from dotenv import dotenv_values
from langchain_openai import ChatOpenAI
from typing import Literal
from tavily import TavilyClient
from deepagents import create_deep_agent


env_vars = dotenv_values('.env')
OPENAI_KEY = env_vars['OPENAI_API_KEY'] 
OPENAI_BASE_URL = env_vars['OPENAI_API_BASE'] 

llm = ChatOpenAI(model="gpt-4o-mini", api_key=OPENAI_KEY, base_url=OPENAI_BASE_URL)
tavily_client = TavilyClient(api_key=env_vars['TAVILY_KEY'] )


def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """Run a web search"""
    return tavily_client.search(
        query,
        max_results=max_results,
        include_raw_content=include_raw_content,
        topic=topic,
    )

# System prompt to steer the agent to be an expert researcher
research_instructions = """You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## `internet_search`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
"""

agent = create_deep_agent(model=llm, tools=[internet_search], system_prompt=research_instructions)

result = agent.invoke({"messages": [{"role": "user", "content": "what is langgraph?"}]})

# Print the agent's response
print(111, result["messages"][-1].content)