Pinecone

# Pinecone Docs > Official Pinecone documentation for the vector database, Assistant, inference APIs, SDKs, and building production search and AI applications. ## Docs - [Pinecone documentation](https://docs.pinecone.io/guides/get-started/overview.md): Pinecone is the vector database for AI agents and applications, built for semantic search, knowledge retrieval, and long-term memory at scale. - [Quickstart](https://docs.pinecone.io/guides/get-started/quickstart.md): Add Pinecone to your AI agent or app in minutes. Build a knowledge retrieval agent, use your IDE assistant, or integrate the SDK directly. - [Agentic IDEs and CLIs](https://docs.pinecone.io/guides/get-started/ai-coding-tools.md): Use Pinecone with agentic IDEs and CLIs like Claude Code, Gemini CLI, and Cursor via MCP server, plugins, and agent skills for vector search. - [Test Pinecone at scale](https://docs.pinecone.io/guides/get-started/test-at-scale.md): Benchmark Pinecone at production scale by importing 10M vectors and measuring semantic search throughput, query latency, and costs. - [Concepts](https://docs.pinecone.io/guides/get-started/concepts.md): Learn core Pinecone concepts (organizations, projects, indexes, documents, namespaces, dense and sparse vectors) and how they relate. - [Architecture](https://docs.pinecone.io/guides/get-started/database-architecture.md): Learn how Pinecone's serverless architecture (API gateway, control plane, data plane, and object storage) powers vector search at scale. - [Indexing overview](https://docs.pinecone.io/guides/index-data/indexing-overview.md): Learn how indexing works in Pinecone: serverless indexes, document schemas, namespaces, integrated embedding, and metadata filtering. - [Create an index](https://docs.pinecone.io/guides/index-data/create-an-index.md): Create a Pinecone serverless index for full-text (BM25), semantic (dense vector), lexical (sparse), or hybrid search with a document schema. - [Data modeling](https://docs.pinecone.io/guides/index-data/data-modeling.md): Model your data in Pinecone using documents with dense_vector, sparse_vector, full-text string, and metadata fields for efficient retrieval. - [Data ingestion overview](https://docs.pinecone.io/guides/index-data/data-ingestion-overview.md): Compare data ingestion options in Pinecone: bulk import from object storage, upsert operations, and hosted embedding via the Inference API. - [Upsert records](https://docs.pinecone.io/guides/index-data/upsert-data.md): Upsert dense, sparse, and text records into Pinecone indexes, batch upserts for higher throughput, and partition data with namespaces. - [Import records](https://docs.pinecone.io/guides/index-data/import-data.md): Import large datasets efficiently from Amazon S3, Google Cloud Storage, or Azure Blob Storage into Pinecone serverless indexes using object st… - [Check data freshness](https://docs.pinecone.io/guides/index-data/check-data-freshness.md): Check data freshness in Pinecone serverless indexes using log sequence numbers (LSNs) and vector counts to verify recent upserts and deletes. - [Implement multitenancy](https://docs.pinecone.io/guides/index-data/implement-multitenancy.md): Implement multitenancy in Pinecone with one namespace per tenant on a serverless index to isolate customer data for SaaS RAG or semantic searc… - [Dedicated Read Nodes](https://docs.pinecone.io/guides/index-data/dedicated-read-nodes.md): Dedicated read nodes use provisioned hardware for read operations, providing predictable, low-latency performance at high query volumes. - [Search overview](https://docs.pinecone.io/guides/search/search-overview.md): Compare Pinecone search types and choose the right retrieval approach: full-text (BM25), semantic (dense vector), sparse lexical, and hybrid. - [Full-text search](https://docs.pinecone.io/guides/search/full-text-search.md): Upsert and search typed JSON documents in Pinecone with BM25 scoring, Lucene query syntax, dense and sparse vector ranking, and metadata filte… […gekürzt]