Chat — Cohere

Generates a text response to a user message and streams it down, token by token. To learn how to use the Chat API with streaming follow our Text Generation guides.

Follow the Migration Guide for instructions on moving from API v1 to API v2.

Headers

X-Client-NamestringOptional

The name of the project that is making the request.

Request

This endpoint expects an object.

streamfalseRequired

Defaults to false.

When true, the response will be a SSE 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

modelstringRequired

The name of a compatible Cohere model (such as command-r or command-r-plus) or the ID of a fine-tuned model.

messageslist of objectsRequired

A list of chat messages in chronological order, representing a conversation between the user and the model.

Messages can be from User, Assistant, Tool and System roles. Learn more about messages and roles in the Chat API guide.

toolslist of objectsOptional

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 content in the response will be empty 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.

documentslist of strings or objectsOptional

A list of relevant documents that the model can cite to generate a more accurate reply. Each document is either a string or document object with content and metadata.

citation_optionsobjectOptional

Options for controlling citation generation.

response_formatobjectOptional

Configuration for forcing the model output to adhere to the specified format. Supported on Command R, Command R+ and newer models.

The model can be forced into outputting JSON objects by setting { "type": "json_object" }.

A JSON Schema 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.

Note: When json_schema is not specified, the generated object can have up to 5 layers of nesting.

Limitation: The parameter is not supported when used in combinations with the documents or tools parameters.

safety_modeenumOptional

Allowed values: CONTEXTUALSTRICTOFF

Used to select the safety instruction inserted into the prompt. Defaults to CONTEXTUAL. When OFF 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 with models Command R 08-2024, Command R+ 08-2024 and newer.

Note: command-r7b-12-2024 only supports "CONTEXTUAL" and "STRICT" modes.

max_tokensintegerOptional

The maximum number of tokens the model will generate as part of the response.

Note: Setting a low value may result in incomplete generations.

stop_sequenceslist of stringsOptional

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.

temperaturedoubleOptional

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.

seedintegerOptional

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.

frequency_penaltydoubleOptional

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.

presence_penaltydoubleOptional

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.

kdoubleOptional

Ensures that only the top k most likely tokens are considered for generation at each step. When k is set to 0, k-sampling is disabled. Defaults to 0, min value of 0, max value of 500.

pdoubleOptional

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.

logprobsbooleanOptional

Defaults to false. When set to true, the log probabilities of the generated tokens will be included in the response.

strict_toolsbooleanOptionalBeta

When set to true, tool calls in the Assistant message will be forced to follow the tool definition strictly. Learn more in the Structured Outputs (Tools) guide.

Note: The first few requests with a new set of tools will take longer to process.

Response

This endpoint returns an object.

idstring

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

finish_reasonenum

Allowed values: COMPLETESTOP_SEQUENCEMAX_TOKENSTOOL_CALLERROR

The reason a chat request has finished.

complete: The model finished sending a complete message.
max_tokens: The number of generated tokens exceeded the model’s context length or the value specified via the max_tokens parameter.
stop_sequence: One of the provided stop_sequence entries was reached in the model’s generation.
tool_call: The model generated a Tool Call and is expecting a Tool Message in return
error: The generation failed due to an internal error

messageobject

A message from the assistant role can contain text and tool call information.

usageobjectOptional

logprobslist of objectsOptional

1	import cohere
2
3	co = cohere.ClientV2()
4
5	response = co.chat(
6	model="command-r-plus-08-2024",
7	messages=[{"role": "user", "content": "hello world!"}],
8	)
9
10	print(response)

1	{
2	"id": "c14c80c3-18eb-4519-9460-6c92edd8cfb4",
3	"finish_reason": "COMPLETE",
4	"message": {
5	"role": "assistant",
6	"content": [
7	{
8	"type": "text",
9	"text": "LLMs stand for Large Language Models, which are a type of neural network model specialized in processing and generating human language. They are designed to understand and respond to natural language input and have become increasingly popular and valuable in recent years.\n\nLLMs are trained on vast amounts of text data, enabling them to learn patterns, grammar, and semantic meanings present in the language. These models can then be used for various natural language processing tasks, such as text generation, summarization, question answering, machine translation, sentiment analysis, and even some aspects of natural language understanding.\n\nSome well-known examples of LLMs include:\n\n1. GPT-3 (Generative Pre-trained Transformer 3) — An open-source LLM developed by OpenAI, capable of generating human-like text and performing various language tasks.\n\n2. BERT (Bidirectional Encoder Representations from Transformers) — A Google-developed LLM that is particularly good at understanding contextual relationships in text, and is widely used for natural language understanding tasks like sentiment analysis and named entity recognition.\n\n3. T5 (Text-to-Text Transfer Transformer) — Also from Google, T5 is a flexible LLM that frames all language tasks as text-to-text problems, where the model learns to generate output text based on input text prompts.\n\n4. RoBERTa (Robustly Optimized BERT Approach) — A variant of BERT that uses additional training techniques to improve performance.\n\n5. DeBERTa (Decoding-enhanced BERT with disentangled attention) — Another variant of BERT that introduces a new attention mechanism.\n\nLLMs have become increasingly powerful and larger in scale, improving the accuracy and sophistication of language tasks. They are also being used as a foundation for developing various applications, including chatbots, content recommendation systems, language translation services, and more. \n\nThe future of LLMs holds the potential for even more sophisticated language technologies, with ongoing research and development focused on enhancing their capabilities, improving efficiency, and exploring their applications in various domains."
10	}
11	]
12	},
13	"usage": {
14	"billed_units": {
15	"input_tokens": 5,
16	"output_tokens": 418
17	},
18	"tokens": {
19	"input_tokens": 71,
20	"output_tokens": 418
21	}
22	}
23	}