Semantic Search API

Semantic Search is a project aimed at providing efficient search capabilities by leveraging semantic similarity between documents. This project utilizes FastAPI for the backend, PostgreSQL for data storage, and the OpenAI text-embedding-ada-002 model for calculating embedding vectors.

Features

• FastAPI Backend: Utilizes FastAPI for building efficient and fast web APIs.
• PostgreSQL Database: Stores and manages documents and their corresponding embedding vectors using psycopg2, pgvector with SQLAlchemy as the ORM.
• Semantic Search: Search documents using cosine distance for semantic similarity.

Running the Project

1. Set your openai API key in the .env file.

   OPENAI_API_KEY=your_api_key
   

2. By using Docker: docker-compose up --build

3. Without Docker:

   • Use poetry to install dependencies: poetry install
   • Enter the virtual environment: poetry shell
   • Set up a PostgreSQL database and update the .env file with corresponding database credentials.

   DB_HOST=localhost
   DB_PORT=5432
   DB_PASS=postgres
   DB_USER=postgres
   DB_BASE=semantic_search
   

   • Run the project using python -m api