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| import os | |
| import json | |
| import re | |
| import gradio as gr | |
| import requests | |
| from duckduckgo_search import DDGS | |
| from typing import List | |
| from pydantic import BaseModel, Field | |
| from tempfile import NamedTemporaryFile | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from llama_parse import LlamaParse | |
| from langchain_core.documents import Document | |
| # Environment variables and configurations | |
| huggingface_token = os.environ.get("HUGGINGFACE_TOKEN") | |
| llama_cloud_api_key = os.environ.get("LLAMA_CLOUD_API_KEY") | |
| # Initialize LlamaParse | |
| llama_parser = LlamaParse( | |
| api_key=llama_cloud_api_key, | |
| result_type="markdown", | |
| num_workers=4, | |
| verbose=True, | |
| language="en", | |
| ) | |
| def load_document(file: NamedTemporaryFile, parser: str = "llamaparse") -> List[Document]: | |
| """Loads and splits the document into pages.""" | |
| if parser == "pypdf": | |
| loader = PyPDFLoader(file.name) | |
| return loader.load_and_split() | |
| elif parser == "llamaparse": | |
| try: | |
| documents = llama_parser.load_data(file.name) | |
| return [Document(page_content=doc.text, metadata={"source": file.name}) for doc in documents] | |
| except Exception as e: | |
| print(f"Error using Llama Parse: {str(e)}") | |
| print("Falling back to PyPDF parser") | |
| loader = PyPDFLoader(file.name) | |
| return loader.load_and_split() | |
| else: | |
| raise ValueError("Invalid parser specified. Use 'pypdf' or 'llamaparse'.") | |
| def get_embeddings(): | |
| return HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") | |
| def update_vectors(files, parser): | |
| if not files: | |
| return "Please upload at least one PDF file." | |
| embed = get_embeddings() | |
| total_chunks = 0 | |
| all_data = [] | |
| for file in files: | |
| data = load_document(file, parser) | |
| all_data.extend(data) | |
| total_chunks += len(data) | |
| if os.path.exists("faiss_database"): | |
| database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True) | |
| database.add_documents(all_data) | |
| else: | |
| database = FAISS.from_documents(all_data, embed) | |
| database.save_local("faiss_database") | |
| return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {parser}." | |
| def generate_chunked_response(prompt, max_tokens=1000, max_chunks=5, temperature=0.3, repetition_penalty=1.1): | |
| API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B-Instruct" | |
| headers = {"Authorization": f"Bearer {huggingface_token}"} | |
| payload = { | |
| "inputs": prompt, | |
| "parameters": { | |
| "max_new_tokens": max_tokens, | |
| "temperature": temperature, | |
| "top_p": 0.4, | |
| "top_k": 40, | |
| "repetition_penalty": repetition_penalty, | |
| "stop": ["</s>", "[/INST]"] | |
| } | |
| } | |
| full_response = "" | |
| for _ in range(max_chunks): | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if isinstance(result, list) and len(result) > 0: | |
| chunk = result[0].get('generated_text', '') | |
| # Remove any part of the chunk that's already in full_response | |
| new_content = chunk[len(full_response):].strip() | |
| if not new_content: | |
| break # No new content, so we're done | |
| full_response += new_content | |
| if chunk.endswith((".", "!", "?", "</s>", "[/INST]")): | |
| break | |
| # Update the prompt for the next iteration | |
| payload["inputs"] = full_response | |
| else: | |
| break | |
| else: | |
| break | |
| # Clean up the response | |
| clean_response = re.sub(r'<s>\[INST\].*?\[/INST\]\s*', '', full_response, flags=re.DOTALL) | |
| clean_response = clean_response.replace("Using the following context:", "").strip() | |
| clean_response = clean_response.replace("Using the following context from the PDF documents:", "").strip() | |
| return clean_response | |
| def duckduckgo_search(query): | |
| with DDGS() as ddgs: | |
| results = ddgs.text(query, max_results=5) | |
| return results | |
| class CitingSources(BaseModel): | |
| sources: List[str] = Field( | |
| ..., | |
| description="List of sources to cite. Should be an URL of the source." | |
| ) | |
| def get_response_from_pdf(query, temperature=0.7, repetition_penalty=1.1): | |
| embed = get_embeddings() | |
| if os.path.exists("faiss_database"): | |
| database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True) | |
| else: | |
| return "No documents available. Please upload PDF documents to answer questions." | |
| retriever = database.as_retriever() | |
| relevant_docs = retriever.get_relevant_documents(query) | |
| context_str = "\n".join([doc.page_content for doc in relevant_docs]) | |
| prompt = f"""<s>[INST] Using the following context from the PDF documents: | |
| {context_str} | |
| Write a detailed and complete response that answers the following user question: '{query}' | |
| Do not include a list of sources in your response. [/INST]""" | |
| generated_text = generate_chunked_response(prompt, temperature=temperature, repetition_penalty=repetition_penalty) | |
| # Clean the response | |
| clean_text = re.sub(r'<s>\[INST\].*?\[/INST\]\s*', '', generated_text, flags=re.DOTALL) | |
| clean_text = clean_text.replace("Using the following context from the PDF documents:", "").strip() | |
| return clean_text | |
| def get_response_with_search(query, temperature=0.7, repetition_penalty=1.1): | |
| search_results = duckduckgo_search(query) | |
| context = "\n".join(f"{result['title']}\n{result['body']}\nSource: {result['href']}\n" | |
| for result in search_results if 'body' in result) | |
| prompt = f"""<s>[INST] Using the following context: | |
| {context} | |
| Write a detailed and complete research document that fulfills the following user request: '{query}' | |
| After writing the document, please provide a list of sources used in your response. [/INST]""" | |
| generated_text = generate_chunked_response(prompt, temperature=temperature, repetition_penalty=repetition_penalty) | |
| # Clean the response | |
| clean_text = re.sub(r'<s>\[INST\].*?\[/INST\]\s*', '', generated_text, flags=re.DOTALL) | |
| clean_text = clean_text.replace("Using the following context:", "").strip() | |
| # Split the content and sources | |
| parts = clean_text.split("Sources:", 1) | |
| main_content = parts[0].strip() | |
| sources = parts[1].strip() if len(parts) > 1 else "" | |
| return main_content, sources | |
| def chatbot_interface(message, history, use_web_search, temperature, repetition_penalty): | |
| if use_web_search: | |
| main_content, sources = get_response_with_search(message, temperature, repetition_penalty) | |
| formatted_response = f"{main_content}\n\nSources:\n{sources}" | |
| else: | |
| response = get_response_from_pdf(message, temperature, repetition_penalty) | |
| formatted_response = response | |
| history.append((message, formatted_response)) | |
| return history | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# AI-powered Web Search and PDF Chat Assistant") | |
| with gr.Row(): | |
| file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"]) | |
| parser_dropdown = gr.Dropdown(choices=["pypdf", "llamaparse"], label="Select PDF Parser", value="llamaparse") | |
| update_button = gr.Button("Upload Document") | |
| update_output = gr.Textbox(label="Update Status") | |
| update_button.click(update_vectors, inputs=[file_input, parser_dropdown], outputs=update_output) | |
| chatbot = gr.Chatbot(label="Conversation") | |
| msg = gr.Textbox(label="Ask a question") | |
| use_web_search = gr.Checkbox(label="Use Web Search", value=False) | |
| with gr.Row(): | |
| temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature") | |
| repetition_penalty_slider = gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, label="Repetition Penalty") | |
| submit = gr.Button("Submit") | |
| gr.Examples( | |
| examples=[ | |
| ["What are the latest developments in AI?"], | |
| ["Tell me about recent updates on GitHub"], | |
| ["What are the best hotels in Galapagos, Ecuador?"], | |
| ["Summarize recent advancements in Python programming"], | |
| ], | |
| inputs=msg, | |
| ) | |
| submit.click(chatbot_interface, | |
| inputs=[msg, chatbot, use_web_search, temperature_slider, repetition_penalty_slider], | |
| outputs=[chatbot]) | |
| msg.submit(chatbot_interface, | |
| inputs=[msg, chatbot, use_web_search, temperature_slider, repetition_penalty_slider], | |
| outputs=[chatbot]) | |
| gr.Markdown( | |
| """ | |
| ## How to use | |
| 1. Upload PDF documents using the file input at the top. | |
| 2. Select the PDF parser (pypdf or llamaparse) and click "Upload Document" to update the vector store. | |
| 3. Ask questions in the textbox. | |
| 4. Toggle "Use Web Search" to switch between PDF chat and web search. | |
| 5. Adjust Temperature and Repetition Penalty sliders to fine-tune the response generation. | |
| 6. Click "Submit" or press Enter to get a response. | |
| """ | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) |