feat: show document artifact after generating report (#658)

* feat: show document artifact after generating report

* keep chat message content as it is

* use artifactEvent from server

* add deep research example

* bump chat-ui for new editor

* import editor css

* hide warning for workflowEvent<{}>() in eject mode

* fix format

* use CL for better testing

* generate artifact after streaming report in Python

* bump chat-ui to support citations

* use isinstance to check stream

* fix document editor spacing

* Create tame-wolves-obey.md

* add sources to document artifact

* add sources to document artifact in python

* type cast

* no need score

* fix lint

* move handle stream logic to server

* refactor: use chunk.text and chunk.raw

* bump chat-ui 0.5.6 to fix citations

* update changset

* fix lock

.changeset/tame-wolves-obey.md

@@ -0,0 +1,7 @@
+---
+"create-llama": patch
+"@llamaindex/server": patch
+"@create-llama/llama-index-server": patch
+---
+
+feat: show document artifact after generating report

packages/create-llama/templates/components/use-cases/python/deep_research/workflow.py

@@ -1,7 +1,7 @@
 import logging
 import os
 import uuid
-from typing import List, Literal, Optional
+from typing import List, Literal, Optional, AsyncGenerator
 
 from app.index import get_index
 from llama_index.core.base.llms.types import (
@@ -23,7 +23,18 @@ from llama_index.core.workflow import (
     Workflow,
     step,
 )
-from llama_index.server.api.models import ChatRequest, SourceNodesEvent, UIEvent
+from llama_index.server.api.models import (
+    ArtifactEvent,
+    ArtifactType,
+    ChatRequest,
+    SourceNodesEvent,
+    UIEvent,
+    Artifact,
+    DocumentArtifactData,
+    DocumentArtifactSource,
+)
+import time
+from llama_index.server.utils.stream import write_response_to_stream
 from pydantic import BaseModel, Field
 
 logger = logging.getLogger("uvicorn")
@@ -365,8 +376,31 @@ class DeepResearchWorkflow(Workflow):
             user_request=self.user_request,
             stream=self.stream,
         )
+
+        final_response = await write_response_to_stream(res, ctx)
+
+        ctx.write_event_to_stream(
+            ArtifactEvent(
+                data=Artifact(
+                    type=ArtifactType.DOCUMENT,
+                    created_at=int(time.time()),
+                    data=DocumentArtifactData(
+                        title="DeepResearch Report",
+                        content=final_response,
+                        type="markdown",
+                        sources=[
+                            DocumentArtifactSource(
+                                id=node.id_,
+                            )
+                            for node in self.context_nodes
+                        ],
+                    ),
+                ),
+            )
+        )
+
         return StopEvent(
-            result=res,
+            result="",
         )
 
 

packages/create-llama/templates/components/use-cases/typescript/deep_research/src/app/workflow.ts

@@ -1,4 +1,4 @@
-import { toSourceEvent } from "@llamaindex/server";
+import { artifactEvent, toSourceEvent } from "@llamaindex/server";
 import {
   agentStreamEvent,
   createStatefulMiddleware,
@@ -339,6 +339,26 @@ export function getWorkflow(index: VectorStoreIndex | LlamaCloudIndex) {
         }),
       );
     }
+
+    // Open the generated report in Canvas
+    sendEvent(
+      artifactEvent.with({
+        type: "artifact",
+        data: {
+          type: "document",
+          created_at: Date.now(),
+          data: {
+            title: "DeepResearch Report",
+            content: response,
+            type: "markdown",
+            sources: state.contextNodes.map((node) => ({
+              id: node.node.id_,
+            })),
+          },
+        },
+      }),
+    );
+
     return stopAgentEvent.with({
       result: response,
     });

packages/server/next/app/layout.tsx

@@ -1,6 +1,7 @@
 import type { Metadata } from "next";
 import { Inter } from "next/font/google";
 
+import "@llamaindex/chat-ui/styles/editor.css";
 import "@llamaindex/chat-ui/styles/markdown.css";
 import "@llamaindex/chat-ui/styles/pdf.css";
 import "./globals.css";

packages/server/project-config/eslint.config.mjs

@@ -18,6 +18,7 @@ const eslintConfig = [
       "react-hooks/exhaustive-deps": "off",
       "@next/next/no-img-element": "off",
       "@next/next/no-assign-module-variable": "off",
+      "@typescript-eslint/no-empty-object-type": "off",
     },
   },
   {

packages/server/src/utils/events.ts

@@ -110,6 +110,7 @@ export type DocumentArtifactData = {
   title: string;
   content: string;
   type: string; // markdown, html,...
+  sources?: { id: string }[]; // sources that are used to render citation numbers in the document
 };
 
 export type CodeArtifact = Artifact<CodeArtifactData> & {

python/llama-index-server/llama_index/server/models/__init__.py

@@ -4,6 +4,7 @@ from llama_index.server.models.artifacts import (
     ArtifactType,
     CodeArtifactData,
     DocumentArtifactData,
+    DocumentArtifactSource,
 )
 from llama_index.server.models.chat import ChatAPIMessage, ChatRequest
 from llama_index.server.models.hitl import HumanInputEvent, HumanResponseEvent
@@ -20,6 +21,7 @@ __all__ = [
     "ArtifactEvent",
     "ArtifactType",
     "DocumentArtifactData",
+    "DocumentArtifactSource",
     "CodeArtifactData",
     "ChatAPIMessage",
     "ChatRequest",

python/llama-index-server/llama_index/server/models/artifacts.py

@@ -1,6 +1,6 @@
 import logging
 from enum import Enum
-from typing import Literal, Optional, Union
+from typing import List, Literal, Optional, Union
 
 from llama_index.core.workflow.events import Event
 from llama_index.server.models.chat import ChatAPIMessage
@@ -21,10 +21,16 @@ class CodeArtifactData(BaseModel):
     language: str
 
 
+class DocumentArtifactSource(BaseModel):
+    id: str
+    # we can add more fields here
+
+
 class DocumentArtifactData(BaseModel):
     title: str
     content: str
     type: Literal["markdown", "html"]
+    sources: Optional[List[DocumentArtifactSource]] = None
 
 
 class Artifact(BaseModel):

python/llama-index-server/llama_index/server/utils/stream.py

@@ -0,0 +1,45 @@
+from typing import AsyncGenerator, Union
+from llama_index.core.base.llms.types import (
+    CompletionResponse,
+    CompletionResponseAsyncGen,
+)
+from llama_index.core.workflow import Context
+from llama_index.core.agent.workflow.workflow_events import AgentStream
+
+
+async def write_response_to_stream(
+    res: Union[CompletionResponse, CompletionResponseAsyncGen],
+    ctx: Context,
+    current_agent_name: str = "assistant",
+) -> str:
+    """
+    Handle both streaming and non-streaming LLM responses.
+
+    Args:
+        res: The LLM response (either streaming or non-streaming)
+        ctx: The workflow context for writing events to stream
+        current_agent_name: The name of the current agent (default: "assistant")
+
+    Returns:
+        The final response text as a string
+    """
+    final_response = ""
+
+    if isinstance(res, AsyncGenerator):
+        # Handle streaming response (CompletionResponseAsyncGen)
+        async for chunk in res:
+            ctx.write_event_to_stream(
+                AgentStream(
+                    delta=chunk.delta or "",
+                    response=final_response,
+                    current_agent_name=current_agent_name,
+                    tool_calls=[],
+                    raw=chunk.raw or "",
+                )
+            )
+            final_response = chunk.text
+    else:
+        # Handle non-streaming response (CompletionResponse)
+        final_response = res.text
+
+    return final_response