> ## Documentation Index > Fetch the complete documentation index at: https://docs.blinkops.com/llms.txt > Use this file to discover all available pages before exploring further. # Generate Chat Completion Generate a chat completion from a model using a conversation history. External Documentation To learn more, visit the [LiteLLM documentation](https://docs.litellm.ai/docs/completion). ## Basic Parameters
| Parameter | Description | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Completions Number | The number of completions to generate. | | Frequency Penalty | Penalize new tokens based on their existing frequency. Accept values from `-2.0` to `2.0`. | | Include Log Probabilities | Select to include the log probabilities for the most likely tokens. | | Include Top Log Probabilities | The number of log probabilities to return for the most likely tokens at each generation step.

Accepts an integer value between `0` and `5`. This parameter is only available when `logprobs` is set to `true`. | | Logit Bias | A JSON object mapping token IDs to bias values. This modifies the probability of generating specific tokens.

Example:
\{
  "2683": -100,
  "7211": 5
}
| | Max Completion Tokens | The maximum number of tokens to generate in the completion. This includes visible output tokens and reasoning tokens.

**Note:** This parameter can't be used along with `Max Tokens`. | | Max Tokens | The maximum number of tokens to generate in the completion.

**Note:** This parameter can't be used along with `Max Completion Tokens`. | | Messages | The list of messages in the conversation.

Example:
\[
  \{
    "role": "system",
    "content": "You are a helpful assistant that answers questions about programming."
  },
  \{
    "role": "user",
    "content": "Can you explain what a JSON map is?"
  }
]
| | Model ID | The ID of the model to get chat completions with. | | Parallel Tool Calls | Select to allow the model to call multiple tools in parallel.

**Note:** To set this parameter to `True`, the parameter `Tools` must be set. | | Presence Penalty | Penalize tokens based on whether they appear in the text so far. Valid range is `-2.0` - `2.0`. | | Provider | Select a provider of the chat model, this will allow to have access to the corresponding parameters. | | Response Format | The format that the model must output.

Set to `{ "type": "json_object" }` to enable JSON mode, which guarantees the model generates valid JSON.

**Important Note:** When using JSON mode, you must also instruct the model to produce JSON via a system or user message. Without this, the model may generate whitespace indefinitely until reaching the token limit. | | Safety Identifier | A unique ID to help track and manage safety-related requests. | | Seed | A seed to make the generated output more deterministic. | | Stop Tokens | A comma-separated list of tokens that will stop the generation.

Example: `\n`, `END`, `---` | | Temperature | The degree of randomness to use in the generation. Valid range is `0.0` - `2.0`. | | Tool Choice | Control which function is called by the model. This parameter accepts either a string or an object.

The optional values for string are:
\* `auto`
\* `required`
\* `none`

Object Example:
\{
    "type": "function",
    "function": \{
      "name": "get\_weather"
    }
  }
| | Tools | A list of JSON objects representing tools that the model can use.

Example:
\[
    \{
      "type": "function",
      "function": \{
        "name": "get\_weather",
        "description": "Fetches the current weather for a given location.",
        "parameters": \{
          "type": "object",
          "properties": \{
            "location": \{
              "type": "string",
              "description": "The city or location to get the weather for."
            },
            "date": \{
              "type": "string",
              "description": "The date for which to get the weather (YYYY-MM-DD)."
            }
          },
          "required": \["location"]
        }
      }
    }
  ]
| | Top P | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with `Top P` probability mass. | | User ID | The ID of the user to associate with the request. |
## Advanced Parameters
| Parameter | Description | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | API Base | The API endpoint you want to call the model with. | | API Version | The API version for the call. This is an Azure specific parameter. | | Additional Parameters | A JSON object for additional body parameters. Values specified in this parameter will override equivalent parameters.

For example:
\{
    "first\_key": 12345,
    "second\_key": "some\_value"
}
The object must follow the vendor's structure as defined in the [API documentation](https://docs.litellm.ai/docs/completion). | | Fallbacks | A List of JSON objects representing fallback models to use in case the primary model fails.

Example:
\[
    \{
      "openai/\*": \[
        "gpt-4o-mini"
      ]
    }
  ]
| | Metadata | Additional metadata to send with the request for logging.

Example:
\{
    "request\_id": "req-8f21c9a2",
    "tenant\_id": "acme-prod",
    "workflow": "blink-workflow",
    "feature": "docs-helper",
    "trace\_id": "trace-91b7e3",
    "user\_id": "user-1234",
    "debug": true
  }
| | Num Retries | The number of times to retry the request in case of `APIError`, `TimeoutError` or `ServiceUnavailableError`. |
## Example Output ```json theme={"dark"} { "id": "chatcmpl-CzHVeyzZj09MzGSXlmesrcTztrr66", "created": 1768721350, "model": "gpt-4o-mini-2024-07-18", "object": "chat.completion", "system_fingerprint": "fp_29330a9688", "choices": [ { "finish_reason": "stop", "index": 0, "message": { "content": "The current weather in New York City is cloudy, with a temperature of 12°C.", "role": "assistant" }, "logprobs": { "content": [ { "token": "The", "bytes": [ 84 ], "logprob": -0.001632451661862433, "top_logprobs": [ { "token": "The", "bytes": [ 84 ], "logprob": -0.001632451661862433 }, { "token": "In", "bytes": [ 73 ], "logprob": -7.001632213592529 }, { "token": "Currently", "bytes": [ 67 ], "logprob": -7.376632213592529 } ] } ] } } ] } ``` ## Workflow Library Example [Generate Chat Completion with Litellm and Send Results Via Email](https://library.blinkops.com/workflows/generate-chat-completion-with-litellm-and-send-results-via-email)
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