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Explorer/MCP/amornpan/py-mcp-qdrant-rag
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py-mcp-qdrant-rag

amornpan/py-mcp-qdrant-rag

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

↗amornpan/py-mcp-qdrant-ragExternal link; availability not verifiedHybrid hosting
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Hasn't been around long. Nothing caught our attention, but there isn't much history to go on yet.
Time indexed (5mo)
0toolsHybrid5moindexed
Checked Apr 18, 2026
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First indexed Apr 18, 2026
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Can run locally or remotely depending on deployment. Review the source repository.
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Source repository is available for review.
↗amornpan/py-mcp-qdrant-ragExternal link; availability not verified
Environment variables (10)
At least one of these required
QDRANT_URL · EMBEDDING_PROVIDER · OLLAMA_URL
At least one of these required
QDRANT_URL · EMBEDDING_PROVIDER · OPENAI_API_KEY
LOG_LEVELstringOPTIONALdefault: INFO
Debug logging level
CHUNK_SIZEstringOPTIONALdefault: 1000
Text chunk size for splitting
OLLAMA_URLstringOPTIONALdefault: http://localhost:11434
Ollama server URL (if using Ollama)
QDRANT_URLstringOPTIONALdefault: http://localhost:6333
Qdrant server URL
CHUNK_OVERLAPstringOPTIONALdefault: 200
Overlap between chunks
Show all 10 environment variables
OPENAI_API_KEYstringOPTIONAL
OpenAI API key (if using OpenAI)
QDRANT_API_KEYstringOPTIONAL
Qdrant API key (if using Qdrant Cloud)
COLLECTION_NAMEstringOPTIONALdefault: documents
Qdrant collection name
EMBEDDING_MODELstringOPTIONAL
Model name for embeddings (e.g., nomic-embed-text for Ollama or text-embedding-3-small for OpenAI)
EMBEDDING_PROVIDERstringOPTIONALdefault: ollama
Embedding provider (ollama or openai)

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