Chat API (v1)

POST/v1/chatChat 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.

Parameters

X-Client-Namestringheader

The name of the project that is making the request.

Acceptsstringheader

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

one of "text/event-stream"

Request body

application/json

object

chat_historyarray of value

A list of previous messages between the user and the model, giving the model conversational context for responding to the user's `message`. Each item represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content. The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

minItems 0

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minItems 0

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oneOf · 2 options

Option 1objectChatMessage

Represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content. The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used.

messagestringrequired

Contents of the chat message.

minLength 1

rolestringrequired

One of `CHATBOT`, `SYSTEM`, `TOOL` or `USER` to identify who the message is coming from.

one of "CHATBOT", "SYSTEM", "USER", "TOOL"

tool_callsarray of object

Show child attributes

Show array items

Contains the tool calls generated by the model. Use it to invoke your tools.

namestringrequired

Name of the tool to call.

parametersobjectrequired

The name and value of the parameters to use when invoking a tool.

Option 2objectChatToolMessage

Represents tool result in the chat history.

rolestringrequired

One of `CHATBOT`, `SYSTEM`, `TOOL` or `USER` to identify who the message is coming from.

one of "CHATBOT", "SYSTEM", "USER", "TOOL"

tool_resultsarray of object

Show child attributes

Show array items

callobjectrequiredToolCall ↑

Contains the tool calls generated by the model. Use it to invoke your tools.

outputsarray of objectrequired

minItems 0

Show child attributes

minItems 0

citation_qualitystring

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

one of "ENABLED", "DISABLED", "FAST", "ACCURATE", "OFF"

connectorsarray of object

Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/connectors), if you've [created](https://docs.cohere.com/v1/docs/creating-and-deploying-a-connector) one. When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG). Compatible Deployments: Cohere Platform

minItems 0

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minItems 0

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The connector used for fetching documents.

continue_on_failureboolean

Defaults to `false`. When `true`, the request will continue if this connector returned an error.

idstringrequired

The identifier of the connector.

minLength 1

optionsobject

Provides the connector with different settings at request time. The key/value pairs of this object are specific to each connector. For example, the connector `web-search` supports the `site` option, which limits search results to the specified domain.

user_access_tokenstring

When specified, this user access token will be passed to the connector in the Authorization header instead of the Cohere generated one.

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

documentsarray of object

A list of relevant documents that the model can cite to generate a more accurate reply. Each document is a string-string dictionary. Example: ``` [ { "title": "Tall penguins", "text": "Emperor penguins are the tallest." }, { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica." }, ] ``` Keys and values from each document will be serialized to a string and passed to the model. The resulting generation will include citations that reference some of these documents. Some suggested keys are "text", "author", and "date". For better generation quality, it is recommended to keep the total word count of the strings in the dictionary to under 300 words. An `id` field (string) can be optionally supplied to identify the document in the citations. This field will not be passed to the model. An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model. The omitted fields will still show up in the citation object. The "_excludes" field will not be passed to the model. See ['Document Mode'](https://docs.cohere.com/docs/retrieval-augmented-generation-rag#document-mode) in the guide for more information. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

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Show array items

Relevant information that could be used by the model to generate a more accurate reply. The contents of each document are generally short (under 300 words), and are passed in the form of a dictionary of strings. Some suggested keys are "text", "author", "date". Both the key name and the value will be passed to the model.

idstring

Unique identifier for this document.

force_single_stepboolean

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

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

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

default 0 · maximum 500 · minimum 0

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

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

messagestringrequired

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](https://docs.cohere.com/docs/models) or the ID of a [fine-tuned](https://docs.cohere.com/docs/chat-fine-tuning) model. Compatible Deployments: Cohere Platform, 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

default 0.75 · maximum 0.99 · minimum 0.01

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

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

prompt_truncationstring

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

one of "OFF", "AUTO", "AUTO_PRESERVE_ORDER"

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

response_formatvalue

Configuration for forcing the model output to adhere to the specified format. Supported on [Command R 03-2024](https://docs.cohere.com/docs/command-r), [Command R+ 04-2024](https://docs.cohere.com/docs/command-r-plus) and newer models. The model can be forced into outputting JSON objects (with up to 5 levels of nesting) by setting `{ "type": "json_object" }`. A [JSON Schema](https://json-schema.org/) can optionally be provided, to ensure a specific structure. **Note**: When using `{ "type": "json_object" }` your `message` should always explicitly instruct the model to generate a JSON (eg: _"Generate a JSON ..."_) . Otherwise the model may end up getting stuck generating an infinite stream of characters and eventually run out of context length. **Limitation**: The parameter is not supported in RAG mode (when any of `connectors`, `documents`, `tools`, `tool_results` are provided).

Show child attributes

oneOf · 2 options

Option 1objectChatTextResponseFormat

Text Response

typestringrequired

Defaults to `"text"`. When set to `"json_object"`, the model's output will be a valid JSON Object.

one of "text", "json_object"

Option 2objectJSONResponseFormat

JSON Object Response

schemaobject

A JSON schema object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](https://docs.cohere.com/docs/structured-outputs-json#schema-constraints) for more information. Example (required name and age object): ```json { "type": "object", "properties": { "name": {"type": "string"}, "age": {"type": "integer"} }, "required": ["name", "age"] } ``` **Note**: This field must not be specified when the `type` is set to `"text"`.

typestringrequired

Defaults to `"text"`. When set to `"json_object"`, the model's output will be a valid JSON Object.

one of "text", "json_object"

safety_modevalue

Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) 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](https://docs.cohere.com/docs/command-r#august-2024-release) and [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release). **Note**: `command-r7b-12-2024` and newer models only support `"CONTEXTUAL"` and `"STRICT"` modes. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

one of "CONTEXTUAL", "STRICT", "NONE"

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

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

minimum 0

stop_sequencesarray of string

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

Show child attributes

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

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

maximum 1 · minimum 0

tool_resultsarray of object

A list of results from invoking tools recommended by the model in the previous chat turn. Results are used to produce a text response and will be referenced in citations. When using `tool_results`, `tools` must be passed as well. Each tool_result contains information about how it was invoked, as well as a list of outputs in the form of dictionaries. **Note**: `outputs` must be a list of objects. If your tool returns a single object (eg `{"status": 200}`), make sure to wrap it in a list. ``` tool_results = [ { "call": { "name": <tool name>, "parameters": { <param name>: <param value> } }, "outputs": [{ <key>: <value> }] }, ... ] ``` **Note**: Chat calls with `tool_results` should not be included in the Chat history to avoid duplication of the message text. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

minItems 0

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minItems 0

Items: ToolResult ↑

toolsarray of object

A list of available tools (functions) that the model may suggest invoking before producing a text response. When `tools` is passed (without `tool_results`), the `text` field in the response will be `""` and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty. Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments

minItems 0

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minItems 0

Show array items

descriptionstringrequired

The description of what the tool does, the model uses the description to choose when and how to call the function.

namestringrequired

The name of the tool to be called. Valid names contain only the characters `a-z`, `A-Z`, `0-9`, `_` and must not begin with a digit.

parameter_definitionsobject

The input parameters of the tool. Accepts a dictionary where the key is the name of the parameter and the value is the parameter spec. Valid parameter names contain only the characters `a-z`, `A-Z`, `0-9`, `_` and must not begin with a digit. ``` { "my_param": { "description": <string>, "type": <string>, // any python data type, such as 'str', 'bool' "required": <boolean> } } ```

Example request

{
  "chat_history": [
    {
      "message": "string",
      "role": "CHATBOT",
      "tool_calls": [
        {
          "name": "string",
          "parameters": {
            "additionalProp1": null
          }
        }
      ]
    }
  ],
  "citation_quality": "ACCURATE",
  "connectors": [
    {
      "continue_on_failure": true,
      "id": "string",
      "options": {},
      "user_access_token": "string"
    }
  ],
  "conversation_id": "string",
  "documents": [
    {
      "id": "string"
    }
  ],
  "force_single_step": true,
  "frequency_penalty": 0,
  "k": 0,
  "max_input_tokens": 0,
  "max_tokens": 0,
  "message": "string",
  "model": "string",
  "p": 0.75,
  "preamble": "string",
  "presence_penalty": 0,
  "prompt_truncation": "AUTO",
  "raw_prompting": true,
  "response_format": {
    "type": "text"
  },
  "safety_mode": "CONTEXTUAL",
  "search_queries_only": true,
  "seed": 0,
  "stop_sequences": [
    "string"
  ],
  "stream": true,
  "temperature": 0,
  "tool_results": [
    {
      "call": {
        "name": "string",
        "parameters": {
          "additionalProp1": null
        }
      },
      "outputs": [
        {
          "additionalProp1": null
        }
      ]
    }
  ],
  "tools": [
    {
      "description": "string",
      "name": "string",
      "parameter_definitions": {
        "additionalProp1": {
          "description": "string",
          "required": false,
          "type": "string"
        }
      }
    }
  ]
}

Responses

value

oneOf · 2 options

Option 1objectNonStreamedChatResponse

chat_historyarray of value

A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's `message`.

minItems 0

Show child attributes

minItems 0

Items: Message ↑

citationsarray of object

Inline citations for the generated reply.

minItems 0

Show child attributes

minItems 0

Show array items

A section of the generated reply which cites external knowledge.

document_idsarray of stringrequired

Identifiers of documents cited by this section of the generated reply.

minItems 1

Show child attributes

minItems 1

endintegerrequired

The index of text that the citation ends after, counting from zero. For example, a generation of `Hello, world!` with a citation on `world` would have an end value of `11`. This is because the citation ends after `d`, which is the eleventh character.

startintegerrequired

The index of text that the citation starts at, counting from zero. For example, a generation of `Hello, world!` with a citation on `world` would have a start value of `7`. This is because the citation starts at `w`, which is the seventh character.

textstringrequired

The text of the citation. For example, a generation of `Hello, world!` with a citation of `world` would have a text value of `world`.

minLength 1

typestring

The type of citation which indicates what part of the response the citation is for.

one of "TEXT_CONTENT", "PLAN"

documentsarray of object

Documents seen by the model when generating the reply.

minItems 0

Show child attributes

minItems 0

Items: ChatDocument ↑

finish_reasonstring

one of "COMPLETE", "STOP_SEQUENCE", "ERROR", "ERROR_TOXIC", "ERROR_LIMIT", "USER_CANCEL", "MAX_TOKENS", "TIMEOUT"

generation_idstring · uuid

Unique identifier for the generated reply. Useful for submitting feedback.

is_search_requiredboolean

Denotes that a search for documents is required during the RAG flow.

metaobject

Show child attributes

api_versionobject

Show child attributes

is_deprecatedboolean

is_experimentalboolean

versionstringrequired

billed_unitsobject

Show child attributes

classificationsnumber

The number of billed classifications units.

image_tokensnumber

The number of billed image tokens.

imagesnumber

The number of billed images.

input_tokensnumber

The number of billed input tokens.

output_tokensnumber

The number of billed output tokens.

pagesnumber

The number of billed pages parsed.

search_unitsnumber

The number of billed search units.

cached_tokensnumber

The number of prompt tokens that hit the inference cache.

tokensobject

Show child attributes

input_tokensnumber

The number of tokens used as input to the model.

output_tokensnumber

The number of tokens produced by the model.

warningsarray of string

Show child attributes

response_idstring · uuid

Unique identifier for the response.

search_queriesarray of object

Generated search queries, meant to be used as part of the RAG flow.

Show child attributes

Show array items

The generated search query. Contains the text of the query and a unique identifier for the query.

generation_idstring · uuidrequired

Unique identifier for the generated search query. Useful for submitting feedback.

textstringrequired

The text of the search query.

search_resultsarray of object

Documents retrieved from each of the conducted searches.

minItems 0

Show child attributes

minItems 0

Show array items

connectorobjectrequired

The connector used for fetching documents.

Show child attributes

idstringrequired

The identifier of the connector.

minLength 1

continue_on_failureboolean

Whether a chat request should continue or not if the request to this connector fails.

document_idsarray of stringrequired

Identifiers of documents found by this search query.

minLength 0

Show child attributes

minLength 0

error_messagestring

An error message if the search failed.

search_queryobjectChatSearchQuery ↑

The generated search query. Contains the text of the query and a unique identifier for the query.

textstringrequired

Contents of the reply generated by the model.

minLength 1

tool_callsarray of object

Show child attributes

Items: ToolCall ↑

Option 2valueStreamedChatResponse

StreamedChatResponse is returned in streaming mode (specified with `stream=True` in the request).

oneOf · 9 options

Option 1valueChatStreamStartEvent

allOf · 2 options

Option 1objectChatStreamEvent

event_typestringrequired

one of "stream-start", "search-queries-generation", "search-results", "text-generation", "citation-generation", "stream-end", "debug"

Option 2object

generation_idstring · uuidrequired

Unique identifier for the generated reply. Useful for submitting feedback.

minLength 1

Option 2valueChatSearchQueriesGenerationEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

search_queriesarray of objectrequired

Generated search queries, meant to be used as part of the RAG flow.

Show child attributes

Items: ChatSearchQuery ↑

Option 3valueChatSearchResultsEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

documentsarray of object

Documents fetched from searches or provided by the user.

minItems 0

Show child attributes

minItems 0

Items: ChatDocument ↑

search_resultsarray of object

Conducted searches and the ids of documents retrieved from each of them.

minItems 0

Show child attributes

minItems 0

Items: ChatSearchResult ↑

Option 4valueChatTextGenerationEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

textstringrequired

The next batch of text generated by the model.

minLength 1

Option 5valueChatCitationGenerationEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

citationsarray of objectrequired

Citations for the generated reply.

Show child attributes

Items: ChatCitation ↑

Option 6valueChatToolCallsGenerationEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

textstring

The text generated related to the tool calls generated

tool_callsarray of objectrequired

Show child attributes

Items: ToolCall ↑

Option 7valueChatStreamEndEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

finish_reasonstringrequired

- `COMPLETE` - the model sent back a finished reply - `ERROR_LIMIT` - the reply was cut off because the model reached the maximum number of tokens for its context length - `MAX_TOKENS` - the reply was cut off because the model reached the maximum number of tokens specified by the max_tokens parameter - `ERROR` - something went wrong when generating the reply - `ERROR_TOXIC` - the model generated a reply that was deemed toxic

one of "COMPLETE", "ERROR_LIMIT", "MAX_TOKENS", "ERROR", "ERROR_TOXIC"

responseobjectrequiredNonStreamedChatResponse ↑

Option 8valueChatToolCallsChunkEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

textstring

tool_call_deltaobjectrequired

Contains the chunk of the tool call generation in the stream.

Show child attributes

indexnumber

Index of the tool call generated

namestring

Name of the tool call

parametersstring

Chunk of the tool parameters

textstring

Chunk of the tool plan text

Option 9valueChatDebugEvent

allOf · 2 options

Option 1: ChatStreamEvent ↑

Option 2object

promptstring

Example response

{
  "chat_history": [
    {
      "message": "string",
      "role": "CHATBOT",
      "tool_calls": [
        {
          "name": "string",
          "parameters": {
            "additionalProp1": null
          }
        }
      ]
    }
  ],
  "citations": [
    {
      "document_ids": [
        "string"
      ],
      "end": 0,
      "start": 0,
      "text": "string",
      "type": "PLAN"
    }
  ],
  "documents": [
    {
      "id": "string"
    }
  ],
  "finish_reason": "COMPLETE",
  "generation_id": "00000000-0000-0000-0000-000000000000",
  "is_search_required": true,
  "meta": {
    "api_version": {
      "is_deprecated": true,
      "is_experimental": true,
      "version": "string"
    },
    "billed_units": {
      "classifications": 0,
      "image_tokens": 0,
      "images": 0,
      "input_tokens": 0,
      "output_tokens": 0,
      "pages": 0,
      "search_units": 0
    },
    "cached_tokens": 0,
    "tokens": {
      "input_tokens": 0,
      "output_tokens": 0
    },
    "warnings": [
      "string"
    ]
  },
  "response_id": "00000000-0000-0000-0000-000000000000",
  "search_queries": [
    {
      "generation_id": "00000000-0000-0000-0000-000000000000",
      "text": "string"
    }
  ],
  "search_results": [
    {
      "connector": {
        "id": "string"
      },
      "continue_on_failure": true,
      "document_ids": [
        "string"
      ],
      "error_message": "string",
      "search_query": {
        "generation_id": "00000000-0000-0000-0000-000000000000",
        "text": "string"
      }
    }
  ],
  "text": "string",
  "tool_calls": [
    {
      "name": "string",
      "parameters": {
        "additionalProp1": null
      }
    }
  ]
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

Examples

X-Client-Nameheader parameter

Acceptsheader parameter

curl -X POST 'https://api.cohere.com/v1/chat' \
  -H 'Content-Type: application/json' \
  -d '{"chat_history":[{"message":"string","role":"CHATBOT","tool_calls":[{"name":"string","parameters":{"additionalProp1":null}}]}],"citation_quality":"ACCURATE","connectors":[{"continue_on_failure":true,"id":"string","options":{},"user_access_token":"string"}],"conversation_id":"string","documents":[{"id":"string"}],"force_single_step":true,"frequency_penalty":0,"k":0,"max_input_tokens":0,"max_tokens":0,"message":"string","model":"string","p":0.75,"preamble":"string","presence_penalty":0,"prompt_truncation":"AUTO","raw_prompting":true,"response_format":{"type":"text"},"safety_mode":"CONTEXTUAL","search_queries_only":true,"seed":0,"stop_sequences":["string"],"stream":true,"temperature":0,"tool_results":[{"call":{"name":"string","parameters":{"additionalProp1":null}},"outputs":[{"additionalProp1":null}]}],"tools":[{"description":"string","name":"string","parameter_definitions":{"additionalProp1":{"description":"string","required":false,"type":"string"}}}]}'

const response = await fetch('https://api.cohere.com/v1/chat', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({"chat_history":[{"message":"string","role":"CHATBOT","tool_calls":[{"name":"string","parameters":{"additionalProp1":null}}]}],"citation_quality":"ACCURATE","connectors":[{"continue_on_failure":true,"id":"string","options":{},"user_access_token":"string"}],"conversation_id":"string","documents":[{"id":"string"}],"force_single_step":true,"frequency_penalty":0,"k":0,"max_input_tokens":0,"max_tokens":0,"message":"string","model":"string","p":0.75,"preamble":"string","presence_penalty":0,"prompt_truncation":"AUTO","raw_prompting":true,"response_format":{"type":"text"},"safety_mode":"CONTEXTUAL","search_queries_only":true,"seed":0,"stop_sequences":["string"],"stream":true,"temperature":0,"tool_results":[{"call":{"name":"string","parameters":{"additionalProp1":null}},"outputs":[{"additionalProp1":null}]}],"tools":[{"description":"string","name":"string","parameter_definitions":{"additionalProp1":{"description":"string","required":false,"type":"string"}}}]}),
});
const data = await response.json();

const response = await fetch('https://api.cohere.com/v1/chat', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: "{\"chat_history\":[{\"message\":\"string\",\"role\":\"CHATBOT\",\"tool_calls\":[{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}}]}],\"citation_quality\":\"ACCURATE\",\"connectors\":[{\"continue_on_failure\":true,\"id\":\"string\",\"options\":{},\"user_access_token\":\"string\"}],\"conversation_id\":\"string\",\"documents\":[{\"id\":\"string\"}],\"force_single_step\":true,\"frequency_penalty\":0,\"k\":0,\"max_input_tokens\":0,\"max_tokens\":0,\"message\":\"string\",\"model\":\"string\",\"p\":0.75,\"preamble\":\"string\",\"presence_penalty\":0,\"prompt_truncation\":\"AUTO\",\"raw_prompting\":true,\"response_format\":{\"type\":\"text\"},\"safety_mode\":\"CONTEXTUAL\",\"search_queries_only\":true,\"seed\":0,\"stop_sequences\":[\"string\"],\"stream\":true,\"temperature\":0,\"tool_results\":[{\"call\":{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}},\"outputs\":[{\"additionalProp1\":null}]}],\"tools\":[{\"description\":\"string\",\"name\":\"string\",\"parameter_definitions\":{\"additionalProp1\":{\"description\":\"string\",\"required\":false,\"type\":\"string\"}}}]}" });
console.log(await response.json());

import requests

response = requests.request(
    "POST",
    "https://api.cohere.com/v1/chat",
    headers={"Content-Type": "application/json"},
    data='{"chat_history":[{"message":"string","role":"CHATBOT","tool_calls":[{"name":"string","parameters":{"additionalProp1":null}}]}],"citation_quality":"ACCURATE","connectors":[{"continue_on_failure":true,"id":"string","options":{},"user_access_token":"string"}],"conversation_id":"string","documents":[{"id":"string"}],"force_single_step":true,"frequency_penalty":0,"k":0,"max_input_tokens":0,"max_tokens":0,"message":"string","model":"string","p":0.75,"preamble":"string","presence_penalty":0,"prompt_truncation":"AUTO","raw_prompting":true,"response_format":{"type":"text"},"safety_mode":"CONTEXTUAL","search_queries_only":true,"seed":0,"stop_sequences":["string"],"stream":true,"temperature":0,"tool_results":[{"call":{"name":"string","parameters":{"additionalProp1":null}},"outputs":[{"additionalProp1":null}]}],"tools":[{"description":"string","name":"string","parameter_definitions":{"additionalProp1":{"description":"string","required":false,"type":"string"}}}]}',
)
print(response.json())

package main

import (
	"context"
	"net/http"
	"strings"
)

func main() {
	ctx := context.Background()
	body := strings.NewReader(`{"chat_history":[{"message":"string","role":"CHATBOT","tool_calls":[{"name":"string","parameters":{"additionalProp1":null}}]}],"citation_quality":"ACCURATE","connectors":[{"continue_on_failure":true,"id":"string","options":{},"user_access_token":"string"}],"conversation_id":"string","documents":[{"id":"string"}],"force_single_step":true,"frequency_penalty":0,"k":0,"max_input_tokens":0,"max_tokens":0,"message":"string","model":"string","p":0.75,"preamble":"string","presence_penalty":0,"prompt_truncation":"AUTO","raw_prompting":true,"response_format":{"type":"text"},"safety_mode":"CONTEXTUAL","search_queries_only":true,"seed":0,"stop_sequences":["string"],"stream":true,"temperature":0,"tool_results":[{"call":{"name":"string","parameters":{"additionalProp1":null}},"outputs":[{"additionalProp1":null}]}],"tools":[{"description":"string","name":"string","parameter_definitions":{"additionalProp1":{"description":"string","required":false,"type":"string"}}}]}`)
	req, err := http.NewRequestWithContext(ctx, "POST", "https://api.cohere.com/v1/chat", body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Content-Type", "application/json")
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()
}

<?php
$request = curl_init("https://api.cohere.com/v1/chat");
curl_setopt($request, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($request, CURLOPT_POSTFIELDS, "{\"chat_history\":[{\"message\":\"string\",\"role\":\"CHATBOT\",\"tool_calls\":[{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}}]}],\"citation_quality\":\"ACCURATE\",\"connectors\":[{\"continue_on_failure\":true,\"id\":\"string\",\"options\":{},\"user_access_token\":\"string\"}],\"conversation_id\":\"string\",\"documents\":[{\"id\":\"string\"}],\"force_single_step\":true,\"frequency_penalty\":0,\"k\":0,\"max_input_tokens\":0,\"max_tokens\":0,\"message\":\"string\",\"model\":\"string\",\"p\":0.75,\"preamble\":\"string\",\"presence_penalty\":0,\"prompt_truncation\":\"AUTO\",\"raw_prompting\":true,\"response_format\":{\"type\":\"text\"},\"safety_mode\":\"CONTEXTUAL\",\"search_queries_only\":true,\"seed\":0,\"stop_sequences\":[\"string\"],\"stream\":true,\"temperature\":0,\"tool_results\":[{\"call\":{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}},\"outputs\":[{\"additionalProp1\":null}]}],\"tools\":[{\"description\":\"string\",\"name\":\"string\",\"parameter_definitions\":{\"additionalProp1\":{\"description\":\"string\",\"required\":false,\"type\":\"string\"}}}]}");
curl_setopt($request, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);
$response = curl_exec($request);
curl_close($request);

var request = HttpRequest.newBuilder(URI.create("https://api.cohere.com/v1/chat"))
    .method("POST", HttpRequest.BodyPublishers.noBody())
    .header("Content-Type", "application/json")
    .method("POST", HttpRequest.BodyPublishers.ofString("{\"chat_history\":[{\"message\":\"string\",\"role\":\"CHATBOT\",\"tool_calls\":[{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}}]}],\"citation_quality\":\"ACCURATE\",\"connectors\":[{\"continue_on_failure\":true,\"id\":\"string\",\"options\":{},\"user_access_token\":\"string\"}],\"conversation_id\":\"string\",\"documents\":[{\"id\":\"string\"}],\"force_single_step\":true,\"frequency_penalty\":0,\"k\":0,\"max_input_tokens\":0,\"max_tokens\":0,\"message\":\"string\",\"model\":\"string\",\"p\":0.75,\"preamble\":\"string\",\"presence_penalty\":0,\"prompt_truncation\":\"AUTO\",\"raw_prompting\":true,\"response_format\":{\"type\":\"text\"},\"safety_mode\":\"CONTEXTUAL\",\"search_queries_only\":true,\"seed\":0,\"stop_sequences\":[\"string\"],\"stream\":true,\"temperature\":0,\"tool_results\":[{\"call\":{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}},\"outputs\":[{\"additionalProp1\":null}]}],\"tools\":[{\"description\":\"string\",\"name\":\"string\",\"parameter_definitions\":{\"additionalProp1\":{\"description\":\"string\",\"required\":false,\"type\":\"string\"}}}]}"))
    .build();
var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());

require 'net/http'

uri = URI("https://api.cohere.com/v1/chat")
request = Net::HTTPGenericRequest.new("POST", true, true, uri)
request['Content-Type'] = 'application/json'
request.body = "{\"chat_history\":[{\"message\":\"string\",\"role\":\"CHATBOT\",\"tool_calls\":[{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}}]}],\"citation_quality\":\"ACCURATE\",\"connectors\":[{\"continue_on_failure\":true,\"id\":\"string\",\"options\":{},\"user_access_token\":\"string\"}],\"conversation_id\":\"string\",\"documents\":[{\"id\":\"string\"}],\"force_single_step\":true,\"frequency_penalty\":0,\"k\":0,\"max_input_tokens\":0,\"max_tokens\":0,\"message\":\"string\",\"model\":\"string\",\"p\":0.75,\"preamble\":\"string\",\"presence_penalty\":0,\"prompt_truncation\":\"AUTO\",\"raw_prompting\":true,\"response_format\":{\"type\":\"text\"},\"safety_mode\":\"CONTEXTUAL\",\"search_queries_only\":true,\"seed\":0,\"stop_sequences\":[\"string\"],\"stream\":true,\"temperature\":0,\"tool_results\":[{\"call\":{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}},\"outputs\":[{\"additionalProp1\":null}]}],\"tools\":[{\"description\":\"string\",\"name\":\"string\",\"parameter_definitions\":{\"additionalProp1\":{\"description\":\"string\",\"required\":false,\"type\":\"string\"}}}]}"
response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(request) }

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.cohere.com/v1/chat");
request.Content = new StringContent("{\"chat_history\":[{\"message\":\"string\",\"role\":\"CHATBOT\",\"tool_calls\":[{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}}]}],\"citation_quality\":\"ACCURATE\",\"connectors\":[{\"continue_on_failure\":true,\"id\":\"string\",\"options\":{},\"user_access_token\":\"string\"}],\"conversation_id\":\"string\",\"documents\":[{\"id\":\"string\"}],\"force_single_step\":true,\"frequency_penalty\":0,\"k\":0,\"max_input_tokens\":0,\"max_tokens\":0,\"message\":\"string\",\"model\":\"string\",\"p\":0.75,\"preamble\":\"string\",\"presence_penalty\":0,\"prompt_truncation\":\"AUTO\",\"raw_prompting\":true,\"response_format\":{\"type\":\"text\"},\"safety_mode\":\"CONTEXTUAL\",\"search_queries_only\":true,\"seed\":0,\"stop_sequences\":[\"string\"],\"stream\":true,\"temperature\":0,\"tool_results\":[{\"call\":{\"name\":\"string\",\"parameters\":{\"additionalProp1\":null}},\"outputs\":[{\"additionalProp1\":null}]}],\"tools\":[{\"description\":\"string\",\"name\":\"string\",\"parameter_definitions\":{\"additionalProp1\":{\"description\":\"string\",\"required\":false,\"type\":\"string\"}}}]}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);

{
  "chat_history": [
    {
      "message": "string",
      "role": "CHATBOT",
      "tool_calls": [
        {
          "name": "string",
          "parameters": {
            "additionalProp1": null
          }
        }
      ]
    }
  ],
  "citations": [
    {
      "document_ids": [
        "string"
      ],
      "end": 0,
      "start": 0,
      "text": "string",
      "type": "PLAN"
    }
  ],
  "documents": [
    {
      "id": "string"
    }
  ],
  "finish_reason": "COMPLETE",
  "generation_id": "00000000-0000-0000-0000-000000000000",
  "is_search_required": true,
  "meta": {
    "api_version": {
      "is_deprecated": true,
      "is_experimental": true,
      "version": "string"
    },
    "billed_units": {
      "classifications": 0,
      "image_tokens": 0,
      "images": 0,
      "input_tokens": 0,
      "output_tokens": 0,
      "pages": 0,
      "search_units": 0
    },
    "cached_tokens": 0,
    "tokens": {
      "input_tokens": 0,
      "output_tokens": 0
    },
    "warnings": [
      "string"
    ]
  },
  "response_id": "00000000-0000-0000-0000-000000000000",
  "search_queries": [
    {
      "generation_id": "00000000-0000-0000-0000-000000000000",
      "text": "string"
    }
  ],
  "search_results": [
    {
      "connector": {
        "id": "string"
      },
      "continue_on_failure": true,
      "document_ids": [
        "string"
      ],
      "error_message": "string",
      "search_query": {
        "generation_id": "00000000-0000-0000-0000-000000000000",
        "text": "string"
      }
    }
  ],
  "text": "string",
  "tool_calls": [
    {
      "name": "string",
      "parameters": {
        "additionalProp1": null
      }
    }
  ]
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

Search documentation

/datasets

/connectors

/embed-jobs

/finetuning

/models

/audio/transcriptions

/batches

Operations

Parameters

Request body

Responses

/datasets

/connectors

/embed-jobs

/finetuning

/models

/audio/transcriptions

/batches

Operations