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v51

latestOpenAPI 3.1.0proprietaryraw.githubusercontent.com2026-07-21411711.1 MB

Chat API (v1)

Generates a text response to a user message. To learn how to use the Chat API and RAG follow our Text Generation guides.

post/v1/chat

Headers

X-Client-Namestring

The name of the project that is making the request.

Accepts'text/event-stream'

Pass text/event-stream to receive the streamed response as server-sent events. The default is \n delimited events.

Request body

messagestring required

Text input for the model to respond to.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

modelstring

The name of a compatible Cohere model or the ID of a fine-tuned model.

Compatible Deployments: Cohere Platform, Private Deployments

streamboolean

Defaults to false.

When true, the response will be a JSON stream of events. The final event will contain the complete response, and will have an event_type of "stream-end".

Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

preamblestring

When specified, the default Cohere preamble will be replaced with the provided one. Preambles are a part of the prompt used to adjust the model's overall behavior and conversation style, and use the SYSTEM role.

The SYSTEM role is also used for the contents of the optional chat_history= parameter. When used with the chat_history= parameter it adds content throughout a conversation. Conversely, when used with the preamble= parameter it adds content at the start of the conversation only.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

conversation_idstring

An alternative to chat_history.

Providing a conversation_id creates or resumes a persisted conversation with the specified ID. The ID can be any non empty string.

Compatible Deployments: Cohere Platform

prompt_truncation'OFF' | 'AUTO' | 'AUTO_PRESERVE_ORDER'

Defaults to AUTO when connectors are specified and OFF in all other cases.

Dictates how the prompt will be constructed.

With prompt_truncation set to "AUTO", some elements from chat_history and documents will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.

With prompt_truncation set to "AUTO_PRESERVE_ORDER", some elements from chat_history and documents will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.

With prompt_truncation set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a TooManyTokens error will be returned.

Compatible Deployments:

  • AUTO: Cohere Platform Only
  • AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
search_queries_onlyboolean

Defaults to false.

When true, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's message will be generated.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

citation_quality'ENABLED' | 'DISABLED' | 'FAST' | 'ACCURATE' | 'OFF'

Defaults to "enabled". Citations are enabled by default for models that support it, but can be turned off by setting "type": "disabled".

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

temperaturenumber float

Defaults to 0.3.

A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations.

Randomness can be further maximized by increasing the value of the p parameter.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

max_tokensinteger

The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

max_input_tokensinteger

The maximum number of input tokens to send to the model. If not specified, max_input_tokens is the model's context length limit minus a small buffer.

Input will be truncated according to the prompt_truncation parameter.

Compatible Deployments: Cohere Platform

kinteger

Ensures only the top k most likely tokens are considered for generation at each step. Defaults to 0, min value of 0, max value of 500.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

pnumber

Ensures that only the most likely tokens, with total probability mass of p, are considered for generation at each step. If both k and p are enabled, p acts after k. Defaults to 0.75. min value of 0.01, max value of 0.99.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

seedinteger

If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

stop_sequencesstring[]

A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

frequency_penaltynumber

Defaults to 0.0, min value of 0.0, max value of 1.0.

Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

presence_penaltynumber

Defaults to 0.0, min value of 0.0, max value of 1.0.

Used to reduce repetitiveness of generated tokens. Similar to frequency_penalty, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

raw_promptingboolean

When enabled, the user's prompt will be sent to the model without any pre-processing.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

force_single_stepboolean

Forces the chat to be single step. Defaults to false.

safety_mode'CONTEXTUAL' | 'STRICT' | 'NONE'

Used to select the safety instruction inserted into the prompt. Defaults to CONTEXTUAL. When NONE is specified, the safety instruction will be omitted.

Safety modes are not yet configurable in combination with tools, tool_results and documents parameters.

Note: This parameter is only compatible newer Cohere models, starting with Command R 08-2024 and Command R+ 08-2024.

Note: command-r7b-12-2024 and newer models only support "CONTEXTUAL" and "STRICT" modes.

Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

Response

OK

OR