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.env

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+GOOGLE_API_KEY="AIzaSyB_RB4tirtCyfzcjiIlb4OXasS0kKU24lc"
+

app.py

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+import streamlit as st
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+import google.generativeai as genai
+from dotenv import load_dotenv
+import os
+from ai71 import AI71
+import re
+
+# Load environment variables
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+AI71_API_KEY = "api71-api-4cc4a01e-6d44-424a-b28f-38fe59855776"
+
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Define the function to load the vector store
+def load_vector_store():
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    return vector_store
+
+# Define the function to get the conversational chain
+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=0.3)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+# Define the function to handle user input
+def handle_user_query(user_question):
+    vector_store = load_vector_store()
+    docs = vector_store.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    # initial response generator agent
+    initial_response = response.get("output_text", "No response generated.")
+    # detailed response generator agent
+    detailed_response = generate_detailed_response(initial_response, user_question)
+    # translator agent
+    urdu_response = generate_urdu_response(detailed_response)
+    
+    return detailed_response, urdu_response
+
+def clean_detailed_answer(response_text):
+    # Remove the "Reply:" prefix at the start
+    response_text = re.sub(r'^Reply:\s*', '', response_text, flags=re.IGNORECASE)
+    
+    # Remove the "User:" suffix at the end (if applicable)
+    response_text = re.sub(r'\s*User:\s*$', '', response_text, flags=re.IGNORECASE)
+    
+    return response_text
+
+# Define the function to generate a detailed response using Falcon LLM with streaming
+def generate_detailed_response(initial_response, question):
+    prompt = f"""
+    Provide a detailed and relevant explanation based on the initial response. Avoid any apologies or unnecessary prefaces.
+
+    Initial Response:
+    {initial_response}
+
+    Question:
+    {question}
+
+    Detailed Answer:
+    """
+    
+    detailed_answer = ""
+    for chunk in AI71(AI71_API_KEY).chat.completions.create(
+        model="tiiuae/falcon-180b-chat",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        stream=True
+    ):
+        if chunk.choices[0].delta.content:
+            detailed_answer += chunk.choices[0].delta.content
+            # Optionally, print each chunk for debugging
+            print(chunk.choices[0].delta.content, sep="", end="", flush=True)
+    cleaned_answer = clean_detailed_answer(detailed_answer)
+    return cleaned_answer
+
+# Define the function to generate a response in Urdu using Falcon LLM
+def generate_urdu_response(english_text):
+    prompt = f"""
+    Translate the following text into Urdu while preserving the meaning and details.
+
+    English Text:
+    {english_text}
+
+    Urdu Translation:
+    """
+    
+    urdu_response = ""
+    for chunk in AI71(AI71_API_KEY).chat.completions.create(
+        model="tiiuae/falcon-180b-chat",
+        messages=[
+            {"role": "system", "content": "You are a translation assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        stream=True
+    ):
+        if chunk.choices[0].delta.content:
+            urdu_response += chunk.choices[0].delta.content
+            # Optionally, print each chunk for debugging
+            print(chunk.choices[0].delta.content, sep="", end="", flush=True)
+    
+    return urdu_response
+
+# Define the main function for Streamlit app
+def main():
+    st.set_page_config("Chat with PDF")
+    st.header("ASK about economic studies")
+
+    # Initialize session state if it doesn't exist
+    if 'history' not in st.session_state:
+        st.session_state.history = []
+
+    # Load the vector store initially
+    if 'vector_store' not in st.session_state:
+        st.session_state.vector_store = load_vector_store()
+    
+    # Text input for user query
+    user_question = st.text_input("Ask a Question")
+
+    if st.button("Generate Response"):
+        if user_question:
+            with st.spinner('Generating response, please wait...'):
+                english_response, urdu_response = handle_user_query(user_question)
+                st.markdown("**English Response:**")
+                st.write(english_response)
+                st.markdown("**Urdu Translation:**")
+                st.write(urdu_response)
+                # Add new query and response at the beginning of the history
+                st.session_state.history.insert(0, {
+                    'user_question': user_question,
+                    'english_response': english_response,
+                    'urdu_response': urdu_response
+                })
+
+    # Display the history
+    if st.session_state.history:
+        st.subheader("***----------------------------Response History----------------------------***")
+        for entry in st.session_state.history:
+            st.markdown("**User's Question:**")
+            st.write(entry['user_question'])
+            st.markdown("**English Response:**")
+            st.write(entry['english_response'])
+            st.markdown("**Urdu Translation:**")
+            st.write(entry['urdu_response'])
+
+if __name__ == "__main__":
+    main()

faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:505dd53acb38e528b61efa72d989369cf9b782c53622e5e1443422e5f6be0b3a
+size 501070

requirements.txt

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+ai71 
+streamlit
+python-dotenv
+langchain
+PyPDF2
+faiss-cpu
+langchain_google_genai
+langchain-core
+langchain-community
+google-generativeai