For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.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 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
    • 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
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Tutorials

Cohere Cookbooks: Build AI Agents and Solutions

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Welcome to LLM University!

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Built with

In order to help developers get up and running on using Cohere’s functionality, we’ve put together some cookbooks that work through common use cases.

They’re organized by categories like “Agents,” “Cloud,” and “Summarization” to allow you to quickly find what you’re looking for. To jump to a particular use-case category, click one of the links below:

  • Agents
  • Open Source Software Integrations
  • Search and Embeddings
  • Cloud
  • RAG
  • Summarization
Note

The code examples in this section use the Cohere v1 API. The v2 API counterparts will be published at a later time.

Here are some of the ones we think are most exciting!

  • A Data Analyst Agent Built with Cohere and Langchain - Build a data analyst agent with Python and Cohere’s Command R+ mode and Langchain.
  • Creating a QA Bot From Technical Documentation - Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.
  • Multilingual Search with Cohere and Langchain - Perform searches across a corpus of mixed-language documents with Cohere and Langchain.
  • Using Redis with Cohere - Learn how to use Cohere’s text vectorizer with Redis to create a semantic search index.
  • Wikipedia Semantic Search with Cohere + Weaviate - Search 10 million Wikipedia vectors with Cohere’s multilingual model and Weaviate’s public dataset.
  • Long Form General Strategies - Techniques to address lengthy documents exceeding the context window of LLMs.