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import datasets
from langchain.docstore.document import Document
# Load the dataset
# guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
# Convert dataset entries into Document objects
# docs = [
# Document(
# page_content="\n".join([
# f"Name: {guest['name']}",
# f"Relation: {guest['relation']}",
# f"Description: {guest['description']}",
# f"Email: {guest['email']}"
# ]),
# metadata={"name": guest["name"]}
# )
# for guest in guest_dataset
# ]
# from langchain_community.retrievers import BM25Retriever
# from langchain.tools import Tool
# bm25_retriever = BM25Retriever.from_documents(docs)
# def extract_text(query: str) -> str:
# """Retrieves detailed information about gala guests based on their name or relation."""
# results = bm25_retriever.invoke(query)
# if results:
# return "\n\n".join([doc.page_content for doc in results[:3]])
# else:
# return "No matching guest information found."
# guest_info_tool = Tool(
# name="guest_info_retriever",
# func=extract_text,
# description="Retrieves detailed information about gala guests based on their name or relation."
# )
#######################################################################################################################################################
from typing import TypedDict, Annotated
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage,SystemMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langchain_openai import ChatOpenAI
from Webserch_tool import weather_info_tool
from other_tools import (
wiki_search, arvix_search, web_search, vector_search,
multiply, add, subtract, divide, modulus, power, square_root
)
import os
from dotenv import load_dotenv
load_dotenv()
# Generate the chat interface, including the tools
llm = ChatOpenAI(temperature=0
, model="gpt-4o-mini", openai_api_key=os.getenv("OPENAI_KEY"))
tools = [
weather_info_tool, wiki_search, arvix_search, web_search,
multiply, add, subtract, divide, modulus, power, square_root
]
chat_with_tools = llm.bind_tools(tools)
#setting up prompt
ai_message = SystemMessage(content="""You are a helpful assistant tasked with answering questions using a set of tools and reference materials.
You may be provided with a reference set of questions and answers from a retriever.
If the current question is identical to or semantically equivalent to a reference question, or if a reference answer clearly applies, use that reference answer directly.
Otherwise, reason through the question as needed to determine the correct answer.
Your output must follow these formatting rules:
- If the answer is a number, do not use commas or units (unless specifically requested).
- If the answer is a string, do not use articles, abbreviations, or short forms. Write digits in full unless specified otherwise.
- If the answer is a comma-separated list, apply the above rules to each item and include exactly one space after each comma.
- If the question matches a reference question, return the reference answer exactly as it appears.
Do not include any explanation, prefix, or extra text—output only the final answer.
""")
# Generate the AgentState and Agent graph
from langgraph.graph import MessagesState #the same as AgentState
# class AgentState(TypedDict):
# messages: Annotated[list[AnyMessage], add_messages]
def assistant(state: MessagesState):
return {
"messages": [chat_with_tools.invoke(state["messages"])],
}
def retriever(state: MessagesState):
"""Retriever node"""
similar_question = vector_search(state["messages"][0].content)
if similar_question:
example_msg = HumanMessage(
content=f"Here I provide a similar question and answer for reference: \n\n{similar_question}",
)
print(f"Similar question found: {similar_question}")
return {"messages": [ai_message] + state["messages"] + [example_msg]}
else:
# Handle the case when no similar questions are found
print( "No similar question found.")
return {"messages": [ai_message] + state["messages"]}
## The graph
builder = StateGraph(MessagesState)
# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("retriever", retriever)
builder.add_node("tools", ToolNode(tools))
# Define edges: these determine how the control flow moves
builder.add_edge(START, "retriever")
builder.add_edge("retriever", "assistant")
builder.add_conditional_edges(
"assistant",
# If the latest message requires a tool, route to tools
# Otherwise, provide a direct response
tools_condition,
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()
# messages = [HumanMessage(content="When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?")]
# #messages = [HumanMessage(content="What the remainder of 30 divided by 7?")]
# response = alfred.invoke({"messages": messages})
# print(response['messages'][-1].content) |