MCP-Based Enterprise Knowledge Search Assistant
Enables enterprise knowledge search through a chat interface, comparing traditional RAG with MCP-driven retrieval using hybrid BM25 and dense vector search, Ollama-powered answer generation, and question routing to domain-specific retrieval tools.
README
MCP-Based Enterprise Knowledge Search Assistant
This repository contains a proof-of-concept implementation of an enterprise knowledge assistant that compares a traditional RAG-style experience with an MCP-driven retrieval flow.
Architecture
- Frontend: Claude Desktop style chat experience
- Backend: FastAPI + Python 3.12
- Retrieval: Elasticsearch-style hybrid search abstraction with BM25 + dense vector similarity
- LLM: Ollama with gemma3:latest
- MCP: Custom Python MCP-style tool server exposing domain-specific retrieval tools
Key features
- Question routing to the correct MCP tool
- Hybrid retrieval with ranked chunks and source citations
- Ollama integration for answer generation
- Evaluation metrics logging to JSON
- Docker Compose support for local development
Running locally
- Install dependencies:
pip install -e .
- Start Ollama and ensure
gemma3:latestis available. - Start the API:
uvicorn app.core.app:app --reload
- Call the chat endpoint:
POST /chatwith{ "question": "What is the maternity leave policy?" }
Sample questions
- What is the maternity leave policy?
- How do I submit a travel reimbursement request?
- What are the annual appraisal guidelines?
- What should I do for a security incident?
Evaluation
Run the evaluation helper to write a metrics record:
from app.services.evaluation import EvaluationService
service = EvaluationService()
print(service.compare("What is the maternity leave policy?"))
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