refactor: remove redundant UI event handling in workflows

- Removed unnecessary string conversion for userInput in code_generator and deep_research workflows.
- Updated userRequest type to MessageContent for better type safety.
- Cleaned up the UI event component by removing redundant indicatorClassName logic.

.changeset/calm-women-repair.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Split artifacts use case to document generator and code generator

packages/create-llama/e2e/python/resolve_dependencies.spec.ts

@@ -18,7 +18,8 @@ const useCases: TemplateUseCase[] = [
   "agentic_rag",
   "deep_research",
   "financial_report",
-  "artifacts",
+  "code_generator",
+  "document_generator",
 ];
 const dataSource: string = process.env.DATASOURCE
   ? process.env.DATASOURCE

packages/create-llama/e2e/typescript/resolve_dependencies.spec.ts

@@ -23,7 +23,8 @@ const useCases: TemplateUseCase[] = [
   "agentic_rag",
   "deep_research",
   "financial_report",
-  "artifacts",
+  "code_generator",
+  "document_generator",
 ];
 const dataSource: string = process.env.DATASOURCE
   ? process.env.DATASOURCE
@@ -82,7 +83,7 @@ test.describe("Test resolve TS dependencies", () => {
           });
         });
         // Skipping llamacloud for the use case doesn't use index.
-        if (useCase !== "artifacts") {
+        if (useCase !== "code_generator" && useCase !== "document_generator") {
           test(`llamaParse - ${optionDescription}`, async () => {
             await runTest({
               templateType: templateType,

packages/create-llama/helpers/types.ts

@@ -58,7 +58,8 @@ export type TemplateUseCase =
   | "extractor"
   | "contract_review"
   | "agentic_rag"
-  | "artifacts";
+  | "code_generator"
+  | "document_generator";
 // Config for both file and folder
 export type FileSourceConfig =
   | {

packages/create-llama/helpers/typescript.ts

@@ -77,7 +77,7 @@ const installLlamaIndexServerTemplate = async ({
   }
 
   // Simplify use case code
-  if (useCase === "artifacts") {
+  if (useCase === "code_generator" || useCase === "document_generator") {
     // Artifact use case doesn't use index.
     // We don't need data.ts, generate.ts
     await fs.rm(path.join(root, "src", "app", "data.ts"));
@@ -387,7 +387,7 @@ const providerDependencies: {
   [key in ModelProvider]?: Record<string, string>;
 } = {
   openai: {
-    "@llamaindex/openai": "^0.2.0",
+    "@llamaindex/openai": "^0.3.7",
   },
   gemini: {
     "@llamaindex/google": "^0.2.0",
@@ -513,7 +513,7 @@ async function updatePackageJson({
   if (backend) {
     packageJson.dependencies = {
       ...packageJson.dependencies,
-      "@llamaindex/readers": "^3.0.0",
+      "@llamaindex/readers": "^3.1.3",
     };
 
     if (vectorDb && vectorDb in vectorDbDependencies) {

packages/create-llama/questions/simple.ts

@@ -10,7 +10,8 @@ type AppType =
   | "agentic_rag"
   | "financial_report"
   | "deep_research"
-  | "artifacts";
+  | "code_generator"
+  | "document_generator";
 
 type SimpleAnswers = {
   appType: AppType;
@@ -47,10 +48,14 @@ export const askSimpleQuestions = async (
             "Researches and analyzes provided documents from multiple perspectives, generating a comprehensive report with citations to support key findings and insights.",
         },
         {
-          title: "Artifacts",
-          value: "artifacts",
-          description:
-            "Build your own Vercel's v0 or OpenAI's canvas-styled UI.",
+          title: "Code Generator",
+          value: "code_generator",
+          description: "Build a Vercel v0 styled code generator.",
+        },
+        {
+          title: "Document Generator",
+          value: "document_generator",
+          description: "Build a OpenAI canvas-styled document generator.",
         },
       ],
     },
@@ -76,7 +81,7 @@ export const askSimpleQuestions = async (
   );
   language = newLanguage;
 
-  if (appType !== "artifacts") {
+  if (appType !== "code_generator" && appType !== "document_generator") {
     const { useLlamaCloud: newUseLlamaCloud } = await prompts(
       {
         type: "toggle",
@@ -153,7 +158,13 @@ const convertAnswers = async (
       tools: [],
       modelConfig: MODEL_GPT41,
     },
-    artifacts: {
+    code_generator: {
+      template: "llamaindexserver",
+      dataSources: [],
+      tools: [],
+      modelConfig: MODEL_GPT41,
+    },
+    document_generator: {
       template: "llamaindexserver",
       dataSources: [],
       tools: [],

packages/create-llama/templates/components/ui/use-cases/document_generator/ui_event.jsx

@@ -0,0 +1,132 @@
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { Progress } from "@/components/ui/progress";
+import { Skeleton } from "@/components/ui/skeleton";
+import { cn } from "@/lib/utils";
+import { Markdown } from "@llamaindex/chat-ui/widgets";
+import { ListChecks, Loader2, Wand2 } from "lucide-react";
+import { useEffect, useState } from "react";
+
+const STAGE_META = {
+  plan: {
+    icon: ListChecks,
+    badgeText: "Step 1/2: Planning",
+    gradient: "from-blue-100 via-blue-50 to-white",
+    progress: 33,
+    iconBg: "bg-blue-100 text-blue-600",
+    badge: "bg-blue-100 text-blue-700",
+  },
+  generate: {
+    icon: Wand2,
+    badgeText: "Step 2/2: Generating",
+    gradient: "from-violet-100 via-violet-50 to-white",
+    progress: 66,
+    iconBg: "bg-violet-100 text-violet-600",
+    badge: "bg-violet-100 text-violet-700",
+  },
+};
+
+function ArtifactWorkflowCard({ event }) {
+  const [visible, setVisible] = useState(event?.state !== "completed");
+  const [fade, setFade] = useState(false);
+
+  useEffect(() => {
+    if (event?.state === "completed") {
+      setVisible(false);
+    } else {
+      setVisible(true);
+      setFade(false);
+    }
+  }, [event?.state]);
+
+  if (!event || !visible) return null;
+
+  const { state, requirement } = event;
+  const meta = STAGE_META[state];
+
+  if (!meta) return null;
+
+  return (
+    <div className="flex min-h-[180px] w-full items-center justify-center py-2">
+      <Card
+        className={cn(
+          "w-full rounded-xl shadow-md transition-all duration-500",
+          "border-0",
+          fade && "pointer-events-none opacity-0",
+          `bg-gradient-to-br ${meta.gradient}`,
+        )}
+        style={{
+          boxShadow:
+            "0 2px 12px 0 rgba(80, 80, 120, 0.08), 0 1px 3px 0 rgba(80, 80, 120, 0.04)",
+        }}
+      >
+        <CardHeader className="flex flex-row items-center gap-2 px-3 pb-1 pt-2">
+          <div
+            className={cn(
+              "flex items-center justify-center rounded-full p-1",
+              meta.iconBg,
+            )}
+          >
+            <meta.icon className="h-5 w-5" />
+          </div>
+          <CardTitle className="flex items-center gap-2 text-base font-semibold">
+            <Badge className={cn("ml-1", meta.badge, "px-2 py-0.5 text-xs")}>
+              {meta.badgeText}
+            </Badge>
+          </CardTitle>
+        </CardHeader>
+        <CardContent className="px-3 py-1">
+          {state === "plan" && (
+            <div className="flex flex-col items-center gap-2 py-2">
+              <Loader2 className="mb-1 h-6 w-6 animate-spin text-blue-400" />
+              <div className="text-center text-sm font-medium text-blue-900">
+                Analyzing your request...
+              </div>
+              <Skeleton className="mt-1 h-3 w-1/2 rounded-full" />
+            </div>
+          )}
+          {state === "generate" && (
+            <div className="flex flex-col gap-2 py-2">
+              <div className="flex items-center gap-1">
+                <Loader2 className="h-4 w-4 animate-spin text-violet-400" />
+                <span className="text-sm font-medium text-violet-900">
+                  Working on the requirement:
+                </span>
+              </div>
+              <div className="max-h-24 overflow-auto rounded-lg border border-violet-200 bg-violet-50 px-2 py-1 text-xs">
+                {requirement ? (
+                  <Markdown content={requirement} />
+                ) : (
+                  <span className="italic text-violet-400">
+                    No requirements available yet.
+                  </span>
+                )}
+              </div>
+            </div>
+          )}
+        </CardContent>
+        <div className="px-3 pb-2 pt-1">
+          <Progress
+            value={meta.progress}
+            className={cn(
+              "h-1 rounded-full bg-gray-200",
+              state === "plan" && "bg-blue-200",
+              state === "generate" && "bg-violet-200",
+            )}
+          />
+        </div>
+      </Card>
+    </div>
+  );
+}
+
+export default function Component({ events }) {
+  const aggregateEvents = () => {
+    if (!events || events.length === 0) return null;
+    return events[events.length - 1];
+  };
+
+  const event = aggregateEvents();
+
+  return <ArtifactWorkflowCard event={event} />;
+}

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

@@ -1,15 +0,0 @@
-from app.code_workflow import CodeArtifactWorkflow
-
-# from app.document_workflow import DocumentArtifactWorkflow to generate documents
-from llama_index.core.workflow import Workflow
-from llama_index.llms.openai import OpenAI
-from llama_index.server.api.models import ChatRequest
-
-
-def create_workflow(chat_request: ChatRequest) -> Workflow:
-    workflow = CodeArtifactWorkflow(
-        llm=OpenAI(model="gpt-4.1"),
-        chat_request=chat_request,
-        timeout=120.0,
-    )
-    return workflow

packages/create-llama/templates/components/use-cases/python/code_generator/README-template.md

@@ -0,0 +1,65 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Workflows](https://docs.llamaindex.ai/en/stable/understanding/workflows/).
+
+## Getting Started
+
+First, setup the environment with uv:
+
+> **_Note:_** This step is not needed if you are using the dev-container.
+
+```shell
+uv sync
+```
+
+Then check the parameters that have been pre-configured in the `.env` file in this directory.
+Make sure you have set the `OPENAI_API_KEY` for the LLM.
+
+Then, run the development server:
+
+```shell
+uv run fastapi dev
+```
+
+Then open [http://localhost:8000](http://localhost:8000) with your browser to start the chat UI.
+
+To start the app optimized for **production**, run:
+
+```
+uv run fastapi run
+```
+
+## Configure LLM and Embedding Model
+
+You can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides/models/llms) and [embedding model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings) in [settings.py](app/settings.py).
+
+## Use Case
+AI-powered code generator that can help you generate app with a chat interface, code editor and app preview.
+
+To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
+
+You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
+
+```
+curl --location 'localhost:8000/api/chat' \
+--header 'Content-Type: application/json' \
+--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:
+
+```
+uv run generate_ui
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
+- [Workflows Introduction](https://docs.llamaindex.ai/en/stable/understanding/workflows/) - learn about LlamaIndex workflows.
+- [LlamaIndex Server](https://pypi.org/project/llama-index-server/)
+
+You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!

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

@@ -6,6 +6,7 @@ from llama_index.core.chat_engine.types import ChatMessage
 from llama_index.core.llms import LLM
 from llama_index.core.memory import ChatMemoryBuffer
 from llama_index.core.prompts import PromptTemplate
+from llama_index.llms.openai import OpenAI
 from llama_index.core.workflow import (
     Context,
     Event,
@@ -26,6 +27,15 @@ from llama_index.server.api.utils import get_last_artifact
 from pydantic import BaseModel, Field
 
 
+def create_workflow(chat_request: ChatRequest) -> Workflow:
+    workflow = CodeArtifactWorkflow(
+        llm=OpenAI(model="gpt-4.1"),
+        chat_request=chat_request,
+        timeout=120.0,
+    )
+    return workflow
+
+
 class Requirement(BaseModel):
     next_step: Literal["answering", "coding"]
     language: Optional[str] = None

packages/create-llama/templates/components/use-cases/python/document_generator/README-template.md

@@ -33,12 +33,9 @@ You can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides
 
 ## Use Case
 
-We have prepared two artifact workflows:
+AI-powered document generator that can help you generate documents with a chat interface and simple markdown editor.
 
-- [Code Workflow](app/code_workflow.py): To generate code and display it in the UI like Vercel's v0.
-- [Document Workflow](app/document_workflow.py): Generate and update a document like OpenAI's canvas.
-
-Modify the factory method in [`workflow.py`](app/workflow.py) to decide which artifact workflow to use. Without any changes the Code Workflow is used.
+To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
 
 You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
 

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

@@ -4,6 +4,7 @@ from typing import Any, Literal, Optional
 
 from llama_index.core.chat_engine.types import ChatMessage
 from llama_index.core.llms import LLM
+from llama_index.llms.openai import OpenAI
 from llama_index.core.memory import ChatMemoryBuffer
 from llama_index.core.prompts import PromptTemplate
 from llama_index.core.workflow import (
@@ -26,6 +27,15 @@ from llama_index.server.api.utils import get_last_artifact
 from pydantic import BaseModel, Field
 
 
+def create_workflow(chat_request: ChatRequest) -> Workflow:
+    workflow = DocumentArtifactWorkflow(
+        llm=OpenAI(model="gpt-4.1"),
+        chat_request=chat_request,
+        timeout=120.0,
+    )
+    return workflow
+
+
 class DocumentRequirement(BaseModel):
     type: Literal["markdown", "html"]
     title: str

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

@@ -1,12 +0,0 @@
-import { createCodeArtifactWorkflow, UIEventSchema } from "./code-workflow";
-// import { createDocumentArtifactWorkflow, UIEventSchema } from "./doc-workflow";
-
-export const workflowFactory = async (reqBody: any) => {
-  // Uncomment the import and change to createDocumentArtifactWorkflow to use the document workflow
-  const workflow = createCodeArtifactWorkflow(reqBody);
-
-  return workflow;
-};
-
-// Re-export the UIEventSchema for generating the UI by `pnpm generate:ui` command
-export { UIEventSchema };

packages/create-llama/templates/components/use-cases/typescript/code_generator/README-template.md

@@ -30,12 +30,9 @@ npm run generate:ui
 
 ## Use Case
 
-We have prepared two artifact workflows:
+AI-powered code generator that can help you generate app with a chat interface, code editor and app preview.
 
-- [Code Workflow](app/code_workflow.ts): To generate code and display it in the UI like Vercel's v0.
-- [Document Workflow](app/document_workflow.ts): Generate and update a document like OpenAI's canvas.
-
-Modify the factory method in [`workflow.ts`](app/workflow.ts) to decide which artifact workflow to use. Without any changes the Code Workflow is used.
+To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).
 
 You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
 

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

@@ -1,5 +1,5 @@
 import { extractLastArtifact } from "@llamaindex/server";
-import { ChatMemoryBuffer, LLM, Settings } from "llamaindex";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";
 
 import {
   agentStreamEvent,
@@ -12,6 +12,12 @@ import {
 
 import { z } from "zod";
 
+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createCodeArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
 export const RequirementSchema = z.object({
   next_step: z.enum(["answering", "coding"]),
   language: z.string().nullable().optional(),
@@ -40,7 +46,7 @@ export const UIEventSchema = z.object({
 
 export type UIEvent = z.infer<typeof UIEventSchema>;
 const planEvent = workflowEvent<{
-  userInput: string;
+  userInput: MessageContent;
   context?: string | undefined;
 }>();
 
@@ -92,7 +98,7 @@ export function createCodeArtifactWorkflow(reqBody: any, llm?: LLM) {
       content: userInput,
     });
     return planEvent.with({
-      userInput,
+      userInput: userInput,
     });
   });
 
@@ -177,15 +183,6 @@ ${user_msg}
       throw new Error("No JSON block found in the response.");
     }
     const requirement = RequirementSchema.parse(JSON.parse(jsonBlock[1]));
-    sendEvent(
-      uiEvent.with({
-        type: "ui_event",
-        data: {
-          state: "generate",
-          requirement: requirement.requirement,
-        },
-      }),
-    );
     state.memory.put({
       role: "assistant",
       content: `The plan for next step: \n${response.text}`,

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

@@ -10,12 +10,14 @@ import {
 import {
   ChatMemoryBuffer,
   LlamaCloudIndex,
+  MessageContent,
   Metadata,
   MetadataMode,
   NodeWithScore,
   PromptTemplate,
   Settings,
   VectorStoreIndex,
+  extractText,
 } from "llamaindex";
 import { randomUUID } from "node:crypto";
 import { z } from "zod";
@@ -153,7 +155,7 @@ export function getWorkflow(index: VectorStoreIndex | LlamaCloudIndex) {
         chatHistory: [],
       }),
       contextNodes: [] as NodeWithScore<Metadata>[],
-      userRequest: "",
+      userRequest: "" as MessageContent,
       totalQuestions: 0,
       researchResults: [] as ResearchResult[],
     };
@@ -178,7 +180,7 @@ export function getWorkflow(index: VectorStoreIndex | LlamaCloudIndex) {
       }),
     );
 
-    const retrievedNodes = await retriever.retrieve(userInput);
+    const retrievedNodes = await retriever.retrieve({ query: userInput });
 
     sendEvent(toSourceEvent(retrievedNodes));
     sendEvent(
@@ -349,7 +351,7 @@ const createResearchPlan = async (
   memory: ChatMemoryBuffer,
   contextStr: string,
   enhancedPrompt: string,
-  userRequest: string,
+  userRequest: MessageContent,
 ) => {
   const chatHistory = await memory.getMessages();
 
@@ -361,7 +363,7 @@ const createResearchPlan = async (
     context_str: contextStr,
     conversation_context: conversationContext,
     enhanced_prompt: enhancedPrompt,
-    user_request: userRequest,
+    user_request: extractText(userRequest),
   });
 
   const responseFormat = z.object({

packages/create-llama/templates/components/use-cases/typescript/document_generator/README-template.md

@@ -0,0 +1,53 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, install the dependencies:
+
+```
+npm install
+```
+
+Third, run the development server:
+
+```
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the chat UI.
+
+## Configure LLM and Embedding Model
+
+You can configure [LLM model](https://ts.llamaindex.ai/docs/llamaindex/modules/llms) in the [settings file](src/app/settings.ts).
+
+## Custom UI Components
+
+We have a custom component located in `components/ui_event.jsx`. This is used to display the state of artifact workflows in UI. You can regenerate a new UI component from the workflow event schema by running the following command:
+
+```
+npm run generate:ui
+```
+
+## Use Case
+
+AI-powered document generator that can help you generate documents with a chat interface and simple markdown editor.
+
+To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).
+
+You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
+
+```shell
+curl --location 'localhost:3000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
+- [LlamaIndexTS Documentation](https://ts.llamaindex.ai/docs/llamaindex) - learn about LlamaIndex (Typescript features).
+- [Workflows Introduction](https://ts.llamaindex.ai/docs/llamaindex/modules/workflows) - learn about LlamaIndexTS workflows.
+
+You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!

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

@@ -1,5 +1,5 @@
 import { extractLastArtifact } from "@llamaindex/server";
-import { ChatMemoryBuffer, LLM, Settings } from "llamaindex";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";
 
 import {
   agentStreamEvent,
@@ -12,6 +12,12 @@ import {
 
 import { z } from "zod";
 
+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createDocumentArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
 export const DocumentRequirementSchema = z.object({
   type: z.enum(["markdown", "html"]),
   title: z.string(),
@@ -40,7 +46,7 @@ export const UIEventSchema = z.object({
 export type UIEvent = z.infer<typeof UIEventSchema>;
 
 const planEvent = workflowEvent<{
-  userInput: string;
+  userInput: MessageContent;
   context?: string | undefined;
 }>();
 
@@ -175,15 +181,6 @@ export function createDocumentArtifactWorkflow(reqBody: any, llm?: LLM) {
       role: "assistant",
       content: `Planning for the document generation: \n${response.text}`,
     });
-    sendEvent(
-      uiEvent.with({
-        type: "ui_event",
-        data: {
-          state: "generate",
-          requirement: requirement.requirement,
-        },
-      }),
-    );
     return generateArtifactEvent.with({
       requirement,
     });

packages/create-llama/templates/types/llamaindexserver/nextjs/package.json

@@ -9,13 +9,13 @@
     "start": "tsx src/index.ts"
   },
   "dependencies": {
-    "@llamaindex/openai": "0.2.0",
-    "@llamaindex/server": "0.2.0",
-    "@llamaindex/workflow": "1.1.1",
-    "@llamaindex/tools": "0.0.9",
+    "@llamaindex/openai": "^0.3.7",
+    "@llamaindex/server": "^0.2.0",
+    "@llamaindex/workflow": "^1.1.2",
+    "@llamaindex/tools": "^0.0.10",
+    "llamaindex": "^0.10.6",
     "dotenv": "^16.4.7",
-    "zod": "^3.23.8",
-    "llamaindex": "0.10.5"
+    "zod": "^3.23.8"
   },
   "devDependencies": {
     "@types/node": "^20.10.3",

packages/create-llama/templates/types/llamaindexserver/nextjs/src/app/settings.ts

@@ -3,7 +3,7 @@ import { Settings } from "llamaindex";
 
 export function initSettings() {
   Settings.llm = new OpenAI({
-    model: "gpt-4o-mini",
+    model: "gpt-4.1",
   });
   Settings.embedModel = new OpenAIEmbedding({
     model: "text-embedding-3-small",