retriever.py

from globals import *

# model_name = 'qwen3:8b'
model_name = 'llama3.2:latest'


# def load_guest_dataset():
guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")

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
]


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 match!'

guest_info_tool = Tool(
    name='guest_info_retriever',
    func=extract_text,
    description='Retrieves detailed information about gala guests based on their name or relation.'
)