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| import gradio as gr | |
| import os | |
| from langchain_community.document_loaders import PyPDFLoader # Corrected import | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.vectorstores import Chroma # Corrected import | |
| from langchain.chains import ConversationalRetrievalChain # Note: Not from "langchain_community" | |
| from langchain_community.embeddings import HuggingFaceEmbeddings # Corrected import | |
| from langchain_community.llms import HuggingFacePipeline, HuggingFaceHub # Corrected import | |
| from langchain.chains import ConversationChain # Note: Not from "langchain_community" | |
| from langchain.memory import ConversationBufferMemory | |
| from pathlib import Path | |
| import chromadb | |
| from transformers import AutoTokenizer | |
| import transformers | |
| import torch | |
| import tqdm | |
| import accelerate | |
| # LLM model and parameters (adjusted for clarity) | |
| chosen_llm_model = "mistralai/Mistral-7B-Instruct-v0.2" | |
| llm_temperature = 0.7 | |
| max_tokens = 1024 | |
| top_k = 3 | |
| # Chunk size and overlap (adjusted for clarity) | |
| chunk_size = 600 | |
| chunk_overlap = 40 | |
| # Initialize vector database in background | |
| accelerate(initialize_database)() # Function definition moved here | |
| def initialize_database(): | |
| """ | |
| This function initializes the vector database (assumed to be ChromaDB). | |
| Modify this function based on your specific database needs. | |
| """ | |
| # Replace with your ChromaDB connection and schema creation logic | |
| # ... | |
| pass | |
| def demo(): | |
| with gr.Blocks(theme="base") as demo: | |
| qa_chain = gr.State() # Store the initialized QA chain | |
| collection_name = gr.State() | |
| gr.Markdown( | |
| """ | |
| <center><h2>PDF-based chatbot (powered by LangChain and open-source LLMs)</center></h2> | |
| <h3>Ask any questions about your PDF documents, along with follow-ups</h3> | |
| <b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \ | |
| When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i> | |
| <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 an output.<br> | |
| """ | |
| ) | |
| with gr.Row(): | |
| document = gr.Files( | |
| height=100, | |
| file_count="multiple", | |
| file_types=["pdf"], | |
| interactive=True, | |
| label="Upload your PDF documents (single or multiple)", | |
| ) | |
| with gr.Row(): | |
| 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", container=True) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit") | |
| clear_btn = gr.ClearButton([msg, chatbot]) | |
| # Initialize default QA chain when documents are uploaded | |
| document.uploaded(initialize_LLM, inputs=[chosen_llm_model]) | |
| # Chatbot events | |
| msg.submit(conversation, inputs=[qa_chain, msg, chatbot]) | |
| submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot]) | |
| clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page]) | |