vector-embedding-api

Flask API server for generating text embeddings using OpenAI's embedding model or the SentenceTransformers library. SentenceTransformers supports over 500 models via HuggingFace Hub.

Features 🎯

• POST endpoint to create text embedding models
  • sentence_transformers
  • OpenAI text-embedding-ada-002
• Easy setup with configuration file
• Simple integration with other applications
• Python client utility for submitting text

Installation 💻

To run this server locally, follow the steps below:

Clone the repository: 📦

git clone https://github.com/deadbits/vector-embedding-api.git
cd text-embeddings-server

Set up a virtual environment (optional but recommended): 🐍

python3 -m venv venv
source venv/bin/activate

Install the required dependencies: 🛠️

pip install -r requirements.txt

Usage

Before running the server, make sure you have obtained an API key from OpenAI to use their model. You also need to set the SentenceTransformers model you want to use in the server.conf file.

Modify the server.conf configuration file: ⚙️

[main]
openai_api_key = YOUR_OPENAI_API_KEY
sent_transformers_model = sentence-transformers/all-MiniLM-L6-v2

Start the server: 🚀

python server.py

The server should now be running on http://127.0.0.1:5000/.

API Endpoints 🌐

POST /submit

Submit text to be converted to embeddings. The sentence transformers model will be used by default, but you can change the "model" field to "openai" to use text-embedding-ada-002.

POST data: {"text": 'Put your text here', "model": "local"} {"text": 'Put your text here', "model": "openai"}

The default is to use the SentenceTransformers model.

Example Request:

curl -X POST -H "Content-Type: application/json" -d '{"text": 'Put your text here', "model": "local"}' http://127.0.0.1:5000/submit

Example Response:

{
    "embedding": [0.123, 0.456, ..., 0.789],
    "status": "success"
}