File size: 10,141 Bytes
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import time
from pathlib import Path
from typing import Literal
from openai import OpenAI
from tqdm import trange, tqdm
from virtual_lab.agent import Agent
from virtual_lab.constants import CONSISTENT_TEMPERATURE, PUBMED_TOOL_DESCRIPTION
from virtual_lab.prompts import (
individual_meeting_agent_prompt,
individual_meeting_critic_prompt,
individual_meeting_start_prompt,
SCIENTIFIC_CRITIC,
team_meeting_start_prompt,
team_meeting_team_lead_initial_prompt,
team_meeting_team_lead_intermediate_prompt,
team_meeting_team_lead_final_prompt,
team_meeting_team_member_prompt,
)
from virtual_lab.utils import (
convert_messages_to_discussion,
count_discussion_tokens,
count_tokens,
get_messages,
get_summary,
print_cost_and_time,
run_tools,
save_meeting,
)
def run_meeting(
meeting_type: Literal["team", "individual"],
agenda: str,
save_dir: Path,
save_name: str = "discussion",
team_lead: Agent | None = None,
team_members: tuple[Agent, ...] | None = None,
team_member: Agent | None = None,
agenda_questions: tuple[str, ...] = (),
agenda_rules: tuple[str, ...] = (),
summaries: tuple[str, ...] = (),
contexts: tuple[str, ...] = (),
num_rounds: int = 0,
temperature: float = CONSISTENT_TEMPERATURE,
pubmed_search: bool = False,
return_summary: bool = False,
) -> str:
"""Runs a meeting with a LLM agents.
:param meeting_type: The type of meeting.
:param agenda: The agenda for the meeting.
:param save_dir: The directory to save the discussion.
:param save_name: The name of the discussion file that will be saved.
:param team_lead: The team lead for a team meeting (None for individual meeting).
:param team_members: The team members for a team meeting (None for individual meeting).
:param team_member: The team member for an individual meeting (None for team meeting).
:param agenda_questions: The agenda questions to answer by the end of the meeting.
:param agenda_rules: The rules for the meeting.
:param summaries: The summaries of previous meetings.
:param contexts: The contexts for the meeting.
:param num_rounds: The number of rounds of discussion.
:param temperature: The sampling temperature.
:param pubmed_search: Whether to include a PubMed search tool.
:param return_summary: Whether to return the summary of the meeting.
:return: The summary of the meeting (i.e., the last message) if return_summary is True, else None.
"""
# Validate meeting type
if meeting_type == "team":
if team_lead is None or team_members is None or len(team_members) == 0:
raise ValueError("Team meeting requires team lead and team members")
if team_member is not None:
raise ValueError("Team meeting does not require individual team member")
if team_lead in team_members:
raise ValueError("Team lead must be separate from team members")
if len(set(team_members)) != len(team_members):
raise ValueError("Team members must be unique")
elif meeting_type == "individual":
if team_member is None:
raise ValueError("Individual meeting requires individual team member")
if team_lead is not None or team_members is not None:
raise ValueError(
"Individual meeting does not require team lead or team members"
)
else:
raise ValueError(f"Invalid meeting type: {meeting_type}")
# Start timing the meeting
start_time = time.time()
# Set up client
client = OpenAI()
# Set up team
if meeting_type == "team":
team = [team_lead] + list(team_members)
else:
team = [team_member] + [SCIENTIFIC_CRITIC]
# Set up tools
assistant_params = {"tools": [PUBMED_TOOL_DESCRIPTION]} if pubmed_search else {}
# Set up the assistants
agent_to_assistant = {
agent: client.beta.assistants.create(
name=agent.title,
instructions=agent.prompt,
model=agent.model,
**assistant_params,
)
for agent in team
}
# Map assistant IDs to agents
assistant_id_to_title = {
assistant.id: agent.title for agent, assistant in agent_to_assistant.items()
}
# Set up tool token count
tool_token_count = 0
# Set up the thread
thread = client.beta.threads.create()
# Initial prompt for team meeting
if meeting_type == "team":
client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content=team_meeting_start_prompt(
team_lead=team_lead,
team_members=team_members,
agenda=agenda,
agenda_questions=agenda_questions,
agenda_rules=agenda_rules,
summaries=summaries,
contexts=contexts,
num_rounds=num_rounds,
),
)
# Loop through rounds
for round_index in trange(num_rounds + 1, desc="Rounds (+ Final Round)"):
round_num = round_index + 1
# Loop through team and elicit responses
for agent in tqdm(team, desc="Team"):
# Prompt based on agent and round number
if meeting_type == "team":
# Team meeting prompts
if agent == team_lead:
if round_index == 0:
prompt = team_meeting_team_lead_initial_prompt(
team_lead=team_lead
)
elif round_index == num_rounds:
prompt = team_meeting_team_lead_final_prompt(
team_lead=team_lead,
agenda=agenda,
agenda_questions=agenda_questions,
agenda_rules=agenda_rules,
)
else:
prompt = team_meeting_team_lead_intermediate_prompt(
team_lead=team_lead,
round_num=round_num - 1,
num_rounds=num_rounds,
)
else:
prompt = team_meeting_team_member_prompt(
team_member=agent, round_num=round_num, num_rounds=num_rounds
)
else:
# Individual meeting prompts
if agent == SCIENTIFIC_CRITIC:
prompt = individual_meeting_critic_prompt(
critic=SCIENTIFIC_CRITIC, agent=team_member
)
else:
if round_index == 0:
prompt = individual_meeting_start_prompt(
team_member=team_member,
agenda=agenda,
agenda_questions=agenda_questions,
agenda_rules=agenda_rules,
summaries=summaries,
contexts=contexts,
)
else:
prompt = individual_meeting_agent_prompt(
critic=SCIENTIFIC_CRITIC, agent=team_member
)
# Create message from user to agent
client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content=prompt,
)
# Run the agent
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id,
assistant_id=agent_to_assistant[agent].id,
model=agent.model,
temperature=temperature,
)
# Check if run requires action
if run.status == "requires_action":
# Run the tools
tool_outputs = run_tools(run=run)
# Update tool token count
tool_token_count += sum(
count_tokens(tool_output["output"]) for tool_output in tool_outputs
)
# Submit the tool outputs
run = client.beta.threads.runs.submit_tool_outputs_and_poll(
thread_id=thread.id, run_id=run.id, tool_outputs=tool_outputs
)
# Add tool outputs to the thread so it's visible for later rounds
client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content="Tool Output:\n\n"
+ "\n\n".join(
tool_output["output"] for tool_output in tool_outputs
),
)
# Check run status
if run.status != "completed":
raise ValueError(f"Run failed: {run.status}")
# If final round, only team lead or team member responds
if round_index == num_rounds:
break
# Get messages from the discussion
messages = get_messages(client=client, thread_id=thread.id)
# Convert messages to discussion format
discussion = convert_messages_to_discussion(
messages=messages, assistant_id_to_title=assistant_id_to_title
)
# Count discussion tokens
token_counts = count_discussion_tokens(discussion=discussion)
# Add tool token count to total token count
token_counts["tool"] = tool_token_count
# Print cost and time
# TODO: handle different models for different agents
print_cost_and_time(
token_counts=token_counts,
model=team_lead.model if meeting_type == "team" else team_member.model,
elapsed_time=time.time() - start_time,
)
# Save the discussion as JSON and Markdown
save_meeting(
save_dir=save_dir,
save_name=save_name,
discussion=discussion,
)
# Optionally, return summary
if return_summary:
return get_summary(discussion)
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