Embedding Service API

This is a simple FastAPI server that uses the Sentence Transformers model (all-mpnet-base-v2) to generate text embeddings. It’s designed to be used as a microservice for applications like matching systems, search, and recommendation engines.

Features

• Generate Embeddings: POST text to the /embed endpoint and get back a vector embedding.
• Easy to Deploy: Runs locally or on any cloud platform.
• Fast & Lightweight: Built with FastAPI and Uvicorn for high performance.

Setup

1. Clone the repository
2. Create and activate a virtual environment

python -m venv venv

On Linux/Mac

source venv/bin/activate

On Windows

venv\Scripts\activate

3. Install the dependencies

pip install -r requirements.txt

Running the server

uvicorn main:app --reload

API Usage

Generate Embedding

Endpoint: POST /embed

Request Body

{
  "text": "Explain how AI works"
}

Successful response

{
  "embedding": [0.123, -0.456, ... ]
}

How it works

Text-to-Vector Conversion

• The model processes your text and returns an array of numbers (vector embedding) that encodes the semantic meaning of the text.

Use Cases

These vector embeddings can be used to :-

• Compare text similarity
• Perform matching between user queries and available content or profiles