Release 0.5.5 (#564 )

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
fix: missing default export (#563 )
2026-07-19 15:03:35 -04:00 · 2025-04-15 20:55:53 +07:00 · 2025-04-15 20:54:23 +07:00 · 2025-04-15 18:28:11 +07:00 · 2025-04-15 18:23:06 +07:00 · 2025-04-15 17:32:13 +07:00
19 changed files with 1005 additions and 357 deletions
@@ -2,8 +2,12 @@ name: E2E Tests
 on:
  push:
    branches: [main]
+    paths-ignore:
+      - "llama-index-server/**"
  pull_request:
    branches: [main]
+    paths-ignore:
+      - "llama-index-server/**"

 env:
  POETRY_VERSION: "1.6.1"
@@ -1,5 +1,17 @@
 # create-llama

+## 0.5.5
+
+### Patch Changes
+
+- ee85320: The default custom deep research component does not work.
+
+## 0.5.4
+
+### Patch Changes
+
+- 7c3b279: Support code generation of event components using an LLM (Python)
+
 ## 0.5.3

 ### Patch Changes
@@ -14,27 +14,33 @@ Custom UI components are a powerful feature that enables you to:

 ### Workflow events

-Your workflow must emit events that fit this structure, allowing the LlamaIndex server to display the right UI components based on the event type.
-
-```json
-{
-    "type": "<event_name>",
-    "data": <data model>
-}
-```
-
-In Pydantic, this is equivalent to:
+To display custom UI components, your workflow needs to emit `UIEvent` events with data that conforms to the data model of your custom UI component.

 ```python
-from pydantic import BaseModel
+from llama_index.server import UIEvent
+from pydantic import BaseModel, Field
 from typing import Literal, Any

-class MyCustomEvent(BaseModel):
-    type: Literal["<my_custom_event_name>"]
-    data: dict | Any
+# Define a Pydantic model for your event data
+class DeepResearchEventData(BaseModel):
+    id: str = Field(description="The unique identifier for the event")
+    type: Literal["retrieval", "analysis"] = Field(description="DeepResearch has two main stages: retrieval and analysis")
+    status: Literal["pending", "completed", "failed"] = Field(description="The current status of the event")
+    content: str = Field(description="The textual content of the event")

-    def to_response(self):
-        return self.model_dump()
+
+# In your workflow, emit the data model with UIEvent
+ctx.write_event_to_stream(
+    UIEvent(
+        type="deep_research_event",
+        data=DeepResearchEventData(
+            id="123",
+            type="retrieval",
+            status="pending",
+            content="Retrieving data...",
+        ),
+    )
+)
 ```

 ### Server Setup
@@ -67,3 +73,31 @@ server = LlamaIndexServer(
       );
   }
   ```
+
+### Generate UI Component
+
+We provide a `generate_event_component` function that uses LLMs to automatically generate UI components for your workflow events.
+
+```python
+from llama_index.server.gen_ui import generate_event_component
+from llama_index.llms.openai import OpenAI
+
+# Generate a component using the event class you defined in your workflow
+from your_workflow import DeepResearchEvent
+ui_code = await generate_event_component(
+    event_cls=DeepResearchEvent,
+    llm=OpenAI(model="gpt-4.1"), # Default LLM is Claude 3.7 Sonnet if not provided
+)
+
+# Alternatively, generate from your workflow file
+ui_code = await generate_event_component(
+    workflow_file="your_workflow.py",
+)
+print(ui_code)
+
+# Save the generated code to a file for use in your project
+with open("deep_research_event.jsx", "w") as f:
+    f.write(ui_code)
+```
+
+> **Tip:** For optimal results, add descriptive documentation to each field in your event data class. This helps the LLM better understand your data structure and generate more appropriate UI components. We also recommend using GPT 4.1, Claude 3.7 Sonnet and Gemini 2.5 Pro for better results.
@@ -1,3 +1,4 @@
+from .api.models import UIEvent
 from .server import LlamaIndexServer, UIConfig

-__all__ = ["LlamaIndexServer", "UIConfig"]
+__all__ = ["LlamaIndexServer", "UIConfig", "UIEvent"]
@@ -140,3 +140,14 @@ class ComponentDefinition(BaseModel):
    type: str
    code: str
    filename: str
+
+
+class UIEvent(Event):
+    type: str
+    data: BaseModel
+
+    def to_response(self) -> dict:
+        return {
+            "type": self.type,
+            "data": self.data.model_dump(),
+        }
@@ -5,7 +5,7 @@ from typing import Optional

 import requests

-CHAT_UI_VERSION = "0.1.0"
+CHAT_UI_VERSION = "0.1.2"


 def download_chat_ui(
@@ -0,0 +1,4 @@
+from .main import generate_event_component
+from .parse_workflow_code import get_workflow_event_schemas
+
+__all__ = ["generate_event_component", "get_workflow_event_schemas"]
@@ -0,0 +1,424 @@
+import re
+from typing import Any, Dict, List, Optional, Type
+
+from llama_index.core.llms import LLM
+from llama_index.core.prompts import PromptTemplate
+from llama_index.core.workflow import (
+    Context,
+    Event,
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+)
+from llama_index.server.gen_ui.parse_workflow_code import get_workflow_event_schemas
+from pydantic import BaseModel
+from rich.console import Console
+from rich.live import Live
+from rich.panel import Panel
+
+
+class PlanningEvent(Event):
+    """
+    Event for planning the UI.
+    """
+
+    events: List[Dict[str, Any]]
+
+
+class WriteAggregationEvent(Event):
+    """
+    Event for aggregating events.
+    """
+
+    events: List[Dict[str, Any]]
+    ui_description: str
+
+
+class WriteUIComponentEvent(Event):
+    """
+    Event for writing UI component.
+    """
+
+    events: List[Dict[str, Any]]
+    aggregation_function: Optional[str]
+    ui_description: str
+
+
+class RefineGeneratedCodeEvent(Event):
+    """
+    Refine the generated code.
+    """
+
+    generated_code: str
+    aggregation_function_context: Optional[str]
+    events: List[Dict[str, Any]]
+
+
+class ExtractEventSchemaEvent(Event):
+    """
+    Extract the event schema from the event.
+    """
+
+    events: List[Any]
+
+
+class AggregatePrediction(BaseModel):
+    """
+    Prediction for aggregating events or not.
+    If need_aggregation is True, the aggregation_function will be provided.
+    """
+
+    need_aggregation: bool
+    aggregation_function: Optional[str]
+
+
+class GenUIWorkflow(Workflow):
+    """
+    Generate UI component for event from workflow.
+    """
+
+    code_structure: str = """
+        ```jsx
+            // Note: Only shadcn/ui and lucide-react and tailwind css are allowed.
+            // shadcn import pattern: import { ComponentName } from "@/components/ui/<component_path>";
+            // e.g: import { Card, CardHeader, CardTitle, CardContent } from "@/components/ui/card";
+            //      import { Button } from "@/components/ui/button";
+            //      import cn from "@/lib/utils"; // clsx is not supported
+
+            // export the component
+            export default function Component({ events }) {
+                // logic for aggregating events (if needed)
+                const aggregateEvents = () => {
+                    // code for aggregating events here
+                }
+            
+                // State handling
+                // e.g: const [state, setState] = useState({});
+
+                return (
+                    // UI code here
+                )
+            }
+        ```
+    """
+
+    def __init__(self, llm: LLM, **kwargs: Any):
+        super().__init__(**kwargs)
+        self.llm = llm
+        self.console = Console()
+        self._live: Optional[Live] = None
+        self._completed_steps: List[str] = []
+        self._current_step: Optional[str] = None
+
+    def update_status(self, message: str, completed: bool = False) -> None:
+        """Show completed and current steps in a panel."""
+        if completed:
+            if self._current_step:
+                self._completed_steps.append(self._current_step)
+            self._current_step = None
+        else:
+            self._current_step = message
+
+        if self._live is None:
+            self._live = Live("", console=self.console, refresh_per_second=4)
+            self._live.start()
+
+        # Build status display
+        status_lines = []
+        for completed_step in self._completed_steps:
+            status_lines.append(f"[green]✓[/green] {completed_step}")
+        if self._current_step:
+            status_lines.append(f"[yellow]⋯[/yellow] {self._current_step}")
+
+        self._live.update(Panel("\n".join(status_lines)))
+
+    @step
+    async def start(self, ctx: Context, ev: StartEvent) -> PlanningEvent:
+        events = ev.events
+        if not events:
+            raise ValueError(
+                "events is required, provide list of filtered events to generate UI components for"
+            )
+        await ctx.set("events", events)
+        self.update_status("Planning the UI")
+        return PlanningEvent(events=events)
+
+    @step
+    async def planning(self, ctx: Context, ev: PlanningEvent) -> WriteAggregationEvent:
+        prompt_template = """
+            # Your role
+            You are a designer who is designing a UI for given events that are emitted from a backend workflow.
+            Here are the events that you need to work on: {events}
+
+            # Task
+            Your task is to analyze the event schema and data and provide a description that how the UI would look like.
+            The UI should be beautiful, no monotonous, and visually pleasing.
+            Focus on the elements and the layout, don't ask too much on the styles (transition, dark mode, responsive, etc...).
+
+            e.g: Assume that the backend produce list of events with animal name, action, and status.
+            ```
+            A card-based layout displaying animal actions:
+            - Each card shows an animal's image at the top
+            - Below the image: animal name as the card title
+            - Action details in the card body with an icon (eating 🍖, sleeping 😴, playing 🎾)
+            - Status badge in the corner showing if action is ongoing/completed
+            - Expandable section for additional details
+            - Soft color scheme based on action type
+            ```
+            Don't be verbose, just return the description for the UI based on the event schema and data.
+        """
+        response = await self.llm.acomplete(
+            PromptTemplate(prompt_template).format(events=ev.events),
+            formatted=True,
+        )
+        await ctx.set("ui_description", response.text)
+        self.update_status("Planning the UI", completed=True)
+        # Update the planning description to the console
+        self.console.print(
+            Panel(
+                response.text,
+                title="UI Description",
+                border_style="cyan",
+            )
+        )
+        self.update_status("Generating aggregation function")
+        return WriteAggregationEvent(
+            events=ev.events,
+            ui_description=response.text,
+        )
+
+    @step
+    async def generate_event_aggregations(
+        self, ctx: Context, ev: WriteAggregationEvent
+    ) -> WriteUIComponentEvent:
+        prompt_template = """
+            # Your role
+            You are a frontend developer who is developing a React component for given events that are emitted from a backend workflow.
+            Here are the events that you need to work on: {events}
+            Here is the description of the UI:
+            ```
+                {ui_description}
+            ```
+
+            # Task
+            Based on the description of the UI and the list of events, write the aggregation function that will be used to aggregate the events.
+            Take into account that the list of events grows with time. At the beginning, there is only one event in the list, and events are incrementally added. 
+            To render the events in a visually pleasing way, try to aggregate them by their attributes and render the aggregates instead of just rendering a list of all events.
+            Don't add computation to the aggregation function, just group the events by their attributes.
+            Make sure that the aggregation should reflect the description of the UI and the grouped events are not duplicated, make it as simple as possible to avoid unnecessary issues.
+
+            # Answer with the following format:
+            ```jsx
+            const aggregateEvents = () => {
+                // code for aggregating events here if needed otherwise let the jsx code block empty
+            }
+            ```
+            """
+
+        response = await self.llm.acomplete(
+            PromptTemplate(prompt_template).format(
+                events=ev.events,
+                ui_description=ev.ui_description,
+            ),
+            formatted=True,
+        )
+        await ctx.set("aggregation_context", response.text)
+
+        self.update_status("Generating aggregation function", completed=True)
+        self.update_status("Generating UI components")
+        return WriteUIComponentEvent(
+            events=ev.events,
+            aggregation_function=response.text,
+            ui_description=ev.ui_description,
+        )
+
+    @step
+    async def write_ui_component(
+        self, ctx: Context, ev: WriteUIComponentEvent
+    ) -> RefineGeneratedCodeEvent:
+        prompt_template = """
+            # Your role
+            You are a frontend developer who is developing a React component using shadcn/ui (@/components/ui/<component_name>) and lucide-react for the UI.
+            You are given a list of events and other context.
+            Your task is to write a beautiful UI for the events that will be included in a chat UI.
+
+            # Context:
+            Here are the events that you need to work on: {events}
+            {aggregation_function_context}
+            Here is the description of the UI:
+            ```
+                {ui_description}
+            ```
+
+            # Requirements:
+            - Write beautiful UI components for the events using shadcn/ui and lucide-react.
+            - The component text/label should be specified for each event type.
+
+            # Instructions:
+            ## Event and schema notice
+            - Based on the provided list of events, determine their types and attributes.
+            - It's normal that the schema is applied to all events, but the events might completely different which some of schema attributes aren't used.
+            - You should make the component visually distinct for each event type.
+              e.g: A simple cat schema
+                ```{"type": "cat", "action": ["jump", "run", "meow"], "jump": {"height": 10, "distance": 20}, "run": {"distance": 100}}```
+                You should display the jump, run and meow actions in different ways. don't try to render "height" for the "run" and "meow" action.
+
+            ## UI notice
+            - Use shadcn/ui and lucide-react and tailwind CSS for the UI.
+            - Be careful on state handling, make sure the update should be updated in the state and there is no duplicate state.
+            - For a long content, consider to use markdown along with dropdown to show the full content.
+                e.g:
+                ```jsx
+                import { Markdown } from "@llamaindex/chat-ui/widgets";
+                <Markdown content={content} />
+                ```
+            - Try to make the component placement not monotonous, consider use row/column/flex/grid layout.
+            """
+
+        aggregation_function_context = (
+            f"\nBefore rendering the events, we're using the following aggregation function: {ev.aggregation_function}"
+            if ev.aggregation_function
+            else ""
+        )
+
+        prompt = PromptTemplate(prompt_template).format(
+            events=ev.events,
+            aggregation_function_context=aggregation_function_context,
+            code_structure=self.code_structure,
+            ui_description=ev.ui_description,
+        )
+        response = await self.llm.acomplete(prompt, formatted=True)
+
+        self.update_status("Generating UI components", completed=True)
+        self.update_status("Refining generated code")
+        return RefineGeneratedCodeEvent(
+            generated_code=response.text,
+            events=ev.events,
+            aggregation_function_context=aggregation_function_context,
+        )
+
+    @step
+    async def refine_code(
+        self, ctx: Context, ev: RefineGeneratedCodeEvent
+    ) -> StopEvent:
+        prompt_template = """
+            # Your role
+            You are a frontend developer who is developing a React component for given events that are emitted from a backend workflow.
+            Your task is to assemble the pieces of code into a complete code segment that follows the specified code structure.
+
+            # Context:
+            ## Here is the generated code:
+            {generated_code}
+
+            {aggregation_function_context}
+
+            ## The generated code should follow the following structure:
+            {code_structure}
+
+            # Requirements:
+            - Refine the code if needed to ensure there are no potential bugs. 
+            - Be careful on code placement, make sure it doesn't call any undefined code.
+            - Make sure the import statements are correct. 
+              e.g: import { Button, Card, Accordion } from "@/components/ui" is correct because Button, Card are defined in different shadcn/ui components.
+              -> correction: import { Button } from "@/components/ui/button";
+                             import { Card } from "@/components/ui/card";
+            - Don't be verbose, only return the code, wrap it in ```jsx <code>```
+        """
+        prompt = PromptTemplate(prompt_template).format(
+            generated_code=ev.generated_code,
+            code_structure=self.code_structure,
+            aggregation_function_context=ev.aggregation_function_context,
+        )
+
+        response = await self.llm.acomplete(prompt, formatted=True)
+
+        # Extract code from response, handling case where code block is missing
+        code_match = re.search(r"```jsx(.*)```", response.text, re.DOTALL)
+        if code_match is None:
+            # If no code block found, use full response
+            code = response.text
+        else:
+            code = code_match.group(1).strip()
+
+        self.update_status("Refining generated code", completed=True)
+        if self._live is not None:
+            self._live.stop()
+            self._live = None
+
+        return StopEvent(
+            result=code,
+        )
+
+
+async def generate_event_component(
+    workflow_file: Optional[str] = None,
+    event_cls: Optional[Type[BaseModel]] = None,
+    llm: Optional[LLM] = None,
+) -> str:
+    """
+    Generate UI component for events from workflow.
+    Either workflow_file or event_cls must be provided.
+
+    Args:
+        workflow_file: The path to the workflow file that contains the event to generate UI for. e.g: `app/workflow.py`.
+        event_cls: A Pydantic class to generate UI for. e.g: `DeepResearchEvent`.
+        llm: The LLM to use for the generation. Default is Anthropic's Claude 3.7 Sonnet.
+             We recommend using these LLMs:
+                - Anthropic's Claude 3.7 Sonnet
+                - OpenAI's GPT-4.1
+                - Google Gemini 2.5 Pro
+    Returns:
+        The generated UI component code.
+    """
+    if workflow_file is None and event_cls is None:
+        raise ValueError(
+            "Either workflow_file or event_cls must be provided. Please provide one of them."
+        )
+    if workflow_file is not None and event_cls is not None:
+        raise ValueError(
+            "Only one of workflow_file or event_cls can be provided. Please provide only one of them."
+        )
+    if llm is None:
+        from llama_index.llms.anthropic import Anthropic
+
+        llm = Anthropic(model="claude-3-7-sonnet-latest", max_tokens=8192)
+
+    console = Console()
+
+    # Get event schemas
+    if workflow_file is not None:
+        # Get event schemas from the input file
+        console.rule("[bold blue]Analyzing Events[/bold blue]")
+        event_schemas = get_workflow_event_schemas(workflow_file)
+        if len(event_schemas) == 0:
+            console.print(
+                Panel(
+                    "[red]No events found that are used with write_event_to_stream[/red]",
+                    title="❌ Error",
+                    border_style="red",
+                )
+            )
+            raise RuntimeError(
+                "No events found that are used with write_event_to_stream. Please check the workflow file."
+            )
+    elif event_cls is not None:
+        event_schemas = [
+            {"type": event_cls.__name__, "schema": event_cls.model_json_schema()}
+        ]
+
+    # Generate UI component from event schemas
+    console.rule("[bold blue]Generate UI Components[/bold blue]")
+
+    workflow = GenUIWorkflow(llm=llm, timeout=500.0)
+    code = await workflow.run(events=event_schemas)
+
+    console.print(
+        Panel(
+            "[green]UI component has been generated successfully![/green]\n",
+            title="✨ Complete",
+            border_style="green",
+        )
+    )
+
+    return code
@@ -0,0 +1,93 @@
+import ast
+import importlib
+import inspect
+import os
+import sys
+from typing import Any, Dict, List
+
+
+class EventAnalyzer(ast.NodeVisitor):
+    """
+    Parse the workflow code to find UIEvent instances passed to write_event_to_stream.
+    """
+
+    def __init__(self) -> None:
+        self.found_ui_event = False
+
+    def visit_Call(self, node: ast.Call) -> None:
+        # Check for ctx.write_event_to_stream call with UIEvent arg
+        if (
+            isinstance(node.func, ast.Attribute)
+            and isinstance(node.func.value, ast.Name)
+            and node.func.attr == "write_event_to_stream"
+            and node.args
+            and isinstance(node.args[0], ast.Call)
+            and isinstance(node.args[0].func, ast.Name)
+            and node.args[0].func.id == "UIEvent"
+        ):
+            self.found_ui_event = True
+
+        self.generic_visit(node)
+
+
+def get_workflow_event_schemas(file_path: str) -> List[Dict[str, Any]]:
+    """
+    Find UIEvent instances passed to write_event_to_stream and return their data type schema.
+    """
+    # Get absolute path for module importing
+    abs_file_path = os.path.abspath(file_path)
+    project_root = os.path.dirname(os.path.dirname(abs_file_path))
+
+    # Convert file path to module name
+    rel_path = os.path.relpath(abs_file_path, project_root)
+    module_name = rel_path.replace(os.sep, ".").replace(".py", "")
+
+    # Temporarily modify sys.path to allow imports
+    original_path = list(sys.path)
+    if project_root not in sys.path:
+        sys.path.insert(0, project_root)
+
+    try:
+        # Import the module
+        module = importlib.import_module(module_name)
+        importlib.reload(module)
+    except ImportError as e:
+        print(f"Error importing module {module_name}: {e}")
+        sys.path = original_path
+        return []
+    finally:
+        # Restore original path
+        if project_root in sys.path and project_root not in original_path:
+            sys.path.remove(project_root)
+
+    # Parse the file to check for UIEvent usage
+    try:
+        with open(file_path, "r") as f:
+            tree = ast.parse(f.read())
+    except (FileNotFoundError, SyntaxError) as e:
+        print(f"Error parsing {file_path}: {e}")
+        return []
+
+    # Check if UIEvent is passed to write_event_to_stream
+    analyzer = EventAnalyzer()
+    analyzer.visit(tree)
+
+    schema_list = []
+
+    # Only proceed if UIEvent was found and the module has the class
+    if analyzer.found_ui_event and hasattr(module, "UIEvent"):
+        # Look for class names containing "EventData" in the module
+        for name, obj in inspect.getmembers(module):
+            if (
+                inspect.isclass(obj)
+                and name.endswith("EventData")
+                and hasattr(obj, "model_json_schema")
+            ):
+                try:
+                    schema = obj.model_json_schema()
+                    if schema:
+                        schema_list.append(schema)
+                except Exception:
+                    pass
+
+    return schema_list
@@ -935,37 +935,37 @@ typing-inspect = ">=0.4.0,<1"

 [[package]]
 name = "debugpy"
-version = "1.8.13"
+version = "1.8.14"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "debugpy-1.8.13-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:06859f68e817966723ffe046b896b1bd75c665996a77313370336ee9e1de3e90"},
-    {file = "debugpy-1.8.13-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb56c2db69fb8df3168bc857d7b7d2494fed295dfdbde9a45f27b4b152f37520"},
-    {file = "debugpy-1.8.13-cp310-cp310-win32.whl", hash = "sha256:46abe0b821cad751fc1fb9f860fb2e68d75e2c5d360986d0136cd1db8cad4428"},
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 typing-extensions = ">=4.1.0"

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 [[package]]
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-version = "4.0.13"
+version = "4.0.14"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
-    {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
+    {file = "widgetsnbextension-4.0.14-py3-none-any.whl", hash = "sha256:4875a9eaf72fbf5079dc372a51a9f268fc38d46f767cbf85c43a36da5cb9b575"},
+    {file = "widgetsnbextension-4.0.14.tar.gz", hash = "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af"},
 ]

 [[package]]
@@ -26,7 +26,7 @@ license = "MIT"
 name = "llama-index-server"
 packages = [{include = "llama_index/"}]
 readme = "README.md"
-version = "0.1.10"
+version = "0.1.12"

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
@@ -62,3 +62,4 @@ types-setuptools = "67.1.0.0"
 xhtml2pdf = "^0.2.17"
 pytest-cov = "^6.0.0"
 llama-cloud = "^0.1.17"
+
@@ -1,6 +1,6 @@
 {
  "name": "create-llama",
-  "version": "0.5.3",
+  "version": "0.5.5",
  "description": "Create LlamaIndex-powered apps with one command",
  "keywords": [
    "rag",
@@ -1,4 +1,4 @@
-function Component({ events }) {
+export default function DeepResearchComponent({ events }) {
  // Aggregate events by type and track their state progression
  const aggregateEvents = () => {
    const retrieveEvents = events.filter((e) => e.event === "retrieve");
@@ -49,6 +49,16 @@ curl --location 'localhost:8000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Create a report comparing the finances of Apple and Tesla" }] }'
 ```

+## Customize the UI
+
+To customize the UI, you can start by modifying the [./components/ui_event.jsx](./components/ui_event.jsx) file.
+
+You can also generate a new code for the workflow using LLM by running the following command:
+
+```
+poetry run generate:ui
+```
+
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -23,8 +23,7 @@ from llama_index.core.workflow import (
    Workflow,
    step,
 )
-from llama_index.server.api.models import SourceNodesEvent
-from llama_index.server.api.models import ChatRequest
+from llama_index.server.api.models import ChatRequest, SourceNodesEvent, UIEvent
 from pydantic import BaseModel, Field

 logger = logging.getLogger("uvicorn")
@@ -66,20 +65,29 @@ class ReportEvent(Event):


 # Events that are streamed to the frontend and rendered there
-class DeepResearchEventData(BaseModel):
-    event: Literal["retrieve", "analyze", "answer"]
-    state: Literal["pending", "inprogress", "done", "error"]
-    id: Optional[str] = None
-    question: Optional[str] = None
-    answer: Optional[str] = None
+class UIEventData(BaseModel):
+    """
+    Events for DeepResearch workflow which has 3 main stages:
+    - Retrieve: Retrieve information from the knowledge base.
+    - Analyze: Analyze the retrieved information and provide list of questions for answering.
+    - Answer: Answering the provided questions. There are multiple answer events, each with its own id that is used to display the answer for a particular question.
+    """

-
-class DataEvent(Event):
-    type: Literal["deep_research_event"]
-    data: DeepResearchEventData
-
-    def to_response(self):
-        return self.model_dump()
+    id: Optional[str] = Field(default=None, description="The id of the event")
+    event: Literal["retrieve", "analyze", "answer"] = Field(
+        default="retrieve", description="The event type"
+    )
+    state: Literal["pending", "inprogress", "done", "error"] = Field(
+        default="pending", description="The state of the event"
+    )
+    question: Optional[str] = Field(
+        default=None,
+        description="Used by answer event to display the question",
+    )
+    answer: Optional[str] = Field(
+        default=None,
+        description="Used by answer event to display the answer of the question",
+    )


 class DeepResearchWorkflow(Workflow):
@@ -137,12 +145,12 @@ class DeepResearchWorkflow(Workflow):
            ]
        )
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "retrieve",
-                    "state": "inprogress",
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="retrieve",
+                    state="inprogress",
+                ),
            )
        )
        retriever = self.index.as_retriever(
@@ -151,12 +159,12 @@ class DeepResearchWorkflow(Workflow):
        nodes = retriever.retrieve(self.user_request)
        self.context_nodes.extend(nodes)  # type: ignore
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "retrieve",
-                    "state": "done",
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="retrieve",
+                    state="done",
+                ),
            )
        )
        # Send source nodes to the stream
@@ -177,12 +185,12 @@ class DeepResearchWorkflow(Workflow):
        """
        logger.info("Analyzing the retrieved information")
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "analyze",
-                    "state": "inprogress",
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="analyze",
+                    state="inprogress",
+                ),
            )
        )
        total_questions = await ctx.get("total_questions")
@@ -194,12 +202,12 @@ class DeepResearchWorkflow(Workflow):
        )
        if res.decision == "cancel":
            ctx.write_event_to_stream(
-                DataEvent(
-                    type="deep_research_event",
-                    data={
-                        "event": "analyze",
-                        "state": "done",
-                    },
+                UIEvent(
+                    type="ui_event",
+                    data=UIEventData(
+                        event="analyze",
+                        state="done",
+                    ),
                )
            )
            return StopEvent(
@@ -210,12 +218,12 @@ class DeepResearchWorkflow(Workflow):
            # It's a LLM hallucination.
            if total_questions == 0:
                ctx.write_event_to_stream(
-                    DataEvent(
-                        type="deep_research_event",
-                        data={
-                            "event": "analyze",
-                            "state": "done",
-                        },
+                    UIEvent(
+                        type="ui_event",
+                        data=UIEventData(
+                            event="analyze",
+                            state="done",
+                        ),
                    )
                )
                return StopEvent(
@@ -245,15 +253,15 @@ class DeepResearchWorkflow(Workflow):
            for question in res.research_questions:
                question_id = str(uuid.uuid4())
                ctx.write_event_to_stream(
-                    DataEvent(
-                        type="deep_research_event",
-                        data={
-                            "event": "answer",
-                            "state": "pending",
-                            "id": question_id,
-                            "question": question,
-                            "answer": None,
-                        },
+                    UIEvent(
+                        type="ui_event",
+                        data=UIEventData(
+                            event="answer",
+                            state="pending",
+                            id=question_id,
+                            question=question,
+                            answer=None,
+                        ),
                    )
                )
                ctx.send_event(
@@ -264,12 +272,12 @@ class DeepResearchWorkflow(Workflow):
                    )
                )
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "analyze",
-                    "state": "done",
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="analyze",
+                    state="done",
+                ),
            )
        )
        return None
@@ -280,14 +288,14 @@ class DeepResearchWorkflow(Workflow):
        Answer the question
        """
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "answer",
-                    "state": "inprogress",
-                    "id": ev.question_id,
-                    "question": ev.question,
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="answer",
+                    state="inprogress",
+                    id=ev.question_id,
+                    question=ev.question,
+                ),
            )
        )
        try:
@@ -299,15 +307,15 @@ class DeepResearchWorkflow(Workflow):
            logger.error(f"Error answering question {ev.question}: {e}")
            answer = f"Got error when answering the question: {ev.question}"
        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "answer",
-                    "state": "done",
-                    "id": ev.question_id,
-                    "question": ev.question,
-                    "answer": answer,
-                },
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    event="answer",
+                    state="done",
+                    id=ev.question_id,
+                    question=ev.question,
+                    answer=answer,
+                ),
            )
        )

@@ -128,7 +128,7 @@ type DeepResearchEventData = {
 };

 class DeepResearchEvent extends WorkflowEvent<{
-  type: "deep_research_event";
+  type: "ui_event";
  data: DeepResearchEventData;
 }> {}

@@ -201,7 +201,7 @@ class DeepResearchWorkflow extends Workflow<

    ctx.sendEvent(
      new DeepResearchEvent({
-        type: "deep_research_event",
+        type: "ui_event",
        data: { event: "retrieve", state: "inprogress" },
      }),
    );
@@ -212,7 +212,7 @@ class DeepResearchWorkflow extends Workflow<

    ctx.sendEvent(
      new DeepResearchEvent({
-        type: "deep_research_event",
+        type: "ui_event",
        data: { event: "retrieve", state: "done" },
      }),
    );
@@ -228,7 +228,7 @@ class DeepResearchWorkflow extends Workflow<
  ): Promise<ResearchEvent | ReportEvent | StopEvent> => {
    ctx.sendEvent(
      new DeepResearchEvent({
-        type: "deep_research_event",
+        type: "ui_event",
        data: { event: "analyze", state: "inprogress" },
      }),
    );
@@ -240,7 +240,7 @@ class DeepResearchWorkflow extends Workflow<
    if (decision === "cancel") {
      ctx.sendEvent(
        new DeepResearchEvent({
-          type: "deep_research_event",
+          type: "ui_event",
          data: { event: "analyze", state: "done" },
        }),
      );
@@ -263,7 +263,7 @@ class DeepResearchWorkflow extends Workflow<
      researchQuestions.forEach(({ questionId: id, question }) => {
        ctx.sendEvent(
          new DeepResearchEvent({
-            type: "deep_research_event",
+            type: "ui_event",
            data: { event: "answer", state: "pending", id, question },
          }),
        );
@@ -280,7 +280,7 @@ class DeepResearchWorkflow extends Workflow<

    ctx.sendEvent(
      new DeepResearchEvent({
-        type: "deep_research_event",
+        type: "ui_event",
        data: { event: "analyze", state: "done" },
      }),
    );
@@ -299,7 +299,7 @@ class DeepResearchWorkflow extends Workflow<
      researchQuestions.map(async ({ questionId: id, question }) => {
        ctx.sendEvent(
          new DeepResearchEvent({
-            type: "deep_research_event",
+            type: "ui_event",
            data: { event: "answer", state: "inprogress", id, question },
          }),
        );
@@ -308,7 +308,7 @@ class DeepResearchWorkflow extends Workflow<

        ctx.sendEvent(
          new DeepResearchEvent({
-            type: "deep_research_event",
+            type: "ui_event",
            data: { event: "answer", state: "done", id, question, answer },
          }),
        );
@@ -1,19 +1,24 @@
 import logging
 import os

-from app.index import STORAGE_DIR
-from app.settings import init_settings
 from dotenv import load_dotenv
-from llama_index.core.indices import (
-    VectorStoreIndex,
-)
-from llama_index.core.readers import SimpleDirectoryReader
+from llama_index.llms.openai import OpenAI

 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger()


-def generate_datasource():
+def generate_index():
+    """
+    Index the documents in the data directory.
+    """
+    from app.index import STORAGE_DIR
+    from app.settings import init_settings
+    from llama_index.core.indices import (
+        VectorStoreIndex,
+    )
+    from llama_index.core.readers import SimpleDirectoryReader
+
    load_dotenv()
    init_settings()

@@ -31,3 +36,28 @@ def generate_datasource():
    # store it for later
    index.storage_context.persist(STORAGE_DIR)
    logger.info(f"Finished creating new index. Stored in {STORAGE_DIR}")
+
+
+def generate_ui_for_workflow():
+    """
+    Generate UI for UIEventData event in app/workflow.py
+    """
+    import asyncio
+
+    from main import COMPONENT_DIR
+
+    # To generate UI components for additional event types,
+    # import the corresponding data model (e.g., MyCustomEventData)
+    # and run the generate_ui_for_workflow function with the imported model.
+    # Make sure the output filename of the generated UI component matches the event type (here `ui_event`)
+    try:
+        from app.workflow import UIEventData
+    except ImportError:
+        raise ImportError("Couldn't generate UI component for the current workflow.")
+    from llama_index.server.gen_ui import generate_event_component
+
+    # works also well with Claude 3.7 Sonnet or Gemini Pro 2.5
+    llm = OpenAI(model="gpt-4.1")
+    code = asyncio.run(generate_event_component(event_cls=UIEventData, llm=llm))
+    with open(f"{COMPONENT_DIR}/ui_event.jsx", "w") as f:
+        f.write(code)
@@ -9,6 +9,9 @@ from llama_index.server import LlamaIndexServer, UIConfig

 logger = logging.getLogger("uvicorn")

+# A path to a directory where the customized UI code is stored
+COMPONENT_DIR = "components"
+

 def create_app():
    env = os.environ.get("APP_ENV")
@@ -16,7 +19,7 @@ def create_app():
    app = LlamaIndexServer(
        workflow_factory=create_workflow,  # A factory function that creates a new workflow for each request
        ui_config=UIConfig(
-            component_dir="components",
+            component_dir=COMPONENT_DIR,
            app_title="Chat App",
        ),
        env=env,
@@ -7,7 +7,9 @@ authors = ["Marcus Schiesser <mail@marcusschiesser.de>"]
 readme = "README.md"

 [tool.poetry.scripts]
-generate = "generate:generate_datasource"
+"generate" = "generate:generate_index"
+"generate:index" = "generate:generate_index"
+"generate:ui" = "generate:generate_ui_for_workflow"
 dev = "main:run('dev')"
 prod = "main:run('prod')"

@@ -17,7 +19,7 @@ python-dotenv = "^1.0.0"
 pydantic = "<2.10"
 aiostream = "^0.5.2"
 llama-index-core = "^0.12.28"
-llama-index-server = "^0.1.10"
+llama-index-server = "^0.1.12"

 [tool.poetry.group.dev.dependencies]
 mypy = "^1.8.0"
Author	SHA1	Message	Date
github-actions[bot]	60f10c5b5d	Release 0.5.5 (#564 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-15 20:55:53 +07:00
Huu Le	ee85320701	fix: missing default export (#563 )	2025-04-15 20:54:23 +07:00
github-actions[bot]	b12dc6f1e8	Release 0.5.4 (#562 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-15 18:28:11 +07:00
Huu Le	7c3b279417	support code generation of event components using an LLM (Python) (#557 ) --------- Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-04-15 18:23:06 +07:00
github-actions[bot]	1514a555d5	chore(release): bump version to 0.1.12 (#559 ) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-04-15 17:32:13 +07:00
Huu Le	cddb4f6bcc	chore: bump chat UI version to 0.1.2 and rename generate_ui_for_workflow (#560 ) * chore: bump chat UI version to 0.1.2 and rename generate_ui_for_workflow * feat: add exports for event component generation in gen_ui module * update document * refine prompt	2025-04-15 17:27:22 +07:00
github-actions[bot]	c82e4f5791	chore(release): bump version to 0.1.11 (#555 ) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-04-15 13:11:15 +07:00
Huu Le	1f7e0e3c69	add GenUIWorkflow for generating UI components from workflow events (#549 ) * feat: add GenUIWorkflow for generating UI components from workflow events * feat: enhance GenUIWorkflow to support event handling and UI generation * add cache, split code * use gemini model * refactor: update GenUIWorkflow to use Anthropic model and add pre-run checks for API key and package installation * feat: introduce PlanningEvent and enhance GenUIWorkflow for improved UI planning and aggregation function generation * feat: add gen ui to llamaindexserver * refactor: remove unused gen_ui.py file * simplify * update for tailwindcss * simplify code and add document * refine text * feat: add UIEvent model and update exports in server module * use default UIEvent * fix wrong model, update template * add missing doc * fix linting * revert change on template * fix mypy * disable e2e for the change from llama-index-server * remove unused script entry from pyproject.toml and refine UI notice text in GenUIWorkflow * update workflow, bump chat ui * Refine GenUIWorkflow documentation and improve code structure notes; add llm parameter to generate_ui_for_workflow function.	2025-04-15 13:06:55 +07:00