> ## Documentation Index
> Fetch the complete documentation index at: https://docs.keywordsai.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Update prompt version

You can update a prompt version by sending a PATCH request to the prompt version endpoint. The request should include the name of the prompt version, and the list of fallback models, load balance models, tools, and whether to deploy this version as the live version.

<ParamField path="messages" type="array" required={true}>
  The list of messages for the prompt version. If you want to add a variable, you can use the following format: `{{variable_name}}`.

  <Accordion title="Example">
    ```json theme={"system"}
    {
    "messages": [
        {"role": "system", "content": "You are a helpful {{role}}."}, // role could be assistant, teacher, etc.
        {"role": "user", "content": "Hello, how are you?"}
      ],
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="model" type="string" required>
  Speciy the model you want to use in this version.
</ParamField>

<ParamField path="description" type="string">
  Description of the prompt version
</ParamField>

<ParamField path="stream" type="boolean">
  Whether the prompt version should be streamed or not. Default is `false`.
</ParamField>

<ParamField path="temperature" type="float">
  The temperature of the model.
</ParamField>

<ParamField path="max_tokens" type="integer">
  The maximum number of tokens to generate.
</ParamField>

<ParamField path="top_p" type="float">
  The nucleus sampling probability.
</ParamField>

<ParamField path="frequency_penalty" type="float">
  Specify how much to penalize new tokens based on their existing frequency in the text so far. Decreases the model's likelihood of repeating the same line verbatim
</ParamField>

<ParamField path="presence_penalty" type="float">
  Specify how much to penalize new tokens based on whether they appear in the text so far. Increases the model’s likelihood of talking about new topics
</ParamField>

<ParamField path="variables" type="object">
  The list of variables for the prompt version. You can use these variables in the messages.

  <Accordion title="Example">
    ```json theme={"system"}
    {
      "variables": {
        "role": ["assistant", "teacher", "student"]
      }
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="fallback_models" type="array">
  The list of fallback models for the prompt version. Check out [fallback models](/documentation/products/gateway/traffic_management/fallbacks) for more information.

  <Accordion title="Example">
    ```json theme={"system"}
    {
      "fallback_models": ["gpt-4o", "gpt-4o-mini"]
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="load_balance_models" type="array">
  The list of models to load balance the prompt version. Check out [load balancing](/documentation/products/gateway/traffic_management/load-balancing) for more information.

  <Accordion title="Example">
    ```json theme={"system"}
    {
    "load_balance_models": [
        {"model": "gpt-4o", "weight": 0.7},
        {"model": "gpt-4o-mini", "weight": 0.3}
      ],
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="tools" type="array">
  The list of tools to use for the prompt version. Check out [tools](/documentation/products/gateway/advanced_features/function_calling) for more information.

  <Accordion title="Example">
    ```json theme={"system"}
    {
    "tools": [
        {
          "type": "function",
          "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
              "type": "object",
              "properties": {
                "location": {
                  "type": "string",
                  "description": "The city and state, e.g. San Francisco, CA"
                }
              },
              "required": ["location"]
            }
          }
        }
      ],
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="deploy" type="boolean">
  Whether to deploy this version as the live version Default is `false`.
</ParamField>

<RequestExample>
  ```Python Python theme={"system"}
  import requests

  url = "https://api.keywordsai.co/api/prompts/<prompt_id>/versions/"
  api_key = "YOUR_KEYWORDS_AI_API_KEY" # Replace with your actual Keywords AI API key
  data = {
  "description": "A description of the prompt version",
    "messages": [
      {"role": "system", "content": "You are a helpful {{role}}."},
      {"role": "user", "content": "Hello, how are you?"}
    ],
    "model": "gpt-3.5-turbo",
    "stream": false,
    "temperature": 0.7,
    "max_tokens": 256,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 0.0,
    "variables": {},
    "fallback_models": ["gpt-3.5-turbo-16k", "gpt-4"],
    "load_balance_models": [
      {"model": "gpt-3.5-turbo", "weight": 0.7},
      {"model": "gpt-4", "weight": 0.3}
    ],
  }
  headers = {
      "Authorization": f"Bearer {api_key}",
      "Content-Type": "application/json"
  }

  response = requests.post(url, headers=headers, json=data)
  print(response.json())
  ```

  ```TypeScript TypeScript theme={"system"}
  // Define the function with TypeScript

  fetch('https://api.keywordsai.co/api/prompts/<prompt_id>/versions/', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_KEYWORDS_AI_API_KEY'
      },
      body: JSON.stringify({
          description: "A description of the prompt version",
          messages: [
              {role: "system", content: "You are a helpful {{role}}."},
              {role: "user", content: "Hello, how are you?"}
          ],
          model: "gpt-3.5-turbo",
          stream: false,
          temperature: 0.7,
          max_tokens: 256,
          top_p: 1.0,
          frequency_penalty: 0.0,
          presence_penalty: 0.0,
          variables: {},
          fallback_models: ["gpt-3.5-turbo-16k", "gpt-4"],
          load_balance_models: [
              {model: "gpt-3.5-turbo", weight: 0.7},
              {model: "gpt-4", weight: 0.3}
          ],
      })
  })
  .then(response => response.json())
  .then(data => console.log(data));
  ```
</RequestExample>