chore: import upstream snapshot with attribution
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"""Dynamic A2UI tool: LLM-generated UI from conversation context.
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This module provides the data preparation for a secondary LLM call that
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generates v0.9 A2UI components. The actual LLM call is made by the
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framework-specific wrapper (LangGraph, CrewAI, etc.) since each framework
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has its own way of invoking LLMs.
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Optional
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_logger = logging.getLogger(__name__)
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CUSTOM_CATALOG_ID = "copilotkit://app-dashboard-catalog"
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# The _design_a2ui_surface tool schema that the secondary LLM is bound to.
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DESIGN_A2UI_SURFACE_TOOL_SCHEMA = {
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"name": "_design_a2ui_surface",
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"description": (
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"Render a dynamic A2UI v0.9 surface.\n\n"
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"Args:\n"
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" surfaceId: Unique surface identifier.\n"
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' catalogId: The catalog ID (use "copilotkit://app-dashboard-catalog").\n'
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" components: A2UI v0.9 component array (flat format). "
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'The root component must have id "root".\n'
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" data: Optional initial data model for the surface."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"surfaceId": {
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"type": "string",
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"description": "Unique surface identifier.",
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},
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"catalogId": {"type": "string", "description": "The catalog ID."},
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"components": {
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"type": "array",
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"items": {"type": "object"},
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"description": "A2UI v0.9 component array (flat format).",
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},
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"data": {
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"type": "object",
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"description": "Optional initial data model for the surface.",
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},
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},
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"required": ["surfaceId", "catalogId", "components"],
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},
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}
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def generate_a2ui_impl(
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messages: list[dict[str, Any]],
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context_entries: Optional[list[dict[str, Any]]] = None,
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) -> dict[str, Any]:
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"""Prepare inputs for a secondary LLM call that generates A2UI components.
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Returns a dict with:
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- system_prompt: The system prompt for the secondary LLM (built from context)
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- tool_schema: The _design_a2ui_surface tool schema to bind to the LLM
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- tool_choice: The tool name to force
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- messages: The conversation messages to pass through
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- catalog_id: The default catalog ID
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The framework wrapper should:
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1. Make an LLM call with these inputs
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2. Extract the tool call args (surfaceId, catalogId, components, data)
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3. Build a2ui_operations from the args and return them
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"""
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context_text = ""
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if context_entries:
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context_text = "\n\n".join(
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entry.get("value", "")
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for entry in context_entries
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if isinstance(entry, dict) and entry.get("value")
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)
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return {
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"system_prompt": context_text,
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"tool_schema": DESIGN_A2UI_SURFACE_TOOL_SCHEMA,
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"tool_choice": "_design_a2ui_surface",
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"messages": messages,
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"catalog_id": CUSTOM_CATALOG_ID,
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}
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def build_a2ui_operations_from_tool_call(args: dict[str, Any]) -> dict[str, Any]:
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"""Build a2ui_operations dict from the secondary LLM's tool call args.
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Call this after the framework wrapper extracts the tool call arguments.
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The returned operations use the A2UI v0.9 NESTED shape (mirroring
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`copilotkit.a2ui.create_surface` / `update_components` / `update_data_model`
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from langgraph-python). The `@ag-ui/a2ui-middleware` extracts the
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surfaceId via `op.createSurface?.surfaceId ?? op.updateComponents?.surfaceId
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?? ...` — a flat `{"type": "create_surface", "surfaceId": ...}` shape leaves
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every op under a "default" surface and the renderer never binds to the
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registered catalog.
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"""
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surface_id = args.get("surfaceId", "dynamic-surface")
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catalog_id = args.get("catalogId", CUSTOM_CATALOG_ID)
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components = args.get("components", [])
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if not components:
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_logger.warning(
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"build_a2ui_operations_from_tool_call received empty components list"
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)
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data = args.get("data")
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ops: list[dict[str, Any]] = [
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{
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"version": "v0.9",
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"createSurface": {
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"surfaceId": surface_id,
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"catalogId": catalog_id,
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},
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},
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{
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"version": "v0.9",
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"updateComponents": {
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"surfaceId": surface_id,
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"components": components,
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},
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},
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]
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if data:
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ops.append(
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{
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"version": "v0.9",
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"updateDataModel": {
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"surfaceId": surface_id,
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"path": "/",
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"value": data,
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},
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}
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)
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return {"a2ui_operations": ops}
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