This is a simple FastAPI application that generates text embeddings using the all-MiniLM-L6-v2 model from the sentence-transformers library.

• POST /embeddings: Accepts a text input and returns its embeddings as a list.
• GET /: Returns a welcome message with basic API information.

• Python 3.8+
• FastAPI
• Pydantic
• Sentence Transformers
• NumPy

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install fastapi uvicorn sentence-transformers numpy

1. Run the FastAPI server:

   uvicorn main:app --reload

   The server will start at http://127.0.0.1:8000.

2. Access the API:

   • GET Welcome Message:

     curl http://127.0.0.1:8000/

     Response:

     {"message": "Welcome to the Text Embeddings API. Use POST /embeddings to get embeddings."}

   • POST Text Embeddings:

     curl -X POST http://127.0.0.1:8000/embeddings -H "Content-Type: application/json" -d '{"text": "This is a sample text"}'

     Response:

     {"embeddings": [0.12, -0.34, ..., 0.56]}

3. Interactive API documentation is available at http://127.0.0.1:8000/docs.

• main.py: Contains the FastAPI application with two endpoints:
  • GET /: Returns a welcome message.
  • POST /embeddings: Takes a text input and returns its embeddings.
• Model: Uses all-MiniLM-L6-v2 from sentence-transformers to generate embeddings.
• Input Validation: Uses Pydantic's BaseModel to validate incoming text input.

• If an error occurs during embedding generation, the API returns a 500 status code with a detailed error message.