Prompt Render

Renders a prompt template with its variable values filled in

Given a prompt ID, variable values, and optionally any hyperparameters, this API returns a JSON object containing the raw prompt template.

Note: Unlike inference requests, Prompt Render API calls are processed through Portkey’s Control Plane services.

Example: Using Prompt Render output in a new request

Here’s how you can take the output from the render API and use it for making a separate LLM call. We’ll take example of OpenAI SDKs, but you can use it simlarly for any other frameworks like Langchain etc. as well.

OpenAI Python

from portkey_ai import Portkey
from openai import OpenAI

# Retrieving the Prompt from Portkey

portkey = Portkey(
  api_key="PORTKEY_API_KEY"
)

render_response = portkey.prompts.render(
  prompt_id="PROMPT_ID",
  variables={ "movie":"Dune 2" }
)

PROMPT_TEMPLATE = render_response.data

# Making a Call to OpenAI with the Retrieved Prompt

openai = OpenAI(
    api_key = "OPENAI_API_KEY",
    base_url = "https://api.portkey.ai/v1",
    default_headers = {
      'x-portkey-provider': 'openai',
      'x-portkey-api-key': 'PORTKEY_API_KEY',
      'Content-Type': 'application/json',
    }
)

chat_complete = openai.chat.completions.create(**PROMPT_TEMPLATE)

print(chat_complete.choices[0].message.content)

Authorizations

x-portkey-api-key · string · header · required

Path Parameters

promptId · string · required

The unique identifier of the prompt template to render

Body

application/json

Note: Although hyperparameters are shown grouped here (like messages, max_tokens, temperature, etc.), they should only be passed at the root level, alongside 'variables' and 'stream'.

Response

200 - application/json

Successful rendered prompt

The response is of type object.