Using the Cohere Chat API for Text Generation
The Chat API endpoint is used to generate text with Cohere LLMs. This endpoint facilitates a conversational interface, allowing users to send messages to the model and receive text responses.
import cohere
co = cohere.Client(api_key="<YOUR API KEY>")
response = co.chat(
model="command-a-03-2025",
message="Write a title for a blog post about API design. Only output the title text.",
)
print(response.text)
# "The Art of API Design: Crafting Elegant and Powerful Interfaces"
public class ChatPost {
public static void main(String[] args) {
Cohere cohere = Cohere.builder().token("<YOUR API KEY>").build();
NonStreamedChatResponse response = cohere.chat(
ChatRequest.builder()
.model("command-a-03-2025")
.message("Write a title for a blog post about API design. Only output the title text.")
)
System.out.println(response); // "The Art of API Design: Crafting Elegant and Powerful Interfaces"
}
}const { CohereClient } = require('cohere-ai');
const cohere = new CohereClient({
token: '<YOUR API KEY>',
});
(async () => {
const response = await cohere.chat({
message: 'Write a title for a blog post about API design. Only output the title text.',
});
console.log(response.text)
})();Response Structure
Section titled “Response Structure”Below is a sample response from the Chat API
{
"text": "The Art of API Design: Crafting Elegant and Powerful Interfaces",
"generation_id": "dd78b9fe-988b-4c18-9419-8fbdf9968948",
"chat_history": [
{
"role": "USER",
"message": "Write a title for a blog post about API design. Only output the title text."
},
{
"role": "CHATBOT",
"message": "The Art of API Design: Crafting Elegant and Powerful Interfaces"
}
],
"finish_reason": "COMPLETE",
"meta": {
"api_version": {
"version": "1"
},
"billed_units": {
"input_tokens": 17,
"output_tokens": 12
},
"tokens": {
"input_tokens": 83,
"output_tokens": 12
}
}
}Every response contains the following fields:
textthe generated message from the model.generation_idthe ID corresponding to this response. Can be used together with the Feedback API endpoint to promote great responses and flag bad ones.chat_historythe conversation presented in a chat log formatfinish_reasoncan be one of the following:COMPLETEthe model successfully finished generating the messageMAX_TOKENSthe model's context limit was reached before the generation could be completed
metacontains information with token counts, billing etc.
Multi-turn conversations
Section titled “Multi-turn conversations”The user message in the Chat request can be sent together with a chat_history to provide the model with conversational context:
import cohere
co = cohere.Client(api_key="<YOUR API KEY>")
message = "Can you tell me about LLMs?"
response = co.chat(
model="command-a-03-2025",
chat_history=[
{"role": "USER", "text": "Hey, my name is Michael!"},
{
"role": "CHATBOT",
"text": "Hey Michael! How can I help you today?",
},
],
message=message,
)
print(response.text) # "Sure thing Michael, LLMs are ..."Instead of manually building the chat_history, we can grab it from the response of the previous turn.
chat_history = []
max_turns = 10
for _ in range(max_turns):
# get user input
message = input("Send the model a message: ")
# generate a response with the current chat history
response = co.chat(
model="command-a-03-2025",
message=message,
chat_history=chat_history,
)
# print the model's response on this turn
print(response.text)
# set the chat history for next turn
chat_history = response.chat_historyUsing conversation_id to Save Chat History
Section titled “Using conversation_id to Save Chat History”Providing the model with the conversation history is one way to have a multi-turn conversation with the model. Cohere has developed another option for users who do not wish to save the conversation history, and it works through a user-defined conversation_id.
import cohere
co = cohere.Client("<YOUR API KEY>")
response = co.chat(
model="command-a-03-2025",
message="The secret word is 'fish', remember that.",
conversation_id="user_defined_id_1",
)
answer = response.textThen, if you wanted to continue the conversation, you could do so like this (keeping the id consistent):
response2 = co.chat(
model="command-a-03-2025",
message="What is the secret word?",
conversation_id="user_defined_id_1",
)
print(response2.text) # "The secret word is 'fish'"Note that the conversation_id should not be used in conjunction with the chat_history. They are mutually exclusive.