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app.py

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+import gradio as gr
+import os
+from dotenv import load_dotenv
+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.llms import HuggingFaceHub
+from pathlib import Path
+import chromadb
+load_dotenv()
+huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# default_persist_directory = './chroma_HF/'
+list_llm = ["mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
+    "google/gemma-7b-it","google/gemma-2b-it", \
+    "HuggingFaceH4/zephyr-7b-beta", \
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "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
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = chunk_size, 
+        chunk_overlap = chunk_overlap)
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+def load_doc_for_openai(list_file_path):
+    # Processing for one document only
+    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 = 40)
+    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( 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...")    
+    # 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 = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
+        )
+    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
+        llm = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
+        )
+    else:
+        llm = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"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
+    )
+    progress(0.8, desc="Defining retrieval chain...")
+    retriever = vector_db.as_retriever()
+    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
+
+
+# Initialize database
+def initialize_database(list_file_obj, chunk_size, chunk_overlap, vector_db, 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 = Path(list_file_path[0]).stem
+    # Fix potential issues from naming convention
+    ## Remove space
+    collection_name = collection_name.replace(" ","-") 
+    ## Limit lenght to 50 characters
+    collection_name = collection_name[:50]
+    ## Enforce start and end as alphanumeric character
+    if not collection_name[0].isalnum():
+        collection_name[0] = 'A'
+    if not collection_name[-1].isalnum():
+        collection_name[-1] = 'Z'
+    # print('list_file_path: ', list_file_path)
+    print('Collection name: ', collection_name)
+    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(0.7, desc="Generating vector database...")
+    # global vector_db
+    vector_db = create_db(doc_splits, collection_name)
+    return vector_db, collection_name, "Complete!"
+
+
+def re_initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
+    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)
+    return qa_chain
+
+
+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, llm_option):
+    formatted_chat_history = format_chat_history(message, 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]
+    new_history = history + [(message, response_answer)]
+    return qa_chain, gr.update(value = ""), new_history
+    
+
+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)
+    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(
+        '''
+        <div style="text-align:center;">
+            <span style="font-size:3em; font-weight:bold;">PDF Document Chatbot</span>
+        </div>
+        ''')
+        with gr.Row():
+            with gr.Row():
+                with gr.Column():
+                    document = gr.Files(file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
+                    with gr.Row():
+                        db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database", visible = False)
+                    with gr.Accordion("Advanced options - Document text splitter", open=False, visible = 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, visible = False)
+                        with gr.Row():
+                            slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True, visible = False)
+                    llm_btn = gr.Radio(list_llm_simple, label = "LLM models", type = "index", info = "Choose your LLM model")
+                    db_progress = gr.Textbox(label="Vector database initialization", value="None")
+                    with gr.Row():
+                        submit_file = gr.Button("Submit File")
+            with gr.Row():
+                with gr.Column():
+                    chatbot = gr.Chatbot()
+                    msg = gr.Textbox(placeholder = "Type Your Message")
+                    with gr.Accordion("Advanced options - LLM model", open = False):
+                        with gr.Row():
+                            slider_temperature = gr.Slider(minimum = 0.0, 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():
+                        submit_btn = gr.Button("Submit")
+                        # clear_btn = gr.ClearButton([msg2, chatbot])
+        # Preprocessing events
+        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
+        submit_file.click(initialize_database, \
+            inputs=[document, slider_chunk_size, slider_chunk_overlap, vector_db], \
+            outputs = [vector_db, collection_name, db_progress])
+        llm_btn.change(
+            re_initialize_LLM, \
+            inputs = [llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
+            outputs = [qa_chain]
+        )
+        msg.submit(conversation, \
+            inputs=[qa_chain, msg, chatbot, llm_btn], \
+            outputs=[qa_chain, msg, chatbot], \
+            queue=False)
+        submit_btn.click(conversation, \
+            inputs=[qa_chain, msg, chatbot, llm_btn], \
+            outputs=[qa_chain, msg, chatbot], \
+            queue=False)
+    demo.queue().launch(share = True, debug = True)
+
+
+if __name__ == "__main__":
+    demo()

requirements.txt

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+torch
+transformers
+sentence-transformers
+langchain
+tqdm
+accelerate
+pypdf
+chromadb
+langchain-community
+weasyprint
+openai
+tiktoken
+pypdf
+pdf2image
+gradio