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app (3).py

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+"""
+PDF-based chatbot with Retrieval-Augmented Generation
+"""
+
+import os
+import gradio as gr
+
+from dotenv import load_dotenv
+
+import indexing
+import retrieval
+
+
+# default_persist_directory = './chroma_HF/'
+list_llm = [
+    "mistralai/Mistral-7B-Instruct-v0.3",
+    "microsoft/Phi-3.5-mini-instruct",
+    "meta-llama/Llama-3.1-8B-Instruct",
+    "meta-llama/Llama-3.2-3B-Instruct",
+    "meta-llama/Llama-3.2-1B-Instruct",
+    "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    "HuggingFaceH4/zephyr-7b-beta",
+    "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "google/gemma-2-2b-it",
+    "google/gemma-2-9b-it",
+    "Qwen/Qwen2.5-1.5B-Instruct",
+    "Qwen/Qwen2.5-3B-Instruct",
+    "Qwen/Qwen2.5-7B-Instruct",
+]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+
+# Load environment file - HuggingFace API key
+def retrieve_api():
+    """Retrieve HuggingFace API Key"""
+    _ = load_dotenv()
+    global huggingfacehub_api_token
+    huggingfacehub_api_token = os.environ.get("HUGGINGFACE_API_KEY")
+
+
+# Initialize database
+def initialize_database(
+    list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()
+):
+    """Initialize database"""
+
+    # Create list of documents (when valid)
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+
+    # Create collection_name for vector database
+    progress(0.1, desc="Creating collection name...")
+    collection_name = indexing.create_collection_name(list_file_path[0])
+
+    progress(0.25, desc="Loading document...")
+    # Load document and create splits
+    doc_splits = indexing.load_doc(list_file_path, chunk_size, chunk_overlap)
+
+    # Create or load vector database
+    progress(0.5, desc="Generating vector database...")
+
+    # global vector_db
+    vector_db = indexing.create_db(doc_splits, collection_name)
+
+    return vector_db, collection_name, "Complete!"
+
+
+# Initialize LLM
+def initialize_llm(
+    llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()
+):
+    """Initialize LLM"""
+
+    # print("llm_option",llm_option)
+    llm_name = list_llm[llm_option]
+    print("llm_name: ", llm_name)
+    qa_chain = retrieval.initialize_llmchain(
+        llm_name, huggingfacehub_api_token, llm_temperature, max_tokens, top_k, vector_db, progress
+    )
+    return qa_chain, "Complete!"
+
+
+# Chatbot conversation
+def conversation(qa_chain, message, history):
+    """Chatbot conversation"""
+
+    qa_chain, new_history, response_sources = retrieval.invoke_qa_chain(
+        qa_chain, message, history
+    )
+
+    # Format output gradio components
+    response_source1 = response_sources[0].page_content.strip()
+    response_source2 = response_sources[1].page_content.strip()
+    response_source3 = response_sources[2].page_content.strip()
+    # Langchain sources are zero-based
+    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
+
+    return (
+        qa_chain,
+        gr.update(value=""),
+        new_history,
+        response_source1,
+        response_source1_page,
+        response_source2,
+        response_source2_page,
+        response_source3,
+        response_source3_page,
+    )
+
+
+SPACE_TITLE = """
+<center><h2>PDF-based chatbot</center></h2>
+<h3>Ask any questions about your PDF documents</h3>
+"""
+
+SPACE_INFO = """
+<b>Description:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
+The user interface explicitely shows multiple steps to help understand the RAG workflow. 
+This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
+<br><b>Notes:</b> Updated space with more recent LLM models (Qwen 2.5, Llama 3.2, SmolLM2 series) 
+<br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
+"""
+
+
+# Gradio User Interface
+def gradio_ui():
+    """Gradio User Interface"""
+
+    with gr.Blocks(theme="base") as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        collection_name = gr.State()
+
+        gr.Markdown(SPACE_TITLE)
+        gr.Markdown(SPACE_INFO)
+
+        with gr.Tab("Step 1 - Upload PDF"):
+            with gr.Row():
+                document = gr.File(
+                    height=200,
+                    file_count="multiple",
+                    file_types=[".pdf"],
+                    interactive=True,
+                    label="Upload your PDF documents (single or multiple)",
+                )
+
+        with gr.Tab("Step 2 - Process document"):
+            with gr.Row():
+                db_btn = gr.Radio(
+                    ["ChromaDB"],
+                    label="Vector database type",
+                    value="ChromaDB",
+                    type="index",
+                    info="Choose your vector database",
+                )
+            with gr.Accordion("Advanced options - Document text splitter", open=False):
+                with gr.Row():
+                    slider_chunk_size = gr.Slider(
+                        minimum=100,
+                        maximum=1000,
+                        value=600,
+                        step=20,
+                        label="Chunk size",
+                        info="Chunk size",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_chunk_overlap = gr.Slider(
+                        minimum=10,
+                        maximum=200,
+                        value=40,
+                        step=10,
+                        label="Chunk overlap",
+                        info="Chunk overlap",
+                        interactive=True,
+                    )
+            with gr.Row():
+                db_progress = gr.Textbox(
+                    label="Vector database initialization", value="None"
+                )
+            with gr.Row():
+                db_btn = gr.Button("Generate vector database")
+
+        with gr.Tab("Step 3 - Initialize QA chain"):
+            with gr.Row():
+                llm_btn = gr.Radio(
+                    list_llm_simple,
+                    label="LLM models",
+                    value=list_llm_simple[6],
+                    type="index",
+                    info="Choose your LLM model",
+                )
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature = gr.Slider(
+                        minimum=0.01,
+                        maximum=1.0,
+                        value=0.7,
+                        step=0.1,
+                        label="Temperature",
+                        info="Model temperature",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_maxtokens = gr.Slider(
+                        minimum=224,
+                        maximum=4096,
+                        value=1024,
+                        step=32,
+                        label="Max Tokens",
+                        info="Model max tokens",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_topk = gr.Slider(
+                        minimum=1,
+                        maximum=10,
+                        value=3,
+                        step=1,
+                        label="top-k samples",
+                        info="Model top-k samples",
+                        interactive=True,
+                    )
+            with gr.Row():
+                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
+            with gr.Row():
+                qachain_btn = gr.Button("Initialize Question Answering chain")
+
+        with gr.Tab("Step 4 - Chatbot"):
+            chatbot = gr.Chatbot(height=300, type="tuples")
+            with gr.Accordion("Advanced - Document references", 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="Type message (e.g. 'Can you summarize this document in one paragraph?')",
+                    container=True,
+                )
+            with gr.Row():
+                submit_btn = gr.Button("Submit message")
+                clear_btn = gr.ClearButton(
+                    components=[msg, chatbot], value="Clear conversation"
+                )
+
+        # Preprocessing events
+        db_btn.click(
+            initialize_database,
+            inputs=[document, slider_chunk_size, slider_chunk_overlap],
+            outputs=[vector_db, collection_name, db_progress],
+        )
+        qachain_btn.click(
+            initialize_llm,
+            inputs=[
+                llm_btn,
+                slider_temperature,
+                slider_maxtokens,
+                slider_topk,
+                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__":
+    retrieve_api()
+    gradio_ui()

requirements (1).txt

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+transformers[torch]
+sentence-transformers
+langchain
+langchain-community
+langchain-huggingface
+langchain-chroma
+huggingface-hub
+tqdm
+accelerate
+pypdf
+chromadb
+unidecode
+gradio
+python-dotenv

requirements-dev.txt

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+pylint
+black

retrieval.py

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+"""
+LLM chain retrieval
+"""
+
+import json
+import gradio as gr
+
+from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.prompts import PromptTemplate
+
+
+# Add system template for RAG application
+PROMPT_TEMPLATE = """
+You are an assistant for question-answering tasks. Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer concise.
+Question: {question} 
+Context: {context} 
+Helpful Answer:
+"""
+
+
+# Initialize langchain LLM chain
+def initialize_llmchain(
+    llm_model,
+    huggingfacehub_api_token,
+    temperature,
+    max_tokens,
+    top_k,
+    vector_db,
+    progress=gr.Progress(),
+):
+    """Initialize Langchain LLM chain"""
+
+    progress(0.1, desc="Initializing HF tokenizer...")
+    # HuggingFaceHub uses HF inference endpoints
+    progress(0.5, desc="Initializing HF Hub...")
+    # Use of trust_remote_code as model_kwargs
+    # Warning: langchain issue
+    # URL: https://github.com/langchain-ai/langchain/issues/6080
+
+    # if 'Llama' in llm_model:
+    #     task = "conversational"
+    # else:
+    #     task = "text-generation"
+    # print(f"Task: {task}")
+
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        task="text-generation",
+        #task="conversational",
+        provider="hf-inference",
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+        huggingfacehub_api_token=huggingfacehub_api_token,
+    )
+
+    progress(0.75, desc="Defining buffer memory...")
+    memory = ConversationBufferMemory(
+        memory_key="chat_history", output_key="answer", return_messages=True
+    )
+    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
+    retriever = vector_db.as_retriever()
+
+    progress(0.8, desc="Defining retrieval chain...")
+    with open('prompt_template.json', 'r') as file:
+    	system_prompt = json.load(file)
+    prompt_template = system_prompt["prompt"]
+    rag_prompt = PromptTemplate(
+        template=prompt_template, input_variables=["context", "question"]
+    )
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        combine_docs_chain_kwargs={"prompt": rag_prompt},
+        return_source_documents=True,
+        # return_generated_question=False,
+        verbose=False,
+    )
+    progress(0.9, desc="Done!")
+
+    return qa_chain
+
+
+def format_chat_history(message, chat_history):
+    """Format chat history for llm chain"""
+
+    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 invoke_qa_chain(qa_chain, message, history):
+    """Invoke question-answering chain"""
+
+    formatted_chat_history = format_chat_history(message, history)
+    # print("formatted_chat_history",formatted_chat_history)
+
+    # Generate response using QA chain
+    response = qa_chain.invoke(
+        {"question": message, "chat_history": formatted_chat_history}
+    )
+
+    response_sources = response["source_documents"]
+
+    response_answer = response["answer"]
+    if response_answer.find("Helpful Answer:") != -1:
+        response_answer = response_answer.split("Helpful Answer:")[-1]
+
+    # Append user message and response to chat history
+    new_history = history + [(message, response_answer)]
+
+    # print ('chat response: ', response_answer)
+    # print('DB source', response_sources)
+
+    return qa_chain, new_history, response_sources