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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/Meta-Llama-3-8B-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>Note:</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>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[0], | |
| 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) | |
| 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() | |