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import os
import base64
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
import wikipediaapi
import json
import asyncio
import aiohttp
from langchain_core.tools import tool
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_tavily import TavilySearch
import requests
system_prompt = """You are a helpful assistant tasked with answering questions using a set of tools.
Now, 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.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer.
"""
api_key = os.getenv("OPENAI_API_KEY")
model = ChatOpenAI(model="gpt-4o-mini", api_key=api_key, temperature=0)
@tool
def search_wiki(query: str, max_results: int = 3) -> str:
"""
Searches Wikipedia for the given query and returns a maximum of 'max_results'
relevant article summaries, titles, and URLs.
Args:
query (str): The search query for Wikipedia.
max_results (int): The maximum number of search results to retrieve (default is 3).
Returns:
str: A JSON string containing a list of dictionaries, where each dictionary
represents a Wikipedia article with its title, summary, and URL.
Returns an empty list if no results are found or an error occurs.
"""
language_code = 'en'
headers={'User-Agent': 'LangGraphAgent/1.0 ([email protected])'}
base_url = 'https://api.wikimedia.org/core/v1/wikipedia/'
endpoint = '/search/page'
url = base_url + language_code + endpoint
parameters = {'q': query, 'limit': max_results}
response = requests.get(url, headers=headers, params=parameters)
response = json.loads(response.text)
return json.dumps(response, indent=2)
tavily_search_tool = TavilySearch(
max_results=5,
topic="general",
)
tools = [
tavily_search_tool,
search_wiki
]
def build_graph():
"""Build the graph"""
# Bind tools to LLM
llm_with_tools = model.bind_tools(tools)
# Node
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": [llm_with_tools.invoke(state["messages"])]}
builder = StateGraph(MessagesState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools", "assistant")
# Compile graph
return builder.compile()
# test agent
if __name__ == "__main__":
question = "When was St. Thomas Aquinas born?"
# Build the graph
graph = build_graph()
# Run the graph
messages = [
SystemMessage(
content=system_prompt
),
HumanMessage(
content=question
)]
messages = graph.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print() |