"""Guest Information Retrieval Toll"""

from smolagents import Tool
from langchain_community.retrievers import BM25Retriever
from langchain.docstore.document import Document
import datasets


class GuestInfoRetrieverTool(Tool):
    """Derived Class for Guest Information Retrieval Tool"""

    name = "guest_info_retriever"
    description = "Retrieves detailed information about gala guests based on their name or relation."  # pylint: disable=line-too-long
    inputs = {
        "query": {
            "type": "string",
            "description": "The name or relation of the guest you want information about.",
        }
    }
    output_type = "string"

    def __init__(self, docs):  # pylint: disable=super-init-not-called
        self.is_initialized = False
        self.retriever = BM25Retriever.from_documents(docs)

    def forward(self, query: str):  # pylint: disable=arguments-differ
        results = self.retriever.get_relevant_documents(query)
        if results:
            return "\n\n".join([doc.page_content for doc in results[:3]])
        return "No matching guest information found."


def guest_info_retriever_factory():
    """Get Guest Information Retrieval Tool"""

    hf_datasets = [
        "agents-course/unit3-invitees",
        "Data-Gem/agents-course-unit3-invitees-expanded",
    ]

    guest_dataset = []

    for dataset_name in hf_datasets:
        new_dataset = datasets.load_dataset(dataset_name, split="train")
        guest_dataset.append(new_dataset)

    # 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 GuestInfoRetrieverTool(docs)