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.