/localEmbeddingsAPI

Qdrant and embedding - local API

Primary LanguagePython

Qdrant and embedding - local API

The purpose of this repo is to use embedding and Qdrant running locally as an API

How to run

  1. Run the following command:
docker-compose up

Please note that the first run will take a while, as there is a few gigabytes of data to download. Also, the first upsert to Qdrant will take a while, as the model has to be downloaded and the index needs to be built.

  1. Open http://localhost:5000/ in your browser
  2. Set up your environment by clicking "Initialize Qdrant" button
  3. You can now use "upsert" to add new embeddings to the database, "test search" to search for the nearest neighbors of a given embedding. Please note that you should provide UUID in the ID field.

API reference

POST /upsert/

  • ID - UUID of the embedding (required)
  • phrase - phrase to embed and upsert to Qdrant (required)

POST /search/

  • search_term - phrase to embed and search for nearest neighbors (required)

POST /embed/

  • phrase - phrase to embed (required)

Developer notes

New packages in requirements.txt

When you change your requirements.txt file, you'll need to rebuild your Docker image to install the new Python packages.

Here are the steps:

  1. Stop the running Docker containers with the following command:

    docker-compose down

  2. Then rebuild and start your Docker containers:

    docker-compose up --build