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.
LogoLogodocs
DASHBOARDPLAYGROUNDDOCSCOMMUNITYLOG IN
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
    • Aya
    • Embed
    • Rerank
  • Text Generation
    • Introduction to Text Generation at Cohere
    • Using the Chat API
    • Reasoning
    • Image Inputs
    • Streaming Responses
    • 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
  • 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!
    • Agentic RAG
    • Cohere on Azure
  • 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
  • RAG
  • Reranking
  • Semantic Search
  • Text Generation
  • Tool Use & Agents
  • Transcribing Audio
  • Playground
  • FAQs
  • An Overview of Cohere's Models
  • Cohere Transcribe
  • Aya
  • Aya Vision
  • Aya Expanse
  • Tiny Aya
  • Command A+
  • Command A
  • Command A Reasoning
  • Command A Translate
  • Command A Vision
  • Command R7B
  • Command R+
  • Command R
  • Embed
  • North Mini Code
  • Rerank
  • Introduction to Text Generation at Cohere
  • Using the Chat API
  • Reasoning
  • Image Inputs
  • Streaming Responses
  • Structured Outputs
  • Parameter Types in Structured Outputs (JSON)
  • Predictable Outputs
  • Advanced Generation Parameters
  • Basic usage
  • End-to-end example
  • Streaming
  • Citations
  • Tool Use
  • Basic usage
  • Usage patterns
  • Parameter types
  • Streaming
  • Citations
  • Tokens and Tokenizers
  • 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)
  • Model Vault
  • 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
  • Agentic RAG
  • Routing Queries to Data Sources
  • Generating Parallel Queries
  • Performing Tasks Sequentially
  • Generating Multi-Faceted Queries
  • Querying Structured Data (Tables)
  • Querying Structured Data (SQL)
  • Cohere on Azure
  • Text Generation
  • Semantic Search
  • Reranking
  • Retrieval Augmented Generation (RAG)
  • Tool Use & Agents
  • Usage Policy
  • Command R and Command R+ Model Card
  • Cohere Labs Acceptable Use Policy
  • Cohere Toolkit
  • Datasets
  • Improve Cohere Docs
DASHBOARDPLAYGROUNDDOCSCOMMUNITYLOG IN
Integrations

Integrating Embedding Models with Other Tools

Use Cohere's Embeddings with the tools you love.

Cohere supports integrations with a variety of powerful external platforms, which are covered in this section. Find links to specific guides below:

  1. Elasticsearch and Cohere
  2. MongoDB and Cohere
  3. Redis and Cohere
  4. Haystack and Cohere
  5. Open Search and Cohere
  6. Vespa and Cohere
  7. Chroma and Cohere
  8. Qdrant and Cohere
  9. Weaviate and Cohere
  10. Pinecone and Cohere
  11. Milvus and Cohere
Was this page helpful?
Edit this page
Previous

Elasticsearch and Cohere (Integration Guide)

Next
Built with