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from langchain.tools import Tool
from langchain_community.retrievers import BM25Retriever
from langchain.docstore.document import Document
from langchain_core.messages import HumanMessage
import datasets
from langchain_openai import AzureChatOpenAI
import os
from dotenv import load_dotenv
load_dotenv()
# Create LLM instance once
conversation_llm = AzureChatOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
deployment_name=os.getenv("DEPLOYMENT_NAME"),
openai_api_version=os.getenv("OPENAI_API_VERSION"),
temperature=0.75,
streaming=False,
verbose=False
)
def load_guest_dataset():
# 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
]
return docs
docs = load_guest_dataset()
bm25_retriever = BM25Retriever.from_documents(docs)
def generate_conversation_starter(description: str) -> str:
"""Generate a conversation starter based on guest description"""
try:
generate_prompt = (
f"Generate a very simple and short conversation starter from the description of the person.\n\n"
f"For example:\n"
f"Description: Rear Admiral Grace Hopper was a trailblazer in computer programming and helped invent the first compiler. "
f"She's passionate about teaching and loves telling stories about debugging.\n\n"
f"Conversation Starter: Ask her about the time she found a real bug in a computer — she loves that story!\n\n"
f"Description: {description}\n\n"
f"Conversation Starter:"
)
response = conversation_llm.invoke(
[HumanMessage(content=generate_prompt)])
return response.content.strip()
except Exception:
return "Ask them about their background and interests!"
def retrieve_info_from_name(query: str) -> str:
"""Retrieves detailed information about gala guests based on their name or relation."""
results = bm25_retriever.invoke(query)
if results:
guest_info_with_starters = []
for i, doc in enumerate(results[:3], 1):
guest_info = doc.page_content
# Extract description from the content
lines = guest_info.split('\n')
description = ""
for line in lines:
if line.startswith("Description:"):
description = line.replace("Description:", "").strip()
break
# Add guest info
result_text = f"Guest {i}:\n{guest_info}"
# Add conversation starter if description exists
if description:
conversation_starter = generate_conversation_starter(
description)
result_text += f"\n💬 Conversation Starter: {conversation_starter}"
guest_info_with_starters.append(result_text)
return "\n\n" + "="*50 + "\n\n".join(guest_info_with_starters)
else:
return "No matching guest information found."
guest_info_tool = Tool(
name="guest_info_retriever",
func=retrieve_info_from_name,
description="Retrieves detailed information about gala guests based on their name or relation, including conversation starters."
)
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