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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On this page
  • Cohere Toolkit Quick Start
  • Deploying Cohere Toolkit
  • Developing on Cohere Toolkit
  • Working with Cohere Toolkit
More Resources

How to Start with the Cohere Toolkit

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The Cohere Datasets API (and How to Use It)

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Built with

Cohere Toolkit is a collection of pre-built components enabling developers to quickly build and deploy retrieval augmented generation (RAG) applications. With it, you can cut time-to-launch down from months to weeks, and deploy in as little as a few minutes.

The pre-built components fall into two big categories: front-end and back end.

  • Front-end: The Cohere Toolkit front end is a web application built in Next.js. It includes a simple SQL database out of the box to store conversation history, documents, and citations, directly in the app.
  • Back-end: The Cohere Toolkit back-end contains the preconfigured data sources and retrieval code needed to set up RAG on custom data sources, which are called “retrieval chains”). Users can also configure which model to use, selecting from Cohere models hosted on our native platform, Azure, or AWS Sagemaker. By default, we have configured a Langchain data retriever to test RAG on Wikipedia and your own uploaded documents.

Here’s an image that shows how these different components work together:

Cohere Toolkit Quick Start

You can get started quickly with toolkit on Google Cloud Run, Microsoft Azure, or locally. Read this for more details, including CLI commands to run after cloning the repo and environment variables that need to be set.

Deploying Cohere Toolkit

The toolkit can be deployed on single containers, AWS ECS, and GCP. Find out how here.

Developing on Cohere Toolkit

If you want to configure old retrieval chains or add new ones, you’ll need to work through a few steps. These include installing poetry, setting up your local database, testing, etc. More context is available here.

Working with Cohere Toolkit

The toolkit is powerful and flexible. There’s a lot you can do with it, including adding your own model deployment, calling the toolkit’s backend over the API, adding a connector, and much else besides.

Following the links in this document or read the full repository to find everything you need!