init

app.py

@@ -0,0 +1,532 @@
+import gradio as gr
+import os
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+
+from pathlib import Path
+import chromadb
+from unidecode import unidecode
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+import tqdm
+import accelerate
+import re
+
+
+# default_persist_directory = './chroma_HF/'
+list_llm = [
+    "mistralai/Mistral-7B-Instruct-v0.2",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "mistralai/Mistral-7B-Instruct-v0.1",
+    "google/gemma-7b-it",
+    "google/gemma-2b-it",
+    "HuggingFaceH4/zephyr-7b-beta",
+    "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+    "meta-llama/Llama-2-7b-chat-hf",
+    "microsoft/phi-2",
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "mosaicml/mpt-7b-instruct",
+    "tiiuae/falcon-7b-instruct",
+    "google/flan-t5-xxl",
+]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+
+# Load PDF document and create doc splits
+def load_doc(list_file_path, chunk_size, chunk_overlap):
+    # Processing for one document only
+    # loader = PyPDFLoader(file_path)
+    # pages = loader.load()
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+
+# Create vector database
+def create_db(splits, collection_name):
+    embedding = HuggingFaceEmbeddings()
+    new_client = chromadb.EphemeralClient()
+    vectordb = Chroma.from_documents(
+        documents=splits,
+        embedding=embedding,
+        client=new_client,
+        collection_name=collection_name,
+        # persist_directory=default_persist_directory
+    )
+    return vectordb
+
+
+# Load vector database
+def load_db():
+    embedding = HuggingFaceEmbeddings()
+    vectordb = Chroma(
+        # persist_directory=default_persist_directory,
+        embedding_function=embedding
+    )
+    return vectordb
+
+
+# Initialize langchain LLM chain
+def initialize_llmchain(
+    llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()
+):
+    progress(0.1, desc="Initializing HF tokenizer...")
+    # HuggingFacePipeline uses local model
+    # Note: it will download model locally...
+    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
+    # progress(0.5, desc="Initializing HF pipeline...")
+    # pipeline=transformers.pipeline(
+    #     "text-generation",
+    #     model=llm_model,
+    #     tokenizer=tokenizer,
+    #     torch_dtype=torch.bfloat16,
+    #     trust_remote_code=True,
+    #     device_map="auto",
+    #     # max_length=1024,
+    #     max_new_tokens=max_tokens,
+    #     do_sample=True,
+    #     top_k=top_k,
+    #     num_return_sequences=1,
+    #     eos_token_id=tokenizer.eos_token_id
+    #     )
+    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
+
+    # HuggingFaceHub uses HF inference endpoints
+    progress(0.5, desc="Initializing HF Hub...")
+    # Use of trust_remote_code as model_kwargs
+    # Warning: langchain issue
+    # URL: https://github.com/langchain-ai/langchain/issues/6080
+    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            load_in_8bit=True,
+        )
+    elif llm_model in [
+        "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+        "mosaicml/mpt-7b-instruct",
+    ]:
+        raise gr.Error(
+            "LLM model is too large to be loaded automatically on free inference endpoint"
+        )
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+        )
+    elif llm_model == "microsoft/phi-2":
+        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            trust_remote_code=True,
+            torch_dtype="auto",
+        )
+    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
+            temperature=temperature,
+            max_new_tokens=250,
+            top_k=top_k,
+        )
+    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
+        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+        )
+    else:
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+        )
+
+    progress(0.75, desc="Defining buffer memory...")
+    memory = ConversationBufferMemory(
+        memory_key="chat_history", output_key="answer", return_messages=True
+    )
+    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
+    retriever = vector_db.as_retriever()
+    progress(0.8, desc="Defining retrieval chain...")
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        # combine_docs_chain_kwargs={"prompt": your_prompt})
+        return_source_documents=True,
+        # return_generated_question=False,
+        verbose=False,
+    )
+    progress(0.9, desc="Done!")
+    return qa_chain
+
+
+# Generate collection name for vector database
+#  - Use filepath as input, ensuring unicode text
+def create_collection_name(filepath):
+    # Extract filename without extension
+    collection_name = Path(filepath).stem
+    # Fix potential issues from naming convention
+    ## Remove space
+    collection_name = collection_name.replace(" ", "-")
+    ## ASCII transliterations of Unicode text
+    collection_name = unidecode(collection_name)
+    ## Remove special characters
+    # collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
+    collection_name = re.sub("[^A-Za-z0-9]+", "-", collection_name)
+    ## Limit length to 50 characters
+    collection_name = collection_name[:50]
+    ## Minimum length of 3 characters
+    if len(collection_name) < 3:
+        collection_name = collection_name + "xyz"
+    ## Enforce start and end as alphanumeric character
+    if not collection_name[0].isalnum():
+        collection_name = "A" + collection_name[1:]
+    if not collection_name[-1].isalnum():
+        collection_name = collection_name[:-1] + "Z"
+    print("Filepath: ", filepath)
+    print("Collection name: ", collection_name)
+    return collection_name
+
+
+# Initialize database
+def initialize_database(
+    list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()
+):
+    # Create list of documents (when valid)
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    # Create collection_name for vector database
+    progress(0.1, desc="Creating collection name...")
+    collection_name = create_collection_name(list_file_path[0])
+    progress(0.25, desc="Loading document...")
+    # Load document and create splits
+    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
+    # Create or load vector database
+    progress(0.5, desc="Generating vector database...")
+    # global vector_db
+    vector_db = create_db(doc_splits, collection_name)
+    progress(0.9, desc="Done!")
+    return vector_db, collection_name, "Complete!"
+
+
+def initialize_LLM(
+    llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()
+):
+    # print("llm_option",llm_option)
+    llm_name = list_llm[llm_option]
+    print("llm_name: ", llm_name)
+    qa_chain = initialize_llmchain(
+        llm_name, llm_temperature, max_tokens, top_k, vector_db, progress
+    )
+    return qa_chain, "Complete!"
+
+
+def format_chat_history(message, chat_history):
+    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):
+    formatted_chat_history = format_chat_history(message, history)
+    # print("formatted_chat_history",formatted_chat_history)
+
+    # Generate response using QA chain
+    response = qa_chain({"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"]
+    response_source1 = response_sources[0].page_content.strip()
+    response_source2 = response_sources[1].page_content.strip()
+    response_source3 = response_sources[2].page_content.strip()
+    # Langchain sources are zero-based
+    response_source1_page = response_sources[0].metadata["page"] + 1
+    response_source2_page = response_sources[1].metadata["page"] + 1
+    response_source3_page = response_sources[2].metadata["page"] + 1
+    # print ('chat response: ', response_answer)
+    # print('DB source', response_sources)
+
+    # Append user message and response to chat history
+    new_history = history + [(message, response_answer)]
+    # return gr.update(value=""), new_history, response_sources[0], response_sources[1]
+    return (
+        qa_chain,
+        gr.update(value=""),
+        new_history,
+        response_source1,
+        response_source1_page,
+        response_source2,
+        response_source2_page,
+        response_source3,
+        response_source3_page,
+    )
+
+
+def upload_file(file_obj):
+    list_file_path = []
+    for idx, file in enumerate(file_obj):
+        file_path = file_obj.name
+        list_file_path.append(file_path)
+    # print(file_path)
+    # initialize_database(file_path, progress)
+    return list_file_path
+
+
+def demo():
+    with gr.Blocks(theme="base") as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        collection_name = gr.State()
+
+        gr.Markdown(
+            """<center><h2>PDF-based chatbot</center></h2>
+        <h3>Ask any questions about your PDF documents</h3>"""
+        )
+        gr.Markdown(
+            """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
+        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
+        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
+        <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 a reply.
+        """
+        )
+
+        with gr.Tab("Step 1 - Upload PDF"):
+            with gr.Row():
+                document = gr.Files(
+                    height=100,
+                    file_count="multiple",
+                    file_types=["pdf"],
+                    interactive=True,
+                    label="Upload your PDF documents (single or multiple)",
+                )
+                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
+
+        with gr.Tab("Step 2 - Process document"):
+            with gr.Row():
+                db_btn = gr.Radio(
+                    ["ChromaDB"],
+                    label="Vector database type",
+                    value="ChromaDB",
+                    type="index",
+                    info="Choose your vector database",
+                )
+            with gr.Accordion("Advanced options - Document text splitter", open=False):
+                with gr.Row():
+                    slider_chunk_size = gr.Slider(
+                        minimum=100,
+                        maximum=1000,
+                        value=600,
+                        step=20,
+                        label="Chunk size",
+                        info="Chunk size",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_chunk_overlap = gr.Slider(
+                        minimum=10,
+                        maximum=200,
+                        value=40,
+                        step=10,
+                        label="Chunk overlap",
+                        info="Chunk overlap",
+                        interactive=True,
+                    )
+            with gr.Row():
+                db_progress = gr.Textbox(
+                    label="Vector database initialization", value="None"
+                )
+            with gr.Row():
+                db_btn = gr.Button("Generate vector database")
+
+        with gr.Tab("Step 3 - Initialize QA chain"):
+            with gr.Row():
+                llm_btn = gr.Radio(
+                    list_llm_simple,
+                    label="LLM models",
+                    value=list_llm_simple[0],
+                    type="index",
+                    info="Choose your LLM model",
+                )
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature = gr.Slider(
+                        minimum=0.01,
+                        maximum=1.0,
+                        value=0.7,
+                        step=0.1,
+                        label="Temperature",
+                        info="Model temperature",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_maxtokens = gr.Slider(
+                        minimum=224,
+                        maximum=4096,
+                        value=1024,
+                        step=32,
+                        label="Max Tokens",
+                        info="Model max tokens",
+                        interactive=True,
+                    )
+                with gr.Row():
+                    slider_topk = gr.Slider(
+                        minimum=1,
+                        maximum=10,
+                        value=3,
+                        step=1,
+                        label="top-k samples",
+                        info="Model top-k samples",
+                        interactive=True,
+                    )
+            with gr.Row():
+                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
+            with gr.Row():
+                qachain_btn = gr.Button("Initialize Question Answering chain")
+
+        with gr.Tab("Step 4 - Chatbot"):
+            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 (e.g. 'What is this document about?')",
+                    container=True,
+                )
+            with gr.Row():
+                submit_btn = gr.Button("Submit message")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+
+        # Preprocessing events
+        # upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
+        db_btn.click(
+            initialize_database,
+            inputs=[document, slider_chunk_size, slider_chunk_overlap],
+            outputs=[vector_db, collection_name, db_progress],
+        )
+        qachain_btn.click(
+            initialize_LLM,
+            inputs=[
+                llm_btn,
+                slider_temperature,
+                slider_maxtokens,
+                slider_topk,
+                vector_db,
+            ],
+            outputs=[qa_chain, llm_progress],
+        ).then(
+            lambda: [None, "", 0, "", 0, "", 0],
+            inputs=None,
+            outputs=[
+                chatbot,
+                doc_source1,
+                source1_page,
+                doc_source2,
+                source2_page,
+                doc_source3,
+                source3_page,
+            ],
+            queue=False,
+        )
+
+        # Chatbot events
+        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,
+            ],
+            queue=False,
+        )
+        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,
+            ],
+            queue=False,
+        )
+        clear_btn.click(
+            lambda: [None, "", 0, "", 0, "", 0],
+            inputs=None,
+            outputs=[
+                chatbot,
+                doc_source1,
+                source1_page,
+                doc_source2,
+                source2_page,
+                doc_source3,
+                source3_page,
+            ],
+            queue=False,
+        )
+    demo.queue().launch(debug=True)
+
+
+if __name__ == "__main__":
+    demo()

requirements.txt

@@ -0,0 +1,9 @@
+torch
+transformers
+sentence-transformers
+langchain
+tqdm
+accelerate
+pypdf
+chromadb
+unidecode