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import gradio as gr |
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import os |
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from langchain_community.document_loaders import PyPDFLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain_community.vectorstores import Chroma |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain_community.llms import HuggingFacePipeline |
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from langchain.chains import ConversationChain |
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from langchain.memory import ConversationBufferMemory |
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from langchain_community.llms import HuggingFaceEndpoint |
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from pathlib import Path |
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import chromadb |
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from unidecode import unidecode |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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import tqdm |
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import accelerate |
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import re |
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default_persist_directory = './chroma_HF/' |
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list_llm = ["mistralai/Mistral-7B-Instruct-v0.2"] |
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list_llm_simple = [os.path.basename(llm) for llm in list_llm] |
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def load_doc(list_file_path, chunk_size, chunk_overlap): |
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loaders = [PyPDFLoader(x) for x in list_file_path] |
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pages = [] |
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for loader in loaders: |
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pages.extend(loader.load()) |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size = chunk_size, |
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chunk_overlap = chunk_overlap) |
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doc_splits = text_splitter.split_documents(pages) |
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return doc_splits |
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def create_db(splits, collection_name): |
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embedding = HuggingFaceEmbeddings() |
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new_client = chromadb.EphemeralClient() |
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vectordb = Chroma.from_documents( |
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documents=splits, |
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embedding=embedding, |
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client=new_client, |
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collection_name=collection_name, |
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persist_directory=default_persist_directory |
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) |
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return vectordb |
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def load_db(): |
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embedding = HuggingFaceEmbeddings() |
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vectordb = Chroma( |
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persist_directory=default_persist_directory, |
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embedding_function=embedding) |
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return vectordb |
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def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): |
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progress(0.1, desc="Initializing HF tokenizer...") |
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progress(0.5, desc="Initializing HF Hub...") |
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if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1": |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = max_tokens, |
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top_k = top_k, |
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load_in_8bit = True, |
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) |
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elif llm_model == "HuggingFaceH4/zephyr-7b-gemma-v0.1": |
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raise gr.Error("zephyr-7b-gemma-v0.1 is too large to be loaded automatically on free inference endpoint") |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = max_tokens, |
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top_k = top_k, |
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) |
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elif llm_model == "microsoft/phi-2": |
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raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...") |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = max_tokens, |
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top_k = top_k, |
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trust_remote_code = True, |
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torch_dtype = "auto", |
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) |
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elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0": |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = 250, |
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top_k = top_k, |
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) |
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elif llm_model == "meta-llama/Llama-2-7b-chat-hf": |
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raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...") |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = max_tokens, |
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top_k = top_k, |
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) |
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else: |
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llm = HuggingFaceEndpoint( |
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repo_id=llm_model, |
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temperature = temperature, |
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max_new_tokens = max_tokens, |
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top_k = top_k, |
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) |
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progress(0.75, desc="Defining buffer memory...") |
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memory = ConversationBufferMemory( |
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memory_key="chat_history", |
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output_key='answer', |
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return_messages=True |
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) |
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retriever=vector_db.as_retriever() |
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progress(0.8, desc="Defining retrieval chain...") |
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qa_chain = ConversationalRetrievalChain.from_llm( |
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llm, |
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retriever=retriever, |
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chain_type="stuff", |
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memory=memory, |
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return_source_documents=True, |
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verbose=False, |
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) |
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progress(0.9, desc="Done!") |
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return qa_chain |
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def create_collection_name(filepath): |
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collection_name = Path(filepath).stem |
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collection_name = collection_name.replace(" ","-") |
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collection_name = unidecode(collection_name) |
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collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name) |
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collection_name = collection_name[:50] |
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if len(collection_name) < 3: |
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collection_name = collection_name + 'xyz' |
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print('Filepath: ', filepath) |
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print('Collection name: ', collection_name) |
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return collection_name |
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def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): |
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list_file_path = [x.name for x in list_file_obj if x is not None] |
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progress(0.1, desc="Creating collection name...") |
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collection_name = create_collection_name(list_file_path[0]) |
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progress(0.25, desc="Loading document...") |
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doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap) |
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progress(0.5, desc="Generating vector database...") |
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vector_db = create_db(doc_splits, collection_name) |
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progress(0.9, desc="Done!") |
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return vector_db, collection_name, "Complete!" |
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def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): |
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llm_name = list_llm[llm_option] |
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print("llm_name: ",llm_name) |
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qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) |
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return qa_chain, "Complete!" |
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def format_chat_history(message, chat_history): |
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formatted_chat_history = [] |
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for user_message, bot_message in chat_history: |
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formatted_chat_history.append(f"User: {user_message}") |
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formatted_chat_history.append(f"Assistant: {bot_message}") |
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return formatted_chat_history |
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def conversation(qa_chain, message, history): |
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formatted_chat_history = format_chat_history(message, history) |
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response = qa_chain({"question": message, "chat_history": formatted_chat_history}) |
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response_answer = response["answer"] |
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if response_answer.find("Helpful Answer:") != -1: |
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response_answer = response_answer.split("Helpful Answer:")[-1] |
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response_sources = response["source_documents"] |
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response_source1 = response_sources[0].page_content.strip() |
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response_source2 = response_sources[1].page_content.strip() |
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response_source3 = response_sources[2].page_content.strip() |
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response_source1_page = response_sources[0].metadata["page"] + 1 |
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response_source2_page = response_sources[1].metadata["page"] + 1 |
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response_source3_page = response_sources[2].metadata["page"] + 1 |
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new_history = history + [(message, response_answer)] |
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return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page |
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def upload_file(file_obj): |
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list_file_path = [] |
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for idx, file in enumerate(file_obj): |
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file_path = file_obj.name |
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list_file_path.append(file_path) |
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return list_file_path |
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def demo(): |
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with gr.Blocks(theme="base") as demo: |
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vector_db = gr.State() |
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qa_chain = gr.State() |
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collection_name = gr.State() |
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gr.Markdown( |
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"""PDF-based chatbot (by Dr. Aloke Upadhaya)</center></h2> |
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<h3>Ask any questions about your PDF documents, along with follow-ups</h3> |
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""") |
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with gr.Tab("Step 1 - Document pre-processing"): |
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with gr.Row(): |
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document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") |
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with gr.Row(): |
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db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database") |
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with gr.Accordion("Advanced options - Document text splitter", open=False): |
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with gr.Row(): |
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slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) |
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with gr.Row(): |
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slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) |
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with gr.Row(): |
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db_progress = gr.Textbox(label="Vector database initialization", value="None") |
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with gr.Row(): |
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db_btn = gr.Button("Generate vector database...") |
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with gr.Tab("Step 2 - QA chain initialization"): |
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with gr.Row(): |
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llm_btn = gr.Radio(list_llm_simple, \ |
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label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model") |
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with gr.Accordion("Advanced options - LLM model", open=False): |
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with gr.Row(): |
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slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) |
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with gr.Row(): |
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slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) |
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with gr.Row(): |
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slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True) |
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with gr.Row(): |
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llm_progress = gr.Textbox(value="None",label="QA chain initialization") |
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with gr.Row(): |
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qachain_btn = gr.Button("Initialize question-answering chain...") |
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with gr.Tab("Step 3 - Conversation with chatbot"): |
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chatbot = gr.Chatbot(height=300) |
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with gr.Accordion("Advanced - Document references", open=False): |
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with gr.Row(): |
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doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) |
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source1_page = gr.Number(label="Page", scale=1) |
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with gr.Row(): |
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doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) |
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source2_page = gr.Number(label="Page", scale=1) |
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with gr.Row(): |
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doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20) |
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source3_page = gr.Number(label="Page", scale=1) |
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with gr.Row(): |
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msg = gr.Textbox(placeholder="Type message", container=True) |
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with gr.Row(): |
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submit_btn = gr.Button("Submit") |
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clear_btn = gr.ClearButton([msg, chatbot]) |
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db_btn.click(initialize_database, \ |
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inputs=[document, slider_chunk_size, slider_chunk_overlap], \ |
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outputs=[vector_db, collection_name, db_progress]) |
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qachain_btn.click(initialize_LLM, \ |
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inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \ |
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outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \ |
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inputs=None, \ |
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outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ |
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queue=False) |
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msg.submit(conversation, \ |
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inputs=[qa_chain, msg, chatbot], \ |
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outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ |
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queue=False) |
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submit_btn.click(conversation, \ |
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inputs=[qa_chain, msg, chatbot], \ |
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outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ |
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queue=False) |
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clear_btn.click(lambda:[None,"",0,"",0,"",0], \ |
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inputs=None, \ |
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outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \ |
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queue=False) |
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demo.queue().launch(debug=True) |
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if __name__ == "__main__": |
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demo() |