Pinecone is the leading vector database for building accurate and performant AI applications at scale in production.

Database quickstart

Set up a fully managed vector database for high-performance semantic search

Assistant quickstart

Create an AI assistant that answers complex questions about your proprietary data

​
Workflows

Use integrated embedding to upsert and search with text and have Pinecone generate vectors automatically.
1

Create an index

Create an index that is integrated with one of Pinecone’s hosted embedding models. Dense indexes and vectors enable semantic search, while sparse indexes and vectors enable lexical search.
2

Prepare data

Prepare your data for efficient ingestion, retrieval, and management in Pinecone.
3

Upsert text

Upsert your source text and have Pinecone convert the text to vectors automatically. Use namespaces to partition data for faster queries and multitenant isolation between customers.
4

Search with text

Search the index with a query text. Again, Pinecone uses the index’s integrated model to convert the text to a vector automatically.
5

Improve relevance

Filter by metadata to limit the scope of your search, rerank results to increase search accuracy, or add lexical search to capture both semantic understanding and precise keyword matches.

​
Start building

API Reference

Comprehensive details about the Pinecone APIs, SDKs, utilities, and architecture.

Integrated Inference

Simplify vector search with integrated embedding and reranking.

Examples

Hands-on notebooks and sample apps with common AI patterns and tools.

Integrations

Pinecone’s growing number of third-party integrations.

Troubleshooting

Resolve common Pinecone issues with our troubleshooting guide.

Releases

News about features and changes in Pinecone and related tools.