POST
/
api
/
prompts
/
<prompt_id>
/
versions
/
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}
  ],
  "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": True
}
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())
You can create a prompt version by sending a POST request to the prompt versions endpoint. The request should include the list of messages for the prompt version, the model you want to use, and other optional parameters. After you created a prompt version, you can deploy it as the live version by setting the deploy parameter to true. Get the prompt_id from the prompts endpoint.
​
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}}.
​
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.
​
fallback_models
array
The list of fallback models for the prompt version. Check out fallback models for more information.
​
load_balance_models
array
The list of models to load balance the prompt version. Check out load balancing for more information.
​
tools
array
The list of tools to use for the prompt version. Check out tools for more information.
​
deploy
boolean
Whether to deploy this version as the live version Default is false.
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}
  ],
  "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": True
}
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

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