feat: use ink to render workflow

chore: copied doc-workflow example from CL
Release 0.11.0 (#1950 )
2026-07-15 06:52:45 -04:00 · 2025-05-19 13:36:08 +07:00 · 2025-05-19 12:40:45 +07:00 · 2025-05-19 12:23:04 +07:00 · 2025-05-19 11:49:18 +07:00 · 2025-05-19 11:12:57 +07:00
201 changed files with 3176 additions and 1477 deletions
@@ -1,5 +1,21 @@
 # @llamaindex/doc

+## 0.2.19
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+- Updated dependencies [3e66ddc]
+  - @llamaindex/workflow@1.1.3
+  - @llamaindex/core@0.6.6
+  - llamaindex@0.11.0
+  - @llamaindex/openai@0.4.0
+  - @llamaindex/cloud@4.0.8
+  - @llamaindex/node-parser@2.0.6
+  - @llamaindex/readers@3.1.4
+
 ## 0.2.18

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.18",
+  "version": "0.2.19",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -4,7 +4,6 @@ import matter from "gray-matter";
 import path from "path";

 const CONTENT_DIR = path.join(process.cwd(), "src/content/docs");
-const BUILD_DIR = path.join(process.cwd(), ".next");

 // Regular expression to find internal links
 // This captures Markdown links [text](/docs/path) and href attributes href="/docs/path"
@@ -14,6 +13,8 @@ const INTERNAL_LINK_REGEX = /(?:(?:\]\(|\bhref=["'])\/docs\/([^")]+))/g;
 // This captures relative links like [text](./path) or ![alt](../images/image.png)
 const RELATIVE_LINK_REGEX = /(?:\]\()(?:\s*)(?:\.\.?)\//g;

+const ALLOWED_LINKS = ["/docs/llamaflow"];
+
 interface LinkValidationResult {
  file: string;
  invalidLinks: Array<{ link: string; line: number }>;
@@ -28,7 +29,7 @@ interface RelativeLinkResult {
 * Get all valid documentation routes from the content directory
 */
 async function getValidRoutes(): Promise<Set<string>> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.{md,mdx}", { cwd: CONTENT_DIR });

  const routes = new Set<string>();

@@ -124,14 +125,11 @@ function findRelativeLinksInFile(
  return relativeLinks;
 }

-/**
- * Validate internal links in all MDX files
- */
 /**
 * Find relative links in all MDX files
 */
 async function findRelativeLinks(): Promise<RelativeLinkResult[]> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
  const results: RelativeLinkResult[] = [];

  for (const file of mdxFiles) {
@@ -150,7 +148,7 @@ async function findRelativeLinks(): Promise<RelativeLinkResult[]> {
 }

 async function validateLinks(): Promise<LinkValidationResult[]> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
  const validRoutes = await getValidRoutes();

  const results: LinkValidationResult[] = [];
@@ -160,6 +158,11 @@ async function validateLinks(): Promise<LinkValidationResult[]> {
    const links = extractLinksFromFile(filePath);

    const invalidLinks = links.filter(({ link }) => {
+      // Check if the link is in the allowed list
+      if (ALLOWED_LINKS.includes(`/docs/${link}`)) {
+        return false;
+      }
+
      // Check if the link exists in valid routes
      // First normalize the link (remove any query string or hash)
      const baseLink = link.split("?")[0].split("#")[0];
@@ -12,7 +12,7 @@ To use workflows install this package:
 npm i @llamaindex/workflow
 ```

-This package is a stable, production-ready version of our [llama-flow](../../../llamaflow) project. 
+This package is a stable, production-ready version of our [llama-flow](/docs/llamaflow) project. 

 While you can still reference the llama-flow documentation for detailed information about the underlying concepts, we recommend using the `@llamaindex/workflow` package for all new projects to ensure stability and long-term availability.

@@ -18,7 +18,7 @@ In your Discord Application, go to the `OAuth2` tab and generate an invite URL b
 This will invite the bot with the necessary permissions to read messages.
 Copy the URL in your browser and select the server you want your bot to join.

-<include cwd>../../examples/discord/reader.ts</include>
+<include cwd>../../examples/readers/discord/reader.ts</include>

 ### Params

@@ -2,89 +2,43 @@
 title: Azure OpenAI
 ---

-To use Azure OpenAI, you only need to set a few environment variables together with the `OpenAI` class.
-
-For example:
-
-## Environment Variables
-
-```
-export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
-export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
-export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
-```
+To use Azure OpenAI, you only need to install the `@llamaindex/azure` package:

 ## Installation

 ```package-install
-npm i llamaindex @llamaindex/openai
+npm i llamaindex @llamaindex/azure
 ```

 ## Usage

+The class `AzureOpenAI` is used for setting the LLM and `AzureOpenAIEmbedding` is used for setting the embedding model, e.g.:
+
 ```ts
 import { Settings } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+import { AzureOpenAI, AzureOpenAIEmbedding } from "@llamaindex/azure";

-Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
+Settings.llm = new AzureOpenAI({ 
+  apiKey: '[key]',
+  deployment: '[model]',
+  apiVersion: '[version]',
+  endpoint: `https://[deployment].openai.azure.com/`, 
+});
+Settings.embedModel = new AzureOpenAIEmbedding({
+  apiKey: '[key]',
+  deployment: '[embedding-model]',
+  apiVersion: '[version]',
+  endpoint: `https://[deployment].openai.azure.com/`, 
 });
 ```

-## Full Example
+Instead of explicitly setting the API key, deployment, version, and endpoint in the constructor, you can use the following environment variables: `AZURE_OPENAI_DEPLOYMENT` for the model deployment name, `AZURE_OPENAI_KEY` for your API key, `AZURE_OPENAI_ENDPOINT` for your Azure endpoint URL, and `AZURE_OPENAI_API_VERSION` for the API version.

-```ts
-import { Document, VectorStoreIndex, Settings } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+## Examples

-Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // get retriever
-  const retriever = index.asRetriever();
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
+See the [Azure examples](https://github.com/run-llama/LlamaIndexTS/tree/main/examples/storage/azure) for more examples of how to use Azure OpenAI.

 ## API Reference

- [OpenAI](/docs/api/classes/OpenAI)
+- [AzureOpenAI](/docs/api/classes/AzureOpenAI)
+- [AzureOpenAIEmbedding](/docs/api/classes/AzureOpenAIEmbedding)
@@ -55,7 +55,7 @@ const results = await queryEngine.query({

 ## Full Example

-<include cwd>../../examples/groq.ts</include>
+<include cwd>../../examples/models/groq.ts</include>

 ## API Reference

@@ -166,4 +166,4 @@ Want to start a new project with LlamaIndexServer? Check out our [create-llama](

 ## API Reference

- [LlamaIndexServer](/docs/api/classes/LlamaIndexServer)
+- [LlamaIndexServer](https://github.com/run-llama/create-llama/blob/main/packages/server)
@@ -27,7 +27,7 @@ Create the file `example.ts`. This code will
 - index it (which creates embeddings using OpenAI)
 - create a query engine to answer questions about the data

-<include cwd>../../examples/vectorIndex.ts</include>
+<include cwd>../../examples/index/vectorIndex.ts</include>

 Create a `tsconfig.json` file in the same folder:

@@ -24,7 +24,7 @@ Create the file `example.ts`. This code will:
 - Give an example of the data structure we wish to generate
 - Prompt the LLM with instructions and the example, plus a sample transcript

-<include cwd>../../examples/jsonExtract.ts</include>
+<include cwd>../../examples/misc/jsonExtract.ts</include>

 To run the code:

@@ -1,5 +1,19 @@
 # @llamaindex/core-e2e

+## 0.1.1
+
+### Patch Changes
+
+- b0cd530: # Breaking Change
+
+  ## What Changed
+
+  Remove default setting of llm and embedModel in Settings
+
+  ## Migration Guide
+
+  Set the llm provider and embed Model in the top of your code using Settings.llm = and Settings.embedModel
+
 ## 0.1.0

 ### Minor Changes
@@ -1,5 +1,13 @@
 # @llamaindex/cloudflare-worker-agent-test

+## 0.0.161
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.0.160

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.160",
+  "version": "0.0.161",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,5 +1,11 @@
 # @llamaindex/llama-parse-browser-test

+## 0.0.63
+
+### Patch Changes
+
+- @llamaindex/cloud@4.0.8
+
 ## 0.0.62

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/llama-parse-browser-test",
  "private": true,
-  "version": "0.0.62",
+  "version": "0.0.63",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -1,5 +1,13 @@
 # @llamaindex/next-agent-test

+## 0.1.161
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.1.160

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-agent-test",
-  "version": "0.1.160",
+  "version": "0.1.161",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,7 +1,7 @@
 "use server";
+import { OpenAIAgent } from "@llamaindex/openai";
 import { createStreamableUI } from "ai/rsc";
 import type { ChatMessage } from "llamaindex";
-import { OpenAIAgent } from "llamaindex";

 export async function chatWithAgent(
  question: string,
@@ -1,5 +1,13 @@
 # test-edge-runtime

+## 0.1.160
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.1.159

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.159",
+  "version": "0.1.160",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,15 @@
 # @llamaindex/next-node-runtime

+## 0.1.28
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+  - @llamaindex/huggingface@0.1.10
+  - @llamaindex/readers@3.1.4
+
 ## 0.1.27

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.1.27",
+  "version": "0.1.28",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,7 +1,8 @@
 "use server";
 import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-import { OpenAI, OpenAIAgent, Settings, VectorStoreIndex } from "llamaindex";
+import { Settings, VectorStoreIndex } from "llamaindex";

 Settings.llm = new OpenAI({
  apiKey: process.env.NEXT_PUBLIC_OPENAI_KEY ?? "FAKE_KEY_TO_PASS_TESTS",
@@ -1,5 +1,13 @@
 # vite-import-llamaindex

+## 0.0.27
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.0.26

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "vite-import-llamaindex",
  "private": true,
-  "version": "0.0.26",
+  "version": "0.0.27",
  "type": "module",
  "scripts": {
    "build": "vite build",
@@ -1,5 +1,13 @@
 # @llamaindex/waku-query-engine-test

+## 0.0.161
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.0.160

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.160",
+  "version": "0.0.161",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,6 +1,7 @@
 import { ClipEmbedding } from "@llamaindex/clip";
 import type { LoadTransformerEvent } from "@llamaindex/env/multi-model";
 import { setTransformers } from "@llamaindex/env/multi-model";
+import { OpenAIEmbedding } from "@llamaindex/openai";
 import { ImageNode, Settings } from "llamaindex";
 import assert from "node:assert";
 import { type Mock, test } from "node:test";
@@ -19,6 +20,7 @@ test.before(() => {

 test.beforeEach(() => {
  callback.mock.resetCalls();
+  Settings.embedModel = new OpenAIEmbedding();
 });

 await test.skip("clip embedding", async (t) => {
@@ -1,10 +1,6 @@
 import type { TaskStep } from "@llamaindex/core/agent";
-import {
-  LLMSingleSelector,
-  OpenAIAgent,
-  Settings,
-  type ChatMessage,
-} from "llamaindex";
+import { OpenAIAgent } from "@llamaindex/openai";
+import { LLMSingleSelector, Settings, type ChatMessage } from "llamaindex";
 import assert from "node:assert";
 import { test } from "node:test";
 import { divideNumbersTool, sumNumbersTool } from "./fixtures/tools.js";
@@ -1,11 +1,10 @@
 import { extractText } from "@llamaindex/core/utils";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
 import { consola } from "consola";
 import {
  Document,
  FunctionTool,
  ObjectIndex,
-  OpenAI,
-  OpenAIAgent,
  QueryEngineTool,
  SentenceSplitter,
  Settings,
@@ -1,5 +1,6 @@
 import { extractText } from "@llamaindex/core/utils";
-import { OpenAI, ReActAgent, Settings, type LLM } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { ReActAgent, Settings, type LLM } from "llamaindex";
 import { ok } from "node:assert";
 import { beforeEach, test } from "node:test";
 import { getWeatherTool } from "./fixtures/tools.js";
@@ -1,8 +1,9 @@
+import { OpenAIEmbedding } from "@llamaindex/openai";
 import { PGVectorStore } from "@llamaindex/postgres";
 import { config } from "dotenv";
-import { Document, VectorStoreQueryMode } from "llamaindex";
+import { Document, Settings, VectorStoreQueryMode } from "llamaindex";
 import assert from "node:assert";
-import { test } from "node:test";
+import { beforeEach, test } from "node:test";
 import pg from "pg";
 import { registerTypes } from "pgvector/pg";

@@ -14,6 +15,10 @@ const pgConfig = {
  database: "llamaindex_node_test",
 };

+beforeEach(async () => {
+  Settings.embedModel = new OpenAIEmbedding();
+});
+
 await test("init with client", async (t) => {
  const pgClient = new pg.Client(pgConfig);
  await pgClient.connect();
@@ -1,5 +1,6 @@
+import { OpenAI } from "@llamaindex/openai";
 import { agent } from "@llamaindex/workflow";
-import { OpenAI, Settings, tool } from "llamaindex";
+import { Settings, tool } from "llamaindex";
 import { ok } from "node:assert";
 import { test } from "node:test";
 import { z } from "zod";
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/e2e",
  "private": true,
-  "version": "0.1.0",
+  "version": "0.1.1",
  "type": "module",
  "scripts": {
    "e2e": "node --import tsx --import ./mock-register.js --test ./node/**/*.e2e.ts",
@@ -1,5 +1,60 @@
 # examples

+## 0.3.16
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [b0cd530]
+- Updated dependencies [c73c659]
+- Updated dependencies [361a685]
+- Updated dependencies [3e66ddc]
+  - @llamaindex/workflow@1.1.3
+  - @llamaindex/core@0.6.6
+  - llamaindex@0.11.0
+  - @llamaindex/qdrant@0.1.15
+  - @llamaindex/azure@0.1.16
+  - @llamaindex/openai@0.4.0
+  - @llamaindex/cloud@4.0.8
+  - @llamaindex/node-parser@2.0.6
+  - @llamaindex/anthropic@0.3.7
+  - @llamaindex/assemblyai@0.1.5
+  - @llamaindex/clip@0.0.56
+  - @llamaindex/cohere@0.0.20
+  - @llamaindex/deepinfra@0.0.56
+  - @llamaindex/discord@0.1.5
+  - @llamaindex/google@0.3.2
+  - @llamaindex/huggingface@0.1.10
+  - @llamaindex/jinaai@0.0.16
+  - @llamaindex/mistral@0.1.6
+  - @llamaindex/mixedbread@0.0.20
+  - @llamaindex/notion@0.1.5
+  - @llamaindex/ollama@0.1.6
+  - @llamaindex/perplexity@0.0.13
+  - @llamaindex/portkey-ai@0.0.48
+  - @llamaindex/replicate@0.0.48
+  - @llamaindex/astra@0.0.20
+  - @llamaindex/chroma@0.0.20
+  - @llamaindex/elastic-search@0.1.6
+  - @llamaindex/firestore@1.0.13
+  - @llamaindex/milvus@0.1.15
+  - @llamaindex/mongodb@0.0.21
+  - @llamaindex/pinecone@0.1.6
+  - @llamaindex/postgres@0.0.49
+  - @llamaindex/supabase@0.1.5
+  - @llamaindex/upstash@0.0.20
+  - @llamaindex/weaviate@0.0.20
+  - @llamaindex/vercel@0.1.6
+  - @llamaindex/voyage-ai@1.0.12
+  - @llamaindex/readers@3.1.4
+  - @llamaindex/tools@0.0.11
+  - @llamaindex/deepseek@0.0.16
+  - @llamaindex/fireworks@0.0.16
+  - @llamaindex/groq@0.0.71
+  - @llamaindex/together@0.0.16
+  - @llamaindex/vllm@0.0.42
+  - @llamaindex/xai@0.0.3
+
 ## 0.3.15

 ### Patch Changes
@@ -92,7 +92,7 @@ async function multiWeatherAgent() {
      agentInputEvent.include(event) ||
      stopAgentEvent.include(event)
    ) {
-      console.log(event);
+      console.log(event.data);
    } else if (agentStreamEvent.include(event)) {
      for (const chunk of event.data.delta) {
        process.stdout.write(chunk);
@@ -1,9 +1,10 @@
+import { openai } from "@llamaindex/openai";
 import {
  agent,
  agentStreamEvent,
  agentToolCallResultEvent,
 } from "@llamaindex/workflow";
-import { Document, VectorStoreIndex, openai } from "llamaindex";
+import { Document, VectorStoreIndex } from "llamaindex";

 async function main() {
  const index = await VectorStoreIndex.fromDocuments([
@@ -0,0 +1,332 @@
+import { ChatMemoryBuffer, ChatMessage, LLM, MessageContent } from "llamaindex";
+
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+
+import { z } from "zod";
+
+export const DocumentRequirementSchema = z.object({
+  type: z.enum(["markdown", "html"]),
+  title: z.string(),
+  requirement: z.string(),
+});
+
+export type DocumentRequirement = z.infer<typeof DocumentRequirementSchema>;
+
+export const UIEventSchema = z.object({
+  type: z.literal("ui_event"),
+  data: z.object({
+    state: z
+      .enum(["plan", "generate", "completed"])
+      .describe(
+        "The current state of the workflow: 'plan', 'generate', or 'completed'.",
+      ),
+    requirement: z
+      .string()
+      .optional()
+      .describe(
+        "An optional requirement creating or updating a document, if applicable.",
+      ),
+  }),
+});
+export type UIEvent = z.infer<typeof UIEventSchema>;
+export const uiEvent = workflowEvent<UIEvent>();
+
+const planEvent = workflowEvent<{
+  userInput: MessageContent;
+  context?: string | undefined;
+}>();
+
+const generateArtifactEvent = workflowEvent<{
+  requirement: DocumentRequirement;
+}>();
+
+const synthesizeAnswerEvent = workflowEvent<{
+  requirement: DocumentRequirement;
+  generatedArtifact: string;
+}>();
+
+const ArtifactSchema = z.object({
+  type: z.literal("artifact"),
+  data: z.object({
+    type: z.literal("document"),
+    data: z.object({
+      title: z.string(),
+      content: z.string(),
+      type: z.string(),
+    }),
+    created_at: z.number(),
+  }),
+});
+export type Artifact = z.infer<typeof ArtifactSchema>;
+export const artifactEvent = workflowEvent<Artifact>();
+
+export function createDocumentArtifactWorkflow(
+  llm: LLM,
+  chatHistory: ChatMessage[],
+  lastArtifact: Artifact | undefined,
+) {
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({
+        llm,
+        chatHistory: chatHistory,
+      }),
+      lastArtifact: lastArtifact,
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data: { userInput } }) => {
+    // Prepare chat history
+    const { state } = getContext();
+    // Put user input to the memory
+    if (!userInput) {
+      throw new Error("Missing user input to start the workflow");
+    }
+    state.memory.put({
+      role: "user",
+      content: userInput,
+    });
+    return planEvent.with({
+      userInput,
+      context: state.lastArtifact
+        ? JSON.stringify(state.lastArtifact)
+        : undefined,
+    });
+  });
+
+  workflow.handle([planEvent], async ({ data: planData }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "plan",
+        },
+      }),
+    );
+    const user_msg = planData.userInput;
+    const context = planData.context
+      ? `## The context is: \n${planData.context}\n`
+      : "";
+    const prompt = `
+         You are a documentation analyst responsible for analyzing the user's request and providing requirements for document generation or update.
+         Follow these instructions:
+         1. Carefully analyze the conversation history and the user's request to determine what has been done and what the next step should be.
+         2. From the user's request, provide requirements for the next step of the document generation or update.
+         3. Do not be verbose; only return the requirements for the next step of the document generation or update.
+         4. Only the following document types are allowed: "markdown", "html".
+         5. The requirement should be in the following format:
+            \`\`\`json
+            {
+                "type": "markdown" | "html",
+                "title": string,
+                "requirement": string
+            }
+            \`\`\`
+
+         ## Example:
+         User request: Create a project guideline document.
+         You should return:
+         \`\`\`json
+         {
+             "type": "markdown",
+             "title": "Project Guideline",
+             "requirement": "Generate a Markdown document that outlines the project goals, deliverables, and timeline. Include sections for introduction, objectives, deliverables, and timeline."
+         }
+         \`\`\`
+
+         User request: Add a troubleshooting section to the guideline.
+         You should return:
+         \`\`\`json
+         {
+             "type": "markdown",
+             "title": "Project Guideline",
+             "requirement": "Add a 'Troubleshooting' section at the end of the document with common issues and solutions."
+         }
+         \`\`\`
+
+         ${context}
+
+         Now, please plan for the user's request:
+         ${user_msg}
+        `;
+
+    const response = await llm.complete({
+      prompt,
+    });
+    // Parse the response to DocumentRequirement
+    const jsonBlock = response.text.match(/```json\s*([\s\S]*?)\s*```/);
+    if (!jsonBlock) {
+      throw new Error("No JSON block found in the response.");
+    }
+    const requirement = DocumentRequirementSchema.parse(
+      JSON.parse(jsonBlock[1]),
+    );
+    state.memory.put({
+      role: "assistant",
+      content: `Planning for the document generation: \n${response.text}`,
+    });
+    return generateArtifactEvent.with({
+      requirement,
+    });
+  });
+
+  workflow.handle(
+    [generateArtifactEvent],
+    async ({ data: { requirement } }) => {
+      const { sendEvent } = getContext();
+      const { state } = getContext();
+
+      sendEvent(
+        uiEvent.with({
+          type: "ui_event",
+          data: {
+            state: "generate",
+            requirement: requirement.requirement,
+          },
+        }),
+      );
+
+      const previousArtifact = state.lastArtifact
+        ? JSON.stringify(state.lastArtifact)
+        : "";
+      const requirementStr = JSON.stringify(requirement);
+
+      const prompt = `
+         You are a skilled technical writer who can help users with documentation.
+         You are given a task to generate or update a document for a given requirement.
+
+         ## Follow these instructions:
+         **1. Carefully read the user's requirements.**
+            If any details are ambiguous or missing, make reasonable assumptions and clearly reflect those in your output.
+            If the previous document is provided:
+            + Carefully analyze the document with the request to make the right changes.
+            + Avoid making unnecessary changes from the previous document if the request is not to rewrite it from scratch.
+         **2. For document requests:**
+            - If the user does not specify a type, default to Markdown.
+            - Ensure the document is clear, well-structured, and grammatically correct.
+            - Only generate content relevant to the user's request—do not add extra boilerplate.
+         **3. Do not be verbose in your response.**
+            - No other text or comments; only return the document content wrapped by the appropriate code block (\`\`\`markdown or \`\`\`html).
+            - If the user's request is to update the document, only return the updated document.
+         **4. Only the following types are allowed: "markdown", "html".**
+         **5. If there is no change to the document, return the reason without any code block.**
+
+         ## Example:
+         \`\`\`markdown
+         # Project Guideline
+         
+         ## Introduction
+         ...
+         \`\`\`
+
+         The previous content is:
+         ${previousArtifact}
+
+         Now, please generate the document for the following requirement:
+         ${requirementStr}
+      `;
+
+      const response = await llm.complete({
+        prompt,
+      });
+
+      // Extract the document from the response
+      const docMatch = response.text.match(/```(markdown|html)([\s\S]*)```/);
+      const generatedContent = response.text;
+
+      if (docMatch) {
+        const content = docMatch[2].trim();
+        const docType = docMatch[1] as "markdown" | "html";
+
+        // Put the generated document to the memory
+        state.memory.put({
+          role: "assistant",
+          content: `Generated document: \n${response.text}`,
+        });
+
+        // To show the Canvas panel for the artifact
+        sendEvent(
+          artifactEvent.with({
+            type: "artifact",
+            data: {
+              type: "document",
+              created_at: Date.now(),
+              data: {
+                title: requirement.title,
+                content: content,
+                type: docType,
+              },
+            },
+          }),
+        );
+      }
+
+      return synthesizeAnswerEvent.with({
+        requirement,
+        generatedArtifact: generatedContent,
+      });
+    },
+  );
+
+  workflow.handle([synthesizeAnswerEvent], async ({ data }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+
+    const chatHistory = await state.memory.getMessages();
+    const messages = [
+      ...chatHistory,
+      {
+        role: "system" as const,
+        content: `
+                Your responsibility is to explain the work to the user.
+                If there is no document to update, explain the reason.
+                If the document is updated, just summarize what changed. Don't need to include the whole document again in the response.
+                `,
+      },
+    ];
+
+    const responseStream = await llm.chat({
+      messages,
+      stream: true,
+    });
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "completed",
+          requirement: data.requirement.requirement,
+        },
+      }),
+    );
+
+    let response = "";
+    for await (const chunk of responseStream) {
+      response += chunk.delta;
+      sendEvent(
+        agentStreamEvent.with({
+          delta: chunk.delta,
+          response: "",
+          currentAgentName: "assistant",
+          raw: chunk,
+        }),
+      );
+    }
+
+    return stopAgentEvent.with({
+      result: response,
+    });
+  });
+
+  return workflow;
+}
@@ -0,0 +1,162 @@
+import { openai } from "@llamaindex/openai";
+import { Box, render, Text } from "ink";
+import React from "react";
+
+import {
+  agentStreamEvent,
+  startAgentEvent,
+  stopAgentEvent,
+} from "@llamaindex/workflow";
+
+import {
+  artifactEvent,
+  createDocumentArtifactWorkflow,
+  uiEvent,
+  UIEvent,
+} from "./doc-workflow";
+
+// Import Artifact type (assuming it's exported from doc-workflow.ts)
+// If not exported, we'd define a simplified version here or use 'any' temporarily.
+// Based on the read file, Artifact is a Zod schema inference.
+// We can either import it or define a similar structure for props.
+// For now, let's assume it's available from './doc-workflow'.
+import type { Artifact } from "./doc-workflow"; // Assuming Artifact is exported
+
+const llm = openai({ model: "gpt-4.1-mini" });
+
+// Define the props for our UI component
+interface WorkflowUIProps {
+  uiEvent: UIEvent;
+}
+
+// React component to render the UI using Ink
+const WorkflowUI: React.FC<WorkflowUIProps> = ({ uiEvent }) => {
+  const contentWidth = 60;
+  const stateColorMap: { [key: string]: string } = {
+    plan: "yellow",
+    generate: "blue",
+    completed: "green",
+  };
+
+  const state = uiEvent.data.state;
+  const coloredStateText = (
+    <Text color={stateColorMap[state] || "white"}>{state.toUpperCase()}</Text>
+  );
+
+  const title = "💡 Workflow Event";
+  const requirementLabel = "Requirement: ";
+
+  return (
+    <Box
+      borderStyle="round"
+      borderColor="gray"
+      flexDirection="column"
+      paddingX={1}
+      width={contentWidth + 2 + 2}
+    >
+      <Box justifyContent="center" paddingTop={0} paddingBottom={0}>
+        <Text>{title}</Text>
+      </Box>
+
+      <Box borderStyle="single" borderColor="gray" height={1} marginY={1} />
+
+      <Box flexDirection="column">
+        <Box>
+          <Text>State: </Text>
+          {coloredStateText}
+        </Box>
+
+        {uiEvent.data.requirement !== undefined && (
+          <Box flexDirection="row" marginTop={1}>
+            <Text>{requirementLabel}</Text>
+            <Box flexGrow={1} flexShrink={1} marginLeft={1}>
+              <Text>{uiEvent.data.requirement}</Text>
+            </Box>
+          </Box>
+        )}
+      </Box>
+    </Box>
+  );
+};
+
+// --- BEGIN NEW ARTIFACT UI COMPONENT ---
+interface ArtifactUIProps {
+  artifact: Artifact; // Use the imported Artifact type
+}
+
+const ArtifactUI: React.FC<ArtifactUIProps> = ({ artifact }) => {
+  const artifactData = artifact.data.data; // Access nested data
+  const contentWidth = 80; // Allow more width for artifact content
+
+  return (
+    <Box
+      borderStyle="round"
+      borderColor="cyan"
+      flexDirection="column"
+      padding={1}
+      width={contentWidth + 2 + 2} // Account for padding and border
+    >
+      <Box justifyContent="center" paddingBottom={1}>
+        <Text bold color="cyan">
+          📄 Artifact Generated: {artifactData.title}
+        </Text>
+      </Box>
+
+      <Box borderStyle="single" borderColor="gray" marginY={1} />
+
+      <Box flexDirection="column" gap={1}>
+        <Box>
+          <Text bold>Title: </Text>
+          <Text>{artifactData.title}</Text>
+        </Box>
+        <Box>
+          <Text bold>Type: </Text>
+          <Text>{artifactData.type}</Text>
+        </Box>
+        <Box flexDirection="column">
+          <Text bold>Content:</Text>
+          {/* Add a Box for content to allow it to take up space and wrap */}
+          <Box borderStyle="round" borderColor="gray" padding={1} marginTop={1}>
+            <Text>{artifactData.content}</Text>
+          </Box>
+        </Box>
+        <Box marginTop={1}>
+          <Text dimColor>
+            Created At: {new Date(artifact.data.created_at).toLocaleString()}
+          </Text>
+        </Box>
+      </Box>
+    </Box>
+  );
+};
+
+async function main() {
+  const workflow = createDocumentArtifactWorkflow(llm, [], undefined);
+  const { stream, sendEvent } = workflow.createContext();
+
+  const { rerender, unmount, clear } = render(
+    <Text>Waiting for workflow to start...</Text>,
+  );
+
+  sendEvent(
+    startAgentEvent.with({ userInput: "Create a project guideline document." }),
+  );
+
+  for await (const event of stream.until(stopAgentEvent)) {
+    if (agentStreamEvent.include(event)) {
+      process.stdout.write(event.data.delta);
+    } else if (uiEvent.include(event)) {
+      if (event.data.data.state !== "completed") {
+        rerender(<WorkflowUI uiEvent={event.data} />);
+      }
+    } else if (artifactEvent.include(event)) {
+      rerender(<ArtifactUI artifact={event.data} />);
+    }
+  }
+
+  unmount();
+
+  console.log("\nWorkflow finished.");
+}
+
+main().catch(console.error);
@@ -1,12 +1,20 @@
 import fs from "node:fs/promises";

+import { openai, OpenAIEmbedding } from "@llamaindex/openai";
 import {
  Document,
  MetadataMode,
  NodeWithScore,
+  Settings,
  VectorStoreIndex,
 } from "llamaindex";

+Settings.llm = openai({
+  apiKey: process.env.OPENAI_API_KEY,
+  model: "gpt-4o",
+});
+Settings.embedModel = new OpenAIEmbedding();
+
 async function main() {
  // Load essay from abramov.txt in Node
  const path = "node_modules/llamaindex/examples/abramov.txt";
@@ -1,7 +1,8 @@
 {
  "name": "@llamaindex/examples",
-  "version": "0.3.15",
+  "version": "0.3.16",
  "private": true,
+  "type": "module",
  "scripts": {
    "lint": "eslint .",
    "start": "tsx ./starter.ts"
@@ -11,51 +12,51 @@
    "@azure/cosmos": "^4.1.1",
    "@azure/identity": "^4.4.1",
    "@azure/search-documents": "^12.1.0",
-    "@llamaindex/anthropic": "^0.3.6",
-    "@llamaindex/assemblyai": "^0.1.4",
-    "@llamaindex/astra": "^0.0.19",
-    "@llamaindex/azure": "^0.1.15",
-    "@llamaindex/chroma": "^0.0.19",
-    "@llamaindex/clip": "^0.0.55",
-    "@llamaindex/cloud": "^4.0.7",
-    "@llamaindex/cohere": "^0.0.19",
-    "@llamaindex/core": "^0.6.5",
-    "@llamaindex/deepinfra": "^0.0.55",
-    "@llamaindex/deepseek": "^0.0.15",
-    "@llamaindex/discord": "^0.1.4",
-    "@llamaindex/elastic-search": "^0.1.5",
+    "@llamaindex/anthropic": "^0.3.7",
+    "@llamaindex/assemblyai": "^0.1.5",
+    "@llamaindex/astra": "^0.0.20",
+    "@llamaindex/azure": "^0.1.16",
+    "@llamaindex/chroma": "^0.0.20",
+    "@llamaindex/clip": "^0.0.56",
+    "@llamaindex/cloud": "^4.0.8",
+    "@llamaindex/cohere": "^0.0.20",
+    "@llamaindex/core": "^0.6.6",
+    "@llamaindex/deepinfra": "^0.0.56",
+    "@llamaindex/deepseek": "^0.0.16",
+    "@llamaindex/discord": "^0.1.5",
+    "@llamaindex/elastic-search": "^0.1.6",
    "@llamaindex/env": "^0.1.30",
-    "@llamaindex/firestore": "^1.0.12",
-    "@llamaindex/fireworks": "^0.0.15",
-    "@llamaindex/google": "^0.3.1",
-    "@llamaindex/groq": "^0.0.70",
-    "@llamaindex/huggingface": "^0.1.9",
-    "@llamaindex/jinaai": "^0.0.15",
-    "@llamaindex/milvus": "^0.1.14",
-    "@llamaindex/mistral": "^0.1.5",
-    "@llamaindex/mixedbread": "^0.0.19",
-    "@llamaindex/mongodb": "^0.0.20",
-    "@llamaindex/node-parser": "^2.0.5",
-    "@llamaindex/notion": "^0.1.4",
-    "@llamaindex/ollama": "^0.1.5",
-    "@llamaindex/openai": "^0.3.7",
-    "@llamaindex/perplexity": "^0.0.12",
-    "@llamaindex/pinecone": "^0.1.5",
-    "@llamaindex/portkey-ai": "^0.0.47",
-    "@llamaindex/postgres": "^0.0.48",
-    "@llamaindex/qdrant": "^0.1.14",
-    "@llamaindex/readers": "^3.1.3",
-    "@llamaindex/replicate": "^0.0.47",
-    "@llamaindex/supabase": "^0.1.4",
-    "@llamaindex/together": "^0.0.15",
-    "@llamaindex/tools": "^0.0.10",
-    "@llamaindex/upstash": "^0.0.19",
-    "@llamaindex/vercel": "^0.1.5",
-    "@llamaindex/vllm": "^0.0.41",
-    "@llamaindex/voyage-ai": "^1.0.11",
-    "@llamaindex/weaviate": "^0.0.19",
-    "@llamaindex/workflow": "^1.1.2",
-    "@llamaindex/xai": "workspace:^0.0.2",
+    "@llamaindex/firestore": "^1.0.13",
+    "@llamaindex/fireworks": "^0.0.16",
+    "@llamaindex/google": "^0.3.2",
+    "@llamaindex/groq": "^0.0.71",
+    "@llamaindex/huggingface": "^0.1.10",
+    "@llamaindex/jinaai": "^0.0.16",
+    "@llamaindex/milvus": "^0.1.15",
+    "@llamaindex/mistral": "^0.1.6",
+    "@llamaindex/mixedbread": "^0.0.20",
+    "@llamaindex/mongodb": "^0.0.21",
+    "@llamaindex/node-parser": "^2.0.6",
+    "@llamaindex/notion": "^0.1.5",
+    "@llamaindex/ollama": "^0.1.6",
+    "@llamaindex/openai": "^0.4.0",
+    "@llamaindex/perplexity": "^0.0.13",
+    "@llamaindex/pinecone": "^0.1.6",
+    "@llamaindex/portkey-ai": "^0.0.48",
+    "@llamaindex/postgres": "^0.0.49",
+    "@llamaindex/qdrant": "^0.1.15",
+    "@llamaindex/readers": "^3.1.4",
+    "@llamaindex/replicate": "^0.0.48",
+    "@llamaindex/supabase": "^0.1.5",
+    "@llamaindex/together": "^0.0.16",
+    "@llamaindex/tools": "^0.0.11",
+    "@llamaindex/upstash": "^0.0.20",
+    "@llamaindex/vercel": "^0.1.6",
+    "@llamaindex/vllm": "^0.0.42",
+    "@llamaindex/voyage-ai": "^1.0.12",
+    "@llamaindex/weaviate": "^0.0.20",
+    "@llamaindex/workflow": "^1.1.3",
+    "@llamaindex/xai": "workspace:^0.0.3",
    "@notionhq/client": "^2.2.15",
    "@pinecone-database/pinecone": "^4.0.0",
    "@vercel/postgres": "^0.10.0",
@@ -63,15 +64,18 @@
    "ajv": "^8.17.1",
    "commander": "^12.1.0",
    "dotenv": "^16.4.5",
+    "ink": "^5.2.1",
    "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.10.6",
+    "llamaindex": "^0.11.0",
    "mongodb": "6.7.0",
    "postgres": "^3.4.4",
+    "react": "^18.3.1",
    "wikipedia": "^2.1.2",
    "zod": "^3.23.8"
  },
  "devDependencies": {
    "@types/node": "^22.9.0",
+    "@types/react": "^18.3.21",
    "tsx": "^4.19.3",
    "typescript": "^5.7.3"
  },
@@ -8,8 +8,9 @@ import {
  AzureCosmosDBNoSqlVectorStore,
  AzureCosmosNoSqlDocumentStore,
  AzureCosmosNoSqlIndexStore,
+  AzureOpenAI,
+  AzureOpenAIEmbedding,
 } from "@llamaindex/azure";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import {
  Document,
  Settings,
@@ -38,17 +39,13 @@ import {
    "https://cognitiveservices.azure.com/.default",
  );

-  const azure = {
+  Settings.llm = new AzureOpenAI({
    azureADTokenProvider,
    deployment: process.env.AZURE_DEPLOYMENT_NAME,
-  };
-  Settings.llm = new OpenAI({ azure });
-  Settings.embedModel = new OpenAIEmbedding({
-    model: process.env.EMBEDDING_MODEL,
-    azure: {
-      ...azure,
-      deployment: process.env.EMBEDDING_MODEL,
-    },
+  });
+  Settings.embedModel = new AzureOpenAIEmbedding({
+    azureADTokenProvider,
+    deployment: process.env.EMBEDDING_MODEL,
  });
  const docStore = AzureCosmosNoSqlDocumentStore.fromAadToken();
  console.log({ docStore });
@@ -2,7 +2,7 @@ import {
  DefaultAzureCredential,
  getBearerTokenProvider,
 } from "@azure/identity";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import { AzureOpenAI, AzureOpenAIEmbedding } from "@llamaindex/azure";
 import "dotenv/config";

 const AZURE_COGNITIVE_SERVICES_SCOPE =
@@ -15,11 +15,10 @@ const AZURE_COGNITIVE_SERVICES_SCOPE =
    AZURE_COGNITIVE_SERVICES_SCOPE,
  );

-  const azure = {
+  const llm = new AzureOpenAI({
    azureADTokenProvider,
    deployment: process.env.AZURE_DEPLOYMENT_NAME ?? "gpt-35-turbo",
-  };
-  const llm = new OpenAI({ azure });
+  });
  // complete api
  const response1 = await llm.complete({ prompt: "How are you?" });
  console.log(response1.text);
@@ -31,11 +30,9 @@ const AZURE_COGNITIVE_SERVICES_SCOPE =
  console.log(response2.message.content);

  // embeddings
-  const embedModel = new OpenAIEmbedding({
-    azure: {
-      ...azure,
-      deployment: process.env.EMBEDDING_MODEL,
-    },
+  const embedModel = new AzureOpenAIEmbedding({
+    azureADTokenProvider,
+    deployment: process.env.EMBEDDING_MODEL,
  });

  const texts = ["hello", "world"];
@@ -9,11 +9,12 @@ import {
 } from "@azure/search-documents";
 import {
  AzureAISearchVectorStore,
+  AzureOpenAI,
+  AzureOpenAIEmbedding,
  type FilterableMetadataFieldKeysType,
  IndexManagement,
  MetadataIndexFieldType,
 } from "@llamaindex/azure";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
 import dotenv from "dotenv";
 import {
@@ -67,17 +68,13 @@ function processResults(response: NodeWithScore[], mode: VectorStoreQueryMode) {
    "https://cognitiveservices.azure.com/.default",
  );
  // You need to deploy your own embedding model as well as your own chat completion model
-  const azure = {
+  Settings.llm = new AzureOpenAI({
    azureADTokenProvider,
    deployment: process.env.AZURE_DEPLOYMENT_NAME,
-  };
-  Settings.llm = new OpenAI({ azure });
-  Settings.embedModel = new OpenAIEmbedding({
-    model: process.env.EMBEDDING_MODEL,
-    azure: {
-      ...azure,
-      deployment: process.env.EMBEDDING_MODEL,
-    },
+  });
+  Settings.embedModel = new AzureOpenAIEmbedding({
+    azureADTokenProvider,
+    deployment: process.env.EMBEDDING_MODEL,
  });

  // ---------------------------------------------------------
@@ -4,8 +4,7 @@ import {
  DefaultAzureCredential,
  getBearerTokenProvider,
 } from "@azure/identity";
-import { AzureDynamicSessionTool } from "@llamaindex/azure";
-import { OpenAI } from "@llamaindex/openai";
+import { AzureDynamicSessionTool, AzureOpenAI } from "@llamaindex/azure";
 import { ReActAgent } from "llamaindex";

 async function main() {
@@ -15,14 +14,10 @@ async function main() {
    "https://cognitiveservices.azure.com/.default",
  );

-  const azure = {
+  // configure LLM model
+  const llm = new AzureOpenAI({
    azureADTokenProvider,
    deployment: process.env.AZURE_OPENAI_DEPLOYMENT ?? "gpt-35-turbo",
-  };
-
-  // configure LLM model
-  const llm = new OpenAI({
-    azure,
  });

  const azureDynamicSession = new AzureDynamicSessionTool();
@@ -2,10 +2,11 @@ import { CosmosClient } from "@azure/cosmos";
 import { DefaultAzureCredential } from "@azure/identity";
 import {
  AzureCosmosDBNoSQLConfig,
+  AzureOpenAI,
+  AzureOpenAIEmbedding,
  SimpleCosmosDBReader,
  SimpleCosmosDBReaderLoaderConfig,
 } from "@llamaindex/azure";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import * as dotenv from "dotenv";
 import {
  Settings,
@@ -31,23 +32,19 @@ const vectorCollectionName =

 // This example uses Azure OpenAI llm and embedding models
 const llmInit = {
-  azure: {
-    apiVersion: process.env.AZURE_OPENAI_LLM_API_VERSION,
-    endpoint: process.env.AZURE_OPENAI_LLM_ENDPOINT,
-    apiKey: process.env.AZURE_OPENAI_LLM_API_KEY,
-  },
+  apiVersion: process.env.AZURE_OPENAI_LLM_API_VERSION,
+  endpoint: process.env.AZURE_OPENAI_LLM_ENDPOINT,
+  apiKey: process.env.AZURE_OPENAI_LLM_API_KEY,
 };

 const embedModelInit = {
-  azure: {
-    apiVersion: process.env.AZURE_OPENAI_EMBEDDING_API_VERSION,
-    endpoint: process.env.AZURE_OPENAI_EMBEDDING_ENDPOINT,
-    apiKey: process.env.AZURE_OPENAI_EMBEDDING_API_KEY,
-  },
+  apiVersion: process.env.AZURE_OPENAI_EMBEDDING_API_VERSION,
+  endpoint: process.env.AZURE_OPENAI_EMBEDDING_ENDPOINT,
+  apiKey: process.env.AZURE_OPENAI_EMBEDDING_API_KEY,
 };

-Settings.llm = new OpenAI(llmInit);
-Settings.embedModel = new OpenAIEmbedding(embedModelInit);
+Settings.llm = new AzureOpenAI(llmInit);
+Settings.embedModel = new AzureOpenAIEmbedding(embedModelInit);

 // Initialize the CosmosDB client
 async function initializeCosmosClient() {
@@ -1,7 +1,10 @@
 import { CosmosClient } from "@azure/cosmos";
 import { DefaultAzureCredential } from "@azure/identity";
-import { AzureCosmosDBNoSQLConfig } from "@llamaindex/azure";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import {
+  AzureCosmosDBNoSQLConfig,
+  AzureOpenAI,
+  AzureOpenAIEmbedding,
+} from "@llamaindex/azure";
 import * as dotenv from "dotenv";
 import {
  Settings,
@@ -25,23 +28,19 @@ const containerName =
  process.env.AZURE_COSMOSDB_VECTOR_CONTAINER_NAME || "vectorContainer";

 const llmInit = {
-  azure: {
-    apiVersion: process.env.AZURE_OPENAI_LLM_API_VERSION,
-    endpoint: process.env.AZURE_OPENAI_LLM_ENDPOINT,
-    apiKey: process.env.AZURE_OPENAI_LLM_API_KEY,
-  },
+  apiVersion: process.env.AZURE_OPENAI_LLM_API_VERSION,
+  endpoint: process.env.AZURE_OPENAI_LLM_ENDPOINT,
+  apiKey: process.env.AZURE_OPENAI_LLM_API_KEY,
 };

 const embedModelInit = {
-  azure: {
-    apiVersion: process.env.AZURE_OPENAI_EMBEDDING_API_VERSION,
-    endpoint: process.env.AZURE_OPENAI_EMBEDDING_ENDPOINT,
-    apiKey: process.env.AZURE_OPENAI_EMBEDDING_API_KEY,
-  },
+  apiVersion: process.env.AZURE_OPENAI_EMBEDDING_API_VERSION,
+  endpoint: process.env.AZURE_OPENAI_EMBEDDING_ENDPOINT,
+  apiKey: process.env.AZURE_OPENAI_EMBEDDING_API_KEY,
 };

-Settings.llm = new OpenAI(llmInit);
-Settings.embedModel = new OpenAIEmbedding(embedModelInit);
+Settings.llm = new AzureOpenAI(llmInit);
+Settings.embedModel = new AzureOpenAIEmbedding(embedModelInit);

 async function initializeStores() {
  // Create a configuration object for the Azure CosmosDB NoSQL Vector Store
@@ -2,7 +2,8 @@ import { CollectionReference } from "@google-cloud/firestore";
 import "dotenv/config";

 import { FirestoreVectorStore } from "@llamaindex/firestore";
-import { OpenAIEmbedding, Settings } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { Settings } from "llamaindex";

 const indexName = "MovieReviews";

@@ -2,8 +2,8 @@ import { CollectionReference } from "@google-cloud/firestore";
 import { CSVReader } from "@llamaindex/readers/csv";
 import "dotenv/config";

+import { OpenAIEmbedding } from "@llamaindex/openai";
 import {
-  OpenAIEmbedding,
  Settings,
  storageContextFromDefaults,
  VectorStoreIndex,
@@ -1,6 +1,7 @@
 import "dotenv/config";

-import { OpenAIEmbedding, Settings, VectorStoreIndex } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { Settings, VectorStoreIndex } from "llamaindex";

 import { CollectionReference } from "@google-cloud/firestore";
 import { FirestoreVectorStore } from "@llamaindex/firestore";
@@ -10,13 +10,8 @@
    "outDir": "./lib",
    "tsBuildInfoFile": "./lib/.tsbuildinfo",
    "incremental": true,
-    "composite": true
+    "composite": true,
+    "jsx": "react-jsx"
  },
-  "ts-node": {
-    "files": true,
-    "compilerOptions": {
-      "module": "commonjs"
-    }
-  },
-  "include": ["./**/*.ts"]
+  "include": ["./**/*.ts", "./**/*.tsx"]
 }
@@ -1,5 +1,13 @@
 # @llamaindex/autotool

+## 8.0.0
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 7.0.6

 ### Patch Changes
@@ -1,5 +1,14 @@
 # @llamaindex/autotool-01-node-example

+## 0.0.108
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+  - @llamaindex/autotool@8.0.0
+
 ## 0.0.107

 ### Patch Changes
@@ -13,5 +13,5 @@
  "scripts": {
    "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
  },
-  "version": "0.0.107"
+  "version": "0.0.108"
 }
@@ -6,7 +6,7 @@
    "url": "git+https://github.com/run-llama/LlamaIndexTS.git",
    "directory": "packages/autotool"
  },
-  "version": "7.0.6",
+  "version": "8.0.0",
  "description": "auto transpile your JS function to LLM Agent compatible",
  "files": [
    "dist",
@@ -1,5 +1,13 @@
 # @llamaindex/cloud

+## 4.0.8
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/core@0.6.6
+
 ## 4.0.7

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloud",
-  "version": "4.0.7",
+  "version": "4.0.8",
  "type": "module",
  "license": "MIT",
  "scripts": {
@@ -1,5 +1,12 @@
 # @llamaindex/core

+## 0.6.6
+
+### Patch Changes
+
+- 680b529: Remove requireContext from tools - better use binding to pass context
+- 361a685: Remove old workflows - use @llamaindex/workflow package
+
 ## 0.6.5

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/core",
  "type": "module",
-  "version": "0.6.5",
+  "version": "0.6.6",
  "description": "LlamaIndex Core Module",
  "exports": {
    "./agent": {
@@ -136,17 +136,6 @@
      },
      "default": "./indices/dist/index.js"
    },
-    "./workflow": {
-      "require": {
-        "types": "./workflow/dist/index.d.cts",
-        "default": "./workflow/dist/index.cjs"
-      },
-      "import": {
-        "types": "./workflow/dist/index.d.ts",
-        "default": "./workflow/dist/index.js"
-      },
-      "default": "./workflow/dist/index.js"
-    },
    "./memory": {
      "require": {
        "types": "./memory/dist/index.d.cts",
@@ -9,7 +9,7 @@ export function getEmbeddedModel(): BaseEmbedding {
    embeddedModelAsyncLocalStorage.getStore() ?? globalEmbeddedModel;
  if (!currentEmbeddedModel) {
    throw new Error(
-      "Cannot find Embedding, please set `Settings.embedModel = ...` on the top of your code",
+      "Cannot find Embedding, please set `Settings.embedModel = ...` on the top of your code. Check https://ts.llamaindex.ai/docs/llamaindex/modules/models/embeddings for details.",
    );
  }
  return currentEmbeddedModel;
@@ -8,7 +8,7 @@ export function getLLM(): LLM {
  const currentLLM = llmAsyncLocalStorage.getStore() ?? globalLLM;
  if (!currentLLM) {
    throw new Error(
-      "Cannot find LLM, please set `Settings.llm = ...` on the top of your code",
+      "Cannot find LLM, please set `Settings.llm = ...` on the top of your code. Check https://ts.llamaindex.ai/docs/llamaindex/modules/models/llms for details.",
    );
  }
  return currentLLM;
@@ -256,10 +256,6 @@ export type ToolMetadata<
   * @link https://json-schema.org/understanding-json-schema
   */
  parameters?: Parameters;
-  /**
-   * Whether the tool requires workflow context to be passed in.
-   */
-  requireContext?: boolean;
 };

 /**
@@ -134,32 +134,16 @@ export class FunctionTool<
  };

  call = (input: T) => {
-    if (this.#metadata.requireContext) {
-      const inputWithContext = input as Record<string, unknown>;
-      if (!inputWithContext.context) {
-        throw new Error(
-          "Tool call requires context, but context parameter is missing",
-        );
-      }
-    }
+    let params = input;
    if (this.#zodType) {
      const result = this.#zodType.safeParse(input);
      if (result.success) {
-        if (this.#metadata.requireContext) {
-          const { context } = input as Record<string, unknown>;
-          return this.#fn.call(
-            null,
-            { context, ...result.data },
-            this.#additionalArg,
-          );
-        } else {
-          return this.#fn.call(null, result.data, this.#additionalArg);
-        }
+        params = result.data;
      } else {
        console.warn(result.error.errors);
      }
    }
-    return this.#fn.call(null, input, this.#additionalArg);
+    return this.#fn.call(null, params, this.#additionalArg);
  };
 }

@@ -1,114 +0,0 @@
-import { type EventTypes, type WorkflowEvent } from "./events";
-import { type StepFunction, type Workflow } from "./workflow";
-
-export class Context {
-  #workflow: Workflow;
-  #queues: Map<StepFunction, WorkflowEvent[]> = new Map();
-  #eventBuffer: Map<EventTypes, WorkflowEvent[]> = new Map();
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  #globals: Map<string, any> = new Map();
-  #streamingQueue: WorkflowEvent[] = [];
-  running: boolean = true;
-  #verbose: boolean = false;
-
-  constructor(params: { workflow: Workflow; verbose?: boolean }) {
-    this.#workflow = params.workflow;
-    this.#verbose = params.verbose ?? false;
-  }
-
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  set(key: string, value: any): void {
-    this.#globals.set(key, value);
-  }
-
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  get(key: string, defaultValue?: any): any {
-    if (this.#globals.has(key)) {
-      return this.#globals.get(key);
-    } else if (defaultValue !== undefined) {
-      return defaultValue;
-    }
-    throw new Error(`Key '${key}' not found in Context`);
-  }
-
-  collectEvents(
-    event: WorkflowEvent,
-    expected: EventTypes[],
-  ): WorkflowEvent[] | null {
-    const eventType = event.constructor as EventTypes;
-    if (!this.#eventBuffer.has(eventType)) {
-      this.#eventBuffer.set(eventType, []);
-    }
-    this.#eventBuffer.get(eventType)!.push(event);
-
-    const retval: WorkflowEvent[] = [];
-    for (const expectedType of expected) {
-      const events = this.#eventBuffer.get(expectedType);
-      if (events && events.length > 0) {
-        retval.push(events.shift()!);
-      }
-    }
-
-    if (retval.length === expected.length) {
-      return retval;
-    }
-
-    // Put back the events if unable to collect all
-    for (const ev of retval) {
-      const eventType = ev.constructor as EventTypes;
-      if (!this.#eventBuffer.has(eventType)) {
-        this.#eventBuffer.set(eventType, []);
-      }
-      this.#eventBuffer.get(eventType)!.unshift(ev);
-    }
-
-    return null;
-  }
-
-  sendEvent(message: WorkflowEvent, step?: StepFunction): void {
-    const stepName = step?.name ? `step ${step.name}` : "all steps";
-    if (this.#verbose) {
-      console.log(`Sending event ${message} to ${stepName}`);
-    }
-    if (step === undefined) {
-      for (const queue of this.#queues.values()) {
-        queue.push(message);
-      }
-    } else {
-      if (!this.#workflow.hasStep(step)) {
-        throw new Error(`Step ${step} does not exist`);
-      }
-
-      if (!this.#queues.has(step)) {
-        this.#queues.set(step, []);
-      }
-      this.#queues.get(step)!.push(message);
-    }
-  }
-
-  getNextEvent(step: StepFunction): WorkflowEvent | undefined {
-    const queue = this.#queues.get(step);
-    if (queue && queue.length > 0) {
-      return queue.shift();
-    }
-    return undefined;
-  }
-
-  writeEventToStream(event: WorkflowEvent): void {
-    this.#streamingQueue.push(event);
-  }
-
-  async *streamEvents(): AsyncGenerator<WorkflowEvent, void, void> {
-    while (true) {
-      const event = this.#streamingQueue.shift();
-      if (event) {
-        yield event;
-      } else {
-        if (!this.running) {
-          break;
-        }
-        await new Promise((resolve) => setTimeout(resolve, 0));
-      }
-    }
-  }
-}
@@ -1,20 +0,0 @@
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
-export class WorkflowEvent<T extends Record<string, any> = any> {
-  data: T;
-
-  constructor(data: T) {
-    this.data = data;
-  }
-
-  toString() {
-    return `${this.constructor.name}(${JSON.stringify(this.data)})`;
-  }
-}
-
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
-export type EventTypes<T extends Record<string, any> = any> = new (
-  data: T,
-) => WorkflowEvent<T>;
-
-export class StartEvent<T = string> extends WorkflowEvent<{ input: T }> {}
-export class StopEvent<T = string> extends WorkflowEvent<{ result: T }> {}
@@ -1,3 +0,0 @@
-export * from "./context";
-export * from "./events";
-export * from "./workflow";
@@ -1,238 +0,0 @@
-import { Context } from "./context";
-import {
-  type EventTypes,
-  StartEvent,
-  StopEvent,
-  WorkflowEvent,
-} from "./events";
-
-export type StepFunction<T extends WorkflowEvent = WorkflowEvent> = (
-  context: Context,
-  ev: T,
-) => Promise<WorkflowEvent | void>;
-
-type EventTypeParam = EventTypes | EventTypes[];
-
-let once = false;
-
-export class Workflow {
-  #steps: Map<
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    StepFunction<any>,
-    { inputs: EventTypes[]; outputs: EventTypes[] | undefined }
-  > = new Map();
-  #contexts: Set<Context> = new Set();
-  #verbose: boolean = false;
-  #timeout: number | null = null;
-  #validate: boolean = false;
-
-  constructor(
-    params: {
-      verbose?: boolean;
-      timeout?: number;
-      validate?: boolean;
-      ignoreDeprecatedWarning?: boolean;
-    } = {},
-  ) {
-    if (!once && !params.ignoreDeprecatedWarning) {
-      console.warn(
-        "@llamaindex/core/workflow is going to use the new workflow API in the next major version.",
-        "Please update your imports to @llamaindex/workflow",
-      );
-      console.warn(
-        "See https://ts.llamaindex.ai/docs/llamaindex/guide/workflow for more information",
-      );
-      once = true;
-    }
-
-    this.#verbose = params.verbose ?? false;
-    this.#timeout = params.timeout ?? null;
-    this.#validate = params.validate ?? false;
-  }
-
-  addStep<T extends WorkflowEvent>(
-    eventType: EventTypeParam,
-    method: StepFunction<T>,
-    params: { outputs?: EventTypeParam } = {},
-  ) {
-    const inputs = Array.isArray(eventType) ? eventType : [eventType];
-    const outputs = params.outputs
-      ? Array.isArray(params.outputs)
-        ? params.outputs
-        : [params.outputs]
-      : undefined;
-    this.#steps.set(method, { inputs, outputs });
-  }
-
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  hasStep(step: StepFunction<any>): boolean {
-    return this.#steps.has(step);
-  }
-
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  #acceptsEvent(step: StepFunction<any>, event: WorkflowEvent): boolean {
-    const eventType = event.constructor as EventTypes;
-    const stepInfo = this.#steps.get(step);
-    if (!stepInfo) {
-      throw new Error(`No method found for step: ${step.name}`);
-    }
-    return stepInfo.inputs.includes(eventType);
-  }
-
-  async *streamEvents(): AsyncGenerator<WorkflowEvent, void> {
-    if (this.#contexts.size > 1) {
-      throw new Error(
-        "This workflow has multiple concurrent runs in progress and cannot stream events. " +
-          "To be able to stream events, make sure you call `run()` on this workflow only once.",
-      );
-    }
-
-    const context = this.#contexts.values().next().value;
-    if (!context) {
-      throw new Error("No active context found for streaming events.");
-    }
-
-    yield* context.streamEvents();
-  }
-
-  validate(): void {
-    if (this.#verbose) {
-      console.log("Validating workflow...");
-    }
-
-    // Check if all steps have outputs defined
-    // precondition for the validation to work
-    const allStepsHaveOutputs = Array.from(this.#steps.values()).every(
-      (stepInfo) => stepInfo.outputs !== undefined,
-    );
-    if (!allStepsHaveOutputs) {
-      throw new Error(
-        "Not all steps have outputs defined. Can't validate. Add the 'outputs' parameter to each 'addStep' method call to do validation",
-      );
-    }
-
-    // input events that are consumed by any step of the workflow
-    const consumedEvents: Set<EventTypes> = new Set();
-    // output events that are produced by any step of the workflow
-    const producedEvents: Set<EventTypes> = new Set([StartEvent]);
-
-    for (const [, stepInfo] of this.#steps) {
-      stepInfo.inputs.forEach((eventType) => consumedEvents.add(eventType));
-      stepInfo.outputs?.forEach((eventType) => producedEvents.add(eventType));
-    }
-
-    // Check if all consumed events are produced
-    const unconsumedEvents = Array.from(consumedEvents).filter(
-      (event) => !producedEvents.has(event),
-    );
-    if (unconsumedEvents.length > 0) {
-      const names = unconsumedEvents.map((event) => event.name).join(", ");
-      throw new Error(
-        `The following events are consumed but never produced: ${names}`,
-      );
-    }
-
-    // Check if there are any unused produced events (except StopEvent)
-    const unusedEvents = Array.from(producedEvents).filter(
-      (event) => !consumedEvents.has(event) && event !== StopEvent,
-    );
-    if (unusedEvents.length > 0) {
-      const names = unusedEvents.map((event) => event.name).join(", ");
-      throw new Error(
-        `The following events are produced but never consumed: ${names}`,
-      );
-    }
-
-    if (this.#verbose) {
-      console.log("Workflow validation passed");
-    }
-  }
-
-  async run<T = string>(event: StartEvent<T> | string): Promise<StopEvent> {
-    // Validate the workflow before running if #validate is true
-    if (this.#validate) {
-      this.validate();
-    }
-
-    const context = new Context({ workflow: this, verbose: this.#verbose });
-    this.#contexts.add(context);
-
-    const stopWorkflow = () => {
-      if (context.running) {
-        context.running = false;
-        this.#contexts.delete(context);
-      }
-    };
-
-    const startEvent: WorkflowEvent =
-      typeof event === "string" ? new StartEvent({ input: event }) : event;
-
-    if (this.#verbose) {
-      console.log(`Starting workflow with event ${startEvent}`);
-    }
-
-    const workflowPromise = new Promise<StopEvent>((resolve, reject) => {
-      for (const [step] of this.#steps) {
-        // send initial event to step
-        context.sendEvent(startEvent, step);
-        if (this.#verbose) {
-          console.log(`Starting tasks for step ${step.name}`);
-        }
-        queueMicrotask(async () => {
-          try {
-            while (context.running) {
-              const currentEvent = context.getNextEvent(step);
-              if (!currentEvent) {
-                // if there's no event, wait and try again
-                await new Promise((resolve) => setTimeout(resolve, 0));
-                continue;
-              }
-              if (!this.#acceptsEvent(step, currentEvent)) {
-                // step does not accept current event, skip it
-                continue;
-              }
-              if (this.#verbose) {
-                console.log(`Step ${step.name} received event ${currentEvent}`);
-              }
-              const result = await step.call(this, context, currentEvent);
-              if (!context.running) {
-                // workflow was stopped during the execution (e.g. there was a timeout)
-                return;
-              }
-              if (result instanceof StopEvent) {
-                if (this.#verbose) {
-                  console.log(`Stopping workflow with event ${result}`);
-                }
-                resolve(result);
-                return;
-              }
-              if (result instanceof WorkflowEvent) {
-                context.sendEvent(result);
-              }
-            }
-          } catch (error) {
-            if (this.#verbose) {
-              console.error(`Error in calling step ${step.name}:`, error);
-            }
-            reject(error as Error);
-          } finally {
-            stopWorkflow();
-          }
-        });
-      }
-    });
-
-    if (this.#timeout !== null) {
-      const timeout = this.#timeout;
-      const timeoutPromise = new Promise<never>((_, reject) =>
-        setTimeout(() => {
-          stopWorkflow();
-          reject(new Error(`Operation timed out after ${timeout} seconds`));
-        }, timeout * 1000),
-      );
-      return Promise.race([workflowPromise, timeoutPromise]);
-    }
-
-    return workflowPromise;
-  }
-}
@@ -1,236 +0,0 @@
-import { beforeEach, describe, expect, test, vi, type Mocked } from "vitest";
-import type { Context } from "../src/workflow/context.js";
-import {
-  StartEvent,
-  StopEvent,
-  WorkflowEvent,
-} from "../src/workflow/events.js";
-import { Workflow } from "../src/workflow/workflow.js";
-
-// mock OpenAI class for testing
-class OpenAI {
-  complete = vi.fn();
-}
-
-class JokeEvent extends WorkflowEvent<{ joke: string }> {}
-class AnalysisEvent extends WorkflowEvent<{ analysis: string }> {}
-
-describe("Workflow", () => {
-  let mockLLM: Mocked<OpenAI>;
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  let generateJoke: Mocked<any>;
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  let critiqueJoke: Mocked<any>;
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  let analyzeJoke: Mocked<any>;
-  beforeEach(() => {
-    mockLLM = new OpenAI() as Mocked<OpenAI>;
-    mockLLM.complete
-      .mockResolvedValueOnce({
-        text: "Why do pirates make great singers? They can hit the high Cs!",
-      })
-      .mockResolvedValueOnce({
-        text: "This joke is clever but could use improvement...",
-      })
-      .mockResolvedValueOnce({
-        text: "The analysis is insightful and helpful.",
-      });
-
-    generateJoke = vi.fn(async (_context, ev: StartEvent) => {
-      const response = await mockLLM.complete({
-        prompt: `Write your best joke about ${ev.data.input}.`,
-      });
-      return new JokeEvent({ joke: response.text });
-    });
-
-    critiqueJoke = vi.fn(async (_context, ev: JokeEvent) => {
-      const response = await mockLLM.complete({
-        prompt: `Give a thorough critique of the following joke: ${ev.data.joke}`,
-      });
-      return new StopEvent({ result: response.text });
-    });
-
-    analyzeJoke = vi.fn(async (_context: Context, ev: JokeEvent) => {
-      const prompt = `Give a thorough analysis of the following joke: ${ev.data.joke}`;
-      const response = await mockLLM.complete({ prompt });
-      return new AnalysisEvent({ analysis: response.text });
-    });
-  });
-
-  test("addStep", () => {
-    const jokeFlow = new Workflow({ verbose: true });
-
-    jokeFlow.addStep(StartEvent, generateJoke);
-    jokeFlow.addStep(JokeEvent, critiqueJoke);
-
-    expect(jokeFlow.hasStep(generateJoke)).toBe(true);
-    expect(jokeFlow.hasStep(critiqueJoke)).toBe(true);
-  });
-
-  test("run workflow", async () => {
-    const jokeFlow = new Workflow({ verbose: true });
-
-    jokeFlow.addStep(StartEvent, generateJoke);
-    jokeFlow.addStep(JokeEvent, critiqueJoke);
-
-    const result = await jokeFlow.run("pirates");
-
-    expect(generateJoke).toHaveBeenCalledTimes(1);
-    expect(critiqueJoke).toHaveBeenCalledTimes(1);
-    expect(result.data.result).toBe(
-      "This joke is clever but could use improvement...",
-    );
-  });
-
-  test("stream events", async () => {
-    const jokeFlow = new Workflow({ verbose: true });
-
-    jokeFlow.addStep(StartEvent, generateJoke);
-    jokeFlow.addStep(JokeEvent, critiqueJoke);
-
-    const run = jokeFlow.run("pirates");
-    const event = await jokeFlow.streamEvents().next(); // get one event to avoid testing timeout
-    const result = await run;
-
-    expect(generateJoke).toHaveBeenCalledTimes(1);
-    expect(critiqueJoke).toHaveBeenCalledTimes(1);
-    expect(result.data.result).toBe(
-      "This joke is clever but could use improvement...",
-    );
-    expect(event).not.toBeNull();
-  });
-
-  test("workflow timeout", async () => {
-    const TIMEOUT = 1;
-    const jokeFlow = new Workflow({ verbose: true, timeout: TIMEOUT });
-
-    const longRunning = async (_context: Context, ev: StartEvent) => {
-      await new Promise((resolve) => setTimeout(resolve, 2000)); // Wait for 2 seconds
-      return new StopEvent({ result: "We waited 2 seconds" });
-    };
-
-    jokeFlow.addStep(StartEvent, longRunning);
-    const run = jokeFlow.run("Let's start");
-    await expect(run).rejects.toThrow(
-      `Operation timed out after ${TIMEOUT} seconds`,
-    );
-  });
-
-  test("workflow validation", async () => {
-    const jokeFlow = new Workflow({ verbose: true, validate: true });
-    jokeFlow.addStep(StartEvent, generateJoke, { outputs: StopEvent });
-    jokeFlow.addStep(JokeEvent, critiqueJoke, { outputs: StopEvent });
-    const run = jokeFlow.run("pirates");
-    await expect(run).rejects.toThrow(
-      "The following events are consumed but never produced: JokeEvent",
-    );
-  });
-
-  test("collectEvents", async () => {
-    let collectedEvents: WorkflowEvent[] | null = null;
-    const jokeFlow = new Workflow({ verbose: true });
-
-    jokeFlow.addStep(StartEvent, generateJoke);
-    jokeFlow.addStep(JokeEvent, analyzeJoke);
-    jokeFlow.addStep([AnalysisEvent], async (context, ev) => {
-      collectedEvents = context.collectEvents(ev, [AnalysisEvent]);
-      return new StopEvent({ result: "Report generated" });
-    });
-
-    const result = await jokeFlow.run("pirates");
-    expect(generateJoke).toHaveBeenCalledTimes(1);
-    expect(analyzeJoke).toHaveBeenCalledTimes(1);
-    expect(result.data.result).toBe("Report generated");
-    expect(collectedEvents).toHaveLength(1);
-  });
-
-  test("run workflow with object-based StartEvent and StopEvent", async () => {
-    const objectFlow = new Workflow({ verbose: true });
-
-    type Person = { name: string; age: number };
-
-    const processObject = vi.fn(async (_context, ev: StartEvent<Person>) => {
-      const { name, age } = ev.data.input;
-      return new StopEvent({
-        result: { greeting: `Hello ${name}, you are ${age} years old!` },
-      });
-    });
-
-    objectFlow.addStep(StartEvent<Person>, processObject);
-
-    const result = await objectFlow.run(
-      new StartEvent<Person>({
-        input: { name: "Alice", age: 30 },
-      }),
-    );
-
-    expect(processObject).toHaveBeenCalledTimes(1);
-    expect(result.data.result).toEqual({
-      greeting: "Hello Alice, you are 30 years old!",
-    });
-  });
-
-  test("workflow with two concurrent steps", async () => {
-    const concurrentFlow = new Workflow({ verbose: true });
-
-    const step1 = vi.fn(async (_context, _ev: StartEvent) => {
-      await new Promise((resolve) => setTimeout(resolve, 200));
-      return new StopEvent({ result: "Step 1 completed" });
-    });
-
-    const step2 = vi.fn(async (_context, _ev: StartEvent) => {
-      await new Promise((resolve) => setTimeout(resolve, 100));
-      return new StopEvent({ result: "Step 2 completed" });
-    });
-
-    concurrentFlow.addStep(StartEvent, step1);
-    concurrentFlow.addStep(StartEvent, step2);
-
-    const startTime = new Date();
-    const result = await concurrentFlow.run("start");
-    const endTime = new Date();
-    const duration = endTime.getTime() - startTime.getTime();
-
-    expect(step1).toHaveBeenCalledTimes(1);
-    expect(step2).toHaveBeenCalledTimes(1);
-    expect(duration).toBeLessThan(200);
-    expect(result.data.result).toBe("Step 2 completed");
-  });
-
-  test("workflow with two concurrent cyclic steps", async () => {
-    const concurrentCyclicFlow = new Workflow({ verbose: true });
-
-    class Step1Event extends WorkflowEvent {}
-    class Step2Event extends WorkflowEvent {}
-
-    let step2Count = 0;
-
-    const step1 = vi.fn(async (_context, ev: StartEvent | Step1Event) => {
-      await new Promise((resolve) => setTimeout(resolve, 1000));
-      return new Step1Event({ result: "Step 1 completed" });
-    });
-
-    const step2 = vi.fn(async (_context, ev: StartEvent | Step2Event) => {
-      await new Promise((resolve) => setTimeout(resolve, 100));
-      step2Count++;
-      if (step2Count >= 5) {
-        return new StopEvent({ result: "Step 2 completed 5 times" });
-      }
-      return new Step2Event({ result: "Step 2 completed" });
-    });
-
-    concurrentCyclicFlow.addStep([StartEvent, Step1Event], step1);
-    concurrentCyclicFlow.addStep([StartEvent, Step2Event], step2);
-
-    const startTime = new Date();
-    const result = await concurrentCyclicFlow.run("start");
-    const endTime = new Date();
-    const duration = endTime.getTime() - startTime.getTime();
-
-    expect(step1).toHaveBeenCalledTimes(1);
-    expect(step2).toHaveBeenCalledTimes(5);
-    expect(duration).toBeGreaterThan(500); // At least 5 * 100ms for step2
-    expect(duration).toBeLessThan(1000); // Less than 1 second
-    expect(result.data.result).toBe("Step 2 completed 5 times");
-  });
-});
@@ -1,8 +0,0 @@
-{
-  "type": "module",
-  "main": "./dist/index.cjs",
-  "module": "./dist/index.js",
-  "types": "./dist/index.d.ts",
-  "exports": "./dist/index.js",
-  "private": true
-}
@@ -1,5 +1,13 @@
 # @llamaindex/experimental

+## 0.0.177
+
+### Patch Changes
+
+- Updated dependencies [b0cd530]
+- Updated dependencies [361a685]
+  - llamaindex@0.11.0
+
 ## 0.0.176

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/experimental",
  "description": "Experimental package for LlamaIndexTS",
-  "version": "0.0.176",
+  "version": "0.0.177",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
@@ -1,5 +1,30 @@
 # llamaindex

+## 0.11.0
+
+### Minor Changes
+
+- b0cd530: # Breaking Change
+
+  ## What Changed
+
+  Remove default setting of llm and embedModel in Settings
+
+  ## Migration Guide
+
+  Set the llm provider and embed Model in the top of your code using Settings.llm = and Settings.embedModel
+
+- 361a685: Remove old workflows - use @llamaindex/workflow package
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/workflow@1.1.3
+  - @llamaindex/core@0.6.6
+  - @llamaindex/cloud@4.0.8
+  - @llamaindex/node-parser@2.0.6
+
 ## 0.10.6

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "llamaindex",
-  "version": "0.10.6",
+  "version": "0.11.0",
  "license": "MIT",
  "type": "module",
  "keywords": [
@@ -24,7 +24,6 @@
    "@llamaindex/core": "workspace:*",
    "@llamaindex/env": "workspace:*",
    "@llamaindex/node-parser": "workspace:*",
-    "@llamaindex/openai": "workspace:*",
    "@llamaindex/workflow": "1.0.3",
    "@types/lodash": "^4.17.7",
    "@types/node": "^22.9.0",
@@ -8,8 +8,8 @@ import {
 import type { QueryType } from "@llamaindex/core/query-engine";
 import type { BaseOutputParser } from "@llamaindex/core/schema";
 import { extractText, toToolDescriptions } from "@llamaindex/core/utils";
-import { OpenAI } from "@llamaindex/openai";
 import { SubQuestionOutputParser } from "./OutputParser.js";
+import { Settings } from "./Settings.js";
 import type {
  BaseQuestionGenerator,
  SubQuestion,
@@ -30,7 +30,7 @@ export class LLMQuestionGenerator
  constructor(init?: Partial<LLMQuestionGenerator>) {
    super();

-    this.llm = init?.llm ?? new OpenAI();
+    this.llm = init?.llm ?? Settings.llm;
    this.prompt = init?.prompt ?? defaultSubQuestionPrompt;
    this.outputParser = init?.outputParser ?? new SubQuestionOutputParser();
  }
@@ -1,73 +0,0 @@
-import type {
-  NonStreamingChatEngineParams,
-  StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
-import type { MessageContent } from "@llamaindex/core/llms";
-import type { BaseRetriever } from "@llamaindex/core/retriever";
-import { EngineResponse, MetadataMode } from "@llamaindex/core/schema";
-import { OpenAIAgent, type OpenAIAgentParams } from "@llamaindex/openai";
-
-export interface ContextAwareConfig {
-  contextRetriever: BaseRetriever;
-}
-
-export interface ContextAwareState {
-  contextRetriever: BaseRetriever;
-  retrievedContext: string | null;
-}
-
-// TODO: support any LLMAgent
-export type SupportedAgent = typeof OpenAIAgent;
-export type AgentParams = OpenAIAgentParams;
-
-/**
- * ContextAwareAgentRunner enhances the base AgentRunner with the ability to retrieve and inject relevant context
- * for each query. This allows the agent to access and utilize appropriate information from a given index or retriever,
- * providing more informed and context-specific responses to user queries.
- */
-export function withContextAwareness(Base: SupportedAgent) {
-  return class ContextAwareAgent extends Base {
-    public readonly contextRetriever: BaseRetriever;
-    public retrievedContext: string | null = null;
-
-    constructor(params: AgentParams & ContextAwareConfig) {
-      super(params);
-      this.contextRetriever = params.contextRetriever;
-    }
-
-    async retrieveContext(query: MessageContent): Promise<string> {
-      const nodes = await this.contextRetriever.retrieve({ query });
-      return nodes
-        .map((node) => node.node.getContent(MetadataMode.NONE))
-        .join("\n");
-    }
-
-    async injectContext(context: string): Promise<void> {
-      const systemMessage = this.chatHistory.find(
-        (msg) => msg.role === "system",
-      );
-      if (systemMessage) {
-        systemMessage.content = `${context}\n\n${systemMessage.content}`;
-      } else {
-        this.chatHistory.unshift({ role: "system", content: context });
-      }
-    }
-
-    async chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
-    async chat(
-      params: StreamingChatEngineParams,
-    ): Promise<ReadableStream<EngineResponse>>;
-    async chat(
-      params: NonStreamingChatEngineParams | StreamingChatEngineParams,
-    ): Promise<EngineResponse | ReadableStream<EngineResponse>> {
-      const context = await this.retrieveContext(params.message);
-      await this.injectContext(context);
-
-      if ("stream" in params && params.stream === true) {
-        return super.chat(params);
-      } else {
-        return super.chat(params as NonStreamingChatEngineParams);
-      }
-    }
-  };
-}
@@ -1,6 +1,5 @@
 export * from "@llamaindex/core/agent";

-export { OpenAIContextAwareAgent } from "./openai.js";
 export {
  ReACTAgentWorker,
  ReActAgent,
@@ -1,7 +0,0 @@
-import { OpenAIAgent } from "@llamaindex/openai";
-import { withContextAwareness } from "./contextAwareMixin.js";
-
-export const OpenAIContextAwareAgent = withContextAwareness(OpenAIAgent);
-export type { ContextAwareConfig } from "./contextAwareMixin.js";
-
-export * from "@llamaindex/openai";
@@ -14,7 +14,7 @@ import {
 } from "@llamaindex/core/prompts";
 import type { BaseNode } from "@llamaindex/core/schema";
 import { MetadataMode, TextNode } from "@llamaindex/core/schema";
-import { OpenAI } from "@llamaindex/openai";
+import { Settings } from "../Settings.js";
 import { BaseExtractor } from "./types.js";

 const STRIP_REGEX = /(\r\n|\n|\r)/gm;
@@ -64,7 +64,7 @@ export class KeywordExtractor extends BaseExtractor {

    super();

-    this.llm = options?.llm ?? new OpenAI();
+    this.llm = options?.llm ?? Settings.llm;
    this.keywords = options?.keywords ?? 5;
    this.promptTemplate = options?.promptTemplate
      ? new PromptTemplate({
@@ -170,7 +170,7 @@ export class TitleExtractor extends BaseExtractor {
  constructor(options?: TitleExtractorsArgs) {
    super();

-    this.llm = options?.llm ?? new OpenAI();
+    this.llm = options?.llm ?? Settings.llm;
    this.nodes = options?.nodes ?? 5;

    this.nodeTemplate = options?.nodeTemplate
@@ -330,7 +330,7 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {

    super();

-    this.llm = options?.llm ?? new OpenAI();
+    this.llm = options?.llm ?? Settings.llm;
    this.questions = options?.questions ?? 5;
    this.promptTemplate = options?.promptTemplate
      ? new PromptTemplate({
@@ -436,7 +436,7 @@ export class SummaryExtractor extends BaseExtractor {

    super();

-    this.llm = options?.llm ?? new OpenAI();
+    this.llm = options?.llm ?? Settings.llm;
    this.summaries = summaries;
    this.promptTemplate = options?.promptTemplate
      ? new PromptTemplate({
@@ -1,17 +1,6 @@
 //#region initial setup for OpenAI
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import { Settings } from "./Settings.js";

-try {
-  // eslint-disable-next-line @typescript-eslint/no-unused-expressions
-  Settings.llm;
-  // eslint-disable-next-line @typescript-eslint/no-unused-expressions
-  Settings.embedModel;
-} catch {
-  Settings.llm = new OpenAI();
-  Settings.embedModel = new OpenAIEmbedding();
-}
-
 //#endregion

 export {
@@ -67,7 +56,6 @@ export { BaseDocumentStore } from "@llamaindex/core/storage/doc-store"; // expli
 export * from "@llamaindex/core/storage/index-store";
 export * from "@llamaindex/core/storage/kv-store";
 export * from "@llamaindex/core/utils";
-export * from "@llamaindex/openai";
 export * from "./agent/index.js";
 export * from "./cloud/index.js";
 export * from "./engines/index.js";
@@ -85,5 +73,4 @@ export * from "./selectors/index.js";
 export * from "./storage/StorageContext.js";
 export * from "./tools/index.js";
 export * from "./types.js";
-export * from "./workflow.js";
 export { Settings };
@@ -1,19 +0,0 @@
-import { Workflow as OriginalWorkflow } from "@llamaindex/workflow";
-export * from "@llamaindex/workflow";
-
-/**
- * @deprecated The Workflow class is deprecated. Please import directly from "@llamaindex/workflow" in the future.
- */
-export class Workflow<ContextData, Start, Stop> extends OriginalWorkflow<
-  ContextData,
-  Start,
-  Stop
-> {
-  constructor(...args: any[]) {
-    // Need to figure out the constructor args for Workflow
-    console.warn(
-      "The Workflow class exported from 'llamaindex' is deprecated. Please use workflows directly from '@llamaindex/workflow' in the future. See https://ts.llamaindex.ai/docs/llamaindex/modules/agents/workflows for usage.",
-    );
-    super(...args);
-  }
-}
@@ -1,5 +1,22 @@
 # @llamaindex/core-test

+## 0.1.1
+
+### Patch Changes
+
+- b0cd530: # Breaking Change
+
+  ## What Changed
+
+  Remove default setting of llm and embedModel in Settings
+
+  ## Migration Guide
+
+  Set the llm provider and embed Model in the top of your code using Settings.llm = and Settings.embedModel
+
+- Updated dependencies [3e66ddc]
+  - @llamaindex/openai@0.4.0
+
 ## 0.1.0

 ### Minor Changes
@@ -1,4 +1,5 @@
-import { OpenAIEmbedding, SimilarityType, similarity } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { SimilarityType, similarity } from "llamaindex";
 import { beforeAll, describe, expect, test } from "vitest";
 import { mockEmbeddingModel } from "./utility/mockOpenAI.js";

@@ -1,8 +1,7 @@
 import { Document } from "@llamaindex/core/schema";
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import {
  KeywordExtractor,
-  OpenAI,
-  OpenAIEmbedding,
  QuestionsAnsweredExtractor,
  SentenceSplitter,
  Settings,
@@ -1,7 +1,8 @@
 import { describe, expect, test } from "vitest";
 // from unittest.mock import patch

-import { LLMSingleSelector, OpenAI } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { LLMSingleSelector } from "llamaindex";
 import { mocStructuredkLlmGeneration } from "./utility/mockOpenAI.js";

 describe("LLMSelector", () => {
@@ -1,4 +1,9 @@
-import { storageContextFromDefaults, type StorageContext } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import {
+  Settings,
+  storageContextFromDefaults,
+  type StorageContext,
+} from "llamaindex";
 import { existsSync, rmSync } from "node:fs";
 import { mkdtemp } from "node:fs/promises";
 import { tmpdir } from "node:os";
@@ -19,6 +24,7 @@ describe("StorageContext", () => {
  let storageContext: StorageContext;

  beforeAll(async () => {
+    Settings.embedModel = new OpenAIEmbedding();
    storageContext = await storageContextFromDefaults({
      persistDir: testDir,
    });
@@ -1,6 +1,6 @@
+import { OpenAIEmbedding } from "@llamaindex/openai";
 import {
  Document,
-  OpenAIEmbedding,
  Settings,
  SummaryIndex,
  VectorStoreIndex,
@@ -1,8 +1,8 @@
+import { OpenAIEmbedding } from "@llamaindex/openai";
 import type { StorageContext } from "llamaindex";
 import {
  DocStoreStrategy,
  Document,
-  OpenAIEmbedding,
  Settings,
  VectorStoreIndex,
 } from "llamaindex";
@@ -1,7 +0,0 @@
-import { expect, test, vi } from "vitest";
-
-test("init without error", async () => {
-  vi.stubEnv("OPENAI_API_KEY", undefined);
-  const { Settings } = await import("llamaindex");
-  expect(Settings.llm).toBeDefined();
-});
@@ -1,7 +1,7 @@
+import { OpenAIEmbedding } from "@llamaindex/openai";
 import {
  FunctionTool,
  ObjectIndex,
-  OpenAIEmbedding,
  Settings,
  SimpleToolNodeMapping,
  VectorStoreIndex,
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/llamaindex-test",
  "private": true,
-  "version": "0.1.0",
+  "version": "0.1.1",
  "type": "module",
  "scripts": {
    "test": "vitest run"
@@ -9,5 +9,8 @@
  "devDependencies": {
    "llamaindex": "workspace:*",
    "vitest": "^2.1.5"
+  },
+  "dependencies": {
+    "@llamaindex/openai": "workspace:*"
  }
 }
@@ -1,6 +1,7 @@
 import type { CallbackManager } from "@llamaindex/core/global";
-import type { LLMChatParamsBase, OpenAIEmbedding } from "llamaindex";
-import { OpenAI, Settings } from "llamaindex";
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import type { LLMChatParamsBase } from "llamaindex";
+import { Settings } from "llamaindex";
 import { vi } from "vitest";

 export const DEFAULT_LLM_TEXT_OUTPUT = "MOCK_TOKEN_1-MOCK_TOKEN_2";
@@ -1,8 +1,5 @@
-import {
-  BaseEmbedding,
-  OpenAIEmbedding,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { BaseEmbedding, storageContextFromDefaults } from "llamaindex";

 import { mockEmbeddingModel } from "./mockOpenAI.js";

@@ -1,5 +1,13 @@
 # @llamaindex/node-parser

+## 2.0.6
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/core@0.6.6
+
 ## 2.0.5

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/node-parser",
-  "version": "2.0.5",
+  "version": "2.0.6",
  "description": "Node parser for LlamaIndex",
  "type": "module",
  "exports": {
@@ -1,5 +1,13 @@
 # @llamaindex/anthropic

+## 0.3.7
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/core@0.6.6
+
 ## 0.3.6

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/anthropic",
  "description": "Anthropic Adapter for LlamaIndex",
-  "version": "0.3.6",
+  "version": "0.3.7",
  "type": "module",
  "main": "./dist/index.cjs",
  "module": "./dist/index.js",
@@ -1,5 +1,13 @@
 # @llamaindex/assemblyai

+## 0.1.5
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/core@0.6.6
+
 ## 0.1.4

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/assemblyai",
  "description": "AssemblyAI Reader for LlamaIndex",
-  "version": "0.1.4",
+  "version": "0.1.5",
  "type": "module",
  "types": "dist/index.d.ts",
  "main": "dist/index.cjs",
@@ -1,5 +1,13 @@
 # @llamaindex/community

+## 0.0.100
+
+### Patch Changes
+
+- Updated dependencies [680b529]
+- Updated dependencies [361a685]
+  - @llamaindex/core@0.6.6
+
 ## 0.0.99

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/aws",
  "description": "AWS package for LlamaIndexTS",
-  "version": "0.0.99",
+  "version": "0.0.100",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Marcus Schiesser	9aeea6f1ea	feat: use ink to render workflow	2025-05-19 13:36:08 +07:00
Marcus Schiesser	a882bbf9c8	chore: copied doc-workflow example from CL	2025-05-19 12:40:45 +07:00
github-actions[bot]	f63b702bec	Release 0.11.0 (#1950 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-05-19 12:23:04 +07:00
Marcus Schiesser	ccde88fe0b	docs: update azure docs (#1958 )	2025-05-19 11:49:18 +07:00
ANKIT VARSHNEY	b0cd5301bb	remove openai from llamaindex package and remove default setting for llm and embedModel (#1809 ) Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com> Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-05-19 11:12:57 +07:00
Marcus Schiesser	3e66ddc10d	chore: Move Azure models to azure package (#1888 )	2025-05-16 15:50:12 +07:00
Marcus Schiesser	c719b968f3	Fix: broken links in docs (#1956 ) Co-authored-by: Andrew Kostka <apkostka@gmail.com>	2025-05-15 16:49:05 +07:00
Anubhav Rana	c73c659c6d	chore: qdrant version updates (#1913 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-05-15 12:30:24 +07:00
Marcus Schiesser	361a685012	chore: remove old workflows (#1951 )	2025-05-15 10:29:47 +07:00
Marcus Schiesser	680b529e94	chore: remove requireContext from tools (#1949 )	2025-05-14 16:38:44 +07:00