For AI agents: a documentation index is available at the root level at /llms.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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Guides and conceptsAPI ReferenceRelease NotesCookbooks
Guides and conceptsAPI ReferenceRelease NotesCookbooks
  • Get Started
    • Introduction
    • Installation
    • Creating a client
    • Playground
    • FAQs
  • Models
    • An Overview of Cohere's Models
    • Embed
    • Rerank
    • Aya
  • Text Generation
    • Introduction to Text Generation at Cohere
    • Using the Chat API
    • Streaming Responses
    • Structured Outputs
    • Predictable Outputs
    • Advanced Generation Parameters
    • Retrieval Augmented Generation (RAG)
    • Tool Use
    • Tokens and Tokenizers
    • Migrating from the Generate API to the Chat API
    • Summarizing Text
    • Safety Modes
  • Embeddings (Vectors, Search, Retrieval)
    • Introduction to Embeddings at Cohere
    • Semantic Search with Embeddings
    • Multimodal Embeddings
    • Batch Embedding Jobs
  • Going to Production
    • API Keys and Rate Limits
    • Going Live
    • Deprecations
    • How Does Cohere's Pricing Work?
  • Integrations
    • Integrating Embedding Models with Other Tools
      • Elasticsearch and Cohere
      • MongoDB and Cohere
      • Redis and Cohere
      • Haystack and Cohere
      • Pinecone and Cohere
      • Weaviate and Cohere
      • Open Search and Cohere
      • Vespa and Cohere
      • Qdrant and Cohere
      • Milvus and Cohere
      • Zilliz and Cohere
      • Chroma and Cohere
    • Cohere and LangChain
    • LlamaIndex and Cohere
  • Deployment Options
    • Overview
    • SDK Compatibility
  • Tutorials
    • Cookbooks
    • LLM University
    • Build Things with Cohere!
  • Responsible Use
    • Security
    • Usage Policy
    • Command A Technical Report
    • Command R and Command R+ Model Card
  • Cohere Labs
    • Cohere Labs Acceptable Use Policy
  • More Resources
    • Cohere Toolkit
    • Datasets
    • Improve Cohere Docs
  • Introduction
  • Installation
  • Creating a client
  • Text Generation
  • RAG
  • Tool Use & Agents
  • Semantic Search
  • Reranking
  • Playground
  • FAQs
  • An Overview of Cohere's Models
  • Command A+
  • Command A
  • Command A Reasoning
  • Command A Translate
  • Command A Vision
  • Command R7B
  • Command R+
  • Command R
  • Embed
  • North Mini Code
  • Rerank
  • Aya
  • Aya Vision
  • Aya Expanse
  • Tiny Aya
  • Introduction to Text Generation at Cohere
  • Using the Chat API
  • Streaming Responses
  • Structured Outputs
  • Predictable Outputs
  • Advanced Generation Parameters
  • Retrieval Augmented Generation (RAG)
  • Tool Use
  • Multi-step Tool Use (Agents)
  • Implementing a Multi-Step Agent with Langchain
  • Single-Step Tool Use
  • Parameter Types in Tool Use
  • Tokens and Tokenizers
  • Migrating from the Generate API to the Chat API
  • Summarizing Text
  • Safety Modes
  • Introduction to Embeddings at Cohere
  • Semantic Search with Embeddings
  • Multimodal Embeddings
  • Batch Embedding Jobs
  • Rerank Overview
  • Rerank Best Practices
  • API Keys and Rate Limits
  • Going Live
  • Deprecations
  • How Does Cohere's Pricing Work?
  • Integrating Embedding Models with Other Tools
  • Elasticsearch and Cohere
  • MongoDB and Cohere
  • Redis and Cohere
  • Haystack and Cohere
  • Pinecone and Cohere
  • Weaviate and Cohere
  • Open Search and Cohere
  • Vespa and Cohere
  • Qdrant and Cohere
  • Milvus and Cohere
  • Zilliz and Cohere
  • Chroma and Cohere
  • Cohere and LangChain
  • Chat on LangChain
  • Embed on LangChain
  • Rerank on LangChain
  • Tools on LangChain
  • LlamaIndex and Cohere
  • Overview
  • SDK Compatibility
  • Overview
  • Setting Up
  • Model Deployment
  • Model Deployment - AWS
  • Usage
  • Cohere on AWS
  • Amazon Bedrock
  • Amazon SageMaker
  • Deploy Your Own Finetuned Command-R-0824 Model from AWS Marketplace
  • Cohere on Azure
  • Cohere on Oracle Cloud Infrastructure (OCI)
  • Cookbooks
  • LLM University
  • Build Things with Cohere!
  • Cohere Text Generation Tutorial
  • Building a Chatbot with Cohere
  • Semantic Search with Cohere
  • Reranking with Cohere
  • RAG with Cohere
  • Building an Agent with Cohere
  • Usage Policy
  • Command R and Command R+ Model Card
  • Cohere Labs Acceptable Use Policy
  • Cohere Toolkit
  • Datasets
  • Improve Cohere Docs
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IntegrationsIntegrating Embedding Models with Other Tools

Chroma and Cohere (Integration Guide)

Chroma is an open-source vector search engine that’s quick to install and start building with using Python or Javascript.

You can get started with Chroma here.

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Cohere and LangChain (Integration Guide)

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