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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 smolagents import Tool
from langchain_community.retrievers import BM25Retriever
class GuestInfoRetrieverTool(Tool):
name = "guest_info_retriever"
description = "Retrieves detailed information about gala guests based on their name or relation."
inputs = {
"query": {
"type": "string",
"description": "The name or relation of the guest you want information about."
}
}
output_type = "string"
def __init__(self, docs):
self.is_initialized = False
self.retriever = BM25Retriever.from_documents(docs)
def forward(self, query: str):
results = self.retriever.get_relevant_documents(query)
if results:
return "\n\n".join([doc.page_content for doc in results[:3]])
else:
return "No matching guest information found."
# Initialize the tool
guest_info_tool = GuestInfoRetrieverTool(docs)
from smolagents import CodeAgent, InferenceClientModel
# Initialize the Hugging Face model
model = InferenceClientModel()
# Create Alfred, our gala agent, with the guest info tool
alfred = CodeAgent(tools=[guest_info_tool], model=model)
# Example query Alfred might receive during the gala
response = alfred.run("Tell me about our guest named 'Lady Ada Lovelace'.")
print("🎩 Alfred's Response:")
print(response)
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