import os import json import re import gradio as gr import requests import random import urllib.parse from tempfile import NamedTemporaryFile from bs4 import BeautifulSoup from typing import List from pydantic import BaseModel, Field from huggingface_hub import InferenceApi from duckduckgo_search import DDGS from langchain_community.vectorstores import FAISS from langchain_community.document_loaders import PyPDFLoader from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.llms import HuggingFaceHub from langchain_core.documents import Document from sentence_transformers import SentenceTransformer from llama_parse import LlamaParse import logging # Set up logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Environment variables and configurations huggingface_token = os.environ.get("HUGGINGFACE_TOKEN") llama_cloud_api_key = os.environ.get("LLAMA_CLOUD_API_KEY") # Initialize SentenceTransformer and LlamaParse sentence_model = SentenceTransformer('paraphrase-MiniLM-L6-v2') 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 = "pypdf") -> List[Document]: 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 update_vectors(files, parser): if not files: return "Please upload at least one PDF file." embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") 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 clear_cache(): if os.path.exists("faiss_database"): os.remove("faiss_database") return "Cache cleared successfully." else: return "No cache to clear." def get_model(temperature, top_p, repetition_penalty): return HuggingFaceHub( repo_id="mistralai/Mistral-7B-Instruct-v0.3", model_kwargs={ "temperature": temperature, "top_p": top_p, "repetition_penalty": repetition_penalty, "max_length": 1000 }, huggingfacehub_api_token=huggingface_token ) def duckduckgo_search(query): logging.debug(f"Performing DuckDuckGo search for query: {query}") with DDGS() as ddgs: results = list(ddgs.text(query, max_results=5)) logging.debug(f"Search returned {len(results)} results") return results def get_response_with_search(query, temperature, top_p, repetition_penalty, use_pdf=False): logging.debug(f"Getting response for query: {query}") logging.debug(f"Parameters: temperature={temperature}, top_p={top_p}, repetition_penalty={repetition_penalty}, use_pdf={use_pdf}") model = get_model(temperature, top_p, repetition_penalty) embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") if use_pdf and os.path.exists("faiss_database"): logging.debug("Using PDF database for context") database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True) retriever = database.as_retriever() relevant_docs = retriever.get_relevant_documents(query) context = "\n".join([f"Content: {doc.page_content}\nSource: {doc.metadata['source']}\n" for doc in relevant_docs]) else: logging.debug("Using web search for context") 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) logging.debug(f"Context generated. Length: {len(context)} characters") prompt = f"""[INST] Using the following context: {context} Write a detailed and complete research document that fulfills the following user request: '{query}' After the main content, provide a list of sources used in your response, prefixed with 'Sources:'. Do not include any part of these instructions in your response. [/INST]""" logging.debug("Sending prompt to model") response = model(prompt) logging.debug(f"Received response from model. Length: {len(response)} characters") main_content, sources = split_response(response) logging.debug(f"Split response. Main content length: {len(main_content)}, Sources length: {len(sources)}") return main_content, sources def split_response(response): logging.debug("Splitting response") logging.debug(f"Original response: {response[:100]}...") # Log first 100 characters # Remove any remaining instruction text response = re.sub(r'\[/?INST\]', '', response) response = re.sub(r'~~.*?~~', '', response, flags=re.DOTALL) logging.debug(f"After removing instructions: {response[:100]}...") # Log first 100 characters # Split the response into main content and sources parts = response.split("Sources:", 1) main_content = parts[0].strip() sources = parts[1].strip() if len(parts) > 1 else "" logging.debug(f"Main content starts with: {main_content[:100]}...") # Log first 100 characters logging.debug(f"Sources: {sources[:100]}...") # Log first 100 characters return main_content, sources def chatbot_interface(message, history, temperature, top_p, repetition_penalty, use_pdf): logging.debug(f"Chatbot interface called with message: {message}") main_content, sources = get_response_with_search(message, temperature, top_p, repetition_penalty, use_pdf) formatted_response = f"{main_content}\n\nSources:\n{sources}" logging.debug(f"Formatted response: {formatted_response[:100]}...") # Log first 100 characters return formatted_response # 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="pypdf") update_button = gr.Button("Upload PDF") update_output = gr.Textbox(label="Update Status") update_button.click(update_vectors, inputs=[file_input, parser_dropdown], outputs=update_output) with gr.Row(): with gr.Column(scale=2): chatbot = gr.Chatbot(label="Conversation") msg = gr.Textbox(label="Ask a question") submit_button = gr.Button("Submit") with gr.Column(scale=1): temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.7, step=0.1) top_p = gr.Slider(label="Top P", minimum=0.0, maximum=1.0, value=0.95, step=0.05) repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.1, step=0.1) use_pdf = gr.Checkbox(label="Use PDF Documents", value=False) def respond(message, chat_history, temperature, top_p, repetition_penalty, use_pdf): bot_message = chatbot_interface(message, chat_history, temperature, top_p, repetition_penalty, use_pdf) chat_history.append((message, bot_message)) return "", chat_history submit_button.click(respond, inputs=[msg, chatbot, temperature, top_p, repetition_penalty, use_pdf], outputs=[msg, chatbot]) clear_button = gr.Button("Clear Cache") clear_output = gr.Textbox(label="Cache Status") clear_button.click(clear_cache, inputs=[], outputs=clear_output) if __name__ == "__main__": demo.launch()