From 2c0af8d2665c4cd77d2be76f011fb610d7e05b8c Mon Sep 17 00:00:00 2001 From: Ming Du Date: Mon, 29 Sep 2025 14:52:12 -0500 Subject: [PATCH 1/4] FEAT: teachable agent through RAG --- pyproject.toml | 1 + src/eaa/agents/asksage.py | 13 +- src/eaa/agents/base.py | 308 ++++++++++++++++- src/eaa/agents/memory/__init__.py | 13 + src/eaa/agents/memory/manager.py | 130 +++++++ src/eaa/agents/memory/types.py | 41 +++ src/eaa/agents/memory/vector_store.py | 319 ++++++++++++++++++ src/eaa/agents/openai.py | 32 +- src/eaa/task_managers/base.py | 9 +- src/eaa/task_managers/imaging/base.py | 5 +- .../task_managers/imaging/feature_tracking.py | 5 +- src/eaa/task_managers/monitoring/base.py | 5 +- src/eaa/task_managers/tuning/base.py | 7 +- src/eaa/task_managers/tuning/bo.py | 5 +- src/eaa/task_managers/tuning/bo_mic_optics.py | 6 +- src/eaa/task_managers/tuning/focusing.py | 8 +- tests/test_memory_manager.py | 61 ++++ tests/test_postgres_vector_store.py | 46 +++ uv.lock | 63 ++++ 19 files changed, 1053 insertions(+), 24 deletions(-) create mode 100644 src/eaa/agents/memory/__init__.py create mode 100644 src/eaa/agents/memory/manager.py create mode 100644 src/eaa/agents/memory/types.py create mode 100644 src/eaa/agents/memory/vector_store.py create mode 100644 tests/test_memory_manager.py create mode 100644 tests/test_postgres_vector_store.py diff --git a/pyproject.toml b/pyproject.toml index 21c8d48..060e5ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ dependencies = [ "fastmcp>=2.11.3", "fastapi", "uvicorn[standard]", + "psycopg[binary]>=3.2.10", ] dynamic = ["version"] diff --git a/src/eaa/agents/asksage.py b/src/eaa/agents/asksage.py index 6cc9e30..d36d676 100644 --- a/src/eaa/agents/asksage.py +++ b/src/eaa/agents/asksage.py @@ -1,11 +1,12 @@ import os import re -from typing import Dict, Any, List +from typing import Dict, Any, List, Optional, Union from asksageclient import AskSageClient from eaa.agents.base import BaseAgent +from eaa.agents.memory import MemoryManagerConfig from eaa.util import decode_image_base64, get_timestamp @@ -15,6 +16,7 @@ def __init__( self, llm_config: dict, system_message: str = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, ) -> None: """An agent that uses OpenAI-compatible API to generate responses. @@ -31,10 +33,13 @@ def __init__( - `cacert_path`: The path to the CA certificate file (*.pem). system_message : str, optional The system message for the OpenAI-compatible API. + memory_config : dict | MemoryManagerConfig, optional + Optional configuration for persistent memory support. """ super().__init__( - llm_config=llm_config, - system_message=system_message + llm_config=llm_config, + system_message=system_message, + memory_config=memory_config, ) @property @@ -176,4 +181,4 @@ def asksage_response_to_openai_response(self, response: dict) -> dict: ): oai_response["tool_calls"] = response["tool_calls"] return oai_response - \ No newline at end of file + diff --git a/src/eaa/agents/base.py b/src/eaa/agents/base.py index a97f500..f1245db 100644 --- a/src/eaa/agents/base.py +++ b/src/eaa/agents/base.py @@ -89,6 +89,7 @@ Tuple, Optional, Literal, + Sequence, ) from dataclasses import dataclass import json @@ -102,6 +103,7 @@ from eaa.tools.mcp import MCPTool from eaa.comms import get_api_key from eaa.util import encode_image_base64, get_image_path_from_text +from eaa.agents.memory import MemoryManager, MemoryManagerConfig, MemoryQueryResult logger = logging.getLogger(__name__) @@ -254,10 +256,22 @@ def _get_entry(self, tool_name: str) -> ToolEntry: class BaseAgent: + _MEMORY_KEYWORDS: List[Tuple[str, str]] = [ + ("remember", "User asked agent to remember"), + ("note that", "User highlighted a note"), + ("call me", "Preferred form of address"), + ("my name is", "User shared their name"), + ("i prefer", "User shared a preference"), + ("i like", "User shared a preference"), + ("store this", "User requested storage"), + ("keep in mind", "User asked agent to keep context"), + ] + def __init__( self, llm_config: dict, system_message: str = "", + memory_config: Optional[Dict[str, Any] | MemoryManagerConfig] = None, ) -> None: """The base agent class. @@ -271,6 +285,18 @@ def __init__( - `base_url`: The base URL for the OpenAI-compatible API. system_message : str, optional The system message for the OpenAI-compatible API. + memory_config : dict | MemoryManagerConfig, optional + Configuration for long-term memory. When provided, the agent can + optionally store conversation snippets and retrieve them on future + turns using retrieval-augmented generation. Pass a + `MemoryManagerConfig` instance when you only need to adjust the + dataclass fields. Supply a dictionary when you also want to include + auxiliary hooks such as a custom vector store, notability filter, + formatter, or embedder override. The dictionary may contain the + config fields (`enabled`, `write_enabled`, `retrieval_enabled`, + `top_k`, `score_threshold`, `min_content_length`, `embedding_model`, + `vector_store_path`, `injection_role`) plus optional extras + (`vector_store`, `notability_filter`, `formatter`, `embedder`). """ self.llm_config = llm_config @@ -286,6 +312,10 @@ def __init__( self.tool_manager.set_approval_handler(self._approval_handler) self.client = self.create_client() + + self.memory_manager: Optional[MemoryManager] = None + self._memory_injection_role = "system" + self._initialize_memory(memory_config) @property def model(self) -> str: @@ -320,6 +350,218 @@ def api_key(self) -> str: def create_client(self) -> Any: raise NotImplementedError + + # --- Memory lifecycle ------------------------------------------------- + + def supports_memory_embeddings(self) -> bool: + """Indicate whether this agent can produce embeddings for memory.""" + return False + + def embed_texts( + self, + texts: Sequence[str], + *, + model: Optional[str] = None, + ) -> List[List[float]]: + raise NotImplementedError( + f"{self.__class__.__name__} does not implement text embeddings." + ) + + def get_default_embedding_model(self) -> Optional[str]: + return self.llm_config.get("embedding_model") + + def _initialize_memory( + self, + memory_config: Optional[Dict[str, Any] | MemoryManagerConfig], + ) -> None: + if memory_config is None: + return + + config_obj: MemoryManagerConfig + vector_store = None + notability_filter = None + formatter = None + embedder_override = None + + if isinstance(memory_config, MemoryManagerConfig): + config_obj = memory_config + elif isinstance(memory_config, dict): + config_obj = MemoryManagerConfig.from_dict(memory_config) + vector_store = memory_config.get("vector_store") + notability_filter = memory_config.get("notability_filter") + formatter = memory_config.get("formatter") + embedder_override = memory_config.get("embedder") + else: + raise ValueError("memory_config must be a dict or MemoryManagerConfig instance.") + + if config_obj.injection_role not in {"system", "user"}: + raise ValueError( + "Unsupported injection role '%s'; defaulting to 'system'.", + config_obj.injection_role, + ) + + self._memory_injection_role = config_obj.injection_role + + if embedder_override is None and not self.supports_memory_embeddings(): + logger.warning( + "%s does not support embeddings; ignoring memory configuration.", + self.__class__.__name__, + ) + return + + embedding_model = config_obj.embedding_model or self.get_default_embedding_model() + embedder_fn = embedder_override or self._build_memory_embedder(embedding_model) + + try: + self.memory_manager = MemoryManager( + embedder_fn, + config=config_obj, + vector_store=vector_store, + notability_filter=notability_filter, + formatter=formatter, + ) + except Exception as exc: # pragma: no cover - defensive. + logger.warning("Failed to initialise memory manager: %s", exc) + self.memory_manager = None + + def _build_memory_embedder(self, embedding_model: Optional[str]) -> Callable[[Sequence[str]], List[List[float]]]: + model_name = embedding_model + + def embedder(texts: Sequence[str]) -> List[List[float]]: + selected_model = model_name or self.get_default_embedding_model() + if selected_model is None: + raise ValueError("No embedding model provided for memory usage.") + return self.embed_texts(texts, model=selected_model) + + return embedder + + def _extract_message_text(self, message: Optional[Dict[str, Any]]) -> str: + if message is None: + return "" + content = message.get("content") + if isinstance(content, str): + return content + if isinstance(content, list): + texts: List[str] = [] + for item in content: + if isinstance(item, dict) and item.get("type") == "text": + texts.append(item.get("text", "")) + return "\n".join(texts) + return "" + + def _post_response_memory_update( + self, + *, + user_message: Dict[str, Any], + agent_response: Optional[Dict[str, Any]], + recall_results: List[MemoryQueryResult], + ) -> None: + """Capture notable user content after a response has been produced. + + The method inspects the user message, determines whether it should be + stored, and forwards the snippet to the memory manager along with + metadata about the interaction (message id, prior retrieval count, etc.). + + Parameters + ---------- + user_message : Dict[str, Any] + The outgoing OpenAI-formatted user message for the current turn. + agent_response : Optional[Dict[str, Any]] + Processed agent reply, used to detect tool-call signals. + recall_results : List[MemoryQueryResult] + Memories that were retrieved for this turn; logged for metadata and + potential deduplication. + """ + if self.memory_manager is None or not self.memory_manager.write_enabled: + return + + message_text = self._extract_message_text(user_message) + if not message_text: + return + + notable, note_reason = self._is_notable_message(message_text, agent_response) + if not notable: + return + + metadata: Dict[str, Any] = { + "role": "user", + "note": note_reason, + } + reference_id = user_message.get("id") + if reference_id is not None: + metadata["message_id"] = reference_id + if recall_results: + metadata["retrieved_count"] = len(recall_results) + if len(message_text.strip()) < self.memory_manager.config.min_content_length: + metadata["force_store"] = True + + self.memory_manager.remember(message_text, metadata=metadata) + + def _is_notable_message( + self, + content: str, + response: Optional[Dict[str, Any]] = None, + ) -> Tuple[bool, str]: + """Determine whether a user message should be persisted in memory. + + A message is considered notable if any of the following hold: + - It contains one of the heuristic keywords such as "remember", "my name is", + or "keep in mind". + - It is a long declarative statement (more than 140 characters terminating with + punctuation). + - The paired response triggered a `memorize` tool call. + + Parameters + ---------- + content : str + User-facing text content to inspect. + response : Optional[Dict[str, Any]] + Processed agent response, used to detect memory-specific tool calls. + + Returns + ------- + Tuple[bool, str] + Whether the message is notable and a short reason string describing + the path that triggered the decision. + """ + lowered = content.lower() + for keyword, reason in self._MEMORY_KEYWORDS: + if keyword in lowered: + return True, reason + + # Treat long declarative statements as potentially notable. + if len(content.strip()) > 140 and content.strip().endswith(('.', '!', '?')): + return True, "Long-form user statement" + + if response is not None: + tool_calls = response.get("tool_calls") if isinstance(response, dict) else None + if tool_calls: + for call in tool_calls: + if call.get("function", {}).get("name") == "memorize": + return True, "User triggered memory tool" + + return False, "" + + def configure_memory( + self, + *, + enabled: Optional[bool] = None, + write_enabled: Optional[bool] = None, + retrieval_enabled: Optional[bool] = None, + ) -> None: + if self.memory_manager is None: + raise ValueError("Memory manager is not initialised for this agent.") + if enabled is not None: + self.memory_manager.set_enabled(enabled) + if write_enabled is not None: + self.memory_manager.set_write_enabled(write_enabled) + if retrieval_enabled is not None: + self.memory_manager.set_retrieval_enabled(retrieval_enabled) + + def flush_memory(self) -> None: + if self.memory_manager is None: + return + self.memory_manager.flush() def register_tools(self, tools: List[BaseTool]) -> None: """Register BaseTool instances with the agent.""" @@ -440,23 +682,39 @@ def receive( message = generate_openai_message( message, role=role, image=image, image_path=image_path, encoded_image=encoded_image ) - + # Print message. if message is not None: print_message(message, response_requested=request_response) - + + # Retrieve from vector store. + recalled_memory_results = [] + memory_context_messages: List[Dict[str, Any]] = [] + if ( + request_response + and self.memory_manager is not None + and self.memory_manager.enabled + ): + recalled_memory_results, memory_context_messages = self.retrieve_from_vector_store(message) + # Create the list of messages to send. sys_message = self.system_messages if with_system_message else [] - message = [message] if message is not None else [] - if context is None: - context = [] - combined_messages = sys_message + context + message + message_list = [message] if message is not None else [] + context_messages = list(context) if context is not None else [] + combined_messages = sys_message + memory_context_messages + context_messages + message_list # Send messages, get response and print it. if request_response: response = self.send_message_and_get_response(combined_messages) print_message(response) - + + if request_response and self.memory_manager is not None and message is not None: + self._post_response_memory_update( + user_message=message, + agent_response=response if request_response else None, + recall_results=recalled_memory_results, + ) + if not request_response: return None @@ -466,11 +724,45 @@ def receive( else: returns.append(response.choices[0].message.content) if return_outgoing_message: - returns.append(message[0] if len(message) > 0 else None) + returns.append(message_list[0] if len(message_list) > 0 else None) if len(returns) == 1: return returns[0] else: return returns + + def retrieve_from_vector_store(self, message: Dict[str, Any]) -> List[MemoryQueryResult]: + """Retrieve memories from the vector store. + + Parameters + ---------- + message : Dict[str, Any] + The message to be sent to the agent. + + Returns + ------- + List[MemoryQueryResult] + The memories retrieved from the vector store. + List[Dict[str, Any]] + The retrieved memories converted to OpenAI-compatible messages + which can be inserted into the context. + """ + recalled_memory_results = [] + memory_context_messages: List[Dict[str, Any]] = [] + + query_text = self._extract_message_text(message) + recalled_memory_results = self.memory_manager.recall(query_text) + formatted_memory = self.memory_manager.format_results(recalled_memory_results) + if formatted_memory: + injection_role = self._memory_injection_role + if injection_role not in {"system", "user"}: + injection_role = "system" + memory_context_messages.append( + generate_openai_message( + formatted_memory, + role=injection_role, + ) + ) + return recalled_memory_results, memory_context_messages def send_message_and_get_response( self, diff --git a/src/eaa/agents/memory/__init__.py b/src/eaa/agents/memory/__init__.py new file mode 100644 index 0000000..ba2b13e --- /dev/null +++ b/src/eaa/agents/memory/__init__.py @@ -0,0 +1,13 @@ +from .manager import MemoryManager, MemoryManagerConfig +from .types import MemoryQueryResult, MemoryRecord +from .vector_store import LocalVectorStore, PostgresVectorStore, VectorStore + +__all__ = [ + "MemoryManager", + "MemoryManagerConfig", + "MemoryQueryResult", + "MemoryRecord", + "LocalVectorStore", + "PostgresVectorStore", + "VectorStore", +] diff --git a/src/eaa/agents/memory/manager.py b/src/eaa/agents/memory/manager.py new file mode 100644 index 0000000..dd32e7c --- /dev/null +++ b/src/eaa/agents/memory/manager.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Sequence +import logging + +from .types import MemoryQueryResult, MemoryRecord, MemorySchema +from .vector_store import LocalVectorStore, VectorStore + +logger = logging.getLogger(__name__) + +EmbedderFn = Callable[[Sequence[str]], List[List[float]]] +FilterFn = Callable[[str, Dict[str, Any]], bool] +FormatterFn = Callable[[List[MemoryQueryResult]], str] + + +@dataclass +class MemoryManagerConfig(MemorySchema): + enabled: bool = False + write_enabled: bool = True + retrieval_enabled: bool = True + top_k: int = 5 + score_threshold: float = 0.25 + min_content_length: int = 12 + embedding_model: Optional[str] = None + vector_store_path: Optional[str] = None + injection_role: str = "system" + + +class MemoryManager: + """Orchestrates storing and retrieving conversation memories for an agent.""" + + def __init__( + self, + embedder: EmbedderFn, + *, + config: Optional[MemoryManagerConfig] = None, + vector_store: Optional[VectorStore] = None, + notability_filter: Optional[FilterFn] = None, + formatter: Optional[FormatterFn] = None, + ) -> None: + self.embedder = embedder + self.config = config or MemoryManagerConfig() + self.vector_store = vector_store or LocalVectorStore(self.config.vector_store_path) + self.notability_filter = notability_filter + self.formatter = formatter or self._default_formatter + + @property + def enabled(self) -> bool: + return self.config.enabled + + @property + def write_enabled(self) -> bool: + return self.config.write_enabled and self.enabled + + @property + def retrieval_enabled(self) -> bool: + return self.config.retrieval_enabled and self.enabled + + def set_enabled(self, value: bool) -> None: + self.config.enabled = value + + def set_write_enabled(self, value: bool) -> None: + self.config.write_enabled = value + + def set_retrieval_enabled(self, value: bool) -> None: + self.config.retrieval_enabled = value + + def remember(self, content: str, *, metadata: Optional[Dict[str, Any]] = None) -> Optional[MemoryRecord]: + if not self.write_enabled: + return None + if not content: + return None + if metadata is None: + metadata = {} + trimmed = content.strip() + if len(trimmed) < self.config.min_content_length and not metadata.get("force_store"): + return None + if self.notability_filter is not None and not self.notability_filter(content, metadata): + return None + try: + embedding = self.embedder([content])[0] + except Exception as exc: # pragma: no cover - dependent on runtime env. + logger.warning("Failed to embed memory snippet: %s", exc) + return None + record = MemoryRecord(content=content, embedding=embedding, metadata=metadata) + self.vector_store.add([record]) + return record + + def recall(self, prompt: str) -> List[MemoryQueryResult]: + if not self.retrieval_enabled: + return [] + if not prompt: + return [] + try: + embedding = self.embedder([prompt])[0] + except Exception as exc: # pragma: no cover - dependent on runtime env. + logger.warning("Failed to embed recall prompt: %s", exc) + return [] + return self.vector_store.search( + embedding, + top_k=self.config.top_k, + score_threshold=self.config.score_threshold, + ) + + def format_results(self, results: List[MemoryQueryResult]) -> Optional[str]: + if not results: + return None + return self.formatter(results) + + def flush(self) -> None: + try: + self.vector_store.persist() + except AttributeError: + logger.debug("Vector store does not implement `persist`; skipping flush.") + + @staticmethod + def _default_formatter(results: List[MemoryQueryResult]) -> str: + parts = [ + "Relevant stored context (most similar first):" + ] + for item in results: + content = item.record.content.strip().replace("\n", " ") + score = f"{item.score:.2f}" + note = item.record.metadata.get("note") if item.record.metadata else None + if note: + parts.append(f"- ({score}) {note}: {content}") + else: + parts.append(f"- ({score}) {content}") + return "\n".join(parts) diff --git a/src/eaa/agents/memory/types.py b/src/eaa/agents/memory/types.py new file mode 100644 index 0000000..53b63a5 --- /dev/null +++ b/src/eaa/agents/memory/types.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, fields +from typing import Any, Dict, List, Optional, Type, TypeVar +import time +import uuid + +T = TypeVar("T", bound="MemorySchema") + + +class MemorySchema: + """Shared helper with a `from_dict` classmethod for memory dataclasses.""" + + @classmethod + def from_dict(cls: Type[T], payload: Optional[Dict[str, Any]]) -> T: + if payload is None: + return cls() # type: ignore[misc] + if not isinstance(payload, dict): + raise TypeError("memory configuration must be a dictionary") + allowed = {field.name for field in fields(cls)} + kwargs = {key: payload[key] for key in allowed if key in payload} + return cls(**kwargs) # type: ignore[arg-type] + + +@dataclass +class MemoryRecord(MemorySchema): + content: str + embedding: List[float] + metadata: Dict[str, Any] = field(default_factory=dict) + record_id: str = field(default_factory=lambda: str(uuid.uuid4())) + created_at: float = field(default_factory=time.time) + + +@dataclass +class MemoryQueryResult(MemorySchema): + record: MemoryRecord + score: float + highlights: Optional[str] = None + + +__all__ = ["MemorySchema", "MemoryRecord", "MemoryQueryResult"] diff --git a/src/eaa/agents/memory/vector_store.py b/src/eaa/agents/memory/vector_store.py new file mode 100644 index 0000000..f5a3bc9 --- /dev/null +++ b/src/eaa/agents/memory/vector_store.py @@ -0,0 +1,319 @@ +from __future__ import annotations + +from contextlib import contextmanager +from dataclasses import asdict +from datetime import datetime +from pathlib import Path +from typing import Dict, Iterator, List, Optional, Protocol, Sequence +import json +import logging +import time + +import numpy as np + +try: # pragma: no cover - import guard for optional dependency + import psycopg # type: ignore + from psycopg.rows import dict_row # type: ignore +except ImportError: # pragma: no cover + psycopg = None # type: ignore + dict_row = None # type: ignore + +from .types import MemoryQueryResult, MemoryRecord + +logger = logging.getLogger(__name__) + + +class VectorStore(Protocol): + """Protocol describing the minimum surface of a vector store.""" + + def add(self, records: Sequence[MemoryRecord]) -> None: + ... + + def search( + self, + embedding: Sequence[float], + *, + top_k: int = 5, + score_threshold: float = 0.0, + ) -> List[MemoryQueryResult]: + ... + + def delete(self, record_ids: Sequence[str]) -> None: + ... + + def persist(self) -> None: + ... + + +class LocalVectorStore: + """Simple vector store that keeps data locally and optionally persists to disk. + + This is not optimised for large corpora but provides a dependency-free + implementation that can be swapped out with more capable backends later. + """ + + def __init__( + self, + persist_path: Optional[str | Path] = None, + auto_flush: bool = True, + ) -> None: + self._records: List["MemoryRecord"] = [] + self.persist_path = Path(persist_path) if persist_path else None + self.auto_flush = auto_flush + if self.persist_path and self.persist_path.exists(): + self._load() + + def add(self, records: Sequence["MemoryRecord"]) -> None: + if not records: + return + self._records.extend(records) + if self.auto_flush: + self.persist() + + def search( + self, + embedding: Sequence[float], + *, + top_k: int = 5, + score_threshold: float = 0.0, + ) -> List["MemoryQueryResult"]: + if not self._records: + return [] + query = np.asarray(embedding, dtype=np.float32) + if query.ndim != 1: + raise ValueError("Embedding must be a 1D vector.") + matrix = np.asarray([rec.embedding for rec in self._records], dtype=np.float32) + if matrix.size == 0: + return [] + + # Normalize for cosine similarity. + query_norm = np.linalg.norm(query) + if query_norm == 0: + logger.warning("Query embedding has zero norm; returning empty search result.") + return [] + matrix_norms = np.linalg.norm(matrix, axis=1) + matrix_norms[matrix_norms == 0] = 1e-9 + scores = (matrix @ query) / (matrix_norms * query_norm) + top_indices = np.argsort(scores)[::-1][:top_k] + + results: List[MemoryQueryResult] = [] + for idx in top_indices: + score = float(scores[idx]) + if score < score_threshold: + continue + record = self._records[idx] + results.append(MemoryQueryResult(record=record, score=score)) + return results + + def delete(self, record_ids: Sequence[str]) -> None: + if not record_ids: + return + id_set = set(record_ids) + self._records = [rec for rec in self._records if rec.record_id not in id_set] + if self.auto_flush: + self.persist() + + def persist(self) -> None: + if not self.persist_path: + return + data = [self._serialise_record(record) for record in self._records] + self.persist_path.parent.mkdir(parents=True, exist_ok=True) + with self.persist_path.open("w", encoding="utf-8") as handle: + json.dump(data, handle) + + def _load(self) -> None: + if not self.persist_path: + return + try: + with self.persist_path.open("r", encoding="utf-8") as handle: + payload = json.load(handle) + except FileNotFoundError: + logger.debug("No persisted memory found at %s", self.persist_path) + return + except json.JSONDecodeError as exc: + logger.warning("Failed to load persisted memory: %s", exc) + return + + records: List[MemoryRecord] = [] + for item in payload: + try: + records.append(self._deserialise_record(item)) + except Exception as exc: # pragma: no cover - defensive. + logger.warning("Could not deserialise memory record: %s", exc) + self._records = records + + @staticmethod + def _serialise_record(record: MemoryRecord) -> dict: + data = asdict(record) + # `embedding` may be a numpy array; ensure it is JSON serialisable. + data["embedding"] = list(map(float, record.embedding)) + return data + + @staticmethod + def _deserialise_record(payload: dict) -> MemoryRecord: + created_at = payload.get("created_at") + if created_at is None: + created_at = time.time() + return MemoryRecord( + content=payload["content"], + embedding=list(map(float, payload["embedding"])), + metadata=payload.get("metadata", {}), + record_id=payload.get("record_id"), + created_at=created_at, + ) + + +def _vector_literal(values: Sequence[float]) -> str: + return "[" + ",".join(f"{float(v):.8f}" for v in values) + "]" + + +class PostgresVectorStore(VectorStore): + """Vector store backed by PostgreSQL + pgvector.""" + + _METRIC_OPERATORS: Dict[str, str] = { + "cosine": "<#>", + "l2": "<->", + "inner": "<.>", + } + + def __init__( + self, + dsn: str, + *, + table_name: str = "memory_records", + ensure_schema: bool = False, + vector_dimension: Optional[int] = None, + metric: str = "cosine", + ) -> None: + if psycopg is None: + raise ImportError( + "psycopg[binary] is required for PostgresVectorStore." + ) + if metric not in self._METRIC_OPERATORS: + raise ValueError(f"Unsupported metric '{metric}'.") + self.dsn = dsn + self.table_name = table_name + self.metric = metric + self._vector_operator = self._METRIC_OPERATORS[metric] + self._connection_kwargs: Dict[str, str] = {} + + if ensure_schema: + if vector_dimension is None: + raise ValueError("vector_dimension must be provided when ensure_schema=True") + self._ensure_schema(vector_dimension) + + @contextmanager + def _connection(self) -> Iterator[psycopg.Connection]: + with psycopg.connect(self.dsn, **self._connection_kwargs) as conn: # type: ignore[arg-type] + yield conn + + def add(self, records: Sequence[MemoryRecord]) -> None: + if not records: + return + sql = ( + f"INSERT INTO {self.table_name} (record_id, content, embedding, metadata, created_at) " + "VALUES (%s, %s, %s::vector, %s::jsonb, %s) " + "ON CONFLICT (record_id) DO UPDATE SET " + "content = EXCLUDED.content, embedding = EXCLUDED.embedding, " + "metadata = EXCLUDED.metadata, created_at = EXCLUDED.created_at" + ) + payload = [ + ( + record.record_id, + record.content, + _vector_literal(record.embedding), + json.dumps(record.metadata or {}), + datetime.fromtimestamp(record.created_at), + ) + for record in records + ] + with self._connection() as conn: + with conn.cursor() as cur: + cur.executemany(sql, payload) + conn.commit() + + def search( + self, + embedding: Sequence[float], + *, + top_k: int = 5, + score_threshold: float = 0.0, + ) -> List[MemoryQueryResult]: + if not embedding: + return [] + vector_literal = _vector_literal(embedding) + operator = self._vector_operator + sql = f""" + SELECT + record_id, + content, + metadata, + created_at, + embedding::float4[] AS embedding_array, + 1 - (embedding {operator} %s::vector) AS similarity + FROM {self.table_name} + ORDER BY embedding {operator} %s::vector ASC + LIMIT %s + """ + results: List[MemoryQueryResult] = [] + with self._connection() as conn: + with conn.cursor(row_factory=dict_row) as cur: # type: ignore[arg-type] + cur.execute(sql, (vector_literal, vector_literal, top_k)) + rows = cur.fetchall() + for row in rows: + score = float(row["similarity"]) + if score < score_threshold: + continue + metadata = row["metadata"] + if isinstance(metadata, str): + metadata = json.loads(metadata) + created_at = row["created_at"].timestamp() if row["created_at"] else time.time() + record = MemoryRecord( + content=row["content"], + embedding=[float(x) for x in row["embedding_array"]], + metadata=metadata or {}, + record_id=str(row["record_id"]), + created_at=created_at, + ) + results.append(MemoryQueryResult(record=record, score=score)) + return results + + def delete(self, record_ids: Sequence[str]) -> None: + if not record_ids: + return + sql = f"DELETE FROM {self.table_name} WHERE record_id = ANY(%s)" + with self._connection() as conn: + with conn.cursor() as cur: + cur.execute(sql, (record_ids,)) + conn.commit() + + def persist(self) -> None: + return + + def _ensure_schema(self, dimension: int) -> None: + create_extension = "CREATE EXTENSION IF NOT EXISTS vector" + create_table = f""" + CREATE TABLE IF NOT EXISTS {self.table_name} ( + record_id UUID PRIMARY KEY, + content TEXT NOT NULL, + embedding VECTOR({dimension}) NOT NULL, + metadata JSONB DEFAULT '{{}}', + created_at TIMESTAMPTZ DEFAULT NOW() + ) + """ + ivfflat_index = ( + f"CREATE INDEX IF NOT EXISTS {self.table_name}_embedding_ivfflat " + f"ON {self.table_name} USING ivfflat (embedding vector_cosine_ops)" + ) + metadata_index = ( + f"CREATE INDEX IF NOT EXISTS {self.table_name}_metadata_gin " + f"ON {self.table_name} USING gin (metadata)" + ) + with self._connection() as conn: + with conn.cursor() as cur: + cur.execute(create_extension) + cur.execute(create_table) + if self.metric == "cosine": + cur.execute(ivfflat_index) + cur.execute(metadata_index) + conn.commit() diff --git a/src/eaa/agents/openai.py b/src/eaa/agents/openai.py index fd994c3..5cec690 100644 --- a/src/eaa/agents/openai.py +++ b/src/eaa/agents/openai.py @@ -1,8 +1,9 @@ -from typing import Dict, Any, List +from typing import Any, Dict, List, Optional, Sequence, Union from openai import OpenAI from eaa.agents.base import BaseAgent +from eaa.agents.memory import MemoryManagerConfig class OpenAIAgent(BaseAgent): @@ -11,6 +12,7 @@ def __init__( self, llm_config: dict, system_message: str = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, ) -> None: """An agent that uses OpenAI-compatible API to generate responses. @@ -24,10 +26,13 @@ def __init__( - `base_url`: The base URL for the OpenAI-compatible API. system_message : str, optional The system message for the OpenAI-compatible API. + memory_config : dict | MemoryManagerConfig, optional + Optional configuration for persistent memory. """ super().__init__( - llm_config=llm_config, - system_message=system_message + llm_config=llm_config, + system_message=system_message, + memory_config=memory_config, ) @property @@ -39,6 +44,27 @@ def create_client(self) -> OpenAI: api_key=self.api_key, base_url=self.base_url, ) + + def supports_memory_embeddings(self) -> bool: + return True + + def get_default_embedding_model(self) -> Optional[str]: + return self.llm_config.get("embedding_model", "text-embedding-3-small") + + def embed_texts( + self, + texts: Sequence[str], + *, + model: Optional[str] = None, + ) -> List[List[float]]: + selected_model = model or self.get_default_embedding_model() + if selected_model is None: + raise ValueError("No embedding model configured for OpenAIAgent.") + response = self.client.embeddings.create( + model=selected_model, + input=list(texts), + ) + return [item.embedding for item in response.data] def send_message_and_get_response( self, diff --git a/src/eaa/task_managers/base.py b/src/eaa/task_managers/base.py index 89a3f88..654de54 100644 --- a/src/eaa/task_managers/base.py +++ b/src/eaa/task_managers/base.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional, Callable, Literal +from typing import Any, Dict, Optional, Callable, Literal, Union import json import sqlite3 import logging @@ -11,6 +11,7 @@ from eaa.util import get_timestamp from eaa.tools.base import ToolReturnType from eaa.api.llm_config import LLMConfig, OpenAIConfig, AskSageConfig +from eaa.agents.memory import MemoryManagerConfig try: from eaa.agents.asksage import AskSageAgent except ImportError: @@ -31,6 +32,7 @@ class BaseTaskManager: def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[Dict[str, Any], MemoryManagerConfig]] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, @@ -42,6 +44,9 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : dict | MemoryManagerConfig, optional + Optional configuration for long-term memory. Forwarded to the + underlying agent when instantiated. tools : list[BaseTool] A list of tools provided to the agent. message_db_path : Optional[str] @@ -55,6 +60,7 @@ def __init__( self.context = [] self.full_history = [] self.llm_config = llm_config + self.memory_config = memory_config self.agent = None self.tools = tools @@ -112,6 +118,7 @@ def build_agent(self, *args, **kwargs): self.agent = agent_class( llm_config=self.llm_config.to_dict(), system_message=self.assistant_system_message, + memory_config=self.memory_config, ) self.agent.set_tool_approval_handler(self._request_tool_approval_via_task_manager) diff --git a/src/eaa/task_managers/imaging/base.py b/src/eaa/task_managers/imaging/base.py index 1b7eb60..57ea70e 100644 --- a/src/eaa/task_managers/imaging/base.py +++ b/src/eaa/task_managers/imaging/base.py @@ -1,11 +1,12 @@ import logging -from typing import Optional +from typing import Optional, Union from PIL import Image from eaa.task_managers.base import BaseTaskManager from eaa.tools.base import BaseTool from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig from eaa.image_proc import stitch_images logger = logging.getLogger(__name__) @@ -25,6 +26,7 @@ class ImagingBaseTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, @@ -49,6 +51,7 @@ def __init__( """ super().__init__( llm_config=llm_config, + memory_config=memory_config, tools=tools, message_db_path=message_db_path, build=build, diff --git a/src/eaa/task_managers/imaging/feature_tracking.py b/src/eaa/task_managers/imaging/feature_tracking.py index 4f990d2..85eacce 100644 --- a/src/eaa/task_managers/imaging/feature_tracking.py +++ b/src/eaa/task_managers/imaging/feature_tracking.py @@ -1,10 +1,11 @@ -from typing import Optional, Callable, Literal +from typing import Optional, Callable, Literal, Union from eaa.tools.base import BaseTool from eaa.tools.imaging.acquisition import AcquireImage from eaa.tools.imaging.registration import ImageRegistration from eaa.task_managers.imaging.base import ImagingBaseTaskManager from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig from eaa.util import get_image_path_from_text @@ -13,6 +14,7 @@ class FeatureTrackingTaskManager(ImagingBaseTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, image_acquisition_tool: AcquireImage = None, image_registration_tool: ImageRegistration = None, additional_tools: list[BaseTool] = (), @@ -56,6 +58,7 @@ def __init__( tools.append(t) super().__init__( llm_config=llm_config, + memory_config=memory_config, tools=tools, message_db_path=message_db_path, build=build, diff --git a/src/eaa/task_managers/monitoring/base.py b/src/eaa/task_managers/monitoring/base.py index c4f543b..019c5d4 100644 --- a/src/eaa/task_managers/monitoring/base.py +++ b/src/eaa/task_managers/monitoring/base.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Optional, Union import logging import time from textwrap import dedent @@ -6,6 +6,7 @@ from eaa.task_managers.base import BaseTaskManager from eaa.tools.base import BaseTool from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig logger = logging.getLogger(__name__) @@ -15,6 +16,7 @@ class BaseMonitoringTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, @@ -36,6 +38,7 @@ def __init__( """ super().__init__( llm_config=llm_config, + memory_config=memory_config, tools=tools, message_db_path=message_db_path, build=build, diff --git a/src/eaa/task_managers/tuning/base.py b/src/eaa/task_managers/tuning/base.py index 915abfe..e738f77 100644 --- a/src/eaa/task_managers/tuning/base.py +++ b/src/eaa/task_managers/tuning/base.py @@ -1,10 +1,11 @@ -from typing import Optional +from typing import Optional, Union import logging from eaa.tools.imaging.param_tuning import SetParameters from eaa.task_managers.base import BaseTaskManager from eaa.tools.base import BaseTool from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig logger = logging.getLogger(__name__) @@ -14,6 +15,7 @@ class BaseParameterTuningTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, param_setting_tool: SetParameters = None, additional_tools: list[BaseTool] = (), initial_parameters: dict[str, float] = None, @@ -58,6 +60,7 @@ def __init__( super().__init__( llm_config=llm_config, + memory_config=memory_config, tools=[param_setting_tool, *additional_tools], message_db_path=message_db_path, build=build, @@ -77,4 +80,4 @@ def prerun_check(self, *args, **kwargs) -> bool: return super().prerun_check(*args, **kwargs) def run(self, *args, **kwargs) -> None: - raise NotImplementedError \ No newline at end of file + raise NotImplementedError diff --git a/src/eaa/task_managers/tuning/bo.py b/src/eaa/task_managers/tuning/bo.py index f77c3e4..5c12de0 100644 --- a/src/eaa/task_managers/tuning/bo.py +++ b/src/eaa/task_managers/tuning/bo.py @@ -1,5 +1,5 @@ import logging -from typing import Optional, Callable +from typing import Optional, Callable, Union import torch @@ -7,6 +7,7 @@ from eaa.tools.base import BaseTool from eaa.tools.bo import BayesianOptimizationTool from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig logger = logging.getLogger(__name__) @@ -16,6 +17,7 @@ class BayesianOptimizationTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, bayesian_optimization_tool: BayesianOptimizationTool = None, additional_tools: list[BaseTool] = (), initial_points: Optional[torch.Tensor] = None, @@ -80,6 +82,7 @@ def __init__( super().__init__( llm_config=llm_config, + memory_config=memory_config, tools=additional_tools, message_db_path=message_db_path, build=build, diff --git a/src/eaa/task_managers/tuning/bo_mic_optics.py b/src/eaa/task_managers/tuning/bo_mic_optics.py index 4ef8a24..532aacc 100644 --- a/src/eaa/task_managers/tuning/bo_mic_optics.py +++ b/src/eaa/task_managers/tuning/bo_mic_optics.py @@ -1,5 +1,5 @@ import logging -from typing import Optional +from typing import Optional, Union import torch import numpy as np @@ -10,6 +10,7 @@ from eaa.tools.base import BaseTool from eaa.tools.bo import BayesianOptimizationTool from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig logger = logging.getLogger(__name__) @@ -21,6 +22,7 @@ class MicroscopyOpticsTuningBOTaskManager( def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, image_acquisition_tool: BaseTool = None, parameter_setting_tool: BaseTool = None, bayesian_optimization_tool: BayesianOptimizationTool = None, @@ -91,6 +93,7 @@ def __init__( BayesianOptimizationTaskManager.__init__( self, llm_config=llm_config, + memory_config=memory_config, bayesian_optimization_tool=bayesian_optimization_tool, initial_points=initial_points, n_initial_points=n_initial_points, @@ -102,6 +105,7 @@ def __init__( FeatureTrackingTaskManager.__init__( self, llm_config=llm_config, + memory_config=memory_config, image_acquisition_tool=image_acquisition_tool, additional_tools=additional_tools, build=True, diff --git a/src/eaa/task_managers/tuning/focusing.py b/src/eaa/task_managers/tuning/focusing.py index ebd2a92..22e2446 100644 --- a/src/eaa/task_managers/tuning/focusing.py +++ b/src/eaa/task_managers/tuning/focusing.py @@ -1,4 +1,4 @@ -from typing import Optional, Callable +from typing import Optional, Callable, Union import logging import copy @@ -13,6 +13,7 @@ from eaa.tools.imaging.registration import ImageRegistration from eaa.agents.base import print_message from eaa.api.llm_config import LLMConfig +from eaa.agents.memory import MemoryManagerConfig from eaa.util import get_image_path_from_text import eaa.image_proc as ip @@ -24,6 +25,7 @@ class ScanningMicroscopeFocusingTaskManager(BaseParameterTuningTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, param_setting_tool: SetParameters = None, acquisition_tool: AcquireImage = None, image_registration_tool: Optional[ImageRegistration] = None, @@ -117,6 +119,7 @@ def __init__( super().__init__( llm_config=llm_config, + memory_config=memory_config, param_setting_tool=param_setting_tool, additional_tools=[acquisition_tool, *additional_tools], initial_parameters=initial_parameters, @@ -249,6 +252,7 @@ def restore_fov( self.feature_tracking_task_manager = FeatureTrackingTaskManager( llm_config=self.llm_config, + memory_config=self.memory_config, image_acquisition_tool=copy.deepcopy(self.acquisition_tool), image_registration_tool=copy.deepcopy(self.image_registration_tool), message_db_path=self.message_db_path, @@ -503,6 +507,7 @@ class ParameterTuningTaskManager(BaseParameterTuningTaskManager): def __init__( self, llm_config: LLMConfig = None, + memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, param_setting_tool: SetParameters = None, acquisition_tool: AcquireImage = None, initial_parameters: dict[str, float] = None, @@ -548,6 +553,7 @@ def __init__( super().__init__( llm_config=llm_config, + memory_config=memory_config, param_setting_tool=param_setting_tool, additional_tools=[param_setting_tool], initial_parameters=initial_parameters, diff --git a/tests/test_memory_manager.py b/tests/test_memory_manager.py new file mode 100644 index 0000000..e7316e5 --- /dev/null +++ b/tests/test_memory_manager.py @@ -0,0 +1,61 @@ +from eaa.agents.memory import LocalVectorStore, MemoryManager, MemoryManagerConfig + + +def dummy_embedder(texts): + return [[float(len(text))] for text in texts] + + +def test_memory_manager_config_from_dict(): + payload = { + "enabled": True, + "write_enabled": False, + "retrieval_enabled": True, + "top_k": 3, + "score_threshold": 0.5, + "min_content_length": 5, + "embedding_model": "custom", + "vector_store_path": "/tmp/path", + "injection_role": "user", + "ignored": "value", + } + + config = MemoryManagerConfig.from_dict(payload) + + assert config.enabled is True + assert config.write_enabled is False + assert config.top_k == 3 + assert config.score_threshold == 0.5 + assert config.min_content_length == 5 + assert config.embedding_model == "custom" + assert config.vector_store_path == "/tmp/path" + assert config.injection_role == "user" + +def test_memory_manager_force_store_short_snippet(tmp_path): + store_path = tmp_path / "memory.json" + manager = MemoryManager( + dummy_embedder, + config=MemoryManagerConfig(enabled=True, vector_store_path=str(store_path)), + ) + + record = manager.remember("Call me Bo", metadata={"note": "Preferred name", "force_store": True}) + assert record is not None + assert record.metadata["note"] == "Preferred name" + + manager.flush() + assert store_path.exists() + + +def test_memory_manager_recall_returns_ranked_results(tmp_path): + manager = MemoryManager( + dummy_embedder, + config=MemoryManagerConfig(enabled=True, top_k=2), + vector_store=LocalVectorStore(), + ) + manager.remember("Remember that experiment A is sensitive", metadata={"note": "Experiment tip"}) + manager.remember("Keep in mind the threshold is 0.7", metadata={"note": "Threshold"}) + + results = manager.recall("threshold guidance") + assert len(results) == 2 + assert results[0].score >= results[1].score + formatted = manager.format_results(results) + assert "Relevant stored context" in formatted diff --git a/tests/test_postgres_vector_store.py b/tests/test_postgres_vector_store.py new file mode 100644 index 0000000..9c3383e --- /dev/null +++ b/tests/test_postgres_vector_store.py @@ -0,0 +1,46 @@ +import os +import uuid + +import pytest + +pytestmark = pytest.mark.local + +try: + from eaa.agents.memory import MemoryRecord, PostgresVectorStore +except ImportError: # pragma: no cover - psycopg not installed + PostgresVectorStore = None + MemoryRecord = None + + +@pytest.fixture +def pg_store(): + if PostgresVectorStore is None: + pytest.skip("psycopg not installed") + dsn = os.getenv("EAA_TEST_PG_DSN") + if not dsn: + pytest.skip("EAA_TEST_PG_DSN not set") + store = PostgresVectorStore( + dsn, + ensure_schema=True, + vector_dimension=3, + ) + yield store + store.delete([str(uuid.UUID(int=0))]) # no-op cleanup call for interface sanity + + +def _record(content: str, embedding): + return MemoryRecord(content=content, embedding=list(embedding), metadata={}, record_id=str(uuid.uuid4())) + + +@pytest.mark.local +def test_postgres_vector_store_roundtrip(pg_store): + record = _record("Remember the threshold is 0.7", [0.1, 0.2, 0.3]) + pg_store.add([record]) + + results = pg_store.search([0.1, 0.2, 0.3], top_k=1) + assert results + assert results[0].record.record_id == record.record_id + + pg_store.delete([record.record_id]) + assert not pg_store.search([0.1, 0.2, 0.3], top_k=1) + diff --git a/uv.lock b/uv.lock index c084547..aaca108 100644 --- a/uv.lock +++ b/uv.lock @@ -1000,6 +1000,7 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, { name = "openai" }, + { name = "psycopg", extra = ["binary"] }, { name = "scikit-image" }, { name = "scipy" }, { name = "tqdm" }, @@ -1035,6 +1036,7 @@ requires-dist = [ { name = "mic-instrument", marker = "extra == 'aps-mic'", git = "https://github.com/BCDA-APS/bluesky-mic.git" }, { name = "numpy" }, { name = "openai" }, + { name = "psycopg", extras = ["binary"], specifier = ">=3.2.10" }, { name = "pytest", marker = "extra == 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Pass overriding vector store etc. through explicit args --- src/eaa/agents/asksage.py | 21 +++- src/eaa/agents/base.py | 95 +++++++++++-------- src/eaa/agents/memory/manager.py | 28 +++++- src/eaa/agents/openai.py | 21 +++- src/eaa/task_managers/base.py | 34 +++++-- src/eaa/task_managers/imaging/base.py | 6 +- .../task_managers/imaging/feature_tracking.py | 6 +- src/eaa/task_managers/monitoring/base.py | 6 +- src/eaa/task_managers/tuning/base.py | 6 +- src/eaa/task_managers/tuning/bo.py | 6 +- src/eaa/task_managers/tuning/bo_mic_optics.py | 6 +- src/eaa/task_managers/tuning/focusing.py | 14 ++- 12 files changed, 175 insertions(+), 74 deletions(-) diff --git a/src/eaa/agents/asksage.py b/src/eaa/agents/asksage.py index d36d676..4cb9fde 100644 --- a/src/eaa/agents/asksage.py +++ b/src/eaa/agents/asksage.py @@ -1,12 +1,12 @@ import os import re -from typing import Dict, Any, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Sequence from asksageclient import AskSageClient from eaa.agents.base import BaseAgent -from eaa.agents.memory import MemoryManagerConfig +from eaa.agents.memory import MemoryManagerConfig, MemoryQueryResult, VectorStore from eaa.util import decode_image_base64, get_timestamp @@ -16,7 +16,12 @@ def __init__( self, llm_config: dict, system_message: str = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, + *, + memory_vector_store: Optional[VectorStore] = None, + memory_notability_filter: Optional[Callable[[str, Dict[str, Any]], bool]] = None, + memory_formatter: Optional[Callable[[List[MemoryQueryResult]], str]] = None, + memory_embedder: Optional[Callable[[Sequence[str]], List[List[float]]]] = None, ) -> None: """An agent that uses OpenAI-compatible API to generate responses. @@ -33,13 +38,21 @@ def __init__( - `cacert_path`: The path to the CA certificate file (*.pem). system_message : str, optional The system message for the OpenAI-compatible API. - memory_config : dict | MemoryManagerConfig, optional + memory_config : MemoryManagerConfig, optional Optional configuration for persistent memory support. + memory_vector_store, memory_notability_filter, memory_formatter, + memory_embedder : optional + Overrides for the memory backend, storage filter, recall formatting, + and embedding function respectively. """ super().__init__( llm_config=llm_config, system_message=system_message, memory_config=memory_config, + memory_vector_store=memory_vector_store, + memory_notability_filter=memory_notability_filter, + memory_formatter=memory_formatter, + memory_embedder=memory_embedder, ) @property diff --git a/src/eaa/agents/base.py b/src/eaa/agents/base.py index f1245db..969641a 100644 --- a/src/eaa/agents/base.py +++ b/src/eaa/agents/base.py @@ -103,7 +103,12 @@ from eaa.tools.mcp import MCPTool from eaa.comms import get_api_key from eaa.util import encode_image_base64, get_image_path_from_text -from eaa.agents.memory import MemoryManager, MemoryManagerConfig, MemoryQueryResult +from eaa.agents.memory import ( + MemoryManager, + MemoryManagerConfig, + MemoryQueryResult, + VectorStore, +) logger = logging.getLogger(__name__) @@ -271,7 +276,12 @@ def __init__( self, llm_config: dict, system_message: str = "", - memory_config: Optional[Dict[str, Any] | MemoryManagerConfig] = None, + memory_config: Optional[MemoryManagerConfig] = None, + *, + memory_vector_store: Optional[VectorStore] = None, + memory_notability_filter: Optional[Callable[[str, Dict[str, Any]], bool]] = None, + memory_formatter: Optional[Callable[[List[MemoryQueryResult]], str]] = None, + memory_embedder: Optional[Callable[[Sequence[str]], List[List[float]]]] = None, ) -> None: """The base agent class. @@ -285,18 +295,20 @@ def __init__( - `base_url`: The base URL for the OpenAI-compatible API. system_message : str, optional The system message for the OpenAI-compatible API. - memory_config : dict | MemoryManagerConfig, optional - Configuration for long-term memory. When provided, the agent can - optionally store conversation snippets and retrieve them on future - turns using retrieval-augmented generation. Pass a - `MemoryManagerConfig` instance when you only need to adjust the - dataclass fields. Supply a dictionary when you also want to include - auxiliary hooks such as a custom vector store, notability filter, - formatter, or embedder override. The dictionary may contain the - config fields (`enabled`, `write_enabled`, `retrieval_enabled`, - `top_k`, `score_threshold`, `min_content_length`, `embedding_model`, - `vector_store_path`, `injection_role`) plus optional extras - (`vector_store`, `notability_filter`, `formatter`, `embedder`). + memory_config : MemoryManagerConfig, optional + Configuration for long-term memory. Use + `MemoryManagerConfig.from_dict` to build from a mapping when + convenient. When provided, the agent can optionally store + conversation snippets and retrieve them on future turns using + retrieval-augmented generation. + memory_vector_store : VectorStore, optional + Override the default memory backend used for persistence and recall. + memory_notability_filter : Callable[[str, Dict[str, Any]], bool], optional + Custom filter evaluated before storing a snippet. + memory_formatter : Callable[[List[MemoryQueryResult]], str], optional + Formatter used to inject recalled memories back into the prompt. + memory_embedder : Callable[[Sequence[str]], List[List[float]]], optional + Override the embedding function for memory operations. """ self.llm_config = llm_config @@ -315,7 +327,13 @@ def __init__( self.memory_manager: Optional[MemoryManager] = None self._memory_injection_role = "system" - self._initialize_memory(memory_config) + self._initialize_memory( + memory_config, + vector_store=memory_vector_store, + notability_filter=memory_notability_filter, + formatter=memory_formatter, + embedder_override=memory_embedder, + ) @property def model(self) -> str: @@ -372,45 +390,38 @@ def get_default_embedding_model(self) -> Optional[str]: def _initialize_memory( self, - memory_config: Optional[Dict[str, Any] | MemoryManagerConfig], + memory_config: Optional[MemoryManagerConfig], + *, + vector_store: Optional[VectorStore] = None, + notability_filter: Optional[Callable[[str, Dict[str, Any]], bool]] = None, + formatter: Optional[Callable[[List[MemoryQueryResult]], str]] = None, + embedder_override: Optional[Callable[[Sequence[str]], List[List[float]]]] = None, ) -> None: if memory_config is None: return - config_obj: MemoryManagerConfig - vector_store = None - notability_filter = None - formatter = None - embedder_override = None - - if isinstance(memory_config, MemoryManagerConfig): - config_obj = memory_config - elif isinstance(memory_config, dict): - config_obj = MemoryManagerConfig.from_dict(memory_config) - vector_store = memory_config.get("vector_store") - notability_filter = memory_config.get("notability_filter") - formatter = memory_config.get("formatter") - embedder_override = memory_config.get("embedder") - else: - raise ValueError("memory_config must be a dict or MemoryManagerConfig instance.") + config_obj = memory_config if config_obj.injection_role not in {"system", "user"}: - raise ValueError( + logger.warning( "Unsupported injection role '%s'; defaulting to 'system'.", config_obj.injection_role, ) + config_obj.injection_role = "system" self._memory_injection_role = config_obj.injection_role - if embedder_override is None and not self.supports_memory_embeddings(): - logger.warning( - "%s does not support embeddings; ignoring memory configuration.", - self.__class__.__name__, - ) - return - - embedding_model = config_obj.embedding_model or self.get_default_embedding_model() - embedder_fn = embedder_override or self._build_memory_embedder(embedding_model) + if embedder_override is None: + if not self.supports_memory_embeddings(): + logger.warning( + "%s does not support embeddings; ignoring memory configuration.", + self.__class__.__name__, + ) + return + embedding_model = config_obj.embedding_model or self.get_default_embedding_model() + embedder_fn = self._build_memory_embedder(embedding_model) + else: + embedder_fn = embedder_override try: self.memory_manager = MemoryManager( diff --git a/src/eaa/agents/memory/manager.py b/src/eaa/agents/memory/manager.py index dd32e7c..e2a4d46 100644 --- a/src/eaa/agents/memory/manager.py +++ b/src/eaa/agents/memory/manager.py @@ -28,7 +28,15 @@ class MemoryManagerConfig(MemorySchema): class MemoryManager: - """Orchestrates storing and retrieving conversation memories for an agent.""" + """Orchestrates storing and retrieving conversation memories for an agent. + + The manager wraps an embedder function and a vector-store backend so it can + decide when to persist snippets (via :meth:`remember`) and fetch relevant + context (:meth:`recall`). Implementations remain pluggable: pass in a custom + embedder, a different vector store, or a notability filter to control what + gets saved. This class coordinates those pieces and exposes a minimal API + that agents can call during a conversation turn. + """ def __init__( self, @@ -39,6 +47,24 @@ def __init__( notability_filter: Optional[FilterFn] = None, formatter: Optional[FormatterFn] = None, ) -> None: + """Initialise a memory manager with the supplied components. + + Parameters + ---------- + embedder : Callable[[Sequence[str]], List[List[float]]] + Function that converts text snippets into numeric embeddings. + config : MemoryManagerConfig, optional + Runtime configuration controlling toggles, thresholds, and defaults. + vector_store : VectorStore, optional + Backend used to persist and query memories; defaults to a + JSON-backed local store when omitted. + notability_filter : Callable[[str, Dict[str, Any]], bool], optional + Predicate that can veto storage even if heuristics would otherwise + save the snippet. + formatter : Callable[[List[MemoryQueryResult]], str], optional + Converts recall results into text injected back into the agent + prompt; falls back to a bullet-list formatter. + """ self.embedder = embedder self.config = config or MemoryManagerConfig() self.vector_store = vector_store or LocalVectorStore(self.config.vector_store_path) diff --git a/src/eaa/agents/openai.py b/src/eaa/agents/openai.py index 5cec690..d3bbe33 100644 --- a/src/eaa/agents/openai.py +++ b/src/eaa/agents/openai.py @@ -1,9 +1,9 @@ -from typing import Any, Dict, List, Optional, Sequence, Union +from typing import Any, Callable, Dict, List, Optional, Sequence from openai import OpenAI from eaa.agents.base import BaseAgent -from eaa.agents.memory import MemoryManagerConfig +from eaa.agents.memory import MemoryManagerConfig, MemoryQueryResult, VectorStore class OpenAIAgent(BaseAgent): @@ -12,7 +12,12 @@ def __init__( self, llm_config: dict, system_message: str = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, + *, + memory_vector_store: Optional[VectorStore] = None, + memory_notability_filter: Optional[Callable[[str, Dict[str, Any]], bool]] = None, + memory_formatter: Optional[Callable[[List[MemoryQueryResult]], str]] = None, + memory_embedder: Optional[Callable[[Sequence[str]], List[List[float]]]] = None, ) -> None: """An agent that uses OpenAI-compatible API to generate responses. @@ -26,13 +31,21 @@ def __init__( - `base_url`: The base URL for the OpenAI-compatible API. system_message : str, optional The system message for the OpenAI-compatible API. - memory_config : dict | MemoryManagerConfig, optional + memory_config : MemoryManagerConfig, optional Optional configuration for persistent memory. + memory_vector_store, memory_notability_filter, memory_formatter, + memory_embedder : optional + Overrides for the memory backend, storage filter, result + formatting, and embedding function respectively. """ super().__init__( llm_config=llm_config, system_message=system_message, memory_config=memory_config, + memory_vector_store=memory_vector_store, + memory_notability_filter=memory_notability_filter, + memory_formatter=memory_formatter, + memory_embedder=memory_embedder, ) @property diff --git a/src/eaa/task_managers/base.py b/src/eaa/task_managers/base.py index 654de54..094a9c5 100644 --- a/src/eaa/task_managers/base.py +++ b/src/eaa/task_managers/base.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional, Callable, Literal, Union +from typing import Any, Dict, Optional, Callable, Literal, List, Sequence import json import sqlite3 import logging @@ -11,7 +11,7 @@ from eaa.util import get_timestamp from eaa.tools.base import ToolReturnType from eaa.api.llm_config import LLMConfig, OpenAIConfig, AskSageConfig -from eaa.agents.memory import MemoryManagerConfig +from eaa.agents.memory import MemoryManagerConfig, MemoryQueryResult, VectorStore try: from eaa.agents.asksage import AskSageAgent except ImportError: @@ -32,11 +32,16 @@ class BaseTaskManager: def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[Dict[str, Any], MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, - *args, **kwargs + *args, + memory_vector_store: Optional[VectorStore] = None, + memory_notability_filter: Optional[Callable[[str, Dict[str, Any]], bool]] = None, + memory_formatter: Optional[Callable[[List[MemoryQueryResult]], str]] = None, + memory_embedder: Optional[Callable[[Sequence[str]], List[List[float]]]] = None, + **kwargs ): """The base task manager. @@ -44,9 +49,11 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. - memory_config : dict | MemoryManagerConfig, optional - Optional configuration for long-term memory. Forwarded to the - underlying agent when instantiated. + memory_config : MemoryManagerConfig, optional + Optional configuration for long-term memory. Use + `MemoryManagerConfig.from_dict` to build from a mapping if needed. + memory_vector_store, memory_notability_filter, memory_formatter, memory_embedder : optional + Overrides propagated to the agent for custom memory behaviour. tools : list[BaseTool] A list of tools provided to the agent. message_db_path : Optional[str] @@ -60,13 +67,20 @@ def __init__( self.context = [] self.full_history = [] self.llm_config = llm_config + if isinstance(memory_config, dict): # type: ignore[unreachable] + memory_config = MemoryManagerConfig.from_dict(memory_config) # pragma: no cover - legacy path self.memory_config = memory_config self.agent = None self.tools = tools - + self.message_db_path = message_db_path self.message_db_conn = None self.webui_user_input_last_timestamp = 0 + + self._memory_vector_store = memory_vector_store + self._memory_notability_filter = memory_notability_filter + self._memory_formatter = memory_formatter + self._memory_embedder = memory_embedder if build: self.build() @@ -119,6 +133,10 @@ def build_agent(self, *args, **kwargs): llm_config=self.llm_config.to_dict(), system_message=self.assistant_system_message, memory_config=self.memory_config, + memory_vector_store=self._memory_vector_store, + memory_notability_filter=self._memory_notability_filter, + memory_formatter=self._memory_formatter, + memory_embedder=self._memory_embedder, ) self.agent.set_tool_approval_handler(self._request_tool_approval_via_task_manager) diff --git a/src/eaa/task_managers/imaging/base.py b/src/eaa/task_managers/imaging/base.py index 57ea70e..7bdbe6f 100644 --- a/src/eaa/task_managers/imaging/base.py +++ b/src/eaa/task_managers/imaging/base.py @@ -1,5 +1,5 @@ import logging -from typing import Optional, Union +from typing import Optional from PIL import Image @@ -26,7 +26,7 @@ class ImagingBaseTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, @@ -39,6 +39,8 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. tools : list[BaseTool] A list of tools provided to the agent. message_db_path : Optional[str] diff --git a/src/eaa/task_managers/imaging/feature_tracking.py b/src/eaa/task_managers/imaging/feature_tracking.py index 85eacce..c7677e0 100644 --- a/src/eaa/task_managers/imaging/feature_tracking.py +++ b/src/eaa/task_managers/imaging/feature_tracking.py @@ -1,4 +1,4 @@ -from typing import Optional, Callable, Literal, Union +from typing import Optional, Callable, Literal from eaa.tools.base import BaseTool from eaa.tools.imaging.acquisition import AcquireImage @@ -14,7 +14,7 @@ class FeatureTrackingTaskManager(ImagingBaseTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, image_acquisition_tool: AcquireImage = None, image_registration_tool: ImageRegistration = None, additional_tools: list[BaseTool] = (), @@ -29,6 +29,8 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. image_acquisition_tool : AcquireImage The tool to use to acquire images. image_registration_tool : ImageRegistration diff --git a/src/eaa/task_managers/monitoring/base.py b/src/eaa/task_managers/monitoring/base.py index 019c5d4..fb64135 100644 --- a/src/eaa/task_managers/monitoring/base.py +++ b/src/eaa/task_managers/monitoring/base.py @@ -1,4 +1,4 @@ -from typing import Optional, Union +from typing import Optional import logging import time from textwrap import dedent @@ -16,7 +16,7 @@ class BaseMonitoringTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, tools: list[BaseTool] = (), message_db_path: Optional[str] = None, build: bool = True, @@ -28,6 +28,8 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration to forward to the underlying agent. tools : list[BaseTool] The tools to use to monitor the experiment. message_db_path : Optional[str] diff --git a/src/eaa/task_managers/tuning/base.py b/src/eaa/task_managers/tuning/base.py index e738f77..c1f754a 100644 --- a/src/eaa/task_managers/tuning/base.py +++ b/src/eaa/task_managers/tuning/base.py @@ -1,4 +1,4 @@ -from typing import Optional, Union +from typing import Optional import logging from eaa.tools.imaging.param_tuning import SetParameters @@ -15,7 +15,7 @@ class BaseParameterTuningTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, param_setting_tool: SetParameters = None, additional_tools: list[BaseTool] = (), initial_parameters: dict[str, float] = None, @@ -31,6 +31,8 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. param_setting_tool : SetParameters The tool to use to set the parameters. initial_parameters : dict[str, float], optional diff --git a/src/eaa/task_managers/tuning/bo.py b/src/eaa/task_managers/tuning/bo.py index 5c12de0..1efcf0e 100644 --- a/src/eaa/task_managers/tuning/bo.py +++ b/src/eaa/task_managers/tuning/bo.py @@ -1,5 +1,5 @@ import logging -from typing import Optional, Callable, Union +from typing import Optional, Callable import torch @@ -17,7 +17,7 @@ class BayesianOptimizationTaskManager(BaseTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, bayesian_optimization_tool: BayesianOptimizationTool = None, additional_tools: list[BaseTool] = (), initial_points: Optional[torch.Tensor] = None, @@ -33,6 +33,8 @@ def __init__( ---------- llm_config : LLMConfig, optional The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. additional_tools : list[BaseTool], optional A list of tools for the agent (not including the `BayesianOptimizationTool`). diff --git a/src/eaa/task_managers/tuning/bo_mic_optics.py b/src/eaa/task_managers/tuning/bo_mic_optics.py index 532aacc..1d5838e 100644 --- a/src/eaa/task_managers/tuning/bo_mic_optics.py +++ b/src/eaa/task_managers/tuning/bo_mic_optics.py @@ -1,5 +1,5 @@ import logging -from typing import Optional, Union +from typing import Optional import torch import numpy as np @@ -22,7 +22,7 @@ class MicroscopyOpticsTuningBOTaskManager( def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, image_acquisition_tool: BaseTool = None, parameter_setting_tool: BaseTool = None, bayesian_optimization_tool: BayesianOptimizationTool = None, @@ -40,6 +40,8 @@ def __init__( ---------- llm_config : LLMConfig, optional The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the underlying agents. bayesian_optimization_tool : BayesianOptimizationTool The Bayesian optimization tool to use. image_acquisition_tool : BaseTool diff --git a/src/eaa/task_managers/tuning/focusing.py b/src/eaa/task_managers/tuning/focusing.py index 22e2446..a4145dd 100644 --- a/src/eaa/task_managers/tuning/focusing.py +++ b/src/eaa/task_managers/tuning/focusing.py @@ -1,4 +1,4 @@ -from typing import Optional, Callable, Union +from typing import Optional, Callable import logging import copy @@ -25,7 +25,7 @@ class ScanningMicroscopeFocusingTaskManager(BaseParameterTuningTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, param_setting_tool: SetParameters = None, acquisition_tool: AcquireImage = None, image_registration_tool: Optional[ImageRegistration] = None, @@ -63,6 +63,8 @@ def __init__( ---------- llm_config : LLMConfig, optional The LLM configuration to use. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. param_setting_tool : SetParameters The tool to use to set the parameters. acquisition_tool : AcquireImage @@ -256,6 +258,10 @@ def restore_fov( image_acquisition_tool=copy.deepcopy(self.acquisition_tool), image_registration_tool=copy.deepcopy(self.image_registration_tool), message_db_path=self.message_db_path, + memory_vector_store=self._memory_vector_store, + memory_notability_filter=self._memory_notability_filter, + memory_formatter=self._memory_formatter, + memory_embedder=self._memory_embedder, ) self.feature_tracking_task_manager.run_feature_tracking( @@ -507,7 +513,7 @@ class ParameterTuningTaskManager(BaseParameterTuningTaskManager): def __init__( self, llm_config: LLMConfig = None, - memory_config: Optional[Union[dict, MemoryManagerConfig]] = None, + memory_config: Optional[MemoryManagerConfig] = None, param_setting_tool: SetParameters = None, acquisition_tool: AcquireImage = None, initial_parameters: dict[str, float] = None, @@ -523,6 +529,8 @@ def __init__( ---------- llm_config : LLMConfig The configuration for the LLM. + memory_config : MemoryManagerConfig, optional + Memory configuration forwarded to the agent. param_setting_tool : SetParameters The tool to use to set the parameters. acquisition_tool : SimulatedAcquireImage, optional From c51c6fb092fa60d2e8bcfeba2f53fa19ec0a89ff Mon Sep 17 00:00:00 2001 From: Ming Du Date: Tue, 30 Sep 2025 13:42:16 -0500 Subject: [PATCH 3/4] FEAT: ChromaDB vector store --- pyproject.toml | 5 +- src/eaa/agents/memory/__init__.py | 3 +- src/eaa/agents/memory/manager.py | 20 +- src/eaa/agents/memory/vector_store.py | 118 ++++ uv.lock | 893 +++++++++++++++++++++++++- 5 files changed, 1024 insertions(+), 15 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 060e5ff..15e329c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,7 +19,7 @@ dependencies = [ "fastmcp>=2.11.3", "fastapi", "uvicorn[standard]", - "psycopg[binary]>=3.2.10", + "chromadb", ] dynamic = ["version"] @@ -32,6 +32,9 @@ aps_mic = [ asksage = [ "asksageclient==1.31" ] +postgresql_vector_store = [ + "psycopg[binary]>=3.2.10" +] docs = [ "sphinx", "sphinx-rtd-theme" diff --git a/src/eaa/agents/memory/__init__.py b/src/eaa/agents/memory/__init__.py index ba2b13e..5ea8f8c 100644 --- a/src/eaa/agents/memory/__init__.py +++ b/src/eaa/agents/memory/__init__.py @@ -1,12 +1,13 @@ from .manager import MemoryManager, MemoryManagerConfig from .types import MemoryQueryResult, MemoryRecord -from .vector_store import LocalVectorStore, PostgresVectorStore, VectorStore +from .vector_store import ChromaVectorStore, LocalVectorStore, PostgresVectorStore, VectorStore __all__ = [ "MemoryManager", "MemoryManagerConfig", "MemoryQueryResult", "MemoryRecord", + "ChromaVectorStore", "LocalVectorStore", "PostgresVectorStore", "VectorStore", diff --git a/src/eaa/agents/memory/manager.py b/src/eaa/agents/memory/manager.py index e2a4d46..12898ed 100644 --- a/src/eaa/agents/memory/manager.py +++ b/src/eaa/agents/memory/manager.py @@ -5,7 +5,7 @@ import logging from .types import MemoryQueryResult, MemoryRecord, MemorySchema -from .vector_store import LocalVectorStore, VectorStore +from .vector_store import ChromaVectorStore, LocalVectorStore, VectorStore logger = logging.getLogger(__name__) @@ -57,7 +57,8 @@ def __init__( Runtime configuration controlling toggles, thresholds, and defaults. vector_store : VectorStore, optional Backend used to persist and query memories; defaults to a - JSON-backed local store when omitted. + Chroma-backed store when available (falling back to the JSON-backed + local store otherwise). notability_filter : Callable[[str, Dict[str, Any]], bool], optional Predicate that can veto storage even if heuristics would otherwise save the snippet. @@ -67,7 +68,7 @@ def __init__( """ self.embedder = embedder self.config = config or MemoryManagerConfig() - self.vector_store = vector_store or LocalVectorStore(self.config.vector_store_path) + self.vector_store = vector_store or self._create_default_store() self.notability_filter = notability_filter self.formatter = formatter or self._default_formatter @@ -140,6 +141,19 @@ def flush(self) -> None: except AttributeError: logger.debug("Vector store does not implement `persist`; skipping flush.") + def _create_default_store(self) -> VectorStore: + persist_path = self.config.vector_store_path + try: + return ChromaVectorStore(persist_path) + except ImportError: + logger.debug("chromadb not available; falling back to LocalVectorStore.") + except Exception as exc: # pragma: no cover - defensive fallback. + logger.warning( + "Failed to initialise ChromaVectorStore, using LocalVectorStore instead: %s", + exc, + ) + return LocalVectorStore(persist_path) + @staticmethod def _default_formatter(results: List[MemoryQueryResult]) -> str: parts = [ diff --git a/src/eaa/agents/memory/vector_store.py b/src/eaa/agents/memory/vector_store.py index f5a3bc9..2c04b3e 100644 --- a/src/eaa/agents/memory/vector_store.py +++ b/src/eaa/agents/memory/vector_store.py @@ -18,6 +18,13 @@ psycopg = None # type: ignore dict_row = None # type: ignore +try: # pragma: no cover - import guard for optional dependency + import chromadb # type: ignore + from chromadb.config import Settings # type: ignore +except ImportError: # pragma: no cover + chromadb = None # type: ignore + Settings = None # type: ignore + from .types import MemoryQueryResult, MemoryRecord logger = logging.getLogger(__name__) @@ -163,6 +170,117 @@ def _deserialise_record(payload: dict) -> MemoryRecord: ) +class ChromaVectorStore(VectorStore): + """Vector store backed by ChromaDB.""" + + def __init__( + self, + persist_path: str | Path, + *, + collection_name: str = "memory_records", + ) -> None: + if chromadb is None or Settings is None: + raise ImportError("chromadb is required for ChromaVectorStore.") + + # Build DB client + self.collection_name = collection_name + self._persist_path = Path(persist_path) if isinstance(persist_path, str) else persist_path + self._persist_path.parent.mkdir(parents=True, exist_ok=True) + self._client = chromadb.PersistentClient(path=str(self._persist_path)) + + # Build collection + self._collection = self._client.get_or_create_collection( + name=self.collection_name, + metadata={"hnsw:space": "cosine"}, + ) + + def add(self, records: Sequence[MemoryRecord]) -> None: + if not records: + return + ids: List[str] = [] + documents: List[str] = [] + embeddings: List[List[float]] = [] + metadatas: List[Dict[str, object]] = [] + for record in records: + ids.append(str(record.record_id)) + documents.append(record.content) + embeddings.append([float(value) for value in record.embedding]) + metadata = dict(record.metadata or {}) + metadata.setdefault("created_at", record.created_at) + metadatas.append(metadata) + self._collection.upsert( + ids=ids, + documents=documents, + embeddings=embeddings, + metadatas=metadatas, + ) + + def search( + self, + embedding: Sequence[float], + *, + top_k: int = 5, + score_threshold: float = 0.0, + ) -> List[MemoryQueryResult]: + if not embedding: + return [] + query_embedding = [float(value) for value in embedding] + response = self._collection.query( + query_embeddings=[query_embedding], + n_results=max(int(top_k), 1), + include=["distances", "documents", "metadatas", "embeddings"], + ) + ids = response.get("ids", [[]]) + if not ids or not ids[0]: + return [] + distances = response.get("distances", [[]])[0] + documents = response.get("documents", [[]])[0] + metadatas = response.get("metadatas", [[]])[0] + retrieved_embeddings = response.get("embeddings", [[]])[0] + results: List[MemoryQueryResult] = [] + for idx, record_id in enumerate(ids[0]): + distance = None + if distances and len(distances) > idx and distances[idx] is not None: + distance = float(distances[idx]) + score = 1.0 - distance if distance is not None else 0.0 + if score < score_threshold: + continue + content = documents[idx] if documents and len(documents) > idx else "" + metadata = metadatas[idx] if metadatas and len(metadatas) > idx else {} + if metadata is None: + metadata = {} + created_at = metadata.get("created_at", time.time()) + if isinstance(created_at, str): + try: + created_at = float(created_at) + except ValueError: + created_at = time.time() + embedding_values: List[float] = [] + if len(retrieved_embeddings[0]) > 0 and len(retrieved_embeddings) > idx: + embedding_values = [float(value) for value in retrieved_embeddings[idx]] + record = MemoryRecord( + content=content, + embedding=embedding_values, + metadata=metadata if isinstance(metadata, dict) else {}, + record_id=str(record_id), + created_at=float(created_at), + ) + results.append(MemoryQueryResult(record=record, score=score)) + return results + + def delete(self, record_ids: Sequence[str]) -> None: + if not record_ids: + return + self._collection.delete(ids=[str(rid) for rid in record_ids]) + + def persist(self) -> None: + if hasattr(self._client, "persist"): + try: + self._client.persist() + except Exception: # pragma: no cover - best effort persistence + logger.debug("Chroma client persistence failed; continuing without persisting.") + + def _vector_literal(values: Sequence[float]) -> str: return "[" + ",".join(f"{float(v):.8f}" for v in values) + "]" diff --git a/uv.lock b/uv.lock index aaca108..cb02248 100644 --- a/uv.lock +++ b/uv.lock @@ -291,6 +291,85 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" }, ] +[[package]] +name = "backoff" +version = "2.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" } +wheels = [ + { url = 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