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.
- 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/
.
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
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
, optionalk
(int),distance_threshold
(float), and credentials.
Open for collaboration; check the issues page for discussions.