from typing import Any, Dict, List

from fastrag.rankers import QuantizedBiEncoderRanker
from fastrag.retrievers import QuantizedBiEncoderRetriever
from haystack import Pipeline
from haystack.document_stores import InMemoryDocumentStore
from haystack.schema import Document


class EndpointHandler:
    def __init__(self, path=""):
        EXAMPLES = [
            "There is a blue house on Oxford Street.",
            "Paris is the capital of France.",
            "The first commit in fastRAG was in 2022",
        ]
        document_store = InMemoryDocumentStore(use_gpu=False, use_bm25=False, embedding_dim=384, return_embedding=True)

        documents = []
        for i, d in enumerate(EXAMPLES):
            documents.append(Document(content=d, id=i))

        document_store.write_documents(documents)

        model_id = "Intel/bge-small-en-v1.5-rag-int8-static"
        retriever = QuantizedBiEncoderRetriever(document_store=document_store, embedding_model=model_id)
        document_store.update_embeddings(retriever=retriever)

        ranker = QuantizedBiEncoderRanker("Intel/bge-large-en-v1.5-rag-int8-static")

        self.pipe = Pipeline()
        self.pipe.add_node(component=retriever, name="retriever", inputs=["Query"])
        self.pipe.add_node(component=ranker, name="ranker", inputs=["retriever"])

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        query = data.pop("inputs", data)
        results = self.pipe.run(query=query)
        return results