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import os
from langchain.document_loaders import (
PyPDFLoader,
TextLoader,
Docx2txtLoader
)
from langchain.text_splitter import CharacterTextSplitter
# from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from dotenv import load_dotenv
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
llm = model = ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.7)
template = """You are a chatbot created by Mohammed Vasim. He is an AI Engineer and AI Architect.
You are created to be having a conversation with a human.
Given the following extracted parts of a long document and a question, create a final helpful answer.
{context}
If context is not provided, answer a helpful answer.
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"], template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
# chain = load_qa_chain(
# llm=llm, chain_type="stuff", memory=memory, prompt=prompt
# )
def build_qa_chain(llm=llm, prompt=prompt, memory=memory):
chain = load_qa_chain(
llm=llm, chain_type="stuff", memory=memory, prompt=prompt
)
return chain |