from uuid import uuid4
import time
from qdrant_client import QdrantClient, models
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_core.documents import Document
# import logging
import json

# logging.basicConfig(
#     level=logging.DEBUG,  
#     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
#     datefmt='%Y-%m-%d %H:%M:%S',  
#     handlers=[
#         logging.FileHandler("app.log"), 
#         logging.StreamHandler()          
#     ]
# )
class SemanticCache:
    def __init__(self, threshold=0.9):
        self.embeddings = HuggingFaceEmbeddings(model_name="dangvantuan/vietnamese-embedding")
        self.cache_client = QdrantClient(":memory:")
        self.cache_collection_name = "cache"
        self.cache_client.create_collection(
        collection_name=self.cache_collection_name,
        vectors_config=VectorParams(size=768, distance=Distance.COSINE),
    )
        self.vector_store = QdrantVectorStore(
        client=self.cache_client,
        collection_name=self.cache_collection_name,
        embedding=self.embeddings,
)       
        self.threshold = threshold
        self.cache_file = 'new_cache_file.json'
        self.init_cache_file = '/home/justtuananh/AI4TUAN/DOAN2024/offical/pipelines/semantic_cache/cache_init.json'
        self._load_cache_from_json()  
        

    def _load_cache_from_json(self):
        """Hàm để load cache từ file JSON và thêm vào vector_store"""
        try:
            with open(self.init_cache_file, 'r', encoding='utf-8') as f:
                data = json.load(f)
                for entry in data:
                    question = entry.get('question')
                    answer = entry.get('answer')
                    if question and answer:
                        # Tạo tài liệu và thêm vào vector_store
                        document = Document(
                            page_content=question,
                            metadata={"answer": answer}
                        )
                        doc_id = str(uuid4())
                        self.vector_store.add_documents(documents=[document], ids=[doc_id])
                print("Loaded into vector store!")
        except FileNotFoundError:
            print(f"Cache file {self.init_cache_file} not found, starting with empty cache.")
        except json.JSONDecodeError:
            print("Error parsing the JSON file. Please check the file format.")

    def search_cache(self, query):
        search_result = self.vector_store.similarity_search_with_score(
            collection_name=self.cache_collection_name,
            query=query,
            score_threshold = self.threshold,
            k=1
        )
        for doc, score in search_result:
            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
        if search_result:
            first_doc, first_score = search_result[0]
            return first_doc, first_score
        else:
            return [],[]

    def add_to_cache(self, question, response_text):
        document = Document(
            page_content=question,
            metadata={"answer": response_text}
        )
        doc_id = str(uuid4())
        self.vector_store.add_documents(documents=[document], ids=[doc_id])
        print(f"Question: {question} added to cache db!")
    
        
    def checker(self, question):
        start_time = time.time()
        first_doc, first_score = self.search_cache(question)
        if first_score and first_score >= self.threshold:
                print('cache-hit')
                print(f'Found cache with score {first_score:.3f}')
                elapsed_time = time.time() - start_time
                print(f'Match with {first_doc.page_content}')
                print(f"Time taken: {elapsed_time:.3f} seconds")
                return first_doc.metadata
        
        print("No answer found in Cache or Database")
        elapsed_time = time.time() - start_time
        print(f"Time taken: {elapsed_time:.3f} seconds")
        return None