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import re
from langgraph.prebuilt import create_react_agent
from agent_util import Agent_Util
from prompts import *
from tools import *
from langgraph_supervisor import create_supervisor
from langchain.chat_models import init_chat_model
import glob
class Agent:
def __init__(self):
print("Initializing Agent....")
print("--> Audio Agent")
self.audio_agent = create_react_agent(
model="openai:gpt-4o-mini", # gpt-4o-mini-2024-07-18
tools=[extract_text_from_url_tool, extract_text_from_file_tool],
prompt= AUDIO_AGENT_PROMPT,
name="audio_agent",
)
print("--> Web Search Agent")
self.web_search_agent = create_react_agent(
model="openai:gpt-4o-mini", # gpt-4o-mini-2024-07-18
tools=[search_web_tool],
prompt= WEB_SEARCH_AGENT_PROMPT,
name="web_research_agent",
)
print("--> Supervisor")
self.supervisor = create_supervisor(
model=init_chat_model("openai:gpt-4o-mini"),
agents=[self.web_search_agent, self.audio_agent],
tools=[bird_video_count_tool,chess_image_to_fen_tool,chess_fen_get_best_next_move_tool,
get_excel_columns_tool, calculate_excel_sum_by_columns_tool,execute_python_code_tool,
text_inverter_tool, check_table_commutativity_tool],
prompt= SUPERVISOR_PROMPT,
add_handoff_back_messages=True,
output_mode="full_history",
).compile()
print("Agent initialized.")
def __call__(self, question: str, task_id: str, task_file_name: str) -> str:
print(f"Agent received question({task_id}) (first 50 chars): {question[:50]}...")
file_prefix = ""
if task_file_name:
print(f"Task com arquivo {task_file_name}")
File_Util.baixa_arquivo_task(task_file_name)
file_prefix = f"File: {task_file_name} . "
for chunk in self.supervisor.stream(
{
"messages": [
{
"role": "user",
"content": f"{file_prefix}{question}",
}
]
},
):
Agent_Util.pretty_print_messages(chunk, last_message=True)
final_chunk = chunk
agent_answer = final_chunk["supervisor"]["messages"]
final_answer = re.sub(r"^FINAL ANSWER:\s*", "", agent_answer, flags=re.IGNORECASE)
print(f"Agent returning answer for task {task_id}: {final_answer}")
return final_answer |