Merge pull request #1 from farhoud/llmcore

Llmcore

Author: farhoud

README.md

@@ -4,11 +4,11 @@ Rapid assessment and retrieval from knowledge graph using Neo4j GraphRAG.
 
 ## Overview
 
-Scouter is a knowledge graph-based document retrieval system that:
+Scouter is a knowledge graph-based document retrieval system focused on MCP (Model Context Protocol) for agentic search:
 
 - Ingests PDFs and text documents using Neo4j GraphRAG's SimpleKGPipeline
-- Provides fast semantic search with relevance scoring
-- Supports both API and MCP (Model Context Protocol) interfaces
+- Provides agentic semantic search via MCP for LLM integration
+- Includes REST API for document ingestion
 - Includes evaluation framework for retrieval quality assessment
 
 ## Quick Start
@@ -62,31 +62,25 @@ curl -X POST "http://localhost:8000/v1/ingest" \
   -d '{"text": "Your document content", "metadata": {"source": "api"}}'
 ```
 
-### Search
-
-```bash
-# Search documents
-curl "http://localhost:8000/v1/search?query=your%20search%20term&limit=5"
-```
-
 ### Interactive API
 
 Visit <http://localhost:8000/docs> for interactive API documentation.
 
+**Note:** Search functionality is provided via MCP (Model Context Protocol) for agentic retrieval. Direct REST search API is not available.
+
 ## Architecture
 
 ### Components
 
 - **Ingestion Service**: Processes PDFs/text into knowledge graph using SimpleKGPipeline
-- **Search Service**: Performs semantic search with relevance scoring
-- **MCP Server**: Provides Model Context Protocol interface for LLM integration
+- **MCP Server**: Core component providing agentic search via Model Context Protocol for LLM integration
 - **Celery Workers**: Handle async document processing
 - **Redis**: Task queue and caching
 
 ### Data Flow
 
 1. Documents → Ingestion API → Celery Queue → Neo4j GraphRAG
-2. Search Query → Search API → Neo4j → Ranked Results
+2. Search Query → MCP Server → Agentic Search → Neo4j → Ranked Results
 
 ## Development
 
@@ -143,24 +137,26 @@ The project uses Neo4j with APOC plugin for enhanced graph procedures. Docker se
 
 ## Examples
 
-### RAG Chatbot
+### MCP Integration (Primary Use Case)
 
 ```bash
-cd examples/chatbot
-python chatbot.py
+# Start MCP server
+python -m scouter_app.agent.mcp
+
+# Use with Claude Desktop or other MCP-compatible tools
 ```
 
-Interactive chatbot that uses Scouter for retrieval and OpenRouter for generation.
+Scouter's MCP server enables agentic search for LLMs, providing semantic retrieval from the knowledge graph.
 
-### MCP Integration
+### RAG Chatbot
 
 ```bash
-# Start MCP server
-python -m scouter_app.agent.mcp
-
-# Use with Claude Desktop or other MCP-compatible tools
+cd examples/chatbot
+python chatbot.py
 ```
 
+Interactive chatbot that uses Scouter for retrieval and OpenRouter for generation.
+
 ## Project Structure
 
 ```

app_main.py

@@ -4,9 +4,9 @@
 
 from fastapi import FastAPI
 
-from src.scouter_app.agent.mcp import app as mcp_app
-from src.scouter_app.config.llm import get_client_config
-from src.scouter_app.ingestion.api import router as ingestion_router
+from src.scouter.agent.mcp import app as mcp_app
+from src.scouter.config.llm import get_client_config
+from src.scouter.ingestion.api import router as ingestion_router
 
 logger = logging.getLogger(__name__)
 

evals/conftest.py

@@ -10,7 +10,7 @@
 
 import pytest
 
-from scouter_app.ingestion.service import IngestionService
+from scouter.ingestion.service import IngestionService
 
 from .utils import create_light_subset
 

evals/test_retrieval_relevancy.py

@@ -3,6 +3,7 @@
 from deepeval.test_case import LLMTestCase
 
 from examples.chatbot.chatbot import chat_with_rag
+
 from .utils import OpenRouterLLM
 
 THRESHOLD = 0.5

examples/chatbot/chatbot.py

@@ -6,7 +6,7 @@
 from mcp import ClientSession
 from mcp.client.stdio import StdioServerParameters, stdio_client
 
-from scouter_app.config.llm import (
+from scouter.config.llm import (
     DEFAULT_MODEL,
     call_with_rate_limit,
     get_chatbot_client,
@@ -32,8 +32,6 @@ async def chat_with_rag(query: str) -> str:
 
         mcp_tools = await session.list_tools()
 
-        print(mcp_tools)
-
         # Convert MCP tools to OpenAI format
         openai_tools = [
             {
@@ -72,15 +70,15 @@ async def chat_with_rag(query: str) -> str:
                 tool_args = json.loads(tool_call.function.arguments)
                 result = await session.call_tool(tool_name, tool_args)
                 # Add to messages
-                messages.append(  # type: ignore
-                    {"role": "assistant", "content": "", "tool_calls": [tool_call]}  # type: ignore
+                messages.append(  # type: ignore[PGH003]
+                    {"role": "assistant", "content": "", "tool_calls": [tool_call]}  # type: ignore[PGH003]
                 )
-                messages.append(  # type: ignore
+                messages.append(  # type: ignore[PGH003]
                     {
                         "role": "tool",
                         "content": str(result),
                         "tool_call_id": tool_call.id,
-                    }  # type: ignore
+                    }  # type: ignore[PGH003]
                 )
 
             # Call LLM again with updated messages

pyproject.toml

@@ -38,6 +38,9 @@ asyncio_mode = "auto"
 [tool.ruff]
 extend = "ruff.toml"
 
+[tool.hatch.build.targets.wheel]
+packages = ["src/scouter"]
+
 [dependency-groups]
 dev = [
     "pre-commit>=4.5.0",

src/scouter_app/__init__.py src/scouter/__init__.pysrc/scouter_app/__init__.py renamed to src/scouter/__init__.py

@@ -1,7 +1,7 @@
 import json
 
-from scouter_app.agent.tools import get_tools
-from scouter_app.config.llm import (
+from scouter.agent.tools import get_tools
+from scouter.config.llm import (
     DEFAULT_MODEL,
     call_with_rate_limit,
     get_scouter_client,