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Documentation Index

Fetch the complete documentation index at: https://docs.freeplay.ai/llms.txt

Use this file to discover all available pages before exploring further.

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1. Create the Freeplay Client

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2. Fetch the prompt from Freeplay

Freeplay will handle conversion of the messages to the right format for you

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3. Call the LLM via LiteLLM

Route the LLM call through LiteLLM

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4. Record to Freeplay

Record the interaction back to Freeplay

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Examples

from freeplay import Freeplay, RecordPayload, CallInfo, SessionInfo
from openai import OpenAI
from anthropic import Anthropic
from dotenv import load_dotenv
import os
import time
from litellm import completion

load_dotenv("../.env")

fp_client = Freeplay(
    freeplay_api_key=os.getenv("FREEPLAY_API_KEY"),
    api_base=os.getenv("FREEPLAY_URL"),
)

user_question = "What is the capital of France?"
# Fetch the prompt from freeplay
prompt_vars = {"question": user_question}
formatted_prompt = fp_client.prompts.get_formatted(
    project_id="5688ebaf-7f22-4d5d-b9bb-bc715c8faabb",
    template_name="BasicTriviaBot",
    environment="dev",
    variables=prompt_vars,
)

start = time.time()

response = completion(
  model=f"{formatted_prompt.prompt_info.provider}/{formatted_prompt.prompt_info.model}",
  messages=formatted_prompt.llm_prompt,
)
msg = response.choices[0].message
answer = msg.content

end = time.time()

# record to Freeplay
session = fp_client.sessions.create()
fp_client.recordings.create(
    RecordPayload(
				project_id=project_id,
        all_messages=formatted_prompt.all_messages(msg),
        inputs=prompt_vars,
        session_info=session,
        prompt_version_info=formatted_prompt.prompt_info,
        call_info=CallInfo.from_prompt_info(formatted_prompt.prompt_info, start, end)
    )
)

print(f'Question {user_question} \n Answer {answer}')