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Server over Python Faiss serverless implementation to match interfaces used in langchain

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Flagro/VecMetaQ

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🚀 VecMetaQ

Part of a ProjectText Suite. VecMetaQ (Vector Metadata Query) is a FastAPI web app encapsulating a FAISS vector index for easy management of embeddings and metadata.

🌟 Features

  • Add Data: Add text, tag, and metadata using POST /add_data/.
  • Delete Data: Mark data as deleted via tag using DELETE /delete_data/.
  • Search Similar: Search for similar text using POST /search_similar/.

🚀 Getting Started

Make sure to have docker installed on your system and then simply copy and initialize the .env file and do a docker compose up:

mv .env-example .env
docker compose up

Or to use the GHCR you can (make sure to have the .env file ready):

docker pull ghcr.io/flagro/vecmetaq
docker run -it --env-file .env ghcr.io/flagro/vecmetaq

📘 Usage

Accessible by default at 127.0.0.1:8000.

🛠️ API Endpoints

  • Add Data (POST /add_data/): Requires text, tag, metadata, and credentials.
  • Delete Data (DELETE /delete_data/): Needs tag and credentials.
  • Search Similar (POST /search_similar/): Expects query, optional k (int), distance_threshold (float), and credentials.

🤝 Collaboration & Issues

Open for collaboration; check the issues page for discussions.