Multi-Chain Prompt with Traces

Chain multiple prompts using traces to group related completions.

1. Initialization of Freeplay & Clients

2. Session & Trace Creation

Create the session and trace, optionally passing additional information to each such as metadata and agent name

3. Prompt #1 - Call & Record

4. Prompt #2 - Call & Record

5. Record the final output to the Trace

Recording the final output to the trace allows you to record evals across the whole trace and the final output (needs to be a str)

Examples

Python

import os
import time
from freeplay import Freeplay, CallInfo, RecordPayload
from openai import OpenAI
import re

# Configure environment variables for API access
project_id = os.environ.get("FREEPLAY_PROJECT_ID")
freeplay_api_key = os.environ.get("FREEPLAY_API_KEY")
freeplay_url = os.environ.get("FREEPLAY_URL") # ie "https://app.freeplay.ai/api"
openai_api_key = os.environ.get("OPENAI_API_KEY")

# Initialize OpenAI client
openai = OpenAI(api_key=openai_api_key)

# Initialize Freeplay client with development API endpoint
fp_client = Freeplay(
    freeplay_api_key=freeplay_api_key,
    api_base=freeplay_url
)

# Create a new Freeplay session to group related completions
session = fp_client.sessions.create({})

user_input = "My favorite artist is Taylor Swift"

# Create a trace to combine multiple prompts into a single workflow
trace_info = session.create_trace(
    input=user_input, # Commonly user question but any str input to a trace
    agent_name="musicAgent", # Optionally pass an agent name
    custom_metadata={
        "version": "1.0.8"
    }
)

# =============================================================================
# Prompt 1: Generate Album Title
# =============================================================================

# Define variables for the album title generation prompt
prompt_vars_a = {"pop_star": "Taylor Swift"}

# Fetch and format the album generation prompt template
formatted_prompt_a = fp_client.prompts.get_formatted(
    project_id=project_id,           # Freeplay project identifier
    template_name="album_bot",       # Name of the prompt template
    environment="latest",            # Environment tag for prompt versioning
    variables=prompt_vars_a          # Variables to interpolate into the prompt
)

# Execute the OpenAI completion for album title generation
start = time.time()
chat_completion_a = openai.chat.completions.create(
    messages=formatted_prompt_a.llm_prompt,
    model=formatted_prompt_a.prompt_info.model,
    **formatted_prompt_a.prompt_info.model_parameters
)
end = time.time()

# Extract the generated album name from the response
album_name = chat_completion_a.choices[0].message.content
print(f"Album Name: {album_name}")

# Record the first completion to Freeplay for tracking and analysis
fp_client.recordings.create(
    RecordPayload(
        all_messages=formatted_prompt_a.all_messages(new_message={
            "role": chat_completion_a.choices[0].message.role,
            "content": chat_completion_a.choices[0].message.content
        }),
        inputs=prompt_vars_a,
        session_info=session.session_info,
        prompt_info=formatted_prompt_a.prompt_info,
        call_info=CallInfo.from_prompt_info(formatted_prompt_a.prompt_info, start, end),
        trace_info=trace_info
    ),
)

# =============================================================================
# Prompt 2: Generate Song List for Album
# =============================================================================

# Define variables for the song list generation prompt (includes generated album name)
prompt_vars_b = {"album_name": album_name, "pop_star": "Taylor Swift"}

# Fetch and format the song list generation prompt template
formatted_prompt_b = fp_client.prompts.get_formatted(
    project_id=project_id,
    template_name="song_bot",
    environment="latest",
    variables=prompt_vars_b
)

# Execute the OpenAI completion for song list generation
start = time.time()
chat_completion_b = openai.chat.completions.create(
    messages=formatted_prompt_b.llm_prompt,
    model=formatted_prompt_b.prompt_info.model,
    **formatted_prompt_b.prompt_info.model_parameters
)
end = time.time()

song_track = chat_completion_b.choices[0].message.content

# Record the second completion to Freeplay for tracking and analysis
fp_client.recordings.create(
  RecordPayload(
				project_id=project_id,
        all_messages=formatted_prompt_b.all_messages(new_message={
            "role": chat_completion_b.choices[0].message.role,
            "content": song_track
        }),
        inputs=prompt_vars_b,
        session_info=session.session_info,
        prompt_version_info=formatted_prompt_b.prompt_info,
        call_info=CallInfo.from_prompt_info(formatted_prompt_b.prompt_info, start, end),
        trace_info=trace_info
    )
)

# Record the final output to complete the trace
trace_info.record_output(
    project_id=project_id,
    output=song_track, # Final trace output (str)
    # Optional code evals logged to the trace
    eval_results={
      "sentiment": 0.7,
      "songTrackLength": len(re.findall(r'\n', song_track)) + 1, # count number of lines
    }
)