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import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OllamaEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
# from langchain.vectorstores.cassandra import Cassandra
from langchain_community.vectorstores import Cassandra
from langchain_community.llms import Ollama
from cassandra.auth import PlainTextAuthProvider
import tempfile
import cassio
from PyPDF2 import PdfReader
from cassandra.cluster import Cluster
import warnings
# from langchain.vectorstores import Pinecone
from langchain_community.vectorstores import Pinecone
import pinecone
from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
warnings.filterwarnings("ignore")
from dotenv import load_dotenv
import time
load_dotenv()
ASTRA_DB_SECURE_BUNDLE_PATH ="G:/GENAI/Medical_chat_bot/src/secure-connect-medical-bot.zip"
LANGCHAIN_TRACING_V2="true"
LANGCHAIN_API_KEY=os.getenv('LANGCHAIN_API_KEY')
LANGCHAIN_PROJECT="Medical_chatbot"
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
def doc_loader(pdf_reader):
# print('im from doc_loc fn')
encode_kwargs = {'normalize_embeddings': True}
huggigface_embeddings=HuggingFaceBgeEmbeddings(
model_name='BAAI/bge-small-en-v1.5',
# model_name='sentence-transformers/all-MiniLM-16-v2',
model_kwargs={'device':'cpu'},
encode_kwargs=encode_kwargs)
loader=PyPDFLoader(pdf_reader)
documents=loader.load_and_split()
# print('iam after documents loader called')
text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
final_documents=text_splitter.split_documents(documents)
# print('iam after final_documents called',final_documents)
# os.environ['PINECONE_API_KEY'] = os.getenv('PINECONE_API_KEY')
os.environ['PINECONE_API_ENV'] = "pdf_query_db"
pc = Pinecone(api_key=os.getenv('PINECONE_API_KEY'))
index = pc.Index("pdf-query-index")
namespace = "pdf_query_medical"
def namespace_exists(index, namespace):
try:
stats = index.describe_index_stats()
return namespace in stats['namespaces']
except pinecone.core.client.exceptions.NotFoundException:
return False
if namespace_exists(index, namespace):
print(f"Namespace '{namespace}' exist.")
pinecone_vector_store = PineconeVectorStore(embedding=huggigface_embeddings,index_name="pdf-query-index", namespace=namespace)
# pinecone_vector_store = index.query(f"SELECT * FROM {namespace}")
# return pinecone_vector_store
else:
print(f"Namespace '{namespace}' does not exist. It will be created upon upsertion.")
pinecone_vector_store=PineconeVectorStore(embedding=huggigface_embeddings,index_name="pdf-query-index",namespace=namespace)
pinecone_vector_store.add_documents(final_documents)
return pinecone_vector_store
# def doc_loader(pdf_reader):
# # print('im from doc_loc fn')
# encode_kwargs = {'normalize_embeddings': True}
# huggigface_embeddings=HuggingFaceBgeEmbeddings(
# model_name='BAAI/bge-small-en-v1.5',
# # model_name='sentence-transformers/all-MiniLM-16-v2',
# model_kwargs={'device':'cpu'},
# encode_kwargs=encode_kwargs)
# loader=PyPDFLoader(pdf_reader)
# documents=loader.load_and_split()
# # print('iam after documents loader called')
# text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
# final_documents=text_splitter.split_documents(documents)
# # print('iam after final_documents called',final_documents)
# astrasession = Cluster(
# cloud={"secure_connect_bundle": ASTRA_DB_SECURE_BUNDLE_PATH},
# auth_provider=PlainTextAuthProvider("token", ASTRA_DB_APPLICATION_TOKEN),
# ).connect()
# check_table_query = f"""
# SELECT table_name FROM system_schema.tables
# WHERE keyspace_name='{ASTRA_DB_KEYSPACE}' AND table_name='{ASTRA_DB_TABLE}';
# """
# try:
# result = astrasession.execute(check_table_query)
# if result.one():
# return_query=f""" select * from '{ASTRA_DB_KEYSPACE}'.'{ASTRA_DB_TABLE}'; """
# astra_vector_store=astrasession.execute(return_query)
# return astra_vector_store
# else:
# print(f"Table {ASTRA_DB_KEYSPACE}.{ASTRA_DB_TABLE} does not exist. Try to create table.")
# astra_vector_store=Cassandra(
# embedding=huggigface_embeddings,
# table_name='medical_bot_demo',
# session=astrasession,
# keyspace=ASTRA_DB_KEYSPACE
# )
# astra_vector_store.add_documents(final_documents)
# if astra_vector_store:
# print("Vector store created successfully")
# return astra_vector_store
# except Exception as e:
# print(f"Error checking/creating keyspace: {e}")
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