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from typing import Annotated, Optional, TypedDict | |
from dotenv import find_dotenv, load_dotenv | |
from langchain.chat_models import init_chat_model | |
from langchain_core.messages import AnyMessage, HumanMessage | |
from langgraph.graph.message import add_messages | |
from langgraph.prebuilt import create_react_agent | |
from tools import (add, ask_about_image, divide, get_current_time_and_date, | |
get_sum, get_weather_info, get_youtube_transcript, | |
get_youtube_video_info, inspect_file_as_text, multiply, | |
reverse_text, subtract, visit_website, web_search, | |
wiki_search) | |
class AgentState(TypedDict): | |
input_file: Optional[str] # Contains file path | |
messages: Annotated[list[AnyMessage], add_messages] | |
class BasicAgent: | |
def __init__(self): | |
load_dotenv(find_dotenv()) | |
model = init_chat_model("groq:meta-llama/llama-4-scout-17b-16e-instruct") | |
system_prompt = ( | |
"You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer " | |
"with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR " | |
"as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a " | |
"number, don't use comma to write your number neither use units such as $ or percent sign unless specified " | |
"otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), " | |
"and write the digits in plain text unless specified otherwise. If you are asked for a comma separated " | |
"list, apply the above rules depending of whether the element to be put in the list is a number or a string." | |
"Give it all you can: I know for a fact that you have access to all the relevant tools to solve it and find " | |
"the correct answer (the answer does exist). Failure or 'I cannot answer' or 'None found' will not be " | |
"tolerated, success will be rewarded. Run verification steps if that's needed, you must make sure you find " | |
"the correct answer! " | |
) | |
tools = [ | |
get_weather_info, | |
add, | |
get_sum, | |
subtract, | |
multiply, | |
divide, | |
get_current_time_and_date, | |
wiki_search, | |
web_search, | |
visit_website, | |
inspect_file_as_text, | |
ask_about_image, | |
reverse_text, | |
get_youtube_video_info, | |
get_youtube_transcript, | |
] | |
self.agent = create_react_agent(model=model, tools=tools, prompt=system_prompt) | |
print("BasicAgent initialized.") | |
def __call__(self, question: str) -> str: | |
print(f"Agent received question (first 50 chars): {question[:50]}...") | |
messages = [HumanMessage(content=question)] | |
response = self.agent.invoke({"messages": messages}) | |
response_string = response["messages"][-1].content | |
print(f"Agent's response: {response_string}") | |
return response_string | |