Test

app.py

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+import streamlit as st
+import ollama
+from utils.QA_Bot import QA_Bot
+from utils.PDF_Reader import PDF_4_QA
+
+ollama.pull("llama3.1")
+
+# Streamlit app
+def main():
+    st.sidebar.title("Upload PDF")
+    st.sidebar.write("Download Demo PDF file from Below....")
+    with open("Demo_Document/Kia_EV6.pdf", "rb") as file:
+        btn = st.sidebar.download_button(
+            label="Download PDF",
+            data=file,
+            file_name="Kia_EV6.pdf"
+        )
+
+    uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type="pdf")
+    if uploaded_file is not None:
+        st.sidebar.success("File uploaded successfully.")
+        vector_store = PDF_4_QA(uploaded_file)
+        QA_Bot(vector_store)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+langchain==0.3.17
+langchain_ollama==0.2.3
+ollama==0.4.7
+PyPDF2==3.0.1
+streamlit==1.29.0
+streamlit==1.32.2
+streamlit==1.35.0
+streamlit==1.26.0
+streamlit==1.41.1
+~treamlit==1.29.0

utils/PDF_Reader.py

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+import PyPDF2
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+
+def read_pdf(uploaded_file):
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def Chunks(docs):
+    text_splitter = RecursiveCharacterTextSplitter(
+    # Set a really small chunk size, just to show.
+    chunk_size = 1000,
+    chunk_overlap  = 100,
+)
+    doc = text_splitter.split_text(docs)
+    return doc
+
+
+def PDF_4_QA(file):
+    content = read_pdf(file)
+    pdf_chunks = Chunks(docs=content)
+
+    embeddings = HuggingFaceBgeEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                          model_kwargs={'device': 'cpu'})
+    vectorstore_openai = FAISS.from_texts(pdf_chunks, embeddings)
+
+    return vectorstore_openai

utils/QA_Bot.py

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+import streamlit as st
+from utils.QnA import Q_A
+import re,time
+
+
+def QA_Bot(vectorstore):
+    st.title("Q&A Bot")
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # React to user input
+    if prompt := st.chat_input("What is up?"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        ai_response = Q_A(vectorstore,prompt)
+        response = f"Echo: {ai_response}"
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            for chunk in re.split(r'(\s+)', response):
+                full_response += chunk + " "
+                time.sleep(0.01)
+
+                # Add a blinking cursor to simulate typing
+                message_placeholder.markdown(full_response + "▌")
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": full_response})

utils/QnA.py

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+from langchain.chains import RetrievalQA
+from langchain_ollama import ChatOllama
+
+def Q_A(vectorstore,question):
+    ollama_llm = ChatOllama(
+    model="llama3.1",
+    temperature=0.5,
+)
+    qa = RetrievalQA.from_chain_type(llm=ollama_llm, chain_type="stuff", retriever=vectorstore.as_retriever())
+    answer = qa.invoke(question)
+
+    return answer['result']