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chikhio123

Qdrant RAG MCP Server

by chikhio123
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Python
Local

Qdrant RAG Tool

Minimal RAG ingestion and search tool for .md and .txt files.

Layout

  • data/: put documents here

  • ingest.py: chunk documents, embed them, and upsert into Qdrant

  • search.py: embed a query and search Qdrant

  • .env: runtime configuration and secrets

Related MCP server: Qdrant MCP Server

Usage

cd /opt/qdrant/rag
source .venv/bin/activate
python ingest.py
python search.py "Qdrant 是什么"

The ingester uses a stable point ID based on source + chunk_index. Before ingesting a file, it deletes existing chunks for the same source, so rerunning ingestion for the same file does not create duplicates. Use python ingest.py --prune to delete sources from Qdrant after removing their files from data/.

Current defaults:

  • Embedding endpoint: https://ai.gitee.com/v1

  • Embedding model: Qwen3-Embedding-8B

  • Embedding dimensions: 4096

  • Rerank model: Qwen3-Reranker-8B

  • Ask model: deepseek-v4-flash-free through OpenCode Zen

  • Qdrant collection: docs_qwen3_embedding_8b

MCP

The MCP server exposes RAG tools:

  • rag_health

  • rag_search

  • rag_ask

  • rag_source_stats

  • rag_get_chunk

  • rag_get_source

  • rag_update_source

  • rag_delete_source

It listens on 127.0.0.1:8765 by default, with the MCP endpoint at /mcp.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

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Resources

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