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Pinecone Nexus is the knowledge engine for agents. It compiles your data into queryable knowledge once, then serves grounded, cited answers to agents on every call.

Deploy Pinecone Nexus with BYOC, curate a context from your own documents, and run KnowQL queries that return grounded, cited multi-document answers.

Learn the core Pinecone Nexus concepts: sources, workspaces, contexts, manifests, artifacts, tasks, sessions, and queries, and how they connect.

Overview of Pinecone Assistant, a managed service for building production-grade RAG chat and agent applications grounded in your data.

Pinecone is the vector database for AI agents and applications, built for semantic search, knowledge retrieval, and long-term memory at scale.

Understand Pinecone Assistant pricing for ingestion units, chat tokens, and storage, plus plan-based service limits for file uploads and knowledge size.

Track Pinecone Assistant performance in the console with time-series metrics for requests, error counts, chat and context latency, and file processing.

Create custom Pinecone datasets with the pinecone-datasets library, define metadata schema, and upload to your own S3, GCS, or local storage bucket.

Browse Pinecone's catalog of public benchmark datasets like ANN, MSMARCO, and Quora, then load and upsert them with the pinecone-datasets Python library.

Load a pre-built movies sample dataset from the Pinecone console to quickly test indexing, similarity search, and quickstart workflows without uploading your own data.