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Getting started

  • Overview
  • Quickstart
  • Python Client
  • Node.JS Client
  • API Reference
  • Examples
  • Choosing index type and size

Concepts

  • Collections
  • Indexes
  • Sparse-dense embeddings
  • Organizations
  • Projects
  • Multitenancy

Guides

  • Manage indexes
    • Scale indexes
  • Insert data
  • Manage data
  • Query data
  • Metadata filtering
  • Using namespaces
  • Monitoring
  • Performance tuning
  • Troubleshooting
  • Back up indexes
  • Moving to production
  • Manage projects
    • Create a project
    • Add users to projects and organizations
    • Change project pod limit
    • Rename a project
  • Manage billing
    • Setting up billing through AWS Marketplace
    • Setting up billing through GCP Marketplace
    • Changing your billing plan
    • Monitoring your usage
    • Understanding cost
    • Managing cost
  • Manage datasets
    • Pinecone public datasets
    • Using public Pinecone datasets
    • Creating and loading private datasets

Examples

  • Retrieval Enhanced Generative Question Answering with OpenAI
  • Retrieval augmentation
  • GPT4 with Retrieval Augmentation over LangChain docs
  • Langchain Retrieval Augmentation

Integrations

  • OpenAI
  • Cohere
  • Haystack
  • Hugging Face Inference Endpoints
  • Elasticsearch
  • Databricks
  • LangChain

Support

  • Limits
  • Release notes
  • Support Forum
  • Support Portal
  • Status
  • Libraries

Examples

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These examples demonstrate how you might build vector search into your applications with Pinecone. You can view their source code to jumpstart your own application.

Our Learn section explains the basics of vector search and vector databases.

Example Data Model(s) Stack

Updated about 2 months ago


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