RAG Chatbot

A small numpy-based vector index (vector_store.py) over your own documents — no compiled/native dependencies beyond numpy, so it needs no admin rights to install. The LLM/embeddings backend is pluggable via config.LLM_PROVIDER: "ollama" (100% local, no API key) or "gemini" (Google's cloud API, needs a free key).

Setup

Provider: Gemini (config.py → LLM_PROVIDER = "gemini", the current default)

1. Get a free API key from Google AI Studio.

2. Set it as an environment variable (don't paste it into files or commit it):

# macOS/Linux
export GOOGLE_API_KEY="your-key-here"

# Windows PowerShell
$env:GOOGLE_API_KEY = "your-key-here"

Provider: Ollama (set LLM_PROVIDER = "ollama" in config.py)

1. Install Ollama and pull the models used:

ollama pull nomic-embed-text
ollama pull llama3.1

Then, for either provider, install Python dependencies:

pip install -r requirements.txt

Related MCP server: vectorise-mcp

Usage — CLI

1. Drop your files (.pdf, .txt, .md, .docx, .csv, .xlsx) into the docs/ folder.

2. Build the index:

python ingest.py

3. Chat:

python chat.py

Type exit to quit.

Usage — Web app (frontend + backend)

Runs a FastAPI backend that serves a JSON API and a static chat UI, all on one port.

python -m uvicorn server:app --reload --port 8000

Open http://localhost:8000 in a browser. From there you can:

• Upload files (drag/select, click Upload)

• Click Rebuild Index to (re)embed everything currently in docs/

• Chat in the main panel — answers include source file names

API endpoints, if you want to script against it directly:

• GET /api/status — index/model info

• POST /api/upload — multipart file upload, saved into docs/

• POST /api/ingest — rebuilds the index from docs/

• POST /api/chat — {"question": "..."} → {"answer": "...", "sources": [...]}

Usage — MCP server

Exposes the document index as MCP tools (ask, search, rebuild_index, status) so any MCP client (Claude Desktop, Claude Code, etc.) can query your docs. Runs over stdio - the client launches it as a subprocess, no port involved.

Add it to your MCP client config, e.g. Claude Desktop's claude_desktop_config.json:

{
  "mcpServers": {
    "rag-chatbot": {
      "command": "python",
      "args": ["C:/Users/Nikita_Admin/Desktop/mcm/rag-chatbot/mcp_server.py"]
    }
  }
}

For Claude Code, run:

claude mcp add rag-chatbot -- python C:/Users/Nikita_Admin/Desktop/mcm/rag-chatbot/mcp_server.py

Restart the client afterward. The index must already exist (python ingest.py), or call the rebuild_index tool from within the chat once files are in docs/.

Notes

• Re-run python ingest.py after adding/changing files in docs/. It rebuilds index.npz from scratch each time.

• Change models or chunking behavior in config.py.

• Larger/more capable local models (e.g. llama3.1:70b, mixtral) give better answers but need more RAM/VRAM — swap LLM_MODEL in config.py.

• The vector index is a single index.npz file (numpy arrays + JSON), fine for personal/small document sets. For large corpora, swap vector_store.py for a proper vector DB.