import pytest
from typing import Any
from huggingface_hub import snapshot_download
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS


snapshot_download(
    repo_id='KonradSzafer/index-large-notebooks',
    allow_patterns=['*.faiss', '*.pkl'], 
    repo_type='dataset',
    local_dir='indexes/'
)

@pytest.fixture(scope='module')
def embedding_model() -> HuggingFaceInstructEmbeddings:
    model_name = 'hkunlp/instructor-large'
    embed_instruction = 'Represent the Hugging Face library documentation'
    query_instruction = 'Query the most relevant piece of information from the Hugging Face documentation'
    return HuggingFaceInstructEmbeddings(
        model_name=model_name,
        embed_instruction=embed_instruction,
        query_instruction=query_instruction,
    )

@pytest.fixture(scope='module')
def index_path() -> str:
    return 'indexes/'

@pytest.fixture(scope='module')
def index(embedding_model: HuggingFaceInstructEmbeddings, index_path: str):
    return FAISS.load_local(index_path, embedding_model)

@pytest.fixture(scope='module')
def query() -> str:
    return 'How to use the tokenizer?'

def test_load_index(embedding_model: HuggingFaceInstructEmbeddings, index_path: str):
    index = FAISS.load_local(index_path, embedding_model)
    assert index is not None, 'Failed to load index'

def test_index_page_content(index, query: str):
    query_docs = index.similarity_search(query=query, k=3)
    assert isinstance(query_docs[0].page_content, str)

def test_index_metadata(index, query):
    query_docs = index.similarity_search(query=query, k=3)
    assert isinstance(query_docs[0].metadata['source'], str)