app.py

import streamlit as st
import time
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_chroma import Chroma
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv

load_dotenv()
st.title("VAIDYA GPT-1V 🩺🤖")
st.caption("This is the first version of VAIDYA GPT which will answer all your questions based on general human anatomy 🫀. This model works on Gemini pro version 1.5")
loader = PyPDFLoader("C:\\Users\\ASUS\\OneDrive\\Desktop\\RAG\data\\Medical_book.pdf")
data = loader.load()  
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
docs = text_splitter.split_documents(data)
vectorstore = Chroma.from_documents(documents=docs, embedding=GoogleGenerativeAIEmbeddings(model="models/embedding-001"))
retriever=vectorstore.as_retriever(search_type="similarity",search_kwargs={"k":10})
llm=ChatGoogleGenerativeAI(model="gemini-1.5-pro",temperature=0.1,max_tokens=50)
system_prompt = (
    "You are an assistant for question-answering tasks. "
    "Use the following pieces of retrieved context to answer "
    "the question. If you don't know the answer, say that you "
    "don't know. Use three sentences maximum and keep the "
    "answer concise."
    "\n\n"
    "{context}"
)

query = st.chat_input("Say something: ") 
prompt = query


prompt=ChatPromptTemplate.from_messages(
    [
        ("system",system_prompt),
        ("human","{input}"),
    ]
)
if query:
    question_answer_chain = create_stuff_documents_chain(llm, prompt)
    rag_chain = create_retrieval_chain(retriever, question_answer_chain)

    response = rag_chain.invoke({"input": query})
    #print(response["answer"])

    st.write(response["answer"])