pinecone

You can run production AI using a managed vector database that provides auto-scaling and low latency for real-time retrieval. This eliminates infrastructure management by handling scaling automatically for large-scale semantic search workloads.

Hybrid search in a vector database combines dense and sparse vectors to improve query relevance. You need it when building recommendation systems or semantic search applications where matching both contextual meaning and specific keywords is critical.

To run semantic search, you create a serverless index, upsert vectors with metadata attached, and execute a top-k query to retrieve the closest matching results. This workflow supports namespaces and metadata filtering for organized retrieval.

Yes, Pinecone works with LangChain and LlamaIndex through client library integrations. These toolchain integrations allow you to connect the managed vector database directly to your AI application framework for real-time retrieval.

Yes, you can use namespaces and metadata filtering to achieve multi-tenant organization in a vector database. This allows you to partition vector data logically and restrict top-k queries to specific tenant subsets.

Yes, you need the pinecone-client library to connect to the serverless vector database. It provides the necessary interface to create indexes, upsert dense and sparse vectors, and execute hybrid search queries programmatically.