from pinecone.grpc import PineconeGRPC as pinecone
from pinecone import ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_community.embeddings import SentenceTransformerEmbeddings
from sentence_transformers import SentenceTransformer
from dotenv import load_dotenv
import time
import os

load_dotenv()
PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
PINECONE_API_ENV = os.getenv('PINECONE_API_ENV')

index_name = "sample3"
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

pc = pinecone(api_key=PINECONE_API_KEY)

def ensure_index_exists():
    if index_name not in pc.list_indexes().names():
        pc.create_index(
            name=index_name,
            dimension=384,
            metric="cosine",
            spec=ServerlessSpec(
                cloud='aws', 
                region='us-east-1'
            ) 
        )

ensure_index_exists()
index = pc.Index(index_name)
vectorstore = PineconeVectorStore(index_name=index_name, embedding=SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2"))

class Document:
    def __init__(self, page_content, metadata=None):
        self.page_content = page_content
        self.metadata = metadata or {}

def encodeaddData(corpusData, url, pdf, pdf2, uns2):
    documents = []
    
    if url or pdf or pdf2:
        for text in corpusData:
            documents.append(Document(text))
    elif uns2:
        documents.append(Document(corpusData))
    
    vectorstore = PineconeVectorStore.from_documents(documents, embeddings, index_name=index_name)

def delete():
    pc.delete_index(index_name)

def find_k_best_match1(query): 
    time.sleep(5)        
    vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
    docs = vectorstore.similarity_search(query, k=2)
    return docs