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| import gradio as gr | |
| import os | |
| from langchain.vectorstores import Chroma # Chroma als Vektordatenbank | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| list_llm = ["google/flan-t5-small", "sentence-transformers/all-MiniLM-L6-v2"] | |
| def load_doc(list_file_path): | |
| loaders = [PyPDFLoader(x) for x in list_file_path] | |
| pages = [] | |
| for loader in loaders: | |
| pages.extend(loader.load()) | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32) | |
| doc_splits = text_splitter.split_documents(pages) | |
| return doc_splits | |
| def create_db(splits): | |
| embeddings = HuggingFaceEmbeddings() | |
| vectordb = Chroma.from_documents(splits, embeddings) | |
| return vectordb | |
| def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db): | |
| llm = HuggingFaceHub( | |
| repo_id=llm_model, | |
| model_kwargs={ | |
| "temperature": temperature, | |
| "max_length": max_tokens, | |
| "top_k": top_k, | |
| } | |
| ) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| retriever = vector_db.as_retriever() | |
| qa_chain = ConversationalRetrievalChain.from_llm( | |
| llm, | |
| retriever=retriever, | |
| chain_type="stuff", | |
| memory=memory, | |
| return_source_documents=True, | |
| verbose=False | |
| ) | |
| return qa_chain | |
| def initialize_database(list_file_obj): | |
| list_file_path = [x.name for x in list_file_obj if x is not None] | |
| doc_splits = load_doc(list_file_path) | |
| vector_db = create_db(doc_splits) | |
| return vector_db, "Datenbank erfolgreich erstellt!" | |
| def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db): | |
| llm_name = list_llm[llm_option] | |
| qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db) | |
| return qa_chain, "LLM erfolgreich initialisiert! Chatbot ist bereit." | |
| def conversation(qa_chain, message, history): | |
| formatted_chat_history = [(f"User: {m}", f"Assistant: {r}") for m, r in history] | |
| response = qa_chain({"question": message, "chat_history": formatted_chat_history}) | |
| response_answer = response["answer"] | |
| new_history = history + [(message, response_answer)] | |
| return qa_chain, gr.update(value=""), new_history | |
| def demo(): | |
| with gr.Blocks() as demo: | |
| vector_db = gr.State() | |
| qa_chain = gr.State() | |
| gr.HTML("<center><h1>RAG PDF Chatbot (Kostenlose Version)</h1></center>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| document = gr.Files(height=300, file_count="multiple", file_types=[".pdf"], interactive=True) | |
| db_btn = gr.Button("Erstelle Vektordatenbank") | |
| db_progress = gr.Textbox(value="Nicht initialisiert", show_label=False) | |
| llm_btn = gr.Radio(["Flan-T5-Small", "MiniLM"], label="Verfügbare Modelle") | |
| slider_temperature = gr.Slider(0.01, 1.0, value=0.5, label="Temperature") | |
| slider_maxtokens = gr.Slider(64, 512, value=256, label="Max Tokens") | |
| qachain_btn = gr.Button("Initialisiere QA-Chatbot") | |
| with gr.Column(): | |
| chatbot = gr.Chatbot(height=400, type="messages") | |
| msg = gr.Textbox(placeholder="Frage stellen...") | |
| submit_btn = gr.Button("Absenden") | |
| db_btn.click(initialize_database, [document], [vector_db, db_progress]) | |
| qachain_btn.click(initialize_LLM, [llm_btn, slider_temperature, slider_maxtokens, vector_db], [qa_chain]) | |
| submit_btn.click(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot]) | |
| demo.launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() | |