Voice

"""LlamaIndex AG-UI AgentUses llama-index-protocols-ag-ui to expose a LlamaIndex workflow as anAG-UI compatible FastAPI router. The router handles all four demoscenarios (agentic-chat, tool-rendering, hitl, gen-ui-tool-based) througha single endpoint since LlamaIndex's get_ag_ui_workflow_router buildsthe full AG-UI protocol surface automatically.NOTE: Uses FixedAGUIChatWorkflow from hitl_in_chat_agent to fix threeupstream library bugs (duplicate tool-call rendering, missingparent_message_id, and incorrect tool-result message roles). Seehitl_in_chat_agent.py module docstring for details."""import jsonimport osfrom typing import Annotatedfrom llama_index.llms.openai import OpenAIfrom llama_index.protocols.ag_ui.router import get_ag_ui_workflow_routerfrom agents.hitl_in_chat_agent import FixedAGUIChatWorkflow# Import shared tool implementationsfrom tools import (    get_weather_impl,    query_data_impl,    manage_sales_todos_impl,    get_sales_todos_impl,    schedule_meeting_impl,    search_flights_impl,    build_a2ui_operations_from_tool_call,)# --- Frontend tools (executed client-side, agent just returns a confirmation) ---def change_background(    background: Annotated[str, "CSS background value. Prefer gradients."],) -> str:    """Change the background color/gradient of the chat area."""    return f"Background changed to {background}"def generate_haiku(    japanese: Annotated[list[str], "3 lines of haiku in Japanese"],    english: Annotated[list[str], "3 lines of haiku translated to English"],    image_name: Annotated[str, "One relevant image name from the valid set"],    gradient: Annotated[str, "CSS Gradient color for the background"],) -> str:    """Generate a haiku with Japanese text, English translation, and a background image."""    return "Haiku generated!"def generate_task_steps(    steps: Annotated[        list[dict],        "Array of step objects with 'description' (string) and 'status' ('enabled' or 'disabled')",    ],) -> str:    """Generate a list of task steps for the user to review and approve."""    return f"Generated {len(steps)} steps for review"def book_call(    topic: Annotated[str, "What the call is about (e.g. 'Intro with sales')"],    attendee: Annotated[str, "Who the call is with (e.g. 'Alice from Sales')"],) -> str:    """Ask the user to pick a time slot for a call. The picker UI presents fixed candidate slots; the user's choice is returned to the agent."""    return f"Booking call about {topic} with {attendee}"def show_card(    title: Annotated[str, "Short heading for the card."],    body: Annotated[str, "Body text for the card."],) -> str:    """Display a titled card with a short body of text. Rendered on the frontend via useComponent."""    return f"Displayed card: {title}"# --- Backend tools (executed server-side, using shared implementations) ---async def get_weather(    location: Annotated[str, "The location to get the weather for."],) -> str:    """Get the weather for a given location. Returns temperature, conditions, humidity, wind speed, and feels-like temperature."""    return json.dumps(get_weather_impl(location))async def query_data(    query: Annotated[str, "Natural language query for financial data."],) -> str:    """Query financial database for chart data. Always call before showing a chart or graph."""    return json.dumps(query_data_impl(query))async def manage_sales_todos(    todos: Annotated[        list[dict], "Complete list of sales todos to replace the current list."    ],) -> str:    """Manage the sales pipeline by replacing the entire list of todos."""    result = manage_sales_todos_impl(todos)    return json.dumps(        {"status": "updated", "count": len(result), "todos": [dict(t) for t in result]}    )async def get_sales_todos_tool() -> str:    """Get the current sales pipeline todos."""    return json.dumps(get_sales_todos_impl(None))async def schedule_meeting(    reason: Annotated[str, "Reason for the meeting."],) -> str:    """Schedule a meeting with the user. Requires human approval."""    return json.dumps(schedule_meeting_impl(reason))async def search_flights(    flights: Annotated[        list[dict],        "List of flight objects to search and display as rich cards. Return exactly 2 flights.",    ],) -> str:    """Search for flights and display the results as rich A2UI cards.    Each flight must have: airline, airlineLogo, flightNumber, origin, destination,    date, departureTime, arrivalTime, duration, status, statusColor, price, currency.    """    result = search_flights_impl(flights)    return json.dumps(result)async def generate_a2ui(    context: Annotated[str, "Conversation context to generate UI from."],) -> str:    """Generate dynamic A2UI components based on the conversation.    A secondary LLM designs the UI schema and data. The result is    returned as an a2ui_operations container for the middleware to detect.    """    from openai import OpenAI    client = OpenAI()    tool_schema = {        "type": "function",        "function": {            "name": "render_a2ui",            "description": "Render a dynamic A2UI v0.9 surface.",            "parameters": {                "type": "object",                "properties": {                    "surfaceId": {"type": "string"},                    "catalogId": {"type": "string"},                    "components": {"type": "array", "items": {"type": "object"}},                    "data": {"type": "object"},                },                "required": ["surfaceId", "catalogId", "components"],            },        },    }    response = client.chat.completions.create(        model="gpt-4.1",        messages=[            {"role": "system", "content": context or "Generate a useful dashboard UI."},            {                "role": "user",                "content": "Generate a dynamic A2UI dashboard based on the conversation.",            },        ],        tools=[tool_schema],        tool_choice={"type": "function", "function": {"name": "render_a2ui"}},    )    if not response.choices[0].message.tool_calls:        return json.dumps({"error": "LLM did not call render_a2ui"})    tool_call = response.choices[0].message.tool_calls[0]    args = json.loads(tool_call.function.arguments)    result = build_a2ui_operations_from_tool_call(args)    return json.dumps(result)_openai_kwargs = {}if os.environ.get("OPENAI_BASE_URL"):    _openai_kwargs["api_base"] = os.environ["OPENAI_BASE_URL"]_AGENT_SYSTEM_PROMPT = (    "You are a polished, professional demo assistant for CopilotKit. "    "Keep responses brief and clear -- 1 to 2 sentences max.\n\n"    "You can:\n"    "- Chat naturally with the user\n"    "- Change the UI background when asked (via frontend tool)\n"    "- Query data and render charts (via query_data tool)\n"    "- Get weather information (via get_weather tool)\n"    "- Schedule meetings with the user (via schedule_meeting tool)\n"    "- Manage sales pipeline todos (via manage_sales_todos / get_sales_todos tools)\n"    "- Search flights and display rich A2UI cards (via search_flights tool)\n"    "- Generate dynamic A2UI dashboards from conversation context (via generate_a2ui tool)\n"    "- Generate step-by-step plans for user review (human-in-the-loop)\n"    "- Book calls with people (via book_call frontend tool)\n"    "- Show titled cards with a body of text (via show_card frontend tool)\n"    "When asked about weather, always use the get_weather tool. "    "When asked about financial data or charts, use query_data first. "    "When asked to book a call, use the book_call tool with topic and name.")async def _agent_workflow_factory():    wf = FixedAGUIChatWorkflow(        llm=OpenAI(model="gpt-4.1", **_openai_kwargs),        frontend_tools=[            change_background,            generate_haiku,            generate_task_steps,            book_call,            show_card,            get_weather,        ],        backend_tools=[            query_data,            manage_sales_todos,            get_sales_todos_tool,            schedule_meeting,            search_flights,            generate_a2ui,        ],        system_prompt=_AGENT_SYSTEM_PROMPT,        initial_state={            "todos": [],        },    )    # Tools that use useRenderTool on the frontend — emit    # TOOL_CALL_RESULT so the render transitions to "complete".    wf.render_only_tool_names = {"get_weather"}    return wfagent_router = get_ag_ui_workflow_router(    workflow_factory=_agent_workflow_factory,)