Chat

Build Conversational AI into your Apps with RAG

Chat with retrieval-augmented generation (RAG) integrates inputs, sources, and models to build more powerful product experiences. It’s all powered by Command.

Retrieve information with connectors

Enterprise Datastores

Point the model at internal datastores, allowing it to seamlessly (and securely) cite your proprietary data.


The Internet


Point the model at the web, allowing it to generate responses grounded in real-time information.


Specific Documents


Provide the model with manually selected documents, enabling grounded Q&A on particular content.


What’s possible when Chat and RAG come together?

Conversational Knowledge Assistants

Create assistants to automate tasks and give grounded answers to your questions through natural interactions and connections to data.

Customer Support

Provide immediate answers from knowledge bases to support agents and engage customers with prompt resolutions for complex escalations.

Learning apps

Deliver dynamic and personalized learning experiences with interactive lessons, quizzes, and feedback based on evolving user profiles.

Why Chat with RAG?

Conversation
 is the new interface

Chat understands the intent behind messages, remembers conversation history, and responds intelligently through multi-turn conversations. Chat responses are powered by Cohere's Command model.

Reduce hallucinations with grounding, citations

Reduce hallucinations and create trust between generated responses and users with citations to understand where responses are coming from. Command is trained to answer questions from additional sources.


Keep your data private

When privately deployed, the training data, input prompts, and output responses stay private and don't leave your secure environment.


Simple APIs, powerful results

No matter your level of experience with ML/AI, Cohere’s Command model makes it easy to build chat interfaces in your applications.

1import cohere 
2co = cohere.Client('MLZXavfC2EpNaW3dYRG5KwWPcMIvBUyabF1DPBgw') # This is your trial API key
3response = co.chat( 
4  message='<YOUR MESSAGE HERE>',
5  prompt_truncation='auto',
6  connectors=[{"id": "web-search"}]
7) 
8print(response)

Try Chat with RAG in our Coral Showcase

The Coral Showcase is our demo environment to preview Coral’s latest enterprise chat capabilities.

Chat resources

Explore our docs and articles, or get hands-on and build your own demo.

Docs

Learn how to integrate chat capabilities into your apps

Go to docs

Cohere docs

Retrieval Augmented Generation (RAG)

Continue in docs

Get started with Cohere today

Contact us to discuss how chat can help transform your products