from appStore.prep_utils import get_client
from langchain_qdrant import FastEmbedSparse
from torch import cuda
from qdrant_client.http import models
from langchain_huggingface import HuggingFaceEmbeddings

device = 'cuda' if cuda.is_available() else 'cpu'

def hybrid_search(client, query, collection_name, limit=500):
    embeddings = HuggingFaceEmbeddings(
        model_name='BAAI/bge-m3',
        model_kwargs={'device': device},
        encode_kwargs={'normalize_embeddings': True}
    )
    sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")

    # 1) Embed the query
    q_dense = embeddings.embed_query(query)
    q_sparse = sparse_embeddings.embed_query(query)

    # 2) Request more than 10 items
    results = client.search_batch(
        collection_name=collection_name,
        requests=[
            # Dense request
            models.SearchRequest(
                vector=models.NamedVector(
                    name="text-dense",
                    vector=q_dense,
                ),
                limit=limit,  # was 10, now uses the parameter
                with_payload=True,
            ),
            # Sparse request
            models.SearchRequest(
                vector=models.NamedSparseVector(
                    name="text-sparse",
                    vector=models.SparseVector(
                        indices=q_sparse.indices,
                        values=q_sparse.values,
                    ),
                ),
                limit=limit,  # was 10, now uses the parameter
                with_payload=True,
            ),
        ]
    )

    return results