Provides a FastAPI gateway server that exposes REST endpoints for the RAG system, enabling web-based access to document querying and role-based retrieval functionality.
Uses OpenAI's API for embeddings and language model operations to power the RAG system's document retrieval and response generation capabilities.
Offers a Streamlit web UI for interactive document querying with role-based access control, allowing users to search company documents through a user-friendly interface.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@FinRAG-MCPWhat were our Q4 revenue drivers?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
FinRAG-MCP
FinRAG-MCP is a Role-Based Access Control (RBAC) Retrieval-Augmented Generation (RAG) system, integrated with the Model Context Protocol (MCP).
It lets you query company documents (Engineering, Finance, HR, Marketing, General) securely — each role only sees what it’s allowed to.
✅ Works with Claude Desktop (MCP tools)
✅ Optional Streamlit UI + FastAPI gateway
✅ Uses Qdrant (local) for vector search
✅ Documents enriched with metadata + citations
🚀 Why this project
Secure answers → Employees, Managers, and C-Level see different data (RBAC).
Trusted output → Every response includes sources and file chunks.
Flexible → Claude MCP integration + standalone UI.
Practical → Handles Markdown, CSV, reports, handbooks, financial summaries.
📂 Project Structure
FinRAG-MCP
FinRAG-MCP is a Role-Based Access Control (RBAC) Retrieval-Augmented Generation (RAG) system, integrated with the Model Context Protocol (MCP).
It lets you query company documents (Engineering, Finance, HR, Marketing, General) securely — each role only sees what it’s allowed to.
✅ Works with Claude Desktop (MCP tools)
✅ Optional Streamlit UI + FastAPI gateway
✅ Uses Qdrant (local) for vector search
✅ Documents enriched with metadata + citations
🚀 Why this project
Secure answers → Employees, Managers, and C-Level see different data (RBAC).
Trusted output → Every response includes sources and file chunks.
Flexible → Claude MCP integration + standalone UI.
Practical → Handles Markdown, CSV, reports, handbooks, financial summaries.
📂 Project Structure
⚙️ Setup
``bash
Clone repo
git clone https://github.com/YOUR_GITHUB/finrag-mcp.git cd finrag-mcp
Create venv with uv
uv venv .venv source .venv/bin/activate uv sync
Add secrets
cat > env/.env << 'EOF' OPENAI_API_KEY=sk-REPLACE_ME OPENAI_MODEL=gpt-4o-mini EMBED_MODEL=text-embedding-3-large QDRANT_LOCAL_PATH=.qdrant_local QDRANT_COLLECTION_PREFIX=finrag FINRAG_ROLE=EMPLOYEE EOF
Ingest docs
uv run python -m ingest.run_ingest --data-root ./data
Run Options
Claude MCP (Recommended) Open Claude Desktop → Settings → Developer → Local MCP servers Add: Command: .../finrag-mcp/.venv/bin/python Args: -m mcp_server.server Env: from .env Claude can now call tools: set_role("FINANCE") search("Q4 2024 revenue drivers", top_k=5)
Start gateway
uv run uvicorn gateway.app:app --port 8000
Start UI
uv run streamlit run ui/app.py --server.port 8501
Example Queries Employee: “What does the handbook say about leave approval?” Engineering: “List services in the architecture doc.” Finance: “Summarize Q4 revenue drivers.” HR: “What’s the rule for sick leave >2 days?” Marketing: “Which 2024 campaigns had the best ROI?”
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