499 lines
17 KiB
Python
499 lines
17 KiB
Python
import asyncio
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import json
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import logging
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import operator
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import os
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import re
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from datetime import datetime
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from typing import Annotated, Any, Dict, List, TypedDict
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from dotenv import load_dotenv
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from langchain.agents import AgentExecutor, create_openai_functions_agent
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from langchain.prompts import ChatPromptTemplate, PromptTemplate
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from langchain.schema import HumanMessage
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, START, StateGraph
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from graphiti_core import Graphiti
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from graphiti_core.nodes import EpisodeType
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logging.getLogger('langchain.callbacks.tracers.langchain').setLevel(logging.WARNING)
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logging.getLogger('urllib3.connectionpool').setLevel(logging.ERROR)
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DEFAULT_MODEL = 'gpt-4o-mini'
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load_dotenv()
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logging.basicConfig(
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level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger('nba_agent')
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for name in logging.root.manager.loggerDict:
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if name != 'nba_agent':
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logging.getLogger(name).setLevel(logging.WARNING)
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# Initialize Graphiti client
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neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
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neo4j_user = os.environ.get('NEO4J_USER', 'neo4j')
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neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password')
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graphiti_client = Graphiti(neo4j_uri, neo4j_user, neo4j_password)
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openai_api_key = os.getenv('OPENAI_API_KEY')
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if not openai_api_key:
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logger.error('OPENAI_API_KEY is not set in the environment variables.')
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raise ValueError('OPENAI_API_KEY is not set')
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# Define the SimulationState
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class SimulationState(TypedDict):
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messages: Annotated[List[str], operator.add]
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teams: Dict[str, Dict[str, int]] # Changed to only store budget
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event: str
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transfer_offers: Annotated[List[Dict], operator.add]
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current_iteration: int
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all_events: List[str]
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max_iterations: int
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@tool
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async def fetch_all_teams_context(teams: List[str]):
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"""Get the current roster and player summaries for specified teams."""
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teams_with_players_dict = {}
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llm = ChatOpenAI(temperature=0.2, model=DEFAULT_MODEL).bind(
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response_format={'type': 'json_object'}
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)
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for team in teams:
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team_nodes = await graphiti_client.get_nodes_by_query(team, 1)
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if not team_nodes:
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logger.warning(f'No nodes found for team: {team}')
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continue
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team_node = team_nodes[0]
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search_result = await graphiti_client.search(
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f'plays for {team_node.name}',
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center_node_uuid=team_node.uuid,
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num_results=30,
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)
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# Include all facts and timestamps (expired at if exists)
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roster_facts = [get_fact_string(edge) for edge in search_result]
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prompt = PromptTemplate.from_template("""
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Given the following list of facts about players and their teams, extract only the names and provide brief summaries for players who currently play for {team_name}. Follow these guidelines:
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1. Only include players who are currently on the team.
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2. Discard any information about players who are no longer on the team or were never on the team.
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3. Use the 'expired_at' field to determine if a fact is still current. If 'expired_at' is not null, the fact is no longer current.
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4. If there are conflicting facts, use the most recent one based on the 'valid_at' or 'created_at' timestamps.
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Return the information as a JSON object containing a "players" field, which is an array of objects, each containing 'name' and 'summary' fields
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example output:
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{{
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"players": [
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{{
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"name": "Player Name",
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"summary": "Brief summary of the player"
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}},
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...
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]
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}}
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Facts:
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{facts}
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Current players for {team_name}:
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""")
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llm_response = await llm.ainvoke(
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prompt.format(
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team_name=team_node.name,
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facts='\n'.join(roster_facts),
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)
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)
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try:
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result = json.loads(llm_response.content)
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players = result.get('players', [])
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if not isinstance(players, list):
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raise ValueError('Expected a JSON array')
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except json.JSONDecodeError:
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logger.error(f'Failed to parse JSON from LLM response for {team_node.name}')
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players = []
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except ValueError as e:
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logger.error(f'Invalid data structure in LLM response for {team_node.name}: {e}')
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players = []
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teams_with_players_dict[team_node.name] = players
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return teams_with_players_dict
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# Existing tools
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@tool
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async def get_team_roster(team_name: str):
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"""Get the current roster for a specific team."""
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search_result = await graphiti_client.search(f'plays for {team_name}', num_results=30)
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roster_facts = [get_fact_string(edge) for edge in search_result]
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# Use LLM to extract player names
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llm = ChatOpenAI(temperature=0, model=DEFAULT_MODEL)
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prompt = PromptTemplate.from_template("""
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Given the following list of facts about players and their teams, extract only the names of players who play for {team_name}. Return the names as a comma-separated list.
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Facts:
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{facts}
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Players who play for {team_name}:
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""")
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llm_response = await llm.ainvoke(
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prompt.format(team_name=team_name, facts='\n'.join(roster_facts))
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)
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player_names = [name.strip() for name in llm_response.content.split(',')]
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return f"{team_name}'s roster: {', '.join(player_names)}"
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@tool
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async def search_player_info(player_name: str):
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"""Search for information about a specific player."""
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search_result = await graphiti_client.search(f'{player_name}', num_results=30)
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all_facts = [get_fact_string(edge) for edge in search_result]
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# Use LLM to extract relevant player information
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llm = ChatOpenAI(temperature=0, model=DEFAULT_MODEL)
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prompt = PromptTemplate.from_template("""
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Given the following list of facts, extract only the information that is relevant to {player_name}.
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Return the relevant facts as a list, with each fact on a new line.
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Facts:
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{facts}
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Relevant facts about {player_name}:
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""")
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llm_response = await llm.ainvoke(
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prompt.format(player_name=player_name, facts='\n'.join(all_facts))
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)
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relevant_facts = llm_response.content.strip().split('\n')
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return {'name': player_name, 'facts': relevant_facts}
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@tool
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async def propose_transfer(player_name: str, from_team: str, to_team: str, proposed_price: int):
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"""Propose a player transfer from one team to another with a proposed price."""
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return f'TRANSFER PROPOSAL: {to_team} wants to buy {player_name} from {from_team} for ${proposed_price:,}.'
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@tool
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async def execute_transfer(
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player_name: str, from_team: str, to_team: str, price: int
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) -> Dict[str, Any]:
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"""Execute a transfer between two teams."""
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await graphiti_client.add_episode(
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name=f'Transfer {player_name}',
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episode_body=f'{player_name} transferred from {from_team} to {to_team} for ${price:,}',
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source_description='Player Transfer',
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reference_time=datetime.now(),
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source=EpisodeType.message,
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)
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return {
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'messages': [
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HumanMessage(
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content=f'Transfer executed: {player_name} moved from {from_team} to {to_team} for ${price:,}'
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)
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],
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}
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async def add_episode(event_description: str):
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"""Add a new episode to the Graphiti client."""
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await graphiti_client.add_episode(
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name='New Event',
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episode_body=event_description,
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source_description='User Input',
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reference_time=datetime.now(),
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source=EpisodeType.message,
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)
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return f"Episode '{event_description}' added successfully."
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def get_fact_string(edge):
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fact_string = f'{edge.fact} Valid At: {edge.valid_at or edge.created_at}'
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if edge.expired_at:
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fact_string += f' Expired At: {edge.expired_at}'
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return fact_string
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tools = [
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get_team_roster,
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search_player_info,
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execute_transfer,
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]
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# Define the team agent function
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def create_team_agent(team_name: str, valid_teams: List[str]):
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llm = ChatOpenAI(temperature=0.3, model=DEFAULT_MODEL).bind(
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response_format={'type': 'json_object'}
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)
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prompt = ChatPromptTemplate.from_template("""You are the manager of the {team_name} NBA team. Make decisions to improve your team.
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Current event: {event}
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Your task is to decide on an action based on the event. Use the available tools to gather information, but focus on making a decision quickly. If you think a player transfer would benefit your team, propose one following the guidelines below.
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Ensure that you use the current budget info and the current state of your team to make the best decision.
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Current budget: ${budget}
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Valid teams for transfers: {valid_teams}
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IMPORTANT: After gathering information, you MUST make a decision. Your options are:
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1. Propose a transfer
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Note: if you are proposing a transfer make sure to output JSON in the following format:
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{{
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"transfer_proposal": {{
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"to_team": "team_name",
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"from_team": "team_name",
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"player_name": "player_name",
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"proposed_price": price
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}}
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}}
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IMPORTANT: Only propose transfers to teams in the valid teams list. Make sure that the player_name is a valid player on the from_team. Ensure that the the from_team name is a valid team name.
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2. Do nothing (output an empty JSON object)
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Do not ask for more information or clarification. Make a decision based on what you know.
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{agent_scratchpad}""")
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async def team_agent_function(state: SimulationState) -> Dict:
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agent = create_openai_functions_agent(llm, tools, prompt)
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executor = AgentExecutor(
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agent=agent, tools=[get_team_roster, search_player_info], verbose=True
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)
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team_data = state['teams'][team_name]
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result = await executor.ainvoke(
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{
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'team_name': team_name,
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'event': state['event'],
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'budget': team_data['budget'],
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'valid_teams': ', '.join(valid_teams),
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}
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)
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json_result = json.loads(result['output'])
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transfer_offer = None
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if 'transfer_proposal' in json_result:
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transfer_offer = json_result['transfer_proposal']
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if (
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transfer_offer['to_team'] not in valid_teams
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or transfer_offer['from_team'] not in valid_teams
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):
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logger.warning(f'Invalid transfer proposal: {transfer_offer}. Ignoring.')
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transfer_offer = None
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return {
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'transfer_offers': [transfer_offer] if transfer_offer else [],
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}
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return team_agent_function
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def parse_transfer_proposal(proposal: str) -> Dict[str, Any]:
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# Use regex to extract information
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to_team_match = re.search(r'(.*?) wants to buy', proposal)
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player_match = re.search(r'buy (.*?) from', proposal)
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from_team_match = re.search(r'from (.*?) for', proposal)
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price_match = re.search(r'\$([0-9,]+)', proposal)
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if not all([to_team_match, player_match, from_team_match, price_match]):
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raise ValueError(f'Unable to parse transfer proposal: {proposal}')
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to_team = to_team_match.group(1)
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player_name = player_match.group(1)
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from_team = from_team_match.group(1)
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proposed_price = int(price_match.group(1).replace(',', ''))
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return {
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'to_team': to_team,
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'from_team': from_team,
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'player_name': player_name,
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'proposed_price': proposed_price,
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}
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async def process_event(state: SimulationState) -> SimulationState:
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# await add_episode(state['event'])
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return {
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**state,
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'messages': [f"Event processed: {state['event']}"],
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'transfer_offers': [],
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}
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async def process_transfers(state: SimulationState) -> SimulationState:
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if not state['transfer_offers']:
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return state
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# Group offers by player
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offers_by_player = {}
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for offer in state['transfer_offers']:
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player = offer['player_name']
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if player not in offers_by_player:
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offers_by_player[player] = []
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offers_by_player[player].append(offer)
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for player, offers in offers_by_player.items():
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# Sort offers by price, highest first
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best_offer = max(offers, key=lambda x: x['proposed_price'])
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logger.info(
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f"Best offer for {player}: {best_offer['to_team']} wants to buy from {best_offer['from_team']} for ${best_offer['proposed_price']:,}"
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)
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# Execute the transfer
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transfer_result = await execute_transfer.ainvoke(
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{
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'player_name': best_offer['player_name'],
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'from_team': best_offer['from_team'],
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'to_team': best_offer['to_team'],
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'price': best_offer['proposed_price'],
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}
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)
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# Add the transfer result message to the state
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state['messages'].extend(transfer_result['messages'])
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# Update team rosters and budgets
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from_team = best_offer['from_team']
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to_team = best_offer['to_team']
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price = best_offer['proposed_price']
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if from_team in state['teams'] and to_team in state['teams']:
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state['teams'][from_team]['budget'] += price
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state['teams'][to_team]['budget'] -= price
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else:
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logger.warning(f'Cannot process transfer: {from_team} or {to_team} not in simulation.')
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# Clear all processed offers
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state['transfer_offers'] = []
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return state
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def create_simulator_agent():
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llm = ChatOpenAI(
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temperature=0.7, model=DEFAULT_MODEL
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) # Higher temperature for more creative events
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prompt = ChatPromptTemplate.from_template("""
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You are an NBA event simulator. Your role is to generate realistic events based on the current state of NBA teams and players. Use the provided team and player information to create engaging and plausible scenarios.
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Current NBA landscape:
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{teams_context}
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Generate a single, specific event involving one or more teams or players. The event should be impactful enough to potentially influence team decisions. Examples include outstanding performances, injuries, trade rumors, or off-court incidents.
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Output the event as a brief, news-like statement.
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Event:
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""")
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return prompt, llm
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simulator_prompt, simulator_llm = create_simulator_agent()
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async def simulate_event(state: SimulationState) -> SimulationState:
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teams = ['Toronto Raptors', 'Boston Celtics', 'Golden State Warriors']
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teams_context = await fetch_all_teams_context.ainvoke({'teams': teams})
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result = await simulator_llm.ainvoke(
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simulator_prompt.format_prompt(teams_context=json.dumps(teams_context, indent=2))
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)
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new_event = result.content
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existing_events = state['all_events'] or []
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existing_events.append(new_event)
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return {
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**state,
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'event': new_event,
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'all_events': existing_events,
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'transfer_offers': [],
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'current_iteration': state['current_iteration'] + 1,
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}
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# Create the graph
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workflow = StateGraph(SimulationState)
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# Add nodes
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workflow.add_node('simulate_event', simulate_event)
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workflow.add_node('process_event', process_event)
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valid_teams = ['Toronto Raptors', 'Boston Celtics', 'Golden State Warriors']
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for team in valid_teams:
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workflow.add_node(f'agent_{team}', create_team_agent(team, valid_teams))
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workflow.add_node('process_transfers', process_transfers)
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# Add edges
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workflow.add_edge(START, 'simulate_event')
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workflow.add_edge('simulate_event', 'process_event')
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# Add edges from process_event to all agent nodes
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for team in valid_teams:
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workflow.add_edge('process_event', f'agent_{team}')
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for team in valid_teams:
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workflow.add_edge(f'agent_{team}', 'process_transfers')
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def routing_function(state: SimulationState) -> str:
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if state['current_iteration'] >= state['max_iterations']:
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return END
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else:
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return 'simulate_event'
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workflow.add_conditional_edges(
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'process_transfers',
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routing_function,
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)
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# Compile the graph
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app = workflow.compile()
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print(app.get_graph().draw_mermaid())
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async def run_simulation():
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num_iterations = int(input('Enter the number of simulation iterations: '))
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initial_state = SimulationState(
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messages=[],
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teams={
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'Toronto Raptors': {'budget': 100000000},
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'Boston Celtics': {'budget': 100000000},
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'Golden State Warriors': {'budget': 100000000},
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},
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event='',
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transfer_offers=[],
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current_iteration=0,
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max_iterations=num_iterations,
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)
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final_state = await app.ainvoke(initial_state, {'recursion_limit': 200})
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print('\nFinal team states:')
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for team_name, team_data in final_state['teams'].items():
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print(f"{team_name} - Budget: ${team_data['budget']:,}")
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print(f'Steps taken: {final_state["current_iteration"]}')
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for event in final_state['all_events']:
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print('/n')
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print(event)
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print('\n')
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if __name__ == '__main__':
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asyncio.run(run_simulation())
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