Update prompt version

PATCH /api/prompts/<prompt_id>/versions/<prompt_version_id>/

Python

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())

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.

messages · array · required

The list of messages for the prompt version. If you want to add a variable, you can use the following format: {{variable_name}}.

Example

{
"messages": [
    {"role": "system", "content": "You are a helpful {{role}}."}, // role could be assistant, teacher, etc.
    {"role": "user", "content": "Hello, how are you?"}
  ],
}

model · string · required

Speciy the model you want to use in this version.

description · string

Description of the prompt version

stream · boolean

Whether the prompt version should be streamed or not. Default is false.

temperature · float

The temperature of the model.

max_tokens · integer

The maximum number of tokens to generate.

top_p · float

The nucleus sampling probability.

frequency_penalty · 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

presence_penalty · 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

variables · object

The list of variables for the prompt version. You can use these variables in the messages.

Example

{
  "variables": {
    "role": ["assistant", "teacher", "student"]
  }
}

fallback_models · array

The list of fallback models for the prompt version. Check out fallback models for more information.

Example

{
  "fallback_models": ["gpt-4o", "gpt-4o-mini"]
}

load_balance_models · array

The list of models to load balance the prompt version. Check out load balancing for more information.

Example

{
"load_balance_models": [
    {"model": "gpt-4o", "weight": 0.7},
    {"model": "gpt-4o-mini", "weight": 0.3}
  ],
}

tools · array

The list of tools to use for the prompt version. Check out tools for more information.

Example

{
"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"]
        }
      }
    }
  ],
}

deploy · boolean

Whether to deploy this version as the live version Default is false.

Python

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())