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1. Instantiate Clients

Instantiate Clients for Freeplay and your LLM Provider

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2. Fetch Prompt Template

Fetch prompt template from Freeplay

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3. Format Prompt

format prompt including input variables and history

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4. Call LLM

Call your LLM provider, history will be merged into your prompt and ready to pass through

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5. Record Interaction

Record the interaction to Freeplay

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6. Manage history

Determine what to include in conversation history over each turn. Append the select messages to an array

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Examples

import json
import os
import time
from copy import deepcopy
from typing import Optional

import boto3
from anthropic import Anthropic, NotGiven
from openai import OpenAI

from freeplay import Freeplay, RecordPayload, ResponseInfo, CallInfo, SessionInfo, TraceInfo

fpclient = Freeplay(
    freeplay_api_key=os.environ['FREEPLAY_API_KEY'],
    api_base=f"{os.environ['FREEPLAY_API_URL']}/api"
)
project_id = os.environ['FREEPLAY_PROJECT_ID']
enviroment = 'dev'

anthropic_client = Anthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY")
)


articles = [
    "george washington was the first president of the united states",
    "the sky is blue",
    "the earth is round",
    ""
]
questions = [
    "who was the first president of the united states?",
    "what color is the sky?",
    "what shape is the earth?",
    "repeat the first question and answer"
]
input_pairs = list(zip(articles, questions))

template_prompt = fpclient.prompts.get(
    project_id=project_id,
    template_name='History-Basics',
    environment=enviroment
)


def call_and_record(
        project_id: str,
        template_name: str,
        env: str,
        history: list,
        input_variables: dict,
        session_info: SessionInfo,
        trace_info: Optional[TraceInfo] = None
) -> dict:
    formatted_prompt = fpclient.prompts.get_formatted(
        project_id=project_id,
        template_name=template_name,
        environment=env,
        variables=input_variables,
        history=history,
    )

    start = time.time()

    completion = anthropic_client.messages.create(
      system=formatted_prompt.system_content or NotGiven(),
      messages=formatted_prompt.llm_prompt,
      model=formatted_prompt.prompt_info.model,
      **formatted_prompt.prompt_info.model_parameters
    )
    end = time.time()
    llm_response = completion.content[0].text

    print("Completion: %s" % llm_response)

    assistant_response = {'role': 'assistant', 'content': llm_response}
    all_messages = formatted_prompt.all_messages(new_message=assistant_response)

    call_info = CallInfo.from_prompt_info(formatted_prompt.prompt_info, start, end)
    response_info = ResponseInfo(
        is_complete=True,
    )

    record_response = fpclient.recordings.create(
        RecordPayload(
            all_messages=all_messages,
            session_info=session_info,
            inputs=input_variables,
            prompt_info=formatted_prompt.prompt_info,
            call_info=call_info,
            response_info=response_info,
            trace_info=trace_info,
        )
    )

    return {'completion_id': record_response.completion_id,
            'llm_response': assistant_response,
            "all_messages": all_messages}


session = fpclient.sessions.create()

history = []
for inputs in input_pairs:
    input_vars = {'question': inputs[1], 'article': inputs[0]}
    record_response = call_and_record(
        project_id=project_id,
        template_name='History-QA',
        env=enviroment,
        history=history,
        input_variables=input_vars,
        session_info=session.session_info,
    )
    history = [msg for msg in record_response['all_messages'] if msg['role'] != 'system']