Create

API Reference

Chat

Completions

Create

client.chat.completions.create(CompletionCreateParamsbody, RequestOptionsoptions?): completion_messageCompletionMessageidstringmetricsarrayCreateChatCompletionResponse | Stream<eventEventidstringCreateChatCompletionResponseStreamChunk>

post/chat/completions

Generate a chat completion for the given messages using the specified model.

Parameters

CompletionCreateParamsalias

streamfalseCompletionCreateParamsNonStreaming | streamfalseCompletionCreateParamsStreaming

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CompletionCreateParamsBase

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streamfalse

optional

If True, generate an SSE event stream of the response. Defaults to False.

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false

CompletionCreateParamsNonStreaming extends streamfalseCompletionCreateParamsBase

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streamfalse

optional

If True, generate an SSE event stream of the response. Defaults to False.

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false

CompletionCreateParamsNonStreaming extends streamfalseCompletionCreateParamsBase

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streamfalse

optional

If True, generate an SSE event stream of the response. Defaults to False.

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false

Returns

completion_messageCompletionMessageidstringmetricsarrayCreateChatCompletionResponse

HTTPTypeScriptPythonGo

import LlamaAPIClient from 'llama-api-client';

const client = new LlamaAPIClient({
  apiKey: 'My API Key',
});

const createChatCompletionResponse = await client.chat.completions.create({
  messages: [{ content: 'string', role: 'user' }],
  model: 'model',
});

console.log(createChatCompletionResponse.id);

200 Example

{
  "completion_message": {
    "role": "assistant",
    "content": "string",
    "stop_reason": "stop",
    "tool_calls": [
      {
        "id": "id",
        "function": {
          "arguments": "arguments",
          "name": "name"
        }
      }
    ]
  },
  "id": "id",
  "metrics": [
    {
      "metric": "metric",
      "value": 0,
      "unit": "unit"
    }
  ]
}