src/agent.py

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