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

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.