Added detailed debug logging showing message count, character count, model name, role, finish reason. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, `developer` vs `system` role selection) + +### **(v0.239.025)** + +#### Bug Fixes + +* **Export Tag/Classification Rendering Fix** + * Fixed conversation tags and classifications rendering as raw Python dicts (e.g., `{'category': 'model', 'value': 'gpt-5'}`) in both Markdown and PDF exports. + * Tags now display as readable `category: value` strings, with smart handling for participant names, document titles, and generic category/value pairs. + * (Ref: `route_backend_conversation_export.py`, `_format_tag` helper, Markdown/PDF metadata rendering) + +* **Export Summary Error Visibility** + * Added `debug_print` and `log_event` logging to all summary generation error paths, including the empty-response path that previously failed silently. + * The actual error detail is now shown in both Markdown and PDF exports when summary generation fails, replacing the generic "could not be generated" message. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, export error rendering) + +### **(v0.239.024)** + +#### Bug Fixes + +* **Export Summary Model Compatibility Fix** + * Fixed export intro summary generation failing with a 400 error when using reasoning-series (o1, o3) or gpt-5 models, which require `max_completion_tokens` instead of `max_tokens`. + * The export summary API call now detects the model type and uses the correct token-limit parameter, matching the pattern used elsewhere in the codebase. + * (Ref: `route_backend_conversation_export.py`, `max_completion_tokens` vs `max_tokens` parameter handling) + +### **(v0.239.023)** + +#### New Features + +* **PDF Conversation Export** + * Added PDF as a third export format option alongside JSON and Markdown, giving users a print-ready, visually styled conversation archive. + * PDF output renders chat messages with colored bubbles that mirror the live chat UI: blue for user messages, gray for assistant messages, green for file messages, and amber for system messages. + * Message content is converted from Markdown to HTML for rich formatting (bold, italic, code blocks, lists, tables) inside the PDF. + * Full appendix structure is included (metadata, message details, references, processing thoughts, supplemental messages), matching the Markdown export layout. + * Rendering uses PyMuPDF's Story API on US Letter paper with 0.5-inch margins and automatic multi-page overflow. + * Works with both single-file and ZIP packaging; intro summaries are supported in PDF as well. + * Frontend format step updated to a 3-column card grid with a new PDF card using the `bi-filetype-pdf` icon. + * (Ref: `route_backend_conversation_export.py`, `chat-export.js`, PyMuPDF Story API, conversation export workflow) + +### **(v0.239.022)** + +#### New Features + +* **Conversation Export Intro Summaries** + * Added an optional AI-generated intro summary step to the conversation export workflow, so each exported chat can begin with a short abstract before the full transcript. + * Summary model selection now reuses the same model list shown in the chat composer, keeping the export flow aligned with the main chat experience. + * Works for both JSON and Markdown exports, including ZIP exports where each conversation keeps its own summary metadata. + * (Ref: `route_backend_conversation_export.py`, `chat-export.js`, conversation export workflow) + +#### Bug Fixes + +* **Conversation Export Schema and Markdown Refresh** + * Fixed conversation exports lagging behind the live chat schema. JSON exports now include processing thoughts, normalized citations, and the raw document/web/tool citation buckets stored with assistant messages. + * Fixed Markdown exports being too flat and text-heavy by reorganizing them into a transcript-first layout with appendices for metadata, message details, references, thoughts, and supplemental records. + * Fixed exported conversations including content that no longer matched the visible chat by filtering deleted messages and inactive-thread retries, then reapplying thread-aware ordering before export. + * (Ref: `route_backend_conversation_export.py`, `test_conversation_export.py`, conversation export rendering) + +### **(v0.239.021)** #### New Features @@ -120,6 +220,12 @@ #### User Interface Enhancements +* **Agent Responded Thought โ€” Seconds & Total Duration** + * The "responded" thought now shows time in **seconds** instead of milliseconds, and clarifies it is the total time from the initial user message (e.g., `'gpt-5-nano' responded (16.3s from initial message)`). + * A `request_start_time` is now captured at the top of both the non-streaming and streaming chat handlers, so the duration reflects the full request lifecycle โ€” including content safety, hybrid search, and agent invocation โ€” not just the model response time. + * Applies to all three agent paths: local SK agents (non-streaming), Azure AI Foundry agents, and streaming SK agents. + * (Ref: `route_backend_chats.py`, `request_start_time`, agent responded thoughts) + * **Enhanced Agent Execution Thoughts** * Added detailed model-level status messages during agent execution, giving users full visibility into each stage of the AI pipeline. * **Model Identification**: A new "Sending to '{deployment_name}'" thought appears immediately after "Sending to agent", showing the exact model deployment being used (e.g., `gpt-5-nano`). diff --git a/functional_tests/test_conversation_export.py b/functional_tests/test_conversation_export.py index cb8e56d0..654059b1 100644 --- a/functional_tests/test_conversation_export.py +++ b/functional_tests/test_conversation_export.py @@ -1,365 +1,545 @@ #!/usr/bin/env python3 # test_conversation_export.py """ -Functional test for conversation export feature. -Version: 0.237.050 -Implemented in: 0.237.050 - -This test validates the conversation export backend endpoint -and ensures JSON/Markdown formats and single/ZIP packaging work correctly. +Functional test for conversation export enhancements. +Version: 0.239.029 +Implemented in: 0.239.022 (base), 0.239.023 (PDF export), 0.239.025 (tag formatting fix), 0.239.028 (summary token budget fix) + +This test ensures conversation export now includes normalized and raw citations, +processing thoughts, transcript-style Markdown appendices, deleted-message filtering, +optional summary-intro metadata, PDF export with chat-bubble styling, and properly +formatted tag/classification rendering. """ -import sys -import os +import io import json +import os +import sys import zipfile -import io sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app')) +from route_backend_conversation_export import ( # noqa: E402 + _build_citation_counts, + _build_pdf_html_body, + _collect_raw_citation_buckets, + _conversation_to_markdown, + _conversation_to_pdf_bytes, + _filter_messages_for_export, + _format_tag, + _normalize_citations, + _normalize_content, + _pdf_bubble_class, + _safe_filename, + _sanitize_conversation, + _sanitize_message, + _truncate_for_summary, +) + + +def test_filter_messages_for_export(): + """Test that deleted and inactive-thread messages are excluded.""" + print("๐Ÿ” Testing export message filtering...") -def test_sanitize_conversation(): - """Test that _sanitize_conversation strips internal fields.""" - print("๐Ÿ” Testing _sanitize_conversation...") - - raw_conversation = { - 'id': 'conv-123', - 'title': 'Test Conversation', - 'last_updated': '2025-01-01T00:00:00Z', - 'chat_type': 'personal', - 'tags': ['test'], - 'is_pinned': False, - 'context': [], - 'user_id': 'secret-user-id', - '_rid': 'cosmos-internal-rid', - '_self': 'cosmos-self-link', - '_etag': 'some-etag', - '_attachments': 'attachments', - '_ts': 1234567890, - 'partition_key': 'should-not-appear' - } + messages = [ + {'id': 'm1', 'role': 'user', 'content': 'Keep me', 'metadata': {}}, + {'id': 'm2', 'role': 'assistant', 'content': 'Delete me', 'metadata': {'is_deleted': True}}, + {'id': 'm3', 'role': 'assistant', 'content': 'Inactive retry', 'metadata': {'thread_info': {'active_thread': False}}}, + {'id': 'm4', 'role': 'assistant', 'content': 'Active reply', 'metadata': {'thread_info': {'active_thread': True}}}, + {'id': 'm5', 'role': 'user', 'content': 'Legacy message', 'metadata': {'thread_info': {}}}, + ] - # Import after path setup โ€” may fail if dependencies aren't installed - try: - from route_backend_conversation_export import register_route_backend_conversation_export - print(" Module imported successfully (dependencies available)") - except ImportError as ie: - print(f" Skipping import test (missing dependency: {ie})") - print(" Verifying sanitization logic inline instead...") - - # We test the logic manually since inner functions are not directly accessible - sanitized = { - 'id': raw_conversation.get('id'), - 'title': raw_conversation.get('title', 'Untitled'), - 'last_updated': raw_conversation.get('last_updated', ''), - 'chat_type': raw_conversation.get('chat_type', 'personal'), - 'tags': raw_conversation.get('tags', []), - 'is_pinned': raw_conversation.get('is_pinned', False), - 'context': raw_conversation.get('context', []) - } + filtered = _filter_messages_for_export(messages) + filtered_ids = [message['id'] for message in filtered] - assert 'id' in sanitized, "Should retain id" - assert 'title' in sanitized, "Should retain title" - assert 'user_id' not in sanitized, "Should strip user_id" - assert '_rid' not in sanitized, "Should strip Cosmos internal fields" - assert '_etag' not in sanitized, "Should strip _etag" - assert 'partition_key' not in sanitized, "Should strip partition_key" + assert filtered_ids == ['m1', 'm4', 'm5'], f"Unexpected filtered IDs: {filtered_ids}" - print("โœ… _sanitize_conversation test passed!") + print("โœ… Export message filtering test passed!") return True -def test_sanitize_message(): - """Test that _sanitize_message strips internal fields.""" - print("๐Ÿ” Testing _sanitize_message...") +def test_sanitize_message_with_citations_and_thoughts(): + """Test that sanitized messages retain normalized/raw citations and thoughts.""" + print("๐Ÿ” Testing message sanitization with citations and thoughts...") - raw_message = { - 'id': 'msg-456', + assistant_message = { + 'id': 'assistant-1', 'role': 'assistant', - 'content': 'Hello, how can I help?', - 'timestamp': '2025-01-01T00:00:01Z', - 'citations': [{'title': 'Doc1', 'url': 'https://example.com'}], - 'conversation_id': 'conv-123', - 'user_id': 'secret-user-id', - '_rid': 'cosmos-internal', - 'metadata': {'thread_info': {'active_thread': True}}, + 'content': [{'type': 'text', 'text': 'Answer with evidence.'}], + 'timestamp': '2026-03-06T12:00:02Z', + 'augmented': True, + 'hybrid_citations': [ + { + 'file_name': 'paper.pdf', + 'page_number': 4, + 'citation_id': 'doc-1_4', + 'chunk_id': 'chunk-1', + 'metadata_type': 'abstract', + 'metadata_content': 'Study abstract text.' + } + ], + 'web_search_citations': [ + {'title': 'Example Source', 'url': 'https://example.com/source'} + ], + 'agent_citations': [ + { + 'tool_name': 'web_lookup', + 'function_name': 'azure_ai_foundry_web_search', + 'plugin_name': 'azure_ai_foundry', + 'function_arguments': {'query': 'test'}, + 'function_result': {'answer': 'done'}, + 'timestamp': '2026-03-06T12:00:01Z', + 'success': True + } + ], + 'metadata': { + 'reasoning_effort': 'medium', + 'token_usage': { + 'prompt_tokens': 10, + 'completion_tokens': 20, + 'total_tokens': 30 + } + }, + 'model_deployment_name': 'gpt-5-mini', + 'hybridsearch_query': 'refined search query' } + thoughts = [ + { + 'step_index': 0, + 'step_type': 'search', + 'content': 'Searching documents...', + 'detail': 'query=refined search query', + 'duration_ms': 123, + 'timestamp': '2026-03-06T12:00:01Z' + } + ] - result = { - 'role': raw_message.get('role', ''), - 'content': raw_message.get('content', ''), - 'timestamp': raw_message.get('timestamp', ''), - } - if raw_message.get('citations'): - result['citations'] = raw_message['citations'] - - assert result['role'] == 'assistant', "Should retain role" - assert result['content'] == 'Hello, how can I help?', "Should retain content" - assert 'citations' in result, "Should retain citations" - assert 'user_id' not in result, "Should strip user_id" - assert '_rid' not in result, "Should strip Cosmos internal fields" - assert 'conversation_id' not in result, "Should strip conversation_id" - assert 'metadata' not in result, "Should strip metadata" - - print("โœ… _sanitize_message test passed!") + sanitized = _sanitize_message(assistant_message, sequence_index=2, transcript_index=2, thoughts=thoughts) + + assert sanitized['content_text'] == 'Answer with evidence.', 'Content should normalize list-based text.' + assert sanitized['label'] == 'Turn 2', 'Transcript messages should use turn labels.' + assert sanitized['citation_counts']['document'] == 1, 'Document citation count should be preserved.' + assert sanitized['citation_counts']['web'] == 1, 'Web citation count should be preserved.' + assert sanitized['citation_counts']['agent_tool'] == 1, 'Agent citation count should be preserved.' + assert len(sanitized['citations']) == 3, 'Normalized citations should include all raw buckets.' + assert sanitized['hybrid_citations'][0]['file_name'] == 'paper.pdf', 'Raw hybrid citations should be preserved.' + assert sanitized['thoughts'][0]['step_type'] == 'search', 'Thoughts should be attached to the message.' + assert sanitized['details']['generation']['model_deployment'] == 'gpt-5-mini', 'Generation details should include the model.' + + print("โœ… Message sanitization test passed!") + return True + + +def test_conversation_metadata_and_json_shape(): + """Test that conversation-level metadata captures counts for export JSON.""" + print("๐Ÿ” Testing conversation metadata and JSON shape...") + + messages = [ + { + 'id': 'u1', + 'role': 'user', + 'is_transcript_message': True, + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [] + }, + { + 'id': 'a1', + 'role': 'assistant', + 'is_transcript_message': True, + 'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 0, 'legacy': 0, 'total': 2}, + 'thoughts': [{'step_type': 'search'}] + }, + { + 'id': 'f1', + 'role': 'file', + 'is_transcript_message': False, + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [] + } + ] + + conversation = _sanitize_conversation( + { + 'id': 'conv-123', + 'title': 'Export Test', + 'last_updated': '2026-03-06T12:00:00Z', + 'chat_type': 'personal', + 'tags': ['export', 'thoughts'], + 'context': ['workspace-a'], + 'classification': ['research'], + 'strict': False, + 'is_pinned': True, + 'scope_locked': True, + 'locked_contexts': ['workspace-a'] + }, + messages=messages, + role_counts={'user': 1, 'assistant': 1, 'file': 1}, + citation_counts={'document': 1, 'web': 1, 'agent_tool': 0, 'legacy': 0, 'total': 2}, + thought_count=1 + ) + + exported = [{'conversation': conversation, 'summary_intro': {'enabled': False, 'generated': False}, 'messages': messages}] + parsed = json.loads(json.dumps(exported, indent=2, ensure_ascii=False, default=str)) + + assert parsed[0]['conversation']['transcript_message_count'] == 2, 'Transcript count should exclude supplemental messages.' + assert parsed[0]['conversation']['citation_counts']['total'] == 2, 'Conversation citation counts should be included.' + assert parsed[0]['summary_intro']['enabled'] is False, 'Summary intro status should be included in JSON.' + + print("โœ… Conversation metadata and JSON shape test passed!") return True -def test_conversation_to_markdown(): - """Test markdown generation from a conversation entry.""" - print("๐Ÿ” Testing markdown generation...") +def test_markdown_export_structure(): + """Test transcript-style Markdown with appendices, citations, and thoughts.""" + print("๐Ÿ” Testing Markdown export structure...") entry = { 'conversation': { 'id': 'conv-123', - 'title': 'My Test Chat', - 'last_updated': '2025-01-01T12:00:00Z', + 'title': 'My Exported Chat', + 'last_updated': '2026-03-06T12:00:00Z', 'chat_type': 'personal', - 'tags': ['important', 'test'], + 'tags': ['science'], + 'classification': ['research'], + 'context': ['workspace-a'], + 'strict': False, 'is_pinned': False, - 'context': [] + 'scope_locked': True, + 'locked_contexts': ['workspace-a'], + 'message_count': 3, + 'message_counts_by_role': {'user': 1, 'assistant': 1, 'file': 1}, + 'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3}, + 'thought_count': 1 + }, + 'summary_intro': { + 'enabled': True, + 'generated': True, + 'model_deployment': 'gpt-5-mini', + 'generated_at': '2026-03-06T12:05:00Z', + 'content': 'A concise abstract.\n\n- Key point one\n- Key point two', + 'error': None }, 'messages': [ { + 'id': 'u1', 'role': 'user', - 'content': 'Hello!', - 'timestamp': '2025-01-01T12:00:01Z' + 'speaker_label': 'User', + 'label': 'Turn 1', + 'sequence_index': 1, + 'transcript_index': 1, + 'is_transcript_message': True, + 'timestamp': '2026-03-06T12:00:01Z', + 'content': 'What did the paper conclude?', + 'content_text': 'What did the paper conclude?', + 'details': {'interaction_mode': {'workspace_search': {'search_enabled': True, 'document_scope': 'personal'}}}, + 'citations': [], + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [], + 'legacy_citations': [], + 'hybrid_citations': [], + 'web_search_citations': [], + 'agent_citations': [] }, { + 'id': 'a1', 'role': 'assistant', - 'content': 'Hi there! How can I help you?', - 'timestamp': '2025-01-01T12:00:02Z', - 'citations': [{'title': 'Doc1'}] + 'speaker_label': 'Assistant', + 'label': 'Turn 2', + 'sequence_index': 2, + 'transcript_index': 2, + 'is_transcript_message': True, + 'timestamp': '2026-03-06T12:00:02Z', + 'content': 'The paper concluded the intervention improved outcomes.', + 'content_text': 'The paper concluded the intervention improved outcomes.', + 'details': {'generation': {'model_deployment': 'gpt-5-mini', 'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3}}}, + 'citations': [ + {'citation_type': 'document', 'label': 'paper.pdf โ€” Page 4', 'citation_id': 'doc-1_4', 'page_number': 4, 'classification': 'research', 'metadata_type': 'abstract', 'metadata_content': 'Study abstract text.'}, + {'citation_type': 'web', 'label': 'Example Source', 'title': 'Example Source', 'url': 'https://example.com/source'}, + {'citation_type': 'agent_tool', 'label': 'web_lookup'} + ], + 'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3}, + 'thoughts': [{'step_type': 'search', 'content': 'Searching documents...', 'detail': 'query=paper outcome', 'duration_ms': 88, 'timestamp': '2026-03-06T12:00:01Z'}], + 'legacy_citations': [], + 'hybrid_citations': [{'file_name': 'paper.pdf'}], + 'web_search_citations': [{'title': 'Example Source', 'url': 'https://example.com/source'}], + 'agent_citations': [{'tool_name': 'web_lookup', 'function_name': 'azure_ai_foundry_web_search', 'plugin_name': 'azure_ai_foundry', 'function_arguments': {'query': 'paper outcome'}, 'function_result': {'answer': 'done'}, 'timestamp': '2026-03-06T12:00:01Z', 'success': True}] + }, + { + 'id': 'f1', + 'role': 'file', + 'speaker_label': 'File', + 'label': 'Message 3', + 'sequence_index': 3, + 'transcript_index': None, + 'is_transcript_message': False, + 'timestamp': '2026-03-06T12:00:00Z', + 'content': 'paper.pdf', + 'content_text': 'paper.pdf', + 'details': {'message_context': {'filename': 'paper.pdf'}}, + 'citations': [], + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [], + 'legacy_citations': [], + 'hybrid_citations': [], + 'web_search_citations': [], + 'agent_citations': [] } ] } - # Replicate the markdown conversion logic - conv = entry['conversation'] - messages = entry['messages'] - lines = [] - lines.append(f"# {conv['title']}") - lines.append('') - lines.append(f"**Last Updated:** {conv['last_updated']} ") - lines.append(f"**Chat Type:** {conv['chat_type']} ") - if conv.get('tags'): - lines.append(f"**Tags:** {', '.join(conv['tags'])} ") - lines.append(f"**Messages:** {len(messages)} ") - lines.append('') - lines.append('---') - lines.append('') - - for msg in messages: - role = msg.get('role', 'unknown') - role_label = role.capitalize() - if role == 'assistant': - role_label = 'Assistant' - elif role == 'user': - role_label = 'User' - lines.append(f"### {role_label}") - if msg.get('timestamp'): - lines.append(f"*{msg['timestamp']}*") - lines.append('') - lines.append(msg.get('content', '')) - lines.append('') - if msg.get('citations'): - lines.append('**Citations:**') - for cit in msg['citations']: - if isinstance(cit, dict): - source = cit.get('title') or cit.get('filepath') or cit.get('url', 'Unknown') - lines.append(f"- {source}") - lines.append('') - lines.append('---') - lines.append('') - - markdown = '\n'.join(lines) - - assert '# My Test Chat' in markdown, "Should have title as H1" - assert '**Last Updated:**' in markdown, "Should have last updated" - assert '**Tags:** important, test' in markdown, "Should list tags" - assert '### User' in markdown, "Should have user heading" - assert '### Assistant' in markdown, "Should have assistant heading" - assert 'Hello!' in markdown, "Should contain user message" - assert 'Hi there! How can I help you?' in markdown, "Should contain assistant reply" - assert '**Citations:**' in markdown, "Should include citations section" - assert '- Doc1' in markdown, "Should list citation title" - - print("โœ… Markdown generation test passed!") + markdown = _conversation_to_markdown(entry) + + assert '# My Exported Chat' in markdown, 'Markdown should include the title heading.' + assert '## Abstract' in markdown, 'Markdown should include the abstract section.' + assert '## Transcript' in markdown, 'Markdown should include the transcript section.' + assert '## Appendix B โ€” Message Details' in markdown, 'Markdown should include the message-details appendix.' + assert '## Appendix C โ€” References' in markdown, 'Markdown should include the references appendix.' + assert '## Appendix D โ€” Processing Thoughts' in markdown, 'Markdown should include the thoughts appendix.' + assert '## Appendix E โ€” Supplemental Messages' in markdown, 'Markdown should include supplemental messages.' + assert 'paper.pdf โ€” Page 4' in markdown, 'Document citation labels should appear in references.' + assert 'Example Source' in markdown, 'Web citations should appear in references.' + assert 'Searching documents...' in markdown, 'Thought content should appear in the appendix.' + + print("โœ… Markdown export structure test passed!") return True -def test_json_export_structure(): - """Test that JSON export produces the expected structure.""" - print("๐Ÿ” Testing JSON export structure...") +def test_safe_filename_and_zip_packaging(): + """Test filename sanitization and ZIP naming consistency.""" + print("๐Ÿ” Testing safe filename and ZIP packaging...") + + assert _safe_filename('Normal Title') == 'Normal_Title', 'Spaces should become underscores.' + assert _safe_filename('File/With:Bad*Chars') == 'File_With_Bad_Chars', 'Unsafe characters should be replaced.' + assert _safe_filename('A' * 100) == 'A' * 50, 'Long names should be truncated.' + assert _safe_filename('') == 'Untitled', 'Empty titles should use Untitled.' exported = [ - { - 'conversation': { - 'id': 'conv-abc', - 'title': 'Test Convo', - 'last_updated': '2025-01-01T00:00:00Z', - 'chat_type': 'personal', - 'tags': [], - 'is_pinned': False, - 'context': [] - }, - 'messages': [ - {'role': 'user', 'content': 'Hello', 'timestamp': '2025-01-01T00:00:01Z'}, - {'role': 'assistant', 'content': 'World', 'timestamp': '2025-01-01T00:00:02Z'} - ] - } + {'conversation': {'id': 'conv-001-extra', 'title': 'First Chat'}, 'messages': []}, + {'conversation': {'id': 'conv-002-extra', 'title': 'Second Chat'}, 'messages': []} ] - content = json.dumps(exported, indent=2, ensure_ascii=False, default=str) - parsed = json.loads(content) + buffer = io.BytesIO() + with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as archive: + for entry in exported: + conversation = entry['conversation'] + safe_title = _safe_filename(conversation.get('title', 'Untitled')) + conversation_id_short = conversation.get('id', 'unknown')[:8] + archive.writestr(f"{safe_title}_{conversation_id_short}.json", json.dumps(entry)) - assert isinstance(parsed, list), "Export should be a list" - assert len(parsed) == 1, "Should have one conversation" - assert 'conversation' in parsed[0], "Each entry should have conversation" - assert 'messages' in parsed[0], "Each entry should have messages" - assert len(parsed[0]['messages']) == 2, "Should have 2 messages" - assert parsed[0]['conversation']['title'] == 'Test Convo', "Title should match" + buffer.seek(0) + with zipfile.ZipFile(buffer, 'r') as archive: + names = archive.namelist() + assert 'First_Chat_conv-001.json' in names, f"Unexpected ZIP entries: {names}" + assert 'Second_Chat_conv-002.json' in names, f"Unexpected ZIP entries: {names}" - print("โœ… JSON export structure test passed!") + print("โœ… Safe filename and ZIP packaging test passed!") return True -def test_zip_packaging(): - """Test that ZIP packaging creates valid archive with correct entries.""" - print("๐Ÿ” Testing ZIP packaging...") +def test_content_and_citation_helpers(): + """Test content normalization, citation normalization, and summary truncation helpers.""" + print("๐Ÿ” Testing helper utilities...") - exported = [ - { - 'conversation': { - 'id': 'conv-001-abc-def', - 'title': 'First Chat', - 'last_updated': '2025-01-01', - 'chat_type': 'personal', - 'tags': [], - 'is_pinned': False, - 'context': [] - }, - 'messages': [ - {'role': 'user', 'content': 'Hello', 'timestamp': '2025-01-01'} - ] - }, - { - 'conversation': { - 'id': 'conv-002-xyz-ghi', - 'title': 'Second Chat', - 'last_updated': '2025-01-02', - 'chat_type': 'personal', - 'tags': [], - 'is_pinned': False, - 'context': [] - }, - 'messages': [ - {'role': 'user', 'content': 'Goodbye', 'timestamp': '2025-01-02'} - ] - } - ] + content_text = _normalize_content([ + {'type': 'text', 'text': 'Line one'}, + {'type': 'image_url', 'image_url': {'url': 'https://example.com/image.png'}}, + {'type': 'text', 'text': 'Line two'} + ]) + assert content_text == 'Line one\n[Image]\nLine two', 'Content normalization should flatten list-based content.' - import re + raw_buckets = _collect_raw_citation_buckets({ + 'citations': [{'title': 'Legacy'}], + 'hybrid_citations': [{'file_name': 'doc.pdf', 'page_number': 2}], + 'web_search_citations': [{'title': 'Web', 'url': 'https://example.com'}], + 'agent_citations': [{'tool_name': 'lookup'}] + }) + normalized = _normalize_citations(raw_buckets) + counts = _build_citation_counts(normalized) - def safe_filename(title): - safe = re.sub(r'[<>:"/\\|?*]', '_', title) - safe = re.sub(r'\s+', '_', safe) - safe = safe.strip('_. ') - if len(safe) > 50: - safe = safe[:50] - return safe or 'Untitled' + assert counts == {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 1, 'total': 4}, f"Unexpected citation counts: {counts}" - buffer = io.BytesIO() - with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf: - for entry in exported: - conv = entry['conversation'] - safe_title = safe_filename(conv.get('title', 'Untitled')) - conv_id_short = conv.get('id', 'unknown')[:8] - file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str) - file_name = f"{safe_title}_{conv_id_short}.json" - zf.writestr(file_name, file_content) + truncated = _truncate_for_summary('A' * 70000) + assert '[... transcript truncated for export summary generation ...]' in truncated, 'Summary truncation marker should be inserted for long transcripts.' + assert len(truncated) < 70000, 'Truncated summary source should be shorter than the original transcript.' - buffer.seek(0) + print("โœ… Helper utilities test passed!") + return True - with zipfile.ZipFile(buffer, 'r') as zf: - names = zf.namelist() - assert len(names) == 2, f"ZIP should have 2 files, got {len(names)}" - assert 'First_Chat_conv-001.json' in names, f"Expected First_Chat_conv-001.json, got {names}" - assert 'Second_Chat_conv-002.json' in names, f"Expected Second_Chat_conv-002.json, got {names}" - # Verify content - first_content = json.loads(zf.read('First_Chat_conv-001.json')) - assert first_content['conversation']['title'] == 'First Chat' - assert len(first_content['messages']) == 1 +def test_pdf_export_generation(): + """Test PDF export generates valid HTML body and PDF bytes.""" + print("๐Ÿ” Testing PDF export generation...") - print("โœ… ZIP packaging test passed!") + entry = { + 'conversation': { + 'id': 'conv-pdf-001', + 'title': 'PDF Export Test', + 'last_updated': '2026-03-06T14:00:00Z', + 'chat_type': 'personal', + 'tags': ['pdf', 'test'], + 'classification': [], + 'context': [], + 'strict': False, + 'is_pinned': False, + 'scope_locked': False, + 'locked_contexts': [], + 'message_count': 2, + 'message_counts_by_role': {'user': 1, 'assistant': 1}, + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thought_count': 0 + }, + 'summary_intro': { + 'enabled': False, + 'generated': False, + 'model_deployment': None, + 'generated_at': None, + 'content': '', + 'error': None + }, + 'messages': [ + { + 'id': 'u1', + 'role': 'user', + 'speaker_label': 'User', + 'label': 'Turn 1', + 'sequence_index': 1, + 'transcript_index': 1, + 'is_transcript_message': True, + 'timestamp': '2026-03-06T14:00:01Z', + 'content': 'Hello, can you help me?', + 'content_text': 'Hello, can you help me?', + 'details': {}, + 'citations': [], + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [], + 'legacy_citations': [], + 'hybrid_citations': [], + 'web_search_citations': [], + 'agent_citations': [] + }, + { + 'id': 'a1', + 'role': 'assistant', + 'speaker_label': 'Assistant', + 'label': 'Turn 2', + 'sequence_index': 2, + 'transcript_index': 2, + 'is_transcript_message': True, + 'timestamp': '2026-03-06T14:00:02Z', + 'content': 'Of course! How can I assist you today?', + 'content_text': 'Of course! How can I assist you today?', + 'details': {}, + 'citations': [], + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thoughts': [], + 'legacy_citations': [], + 'hybrid_citations': [], + 'web_search_citations': [], + 'agent_citations': [] + } + ] + } + + # Test HTML body generation + html_body = _build_pdf_html_body(entry) + assert '

    PDF Export Test

    ' in html_body, 'HTML should contain the conversation title.' + assert 'user-bubble' in html_body, 'HTML should contain user bubble CSS class.' + assert 'assistant-bubble' in html_body, 'HTML should contain assistant bubble CSS class.' + assert 'Transcript' in html_body, 'HTML should contain Transcript section.' + assert 'Appendix A' in html_body, 'HTML should contain Appendix A.' + assert 'Hello, can you help me?' in html_body, 'HTML should contain user message content.' + assert 'Of course! How can I assist you today?' in html_body, 'HTML should contain assistant message content.' + + # Test bubble class helper + assert _pdf_bubble_class('user') == 'user-bubble', 'User role should map to user-bubble.' + assert _pdf_bubble_class('assistant') == 'assistant-bubble', 'Assistant role should map to assistant-bubble.' + assert _pdf_bubble_class('system') == 'system-bubble', 'System role should map to system-bubble.' + assert _pdf_bubble_class('file') == 'file-bubble', 'File role should map to file-bubble.' + assert _pdf_bubble_class('unknown') == 'other-bubble', 'Unknown roles should map to other-bubble.' + + # Test PDF bytes generation + pdf_bytes = _conversation_to_pdf_bytes(entry) + assert isinstance(pdf_bytes, bytes), 'PDF output should be bytes.' + assert pdf_bytes[:5] == b'%PDF-', f'PDF should start with %PDF- header, got: {pdf_bytes[:10]}' + assert len(pdf_bytes) > 100, 'PDF should have a reasonable size.' + + print("โœ… PDF export generation test passed!") return True -def test_safe_filename(): - """Test filename sanitization.""" - print("๐Ÿ” Testing safe filename generation...") +def test_tag_formatting(): + """Test that dict-style tags and classifications render as readable strings.""" + print("๐Ÿ” Testing tag/classification formatting...") - import re + # Category/value tag + assert _format_tag({'category': 'model', 'value': 'gpt-5'}) == 'model: gpt-5' - def safe_filename(title): - safe = re.sub(r'[<>:"/\\|?*]', '_', title) - safe = re.sub(r'\s+', '_', safe) - safe = safe.strip('_. ') - if len(safe) > 50: - safe = safe[:50] - return safe or 'Untitled' + # Participant tag with name + assert _format_tag({'category': 'participant', 'name': 'Alice', 'user_id': 'u1'}) == 'participant: Alice' - assert safe_filename('Normal Title') == 'Normal_Title', "Spaces should become underscores" - assert safe_filename('File/With:Bad*Chars') == 'File_With_Bad_Chars', "Bad chars should be replaced" - assert safe_filename('A' * 100) == 'A' * 50, "Long names should be truncated" - assert safe_filename('') == 'Untitled', "Empty should become Untitled" - assert safe_filename(' ') == 'Untitled', "Whitespace-only should become Untitled" + # Participant tag with email fallback + assert _format_tag({'category': 'participant', 'email': 'bob@test.com'}) == 'participant: bob@test.com' - print("โœ… Safe filename test passed!") - return True + # Document tag with title + assert _format_tag({'category': 'document', 'title': 'Study.pdf', 'document_id': 'd1'}) == 'document: Study.pdf' + # Document tag with only document_id + assert _format_tag({'category': 'document', 'document_id': 'd1'}) == 'document: d1' -def test_active_thread_filter(): - """Test that only active thread messages are included.""" - print("๐Ÿ” Testing active thread message filtering...") + # Plain string tag (older data) + assert _format_tag('science') == 'science' - messages = [ - {'role': 'user', 'content': 'Hello', 'metadata': {}}, - {'role': 'assistant', 'content': 'Reply 1', 'metadata': {'thread_info': {'active_thread': True}}}, - {'role': 'assistant', 'content': 'Reply 2 (inactive)', 'metadata': {'thread_info': {'active_thread': False}}}, - {'role': 'user', 'content': 'Follow up', 'metadata': {'thread_info': {}}}, - {'role': 'assistant', 'content': 'Final', 'metadata': {'thread_info': {'active_thread': None}}}, - ] + # Category only (no value) + assert _format_tag({'category': 'semantic'}) == 'semantic' + + # Verify Markdown export uses formatted tags + entry = { + 'conversation': { + 'id': 'conv-tags-001', + 'title': 'Tag Format Test', + 'last_updated': '2026-03-08T00:00:00Z', + 'chat_type': 'personal', + 'tags': [ + {'category': 'model', 'value': 'gpt-5'}, + {'category': 'semantic', 'value': 'cubesats'} + ], + 'classification': [], + 'context': [], + 'strict': False, + 'is_pinned': False, + 'scope_locked': False, + 'locked_contexts': [], + 'message_count': 0, + 'message_counts_by_role': {}, + 'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0}, + 'thought_count': 0 + }, + 'summary_intro': {'enabled': False, 'generated': False}, + 'messages': [] + } + + md = _conversation_to_markdown(entry) + assert 'model: gpt-5' in md, f'Markdown should contain formatted tag, got: {md[:500]}' + assert "{'category'" not in md, 'Markdown should not contain raw dict strings' + + html = _build_pdf_html_body(entry) + assert 'model: gpt-5' in html, f'PDF HTML should contain formatted tag' + assert "{'category'" not in html, 'PDF HTML should not contain raw dict strings' - filtered = [] - for msg in messages: - thread_info = msg.get('metadata', {}).get('thread_info', {}) - active = thread_info.get('active_thread') - if active is True or active is None or 'active_thread' not in thread_info: - filtered.append(msg) - - assert len(filtered) == 4, f"Expected 4 active messages, got {len(filtered)}" - contents = [m['content'] for m in filtered] - assert 'Reply 2 (inactive)' not in contents, "Inactive thread message should be excluded" - assert 'Hello' in contents, "Message without thread info should be included" - assert 'Reply 1' in contents, "Active=True message should be included" - assert 'Follow up' in contents, "Message with empty thread_info should be included" - assert 'Final' in contents, "Message with active_thread=None should be included" - - print("โœ… Active thread filter test passed!") + print("โœ… Tag formatting test passed!") return True if __name__ == "__main__": tests = [ - test_sanitize_conversation, - test_sanitize_message, - test_conversation_to_markdown, - test_json_export_structure, - test_zip_packaging, - test_safe_filename, - test_active_thread_filter + test_filter_messages_for_export, + test_sanitize_message_with_citations_and_thoughts, + test_conversation_metadata_and_json_shape, + test_markdown_export_structure, + test_safe_filename_and_zip_packaging, + test_content_and_citation_helpers, + test_pdf_export_generation, + test_tag_formatting, ] results = [] @@ -367,8 +547,8 @@ def test_active_thread_filter(): print(f"\n๐Ÿงช Running {test.__name__}...") try: results.append(test()) - except Exception as e: - print(f"โŒ {test.__name__} failed: {e}") + except Exception as exc: + print(f"โŒ {test.__name__} failed: {exc}") import traceback traceback.print_exc() results.append(False) diff --git a/functional_tests/test_persistent_conversation_summary.py b/functional_tests/test_persistent_conversation_summary.py new file mode 100644 index 00000000..e8d95ffa --- /dev/null +++ b/functional_tests/test_persistent_conversation_summary.py @@ -0,0 +1,279 @@ +#!/usr/bin/env python3 +# test_persistent_conversation_summary.py +""" +Functional test for persistent conversation summaries. +Version: 0.239.030 +Implemented in: 0.239.030 + +This test ensures that: +1. generate_conversation_summary() returns properly structured summary data +2. _build_summary_intro() uses cached summary when message_time_end hasn't changed +3. _build_summary_intro() regenerates when messages are newer than cached summary +4. Summary data includes message_time_start and message_time_end fields +5. The truncation helper works correctly +""" + +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app')) + +from route_backend_conversation_export import ( # noqa: E402 + _build_summary_intro, + _truncate_for_summary, + SUMMARY_SOURCE_CHAR_LIMIT, +) + + +def test_summary_intro_cache_hit(): + """Test that _build_summary_intro returns cached summary when message_time_end matches.""" + print("๐Ÿ” Testing summary cache hit (no new messages)...") + + existing_summary = { + 'content': 'Cached summary text.', + 'model_deployment': 'gpt-4o', + 'generated_at': '2026-01-15T10:00:00', + 'message_time_start': '2026-01-15T09:00:00', + 'message_time_end': '2026-01-15T09:55:00' + } + + conversation = { + 'id': 'conv-test-1', + 'title': 'Test Conversation', + 'summary': existing_summary + } + + messages = [ + {'role': 'user', 'content_text': 'Hello', 'speaker_label': 'USER'}, + {'role': 'assistant', 'content_text': 'Hi there', 'speaker_label': 'ASSISTANT'} + ] + + sanitized_conversation = {'title': 'Test Conversation'} + + result = _build_summary_intro( + messages=messages, + conversation=conversation, + sanitized_conversation=sanitized_conversation, + settings={}, + enabled=True, + summary_model_deployment='gpt-4o', + message_time_start='2026-01-15T09:00:00', + message_time_end='2026-01-15T09:55:00' # Same as cached + ) + + assert result['enabled'] is True, f"Expected enabled=True, got {result['enabled']}" + assert result['generated'] is True, f"Expected generated=True, got {result['generated']}" + assert result['content'] == 'Cached summary text.', f"Expected cached content, got: {result['content']}" + assert result['model_deployment'] == 'gpt-4o', f"Expected gpt-4o, got: {result['model_deployment']}" + assert result['generated_at'] == '2026-01-15T10:00:00', f"Expected cached timestamp, got: {result['generated_at']}" + assert result['error'] is None, f"Expected no error, got: {result['error']}" + + print("โœ… Summary cache hit test passed!") + return True + + +def test_summary_intro_cache_hit_older_message(): + """Test cache hit when cached message_time_end is NEWER than current (edge case).""" + print("๐Ÿ” Testing summary cache hit with older message_time_end...") + + existing_summary = { + 'content': 'Summary covers more messages.', + 'model_deployment': 'gpt-4o', + 'generated_at': '2026-01-15T12:00:00', + 'message_time_start': '2026-01-15T09:00:00', + 'message_time_end': '2026-01-15T11:55:00' + } + + conversation = { + 'id': 'conv-test-2', + 'summary': existing_summary + } + + sanitized_conversation = {'title': 'Test'} + + result = _build_summary_intro( + messages=[{'role': 'user', 'content_text': 'Hi', 'speaker_label': 'USER'}], + conversation=conversation, + sanitized_conversation=sanitized_conversation, + settings={}, + enabled=True, + summary_model_deployment='gpt-4o', + message_time_start='2026-01-15T09:00:00', + message_time_end='2026-01-15T10:00:00' # Older than cached + ) + + assert result['generated'] is True, "Should use cache when cached end >= current end" + assert result['content'] == 'Summary covers more messages.', "Should return cached content" + + print("โœ… Summary cache hit (older message) test passed!") + return True + + +def test_summary_intro_disabled(): + """Test that disabled summary returns immediately.""" + print("๐Ÿ” Testing summary disabled state...") + + result = _build_summary_intro( + messages=[], + conversation={'id': 'conv-disabled'}, + sanitized_conversation={'title': 'Test'}, + settings={}, + enabled=False, + summary_model_deployment='' + ) + + assert result['enabled'] is False, f"Expected enabled=False, got {result['enabled']}" + assert result['generated'] is False, f"Expected generated=False, got {result['generated']}" + assert result['content'] == '', f"Expected empty content, got: {result['content']}" + + print("โœ… Summary disabled state test passed!") + return True + + +def test_summary_intro_stale_cache(): + """Test that a stale cache (newer messages exist) triggers regeneration attempt.""" + print("๐Ÿ” Testing summary stale cache detection...") + + existing_summary = { + 'content': 'Old cached summary.', + 'model_deployment': 'gpt-4o', + 'generated_at': '2026-01-15T10:00:00', + 'message_time_start': '2026-01-15T09:00:00', + 'message_time_end': '2026-01-15T09:55:00' + } + + conversation = { + 'id': 'conv-stale', + 'summary': existing_summary + } + + sanitized_conversation = {'title': 'Test'} + + # message_time_end is NEWER than cached โ€” should try to regenerate + # Since we don't have a real OpenAI client, this will fail with an error, + # which confirms the cache was NOT used. + result = _build_summary_intro( + messages=[ + {'role': 'user', 'content_text': 'New message', 'speaker_label': 'USER'} + ], + conversation=conversation, + sanitized_conversation=sanitized_conversation, + settings={}, + enabled=True, + summary_model_deployment='gpt-4o', + message_time_start='2026-01-15T09:00:00', + message_time_end='2026-01-15T10:30:00' # NEWER than cached 09:55 + ) + + # Without a real GPT client, generation should fail + assert result['generated'] is False, "Without GPT client, generation should fail" + assert result['error'] is not None, "Should have an error when no GPT client available" + # The key assertion: it DID NOT return the cached content + assert result['content'] != 'Old cached summary.', "Should not use stale cached summary" + + print("โœ… Summary stale cache detection test passed!") + return True + + +def test_summary_data_structure(): + """Test that the summary_intro structure contains all expected fields.""" + print("๐Ÿ” Testing summary data structure...") + + result = _build_summary_intro( + messages=[], + conversation={'id': 'conv-struct'}, + sanitized_conversation={'title': 'Test'}, + settings={}, + enabled=True, + summary_model_deployment='gpt-4o', + message_time_start=None, + message_time_end=None + ) + + expected_keys = {'enabled', 'generated', 'model_deployment', 'generated_at', 'content', 'error'} + actual_keys = set(result.keys()) + assert expected_keys.issubset(actual_keys), f"Missing keys: {expected_keys - actual_keys}" + + print("โœ… Summary data structure test passed!") + return True + + +def test_truncation_short_text(): + """Test that short text passes through unchanged.""" + print("๐Ÿ” Testing truncation with short text...") + + short_text = "This is a short transcript." + result = _truncate_for_summary(short_text) + assert result == short_text, "Short text should pass through unchanged" + + print("โœ… Truncation short text test passed!") + return True + + +def test_truncation_long_text(): + """Test that long text gets truncated with marker.""" + print("๐Ÿ” Testing truncation with long text...") + + long_text = "A" * (SUMMARY_SOURCE_CHAR_LIMIT + 1000) + result = _truncate_for_summary(long_text) + + assert len(result) < len(long_text), "Truncated text should be shorter" + assert "transcript truncated" in result, "Should contain truncation marker" + + print("โœ… Truncation long text test passed!") + return True + + +def test_summary_no_content(): + """Test that summary with no message content returns an error.""" + print("๐Ÿ” Testing summary with empty messages...") + + result = _build_summary_intro( + messages=[ + {'role': 'user', 'content_text': '', 'speaker_label': 'USER'}, + {'role': 'assistant', 'content_text': '', 'speaker_label': 'ASSISTANT'} + ], + conversation={'id': 'conv-empty'}, + sanitized_conversation={'title': 'Test'}, + settings={}, + enabled=True, + summary_model_deployment='gpt-4o', + message_time_start=None, + message_time_end=None + ) + + assert result['generated'] is False, "Should not generate with empty content" + assert result['error'] is not None, "Should have an error message" + assert 'No message content' in result['error'], f"Unexpected error: {result['error']}" + + print("โœ… Summary with empty messages test passed!") + return True + + +if __name__ == "__main__": + tests = [ + test_summary_intro_cache_hit, + test_summary_intro_cache_hit_older_message, + test_summary_intro_disabled, + test_summary_intro_stale_cache, + test_summary_data_structure, + test_truncation_short_text, + test_truncation_long_text, + test_summary_no_content, + ] + results = [] + + for test in tests: + print(f"\n๐Ÿงช Running {test.__name__}...") + try: + results.append(test()) + except Exception as e: + print(f"โŒ {test.__name__} failed with exception: {e}") + import traceback + traceback.print_exc() + results.append(False) + + passed = sum(1 for r in results if r) + total = len(results) + print(f"\n๐Ÿ“Š Results: {passed}/{total} tests passed") + sys.exit(0 if all(results) else 1) From 38ea889e0cae8ffe9c0e8abfb60e9b3566d59e3b Mon Sep 17 00:00:00 2001 From: Paul Lizer Date: Mon, 9 Mar 2026 09:47:18 -0400 Subject: [PATCH 8/8] Update release_notes.md --- docs/explanation/release_notes.md | 114 ++++++++---------------------- 1 file changed, 29 insertions(+), 85 deletions(-) diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md index 444c8c87..eafd4dce 100644 --- a/docs/explanation/release_notes.md +++ b/docs/explanation/release_notes.md @@ -4,16 +4,6 @@ ### **(v0.239.031)** -#### Bug Fixes - -* **On-Demand Summary Generation โ€” Content Normalization Fix** - * Fixed the `POST /api/conversations//summary` endpoint failing with an error when generating summaries from the conversation details modal. - * Root cause: message `content` in Cosmos DB can be a list of content parts (e.g., `[{type: "text", text: "..."}]`) rather than a plain string. The endpoint was passing the raw list as `content_text`, which either stringified incorrectly or produced empty transcript text. - * Now uses `_normalize_content()` to properly flatten list/dict content into plain text, matching the export pipeline's behavior. - * (Ref: `route_backend_conversations.py`, `_normalize_content`, `generate_conversation_summary`) - -### **(v0.239.030)** - #### New Features * **Persistent Conversation Summaries** @@ -26,64 +16,6 @@ * Extracted `generate_conversation_summary()` as a shared helper used by both the export pipeline and the new API endpoint. * (Ref: `route_backend_conversation_export.py`, `route_backend_conversations.py`, `chat-conversation-details.js`, `functions_conversation_metadata.py`) -### **(v0.239.029)** - -#### Bug Fixes - -* **Export Summary โ€” Remove Token Constraints** - * Removed all `max_tokens` / `max_completion_tokens` caps from the export summary API call. The model now decides output length naturally based on the prompt, matching how chat calls work in `route_backend_chats.py`. - * Rewrote the system prompt to instruct the model to judge length itself: one paragraph for short conversations, two for longer ones. - * This fixes reasoning models (gpt-5, o1, o3) exhausting their token budget on internal thinking with nothing left for visible output. - * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`) - -### **(v0.239.028)** - -#### Bug Fixes - -* **Export Summary Token Budget for Reasoning Models** - * Fixed export summary returning empty content with reasoning models (gpt-5, o1, o3) that was caused by internal thinking tokens consuming the entire `max_completion_tokens` budget of 512, leaving nothing for output (`finish_reason=length` with empty content). - * Increased `max_completion_tokens` to 16384 for reasoning models to accommodate thinking tokens; standard models use `max_tokens=1024`. - * Added null-safe content extraction to prevent crashes when reasoning models return `None` content. - * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, reasoning model token budgets) - -### **(v0.239.027)** - -#### Bug Fixes - -* **Export Summary Generation Rewrite** - * Fixed export summary returning empty responses with reasoning models (gpt-5, o1, o3) by using the `developer` role instead of `system` for instruction messages. - * Summary now includes ALL messages (user, assistant, system, file, image analysis) instead of only user/assistant transcript messages, giving the model full conversation context. - * Simplified the summary prompt to produce 1-2 factual paragraphs instead of bullet points, with increased token limit (512). - * Added detailed debug logging showing message count, character count, model name, role, finish reason. - * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, `developer` vs `system` role selection) - -### **(v0.239.025)** - -#### Bug Fixes - -* **Export Tag/Classification Rendering Fix** - * Fixed conversation tags and classifications rendering as raw Python dicts (e.g., `{'category': 'model', 'value': 'gpt-5'}`) in both Markdown and PDF exports. - * Tags now display as readable `category: value` strings, with smart handling for participant names, document titles, and generic category/value pairs. - * (Ref: `route_backend_conversation_export.py`, `_format_tag` helper, Markdown/PDF metadata rendering) - -* **Export Summary Error Visibility** - * Added `debug_print` and `log_event` logging to all summary generation error paths, including the empty-response path that previously failed silently. - * The actual error detail is now shown in both Markdown and PDF exports when summary generation fails, replacing the generic "could not be generated" message. - * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, export error rendering) - -### **(v0.239.024)** - -#### Bug Fixes - -* **Export Summary Model Compatibility Fix** - * Fixed export intro summary generation failing with a 400 error when using reasoning-series (o1, o3) or gpt-5 models, which require `max_completion_tokens` instead of `max_tokens`. - * The export summary API call now detects the model type and uses the correct token-limit parameter, matching the pattern used elsewhere in the codebase. - * (Ref: `route_backend_conversation_export.py`, `max_completion_tokens` vs `max_tokens` parameter handling) - -### **(v0.239.023)** - -#### New Features - * **PDF Conversation Export** * Added PDF as a third export format option alongside JSON and Markdown, giving users a print-ready, visually styled conversation archive. * PDF output renders chat messages with colored bubbles that mirror the live chat UI: blue for user messages, gray for assistant messages, green for file messages, and amber for system messages. @@ -94,28 +26,12 @@ * Frontend format step updated to a 3-column card grid with a new PDF card using the `bi-filetype-pdf` icon. * (Ref: `route_backend_conversation_export.py`, `chat-export.js`, PyMuPDF Story API, conversation export workflow) -### **(v0.239.022)** - -#### New Features - * **Conversation Export Intro Summaries** * Added an optional AI-generated intro summary step to the conversation export workflow, so each exported chat can begin with a short abstract before the full transcript. * Summary model selection now reuses the same model list shown in the chat composer, keeping the export flow aligned with the main chat experience. * Works for both JSON and Markdown exports, including ZIP exports where each conversation keeps its own summary metadata. * (Ref: `route_backend_conversation_export.py`, `chat-export.js`, conversation export workflow) -#### Bug Fixes - -* **Conversation Export Schema and Markdown Refresh** - * Fixed conversation exports lagging behind the live chat schema. JSON exports now include processing thoughts, normalized citations, and the raw document/web/tool citation buckets stored with assistant messages. - * Fixed Markdown exports being too flat and text-heavy by reorganizing them into a transcript-first layout with appendices for metadata, message details, references, thoughts, and supplemental records. - * Fixed exported conversations including content that no longer matched the visible chat by filtering deleted messages and inactive-thread retries, then reapplying thread-aware ordering before export. - * (Ref: `route_backend_conversation_export.py`, `test_conversation_export.py`, conversation export rendering) - -### **(v0.239.021)** - -#### New Features - * **Agent & Action User Tracking (created_by / modified_by)** * All agent and action documents (personal, group, and global) now include `created_by`, `created_at`, `modified_by`, and `modified_at` fields that track which user created or last modified the entity. * On updates, the original `created_by` and `created_at` values are preserved while `modified_by` and `modified_at` are refreshed with the current user and timestamp. @@ -165,6 +81,34 @@ #### Bug Fixes +* **On-Demand Summary Generation โ€” Content Normalization Fix** + * Fixed the `POST /api/conversations//summary` endpoint failing with an error when generating summaries from the conversation details modal. + * Root cause: message `content` in Cosmos DB can be a list of content parts (e.g., `[{type: "text", text: "..."}]`) rather than a plain string. The endpoint was passing the raw list as `content_text`, which either stringified incorrectly or produced empty transcript text. + * Now uses `_normalize_content()` to properly flatten list/dict content into plain text, matching the export pipeline's behavior. + * (Ref: `route_backend_conversations.py`, `_normalize_content`, `generate_conversation_summary`) + +* **Export Summary Reasoning-Model Compatibility** + * Fixed export intro summary generation failing or returning empty content with reasoning-series models (gpt-5, o1, o3) through a series of incremental fixes: using `developer` role instead of `system` for instruction messages, removing all `max_tokens` / `max_completion_tokens` caps so the model decides output length naturally, and adding null-safe content extraction for `None` responses. + * Summary now includes ALL messages (user, assistant, system, file, image analysis) for full context, with a simplified prompt producing 1-2 factual paragraphs. + * Added detailed debug logging showing message count, character count, model name, role, and finish reason. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, `generate_conversation_summary`) + +* **Conversation Export Schema and Markdown Refresh** + * Fixed conversation exports lagging behind the live chat schema. JSON exports now include processing thoughts, normalized citations, and the raw document/web/tool citation buckets stored with assistant messages. + * Fixed Markdown exports being too flat and text-heavy by reorganizing them into a transcript-first layout with appendices for metadata, message details, references, thoughts, and supplemental records. + * Fixed exported conversations including content that no longer matched the visible chat by filtering deleted messages and inactive-thread retries, then reapplying thread-aware ordering before export. + * (Ref: `route_backend_conversation_export.py`, `test_conversation_export.py`, conversation export rendering) + +* **Export Tag/Classification Rendering Fix** + * Fixed conversation tags and classifications rendering as raw Python dicts (e.g., `{'category': 'model', 'value': 'gpt-5'}`) in both Markdown and PDF exports. + * Tags now display as readable `category: value` strings, with smart handling for participant names, document titles, and generic category/value pairs. + * (Ref: `route_backend_conversation_export.py`, `_format_tag` helper, Markdown/PDF metadata rendering) + +* **Export Summary Error Visibility** + * Added `debug_print` and `log_event` logging to all summary generation error paths, including the empty-response path that previously failed silently. + * The actual error detail is now shown in both Markdown and PDF exports when summary generation fails, replacing the generic "could not be generated" message. + * (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, export error rendering) + * **Content Safety for Streaming Chat Path** * Added full Azure AI Content Safety checking to the streaming (`/api/chat/stream`) SSE path, matching the existing non-streaming (`/api/chat`) implementation. * Previously, only the non-streaming path performed content safety analysis; streaming conversations bypassed safety checks entirely. @@ -225,7 +169,7 @@ * A `request_start_time` is now captured at the top of both the non-streaming and streaming chat handlers, so the duration reflects the full request lifecycle โ€” including content safety, hybrid search, and agent invocation โ€” not just the model response time. * Applies to all three agent paths: local SK agents (non-streaming), Azure AI Foundry agents, and streaming SK agents. * (Ref: `route_backend_chats.py`, `request_start_time`, agent responded thoughts) - + * **Enhanced Agent Execution Thoughts** * Added detailed model-level status messages during agent execution, giving users full visibility into each stage of the AI pipeline. * **Model Identification**: A new "Sending to '{deployment_name}'" thought appears immediately after "Sending to agent", showing the exact model deployment being used (e.g., `gpt-5-nano`).