pinecone-io/python-sdk

What it solves

It provides a Python client to interact with the Pinecone vector database, allowing developers to store, manage, and search high-dimensional vector embeddings used in AI applications.

How it works

The SDK enables users to create and manage indexes with specific schemas (such as dense vectors), upsert records as JSON documents, and perform similarity searches using query vectors. It supports both synchronous and asynchronous operations via asyncio and provides tools for managing namespaces and metadata.

Who it’s for

Python developers building AI applications that require a scalable vector database for tasks like recommendation systems or retrieval-augmented generation (RAG).

Highlights

• Async Support: Includes an AsyncPinecone client for high-performance asynchronous workflows.
• Schema-based Indexing: Allows the definition of custom fields and dimensions for document indexes.
• Flexible Deployment: Supports managed cloud deployments across different regions and providers.
• Server-side Embedding: Offers specialized index creation for models that handle embedding generation on the server side.