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.env

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+GOOGLE_API_KEY = ""

app.py

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+import os
+import streamlit as st
+import google.generativeai as genai
+
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader # read the PDF file
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings # converting text to vectors
+from langchain.vectorstores import FAISS # for vector embeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain # helps for prompts
+from langchain.prompts import PromptTemplate
+
+
+load_dotenv()
+genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
+                                  
+# read the pdf, and extract the text
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+# divide the text into chunks
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# convert the text to vectors
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index") # save the vector as local, can also save it on Pinecone, DataStax
+
+# creating chain for conversational
+def get_conversational_chain():
+            prompt_template = """
+                    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+                    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+                    Context:\n {context}?\n
+                    Question: \n{question}\n
+                    Answer:
+                    """
+            model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.8)
+            prompt = PromptTemplate(template=prompt_template, input_variables=['context','question'])
+            chain = load_qa_chain(model, chain_type='stuff',prompt=prompt) #stuff for internal summerization
+            return chain
+
+
+# user question besed on the textbox and calling the other functions
+def user_input(user_question):
+     embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+
+     new_db = FAISS.load_local("faiss_index",embeddings) # load the embedding from local
+     docs = new_db.similarity_search(user_question)
+     chain = get_conversational_chain()
+
+     response = chain(
+          {'input_documents': docs, "question": user_question},
+          return_only_outputs=True
+     )
+     print(response)
+     st.write("Reply: ", response["output_text"])
+
+# creating the streamlit Application
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with PDF using Gemini💁")
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+
+
+if __name__ == "__main__":
+    main()
+
+
+

requirements.txt

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+streamlit
+google-generativeai
+langchain
+python-dotenv
+PyPDF2
+faiss-cpu
+langchain_google_genai