pinecone

A managed vector database eliminates infrastructure overhead for production RAG by auto-scaling storage and retrieval. It handles billions of vectors with low latency, simplifying deployment for large-scale similarity search.

Pinecone supports hybrid search by combining dense semantic vectors and sparse keyword vectors. This dual approach improves search accuracy and retrieval relevance across large production datasets.

To set up an auto-scaling vector database, create a new index specifying your vector dimensions, such as 1536, and choose a metric like cosine similarity. The managed service scales automatically.

You can use metadata filtering with vector search to enable precise querying. This allows you to restrict similarity search results based on associated metadata attributes attached to your vectors.

The latency performance of this managed vector database is under 100ms at p95. It ensures fast and relevant retrieval for production AI applications like customer support chatbots.

Yes, you need the pinecone-client dependency to interact with your vector index. It provides the interface to create indexes, upsert vectors, and execute similarity searches within your environment.