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Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
  • 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
    • Reasoning
    • Image Inputs
    • Predictable Outputs
    • Advanced Generation Parameters
    • Tool Use
    • Tokens and Tokenizers
    • Summarizing Text
    • Safety Modes
  • Embeddings (Vectors, Search, Retrieval)
    • Introduction to Embeddings at Cohere
    • Semantic Search with Embeddings
    • Multimodal Embeddings
    • Batch Embedding Jobs
  • Fine-Tuning
    • Introduction
    • Fine-tuning with the Web-UI
    • Programmatic Fine-tuning
    • Fine-tuning for Chat
    • Fine-tuning for Rerank
    • Fine-tuning for Classify
    • FAQs / Troubleshooting
  • Going to Production
    • API Keys and Rate Limits
    • Going Live
    • Deprecations
    • How Does Cohere's Pricing Work?
  • Integrations
    • Integrating Embedding Models with Other Tools
    • Cohere and LangChain
    • LlamaIndex and Cohere
  • Deployment Options
    • Overview
    • SDK Compatibility
  • Tutorials
    • Cookbooks
    • LLM University
    • Build Things with Cohere!
    • Agentic RAG
    • Cohere on Azure
  • Responsible Use
    • Security
    • Usage Policy
    • Command R and Command R+ Model Card
  • Cohere Labs
    • Cohere Labs Acceptable Use Policy
  • More Resources
    • 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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