oscarcitoz/vector-db

A FastAPI-based API for managing vector database operations like creating indexes, upserting data, and similarity searches. Built with Python 3.12, it supports Pinecone as the vector database provider and features metadata filtering for precise search results.

Vector Database API with FastAPI

This project provides a FastAPI-based API to manage vector database operations, including creating indexes, inserting (upserting) data, and performing similarity or ID-based searches. Currently, it supports Pinecone as the vector database provider.

Features

• Create vector indexes in Pinecone.
• Upsert (insert or update) data into the vector database.
• Search for similar vectors or specific IDs with metadata filtering.

Requirements

• Python 3.12
• FastAPI == 0.109.1
• Pinecone Client == 3.0.0
• Pydantic == 2.5.2
• Uvicorn == 0.24.0
• python-dotenv
• Docker (opcional)

Instalación y Ejecución

Usando Docker

1. Construir la imagen:

docker build -t vector-db-api .

2. Ejecutar el contenedor:

docker run -d -p 9000:9000 --name vector-db-container vector-db-api

3. Verificar que está funcionando:

• La API estará disponible en: http://localhost:9000/docs

4. Comandos útiles de Docker:

# Ver logs del contenedor
docker logs vector-db-container

# Detener el contenedor
docker stop vector-db-container

# Eliminar el contenedor
docker rm vector-db-container

Instalación Local

Install the dependencies using:

pip install -r requirements.txt

Environment Variables

The following environment variables are required:

• PINECONE_API_KEY: Your Pinecone API key.
• MODEL_NAME: Name of the embedding model to use.

Usage

1. Start the Server

Run the FastAPI server using Uvicorn:

uvicorn main:app --reload

2. API Endpoints

Postman Collection

Para probar los endpoints más fácilmente, puedes usar nuestra colección de Postman: Vector DB API Collection

Create Index

Creates a vector index in Pinecone.

curl --location 'http://localhost:8000/vector-db/create_index/pinecone' \
--header 'Content-Type: application/json' \
--data '{
    "index_name": "startup",
    "dimension": 1024,
    "metric": "cosine",
    "cloud": "aws",
    "region": "us-east-1"
}'

Upsert Data

Upserts (inserts or updates) data into the specified namespace of a Pinecone index.

curl --location 'http://localhost:8000/vector-db/upsert_data/pinecone/startup' \
--header 'Content-Type: application/json' \
--data '{
    "namespace": "products",
    "records": [
        {
            "id": "pc1",
            "data": {"name": "computadora mac", "price": 213131.03131},
            "metadata": {"tags": ["mac"], "category": "tech", "price": 2000,  "id": "pc1"}
        },
        {
            "id": "pc2",
            "data": {"name": "computadora linux", "price": 200.03131},
            "metadata": {"tags": ["linux"], "category": "tech", "price": 1000,  "id": "pc2"}
        }
    ]
}'

Search

Performs a similarity search with optional metadata filters.

curl --location 'http://localhost:8000/vector-db/search/pinecone/startup' \
--header 'Content-Type: application/json' \
--data '{
    "query": "quiero comprar un pc",
    "top_k": 2,
    "ids": ["pc1", "pc2"],
    "namespace": "products",
    "metadata_filter": {
        "category": {
            "$eq": "tech"
        },
        "price": {
            "$gt": 200
        },
        "id": {
            "$eq": "pc1"
        }
    }
}'

License

This project is licensed under the MIT License.