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| import gradio as gr | |
| import os | |
| from typing import List, Dict | |
| from langchain.text_splitter import ( | |
| RecursiveCharacterTextSplitter, | |
| CharacterTextSplitter, | |
| TokenTextSplitter | |
| ) | |
| from langchain_community.vectorstores import FAISS, Chroma, Qdrant | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| from langchain.memory import ConversationBufferMemory | |
| list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"] | |
| list_llm_simple = [os.path.basename(llm) for llm in list_llm] | |
| api_token = os.getenv("HF_TOKEN") | |
| CHUNK_SIZES = { | |
| "small": {"recursive": 512, "fixed": 512, "token": 256}, | |
| "medium": {"recursive": 1024, "fixed": 1024, "token": 512} | |
| } | |
| def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int = 64): | |
| splitters = { | |
| "recursive": RecursiveCharacterTextSplitter( | |
| chunk_size=chunk_size, | |
| chunk_overlap=chunk_overlap | |
| ), | |
| "fixed": CharacterTextSplitter( | |
| chunk_size=chunk_size, | |
| chunk_overlap=chunk_overlap | |
| ), | |
| "token": TokenTextSplitter( | |
| chunk_size=chunk_size, | |
| chunk_overlap=chunk_overlap | |
| ) | |
| } | |
| return splitters.get(strategy) | |
| def load_doc(list_file_path: List[str], splitting_strategy: str, chunk_size: str): | |
| chunk_size_value = CHUNK_SIZES[chunk_size][splitting_strategy] | |
| loaders = [PyPDFLoader(x) for x in list_file_path] | |
| pages = [] | |
| for loader in loaders: | |
| pages.extend(loader.load()) | |
| text_splitter = get_text_splitter(splitting_strategy, chunk_size_value) | |
| doc_splits = text_splitter.split_documents(pages) | |
| return doc_splits | |
| def create_db(splits, db_choice: str = "faiss"): | |
| embeddings = HuggingFaceEmbeddings() | |
| db_creators = { | |
| "faiss": lambda: FAISS.from_documents(splits, embeddings), | |
| "chroma": lambda: Chroma.from_documents(splits, embeddings), | |
| "qdrant": lambda: Qdrant.from_documents( | |
| splits, | |
| embeddings, | |
| location=":memory:", | |
| collection_name="pdf_docs" | |
| ) | |
| } | |
| return db_creators[db_choice]() | |
| def initialize_database(list_file_obj, splitting_strategy, chunk_size, db_choice, progress=gr.Progress()): | |
| """Initialize vector database with error handling""" | |
| try: | |
| if not list_file_obj: | |
| return None, "No files uploaded. Please upload PDF documents first." | |
| list_file_path = [x.name for x in list_file_obj if x is not None] | |
| if not list_file_path: | |
| return None, "No valid files found. Please upload PDF documents." | |
| doc_splits = load_doc(list_file_path, splitting_strategy, chunk_size) | |
| if not doc_splits: | |
| return None, "No content extracted from documents." | |
| vector_db = create_db(doc_splits, db_choice) | |
| return vector_db, f"Database created successfully using {splitting_strategy} splitting and {db_choice} vector database!" | |
| except Exception as e: | |
| return None, f"Error creating database: {str(e)}" | |
| def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| """Initialize LLM chain with error handling""" | |
| try: | |
| if vector_db is None: | |
| return None, "Please create vector database first." | |
| llm_model = list_llm[llm_choice] | |
| llm = HuggingFaceEndpoint( | |
| repo_id=llm_model, | |
| huggingfacehub_api_token=api_token, | |
| temperature=temperature, | |
| max_new_tokens=max_tokens, | |
| top_k=top_k | |
| ) | |
| 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, | |
| memory=memory, | |
| return_source_documents=True | |
| ) | |
| return qa_chain, "LLM initialized successfully!" | |
| except Exception as e: | |
| return None, f"Error initializing LLM: {str(e)}" | |
| def conversation(qa_chain, message, history): | |
| """Conversation function returning all required outputs""" | |
| response = qa_chain.invoke({ | |
| "question": message, | |
| "chat_history": [(hist[0], hist[1]) for hist in history] | |
| }) | |
| response_answer = response["answer"] | |
| if "Helpful Answer:" in response_answer: | |
| response_answer = response_answer.split("Helpful Answer:")[-1] | |
| sources = response["source_documents"][:3] | |
| source_contents = [] | |
| source_pages = [] | |
| for source in sources: | |
| source_contents.append(source.page_content.strip()) | |
| source_pages.append(source.metadata.get("page", 0) + 1) | |
| while len(source_contents) < 3: | |
| source_contents.append("") | |
| source_pages.append(0) | |
| return ( | |
| qa_chain, | |
| gr.update(value=""), | |
| history + [(message, response_answer)], | |
| source_contents[0], | |
| source_pages[0], | |
| source_contents[1], | |
| source_pages[1], | |
| source_contents[2], | |
| source_pages[2] | |
| ) | |
| def demo(): | |
| with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo: | |
| vector_db = gr.State() | |
| qa_chain = gr.State() | |
| gr.HTML("<center><h1>RAG PDF Chatbot</h1></center>") | |
| with gr.Column(scale=86): | |
| gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>") | |
| with gr.Row(): | |
| document = gr.Files( | |
| height=300, | |
| file_count="multiple", | |
| file_types=["pdf"], | |
| interactive=True, | |
| label="Upload PDF documents" | |
| ) | |
| with gr.Row(): | |
| splitting_strategy = gr.Radio( | |
| ["recursive", "fixed", "token"], | |
| label="Text Splitting Strategy", | |
| value="recursive" | |
| ) | |
| db_choice = gr.Radio( | |
| ["faiss", "chroma", "qdrant"], | |
| label="Vector Database", | |
| value="faiss" | |
| ) | |
| chunk_size = gr.Radio( | |
| ["small", "medium"], | |
| label="Chunk Size", | |
| value="medium" | |
| ) | |
| with gr.Row(): | |
| db_btn = gr.Button("Create vector database") | |
| db_progress = gr.Textbox( | |
| value="Not initialized", | |
| show_label=False | |
| ) | |
| gr.Markdown("<b>Step 2 - Configure LLM</b>") | |
| with gr.Row(): | |
| llm_choice = gr.Radio( | |
| list_llm_simple, | |
| label="Available LLMs", | |
| value=list_llm_simple[0], | |
| type="index" | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("LLM Parameters", open=False): | |
| temperature = gr.Slider( | |
| minimum=0.01, | |
| maximum=1.0, | |
| value=0.5, | |
| step=0.1, | |
| label="Temperature" | |
| ) | |
| max_tokens = gr.Slider( | |
| minimum=128, | |
| maximum=4096, | |
| value=2048, | |
| step=128, | |
| label="Max Tokens" | |
| ) | |
| top_k = gr.Slider( | |
| minimum=1, | |
| maximum=10, | |
| value=3, | |
| step=1, | |
| label="Top K" | |
| ) | |
| with gr.Row(): | |
| init_llm_btn = gr.Button("Initialize LLM") | |
| llm_progress = gr.Textbox( | |
| value="Not initialized", | |
| show_label=False | |
| ) | |
| with gr.Column(scale=200): | |
| gr.Markdown("<b>Step 3 - Chat with Documents</b>") | |
| chatbot = gr.Chatbot(height=505) | |
| with gr.Accordion("Source References", open=False): | |
| with gr.Row(): | |
| source1 = gr.Textbox(label="Source 1", lines=2) | |
| page1 = gr.Number(label="Page") | |
| with gr.Row(): | |
| source2 = gr.Textbox(label="Source 2", lines=2) | |
| page2 = gr.Number(label="Page") | |
| with gr.Row(): | |
| source3 = gr.Textbox(label="Source 3", lines=2) | |
| page3 = gr.Number(label="Page") | |
| with gr.Row(): | |
| msg = gr.Textbox( | |
| placeholder="Ask a question", | |
| show_label=False | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit") | |
| clear_btn = gr.ClearButton( | |
| [msg, chatbot], | |
| value="Clear Chat" | |
| ) | |
| # Event handlers | |
| db_btn.click( | |
| initialize_database, | |
| inputs=[document, splitting_strategy, chunk_size, db_choice], | |
| outputs=[vector_db, db_progress] | |
| ).then( | |
| lambda x: gr.update(interactive=True) if x[0] is not None else gr.update(interactive=False), | |
| inputs=[vector_db], | |
| outputs=[init_llm_btn] | |
| ) | |
| init_llm_btn.click( | |
| initialize_llmchain, | |
| inputs=[llm_choice, temperature, max_tokens, top_k, vector_db], | |
| outputs=[qa_chain, llm_progress] | |
| ).then( | |
| lambda x: gr.update(interactive=True) if x[0] is not None else gr.update(interactive=False), | |
| inputs=[qa_chain], | |
| outputs=[msg] | |
| ) | |
| msg.submit( | |
| conversation, | |
| inputs=[qa_chain, msg, chatbot], | |
| outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3] | |
| ) | |
| submit_btn.click( | |
| conversation, | |
| inputs=[qa_chain, msg, chatbot], | |
| outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3] | |
| ) | |
| clear_btn.click( | |
| lambda: [None, "", 0, "", 0, "", 0], | |
| outputs=[chatbot, source1, page1, source2, page2, source3, page3] | |
| ) | |
| demo.queue().launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() |