wiki-rag-mcp

Automated RAG ingestion pipeline and MCP server for enterprise wikis powered by Qdrant and FastMCP via Docker Compose.

Configuration

Settings are resolved first from Environment Variables, falling back to config.json when present on the host filesystem.

• WIKI_TYPE: Target wiki engine (mediawiki or confluence, default: mediawiki)

• WIKI_URL: Base URL of the wiki API (default: https://en.wikipedia.org/w)

• AUTH_TOKEN: Bearer token or API key if authentication is required (e.g., Confluence)

• QDRANT_HOST: Vector database hostname (default: vector-db)

• QDRANT_PORT: Vector database port (default: 6333)

• EMBED_MODEL_URL: Base URL for generating vector embeddings (default: http://host.docker.internal:1234/v1)

Related MCP server: ollqd

Ingestion Lifecycle & MCP Persistence

• Fresh Snapshot Strategy: On every execution, src/ingest.py automatically drops and recreates the wiki vector collection to clear out stale or deleted wiki pages.

• Zero MCP Restart: The MCP server queries Qdrant via stateless REST calls. When ingest.py recreates the collection, the MCP server immediately queries the new vector snapshot on its next tool call with zero downtime or server restarts.

Option 1: Running on a Local Host Machine

Run the entire containerized stack on your local development machine using Docker Compose. All vector indexes persist to ./qdrant_data on your host disk.

1. Launch the Stack

Spin up Qdrant and the MCP server in background mode:

docker compose up -d

2. Trigger Ingestion

Run the ingestor container on-demand on your local host whenever you need a sync:

docker compose run --rm ingestor

3. Connect Local LLM Client

Point host-level LLM clients (LM Studio, Claude Desktop, Cursor) to the MCP server endpoint running on your local host:

http://localhost:8080/sse

Option 2: Running on a Cloud Host Machine

Deploy the container images to cloud container infrastructure (AWS, GCP, Azure, or a remote Linux host). Because the ingestor container runs to completion and exits cleanly, it can be scheduled on demand without running a 24/7 process.

1. Persistent Services (Vector DB & MCP Server)

Deploy vector-db and mcp-server as long-running, always-on container services on your cloud host / cluster:

• Expose port 8080 internally or behind an API gateway for client connections.

• Mount persistent cloud storage (e.g., AWS EBS, GCP Persistent Disk) to /qdrant/storage.

2. Scheduled Ingestor Execution

Invoke the ingestor container image on a scheduled cron trigger using cloud container orchestration:

• AWS: Trigger ingestor as an ECS / Fargate Task scheduled via Amazon EventBridge.

• GCP: Schedule ingestor as a Cloud Run Job managed by Cloud Scheduler.

• Remote Cloud VM: Schedule docker run --rm ingestor via standard host crontab.

Testing Locally with LM Studio

LM Studio runs your chat model and embedding model locally while Docker Compose runs the MCP server and vector database.

Step 1: Start the Docker Infrastructure

Launch Qdrant and the MCP server:

docker compose up -d

Step 2: Set Up Models in LM Studio

1. Open LM Studio and download an embedding model (e.g., text-embedding-all-minilm-l6-v2) and a chat model (e.g., Qwen2.5-7B-Instruct).

2. Start the Local Server in LM Studio on port 1234.

3. Verify that http://localhost:1234/v1 is active.

Step 3: Trigger Ingestion

Run the ingestor container to pull wiki pages, generate embeddings via LM Studio's embedding endpoint, and write them to Qdrant:

docker compose run --rm ingestor

Step 4: Connect LM Studio to the MCP Server

1. Go to the Programmatic Access / MCP section in LM Studio.

2. Add a new SSE server entry:

   • Server URL: http://localhost:8080/sse

3. Connect to the server. You should see search_wiki listed under available tools.

Step 5: Test the Pipeline

Open a new chat in LM Studio with your loaded chat model, ensure Tools/MCP function calling is enabled, and ask:

"Search the wiki for authentication rules."

LM Studio will invoke search_wiki, retrieve relevant vector context from Qdrant, and output a grounded answer.