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| import gradio as gr | |
| import os | |
| from bs4 import BeautifulSoup | |
| import requests | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| from langchain.schema import Document | |
| # Initialize environment | |
| api_token =os.getenv("HF_TOKEN") | |
| 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] | |
| def scrape_website(url): | |
| """Scrape text content from a website""" | |
| try: | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| for script in soup(["script", "style"]): | |
| script.decompose() | |
| text = soup.get_text() | |
| lines = (line.strip() for line in text.splitlines()) | |
| chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) | |
| text = ' '.join(chunk for chunk in chunks if chunk) | |
| return text | |
| except Exception as e: | |
| return f"Error scraping website: {str(e)}" | |
| def process_text(text): | |
| """Split text into chunks""" | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=1024, | |
| chunk_overlap=64 | |
| ) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def create_db(splits): | |
| """Create vector database""" | |
| embeddings = HuggingFaceEmbeddings() | |
| documents = [Document(page_content=text, metadata={}) for text in splits] | |
| vectordb = FAISS.from_documents(documents, embeddings) | |
| return vectordb | |
| def initialize_database(url, progress=gr.Progress()): | |
| """Initialize database from URL""" | |
| # Scrape website content | |
| text_content = scrape_website(url) | |
| if text_content.startswith("Error"): | |
| return None, text_content | |
| # Create text chunks | |
| doc_splits = process_text(text_content) | |
| # Create vector database | |
| vector_db = create_db(doc_splits) | |
| return vector_db, "Database created successfully!" | |
| def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db): | |
| """Initialize LLM chain""" | |
| 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 | |
| ) | |
| qa_chain = ConversationalRetrievalChain.from_llm( | |
| llm, | |
| retriever=vector_db.as_retriever(), | |
| chain_type="stuff", | |
| memory=memory, | |
| return_source_documents=True, | |
| verbose=False, | |
| ) | |
| return qa_chain | |
| def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| """Initialize LLM with selected options""" | |
| llm_name = list_llm[llm_option] | |
| qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db) | |
| return qa_chain, "QA chain initialized. Chatbot is ready!" | |
| def format_chat_history(message, chat_history): | |
| """Format chat history for the model""" | |
| 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): | |
| """Handle conversation with the model""" | |
| 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"][:3] | |
| sources = [] | |
| for i in range(3): | |
| if i < len(response_sources): | |
| sources.append((response_sources[i].page_content.strip(), 1)) | |
| else: | |
| sources.append(("", 1)) | |
| new_history = history + [(message, response_answer)] | |
| return (qa_chain, gr.update(value=""), new_history, | |
| sources[0][0], sources[0][1], | |
| sources[1][0], sources[1][1], | |
| sources[2][0], sources[2][1]) | |
| 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 Website Chatbot</h1></center>") | |
| gr.Markdown("""<b>Query any website content!</b> This AI agent performs retrieval augmented generation (RAG) on website content.""") | |
| with gr.Row(): | |
| with gr.Column(scale=86): | |
| gr.Markdown("<b>Step 1 - Enter Website URL and Initialize RAG pipeline</b>") | |
| with gr.Row(): | |
| url_input = gr.Textbox(label="Website URL", placeholder="Enter website URL here...") | |
| with gr.Row(): | |
| db_btn = gr.Button("Create vector database") | |
| with gr.Row(): | |
| db_progress = gr.Textbox(value="Not initialized", show_label=False) | |
| gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>") | |
| with gr.Row(): | |
| llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index") | |
| with gr.Row(): | |
| with gr.Accordion("LLM input parameters", open=False): | |
| slider_temperature = gr.Slider(0.01, 1.0, 0.5, step=0.1, label="Temperature") | |
| slider_maxtokens = gr.Slider(128, 9192, 4096, step=128, label="Max New Tokens") | |
| slider_topk = gr.Slider(1, 10, 3, step=1, label="top-k") | |
| with gr.Row(): | |
| qachain_btn = gr.Button("Initialize Question Answering Chatbot") | |
| with gr.Row(): | |
| llm_progress = gr.Textbox(value="Not initialized", show_label=False) | |
| with gr.Column(scale=200): | |
| gr.Markdown("<b>Step 2 - Chat about the Website Content</b>") | |
| chatbot = gr.Chatbot(height=505) | |
| with gr.Accordion("Relevant context from the source", open=False): | |
| with gr.Row(): | |
| doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) | |
| source1_page = gr.Number(label="Section", scale=1) | |
| with gr.Row(): | |
| doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) | |
| source2_page = gr.Number(label="Section", scale=1) | |
| with gr.Row(): | |
| doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20) | |
| source3_page = gr.Number(label="Section", 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") | |
| # Event handlers | |
| db_btn.click(initialize_database, | |
| inputs=[url_input], | |
| outputs=[vector_db, db_progress]) | |
| qachain_btn.click(initialize_LLM, | |
| inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], | |
| outputs=[qa_chain, llm_progress]) | |
| 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]) | |
| 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]) | |
| clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], | |
| inputs=None, | |
| outputs=[chatbot, doc_source1, source1_page, | |
| doc_source2, source2_page, doc_source3, source3_page]) | |
| demo.queue().launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() | |