| Stell deng Froen op Lëtzebuergesch, oder an enger anerer Sprooch : | Äntwert vum OpenAI File-Search Assistent : |
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Dës HuggingFace Space Demo gouf vum Marco Barnig realiséiert. Als kënstlech Intelligenz gëtt, mëttels API, den OpenAI Modell gpt-4o-mini-2024-07-18 benotzt, deen als Kontext bis 128.000 Tokens ka benotzen, eng Äntwert op eng Fro vu maximal 16.384 Tokens ka ginn a bis zu 200.000 Tokens pro Minutt (TPM) ka beaarbechten. Fir dës Demo ginn nëmmen 6 News-JSON-Datei mat enger Gréisst vun je 30 MB benotzt. Et ass méiglech bis zu 10.000 Dateien op en OpenAI Assistent opzelueden. D'Äntwerte vun de Beispiller sinn am Cache gespäichert a ginn duerfir ouni Delai ugewise.
""" myinput = gr.Textbox(lines=3, label="Wat wëllt Der wëssen ?") myexamples = [ "Wat war lass am Juni 2023 ?", "Wat ass gewosst iwwert de SREL ?", "Wat fir eng Katastroph war 2022 zu Lëtzebuerg ?", "Koumen an de leschte Jore gréisser Kriminalfäll viru Geriicht ?" ] class EventHandler(AsyncAssistantEventHandler): def __init__(self) -> None: super().__init__() self.response_text = "" async def on_text_created(self, text) -> None: self.response_text += str(text) async def on_text_delta(self, delta, snapshot): self.response_text += str(delta.value) async def on_text_done(self, text): pass async def on_tool_call_created(self, tool_call): self.response_text += f"\n[Tool Call]: {str(tool_call.type)}\n" async def on_tool_call_delta(self, delta, snapshot): if snapshot.id != getattr(self, "current_tool_call", None): self.current_tool_call = snapshot.id self.response_text += f"\n[Tool Call Delta]: {str(delta.type)}\n" if delta.type == 'code_interpreter': if delta.code_interpreter.input: self.response_text += str(delta.code_interpreter.input) if delta.code_interpreter.outputs: self.response_text += "\n\n[Output]:\n" for output in delta.code_interpreter.outputs: if output.type == "logs": self.response_text += f"\n{str(output.logs)}" async def on_tool_call_done(self, text): pass # Initialize session variables session_data = {"assistant_id": assistantID, "thread_id": None} async def initialize_thread(): # Create a Thread thread = await client.beta.threads.create() # Store thread ID in session_data for later use session_data["thread_id"] = thread.id async def generate_response(user_input): assistant_id = session_data["assistant_id"] thread_id = session_data["thread_id"] # Add a Message to the Thread oai_message = await client.beta.threads.messages.create( thread_id=thread_id, role="user", content=user_input ) # Create and Stream a Run event_handler = EventHandler() async with client.beta.threads.runs.stream( thread_id=thread_id, assistant_id=assistant_id, instructions="Please assist the user with their query.", event_handler=event_handler, ) as stream: # Yield incremental updates async for _ in stream: await asyncio.sleep(0.1) # Small delay to mimic streaming yield event_handler.response_text # Gradio interface function (generator) async def gradio_chat_interface(user_input): # Create a new event loop if none exists (or if we are in a new thread) try: loop = asyncio.get_running_loop() except RuntimeError: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Initialize the thread if not already done if session_data["thread_id"] is None: await initialize_thread() # Generate and yield responses async for response in generate_response(user_input): yield response # Set up Gradio interface with streaming interface = gr.Interface( fn=gradio_chat_interface, inputs=myinput, outputs="markdown", title=mytitle, description=mydescription, article=myarticle, live=False, allow_flagging="never", examples=myexamples ) # Launch the Gradio app interface.launch()