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app.py

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+import gradio as gr
+import os
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+import torch
+
+api_token = os.getenv("HF_TOKEN")
+
+list_llm = ["microsoft/Phi-3-mini-4k-instruct", "mistralai/Mistral-7B-Instruct-v0.3"]  
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+# Load and split PDF document
+def load_doc(list_file_path, chunk_size, chunk_overlap):
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, 
+        chunk_overlap=chunk_overlap 
+    )  
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings()
+    vectordb = FAISS.from_documents(splits, embeddings)
+    return vectordb
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, vector_db, progress=gr.Progress()):
+    llm = HuggingFaceEndpoint(
+        huggingfacehub_api_token=api_token,
+        repo_id=llm_model, 
+        temperature=0.1,
+        max_new_tokens=2000,
+        top_k=3,
+    )
+    
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+
+    retriever = vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain
+
+# Initialize database
+def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
+    vector_db = create_db(doc_splits)
+    if vector_db is None:
+        print("Vector database creation failed")
+    else:
+        print("Embedding database created successfully")
+    return vector_db, "Embedding database created!"
+
+# Initialize LLM
+def initialize_LLM(llm_option, vector_db, progress=gr.Progress()):
+    if vector_db is None:
+        print("Vector database is None")
+        return None, "Failed to initialize RAG System: Vector database is None"
+    
+    llm_name = list_llm[llm_option]
+    qa_chain = initialize_llmchain(llm_name, vector_db, progress)
+    return qa_chain, "RAG System initialized!"
+
+def format_chat_history(message, chat_history):
+    formatted_chat_history = []
+    for user_message, bot_message in chat_history:
+        formatted_chat_history.append(f"User: {user_message}")
+        formatted_chat_history.append(f"Assistant: {bot_message}")
+    return formatted_chat_history
+
+def conversation(qa_chain, message, history):
+    formatted_chat_history = format_chat_history(message, history)
+    response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    if response_answer.find("Helpful Answer:") != -1:
+        response_answer = response_answer.split("Helpful Answer:")[-1]
+    response_sources = response["source_documents"]
+    response_source1 = response_sources[0].page_content.strip()
+    response_source2 = response_sources[1].page_content.strip()
+    response_source3 = response_sources[2].page_content.strip()
+    response_source1_page = response_sources[0].metadata["page"] + 1
+    response_source2_page = response_sources[1].metadata["page"] + 1
+    response_source3_page = response_sources[2].metadata["page"] + 1
+    new_history = history + [(message, response_answer)]
+    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+
+def upload_file(file_obj):
+    list_file_path = []
+    for idx, file in enumerate(file_obj):
+        file_path = file_obj.name
+        list_file_path.append(file_path)
+    return list_file_path
+
+def demo():
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="green")) as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        gr.HTML("<center><h1>RAG System</h1><center>")
+        gr.Markdown("""This App is designed to perform retrieval augmented generation (RAG) on PDF documents. \
+        <b>Please do not upload confidential documents.</b>
+        """)
+        with gr.Row():
+            with gr.Column(scale=86):
+                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize the RAG system</b>")
+                with gr.Row():
+                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                with gr.Row():
+                    slider_chunk_size = gr.Slider(minimum=10, maximum=1000, value=200, step=5, label="Chunk Size")
+                    slider_chunk_overlap = gr.Slider(minimum=0, maximum=512, value=20, step=5, label="Chunk Overlap")
+                with gr.Row():
+                    db_btn = gr.Button("Create Embeddings")
+                with gr.Row():
+                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
+                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM)</b>")
+                with gr.Row():
+                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
+                with gr.Row():
+                    qachain_btn = gr.Button("Initialize RAG system")
+                with gr.Row():
+                    llm_progress = gr.Textbox(value="Not initialized", show_label=False)
+
+            with gr.Column(scale=200):
+                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                chatbot = gr.Chatbot(height=505)
+                with gr.Accordion("Similar context from the source document", open=False):
+                    with gr.Row():
+                        doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
+                        source1_page = gr.Number(label="Page", scale=1)
+                    with gr.Row():
+                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                        source2_page = gr.Number(label="Page", scale=1)
+                    with gr.Row():
+                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
+                        source3_page = gr.Number(label="Page", scale=1)
+                with gr.Row():
+                    msg = gr.Textbox(placeholder="Ask a question", container=True)
+                with gr.Row():
+                    submit_btn = gr.Button("Submit")
+                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
+
+        # Preprocessing events
+        db_btn.click(initialize_database, 
+            inputs=[document, slider_chunk_size, slider_chunk_overlap], 
+            outputs=[vector_db, db_progress])
+        qachain_btn.click(initialize_LLM, 
+            inputs=[llm_btn, vector_db], 
+            outputs=[qa_chain, llm_progress]).then(lambda:[None, "", 0, "", 0, "", 0], 
+            inputs=None, 
+            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
+            queue=False)
+
+        # Chatbot events
+        msg.submit(conversation, 
+            inputs=[qa_chain, msg, chatbot], 
+            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
+            queue=False)
+        submit_btn.click(conversation, 
+            inputs=[qa_chain, msg, chatbot], 
+            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
+            queue=False)
+        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], 
+            inputs=None, 
+            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
+            queue=False)
+    demo.queue().launch(debug=True)
+
+if __name__ == "__main__":
+    demo()

requirements.txt

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+torch
+transformers
+sentence-transformers
+langchain
+langchain-community
+tqdm
+accelerate
+pypdf
+faiss-gpu