Release 0.9.19 (#1844 )

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
Add GPT-4.1 models to OpenAI (#1842 )
2026-07-01 22:14:03 -04:00 · 2025-04-15 00:31:31 -07:00 · 2025-04-15 09:07:38 +02:00 · 2025-04-15 09:06:39 +02:00 · 2025-04-14 20:52:09 +07:00 · 2025-04-14 13:48:21 +02:00
503 changed files with 26615 additions and 3155 deletions
@@ -14,13 +14,14 @@ There are some important folders in the repository:
    all JS runtime environments.
  - `env`: The environment package of LlamaIndex.TS, which contains the environment-specific classes and interfaces. It
    includes compatibility layers for Node.js, Deno, Vercel Edge Runtime, Cloudflare Workers...
+  - `providers/*`: The providers package of LlamaIndex.TS, which contains the providers for LLM and other services.
 - `apps/*`: The applications based on LlamaIndex.TS.
  - `next`: Our documentation website based on Next.js.
 - `examples`: The code examples of LlamaIndex.TS using Node.js.

 ## Getting Started

-Make sure you have Node.js LIS (Long-term Support) installed. You can check your Node.js version by running:
+Make sure you have Node.js LTS (Long-term Support) installed. You can check your Node.js version by running:

 ```shell
 node -v
@@ -30,7 +31,7 @@ node -v
 ### Use pnpm

 ```shell
-corepack enable
+npm install -g pnpm
 ```

 ### Install dependencies
@@ -41,33 +42,65 @@ pnpm install

 ### Build the packages

-You'll need Turbo to build the packages. If you don't have it, you can run it with `pnpx`.
-
 To build all packages, run:

 ```shell
-# Build all packages
-pnpx turbo build --filter "./packages/*"
-
-# Or if you have turbo installed, you can run:
-turbo build --filter "./packages/*"
+pnpm build
 ```

+### Run tests
+
+#### Unit tests
+
+After build, to run all unit tests, call:
+
+```shell
+pnpm test
+```
+
+Unit tests are located in the `tests` folder of each package. They are using their own package (e.g. `@llamaindex/core-tests` for `@llamaindex/core`). The tests are importing the package under test and the test package is not published.
+
+#### E2E tests
+
+To run all E2E tests, call:
+
+```shell
+pnpm e2e
+```
+
+All E2E tests are in the `e2e` folder.
+
 ### Docs

 See the [docs](./apps/next/README.md) for more information.

-## Changeset
+## Adding a new package
+
+Please follow these steps to add a new package:
+
+1. Only add new packages to the `packages/providers` folder.
+2. Use the `package.json` and `tsconfig.json` of an existing packages as template.
+3. Reference your new package in the root `tsconfig.json` file
+4. Add your package to the `examples/package.json` file if you add a new example.
+
+## Before sending a PR
+
+Before sending a PR, make sure of the following:
+
+1. Tests are all running and you added meaningful tests for your change.
+2. If you have a new feature, document it in the `apps/next` docs folder.
+3. If you have a new feature, add a new example in the `examples` folder.
+4. You have a descriptive changeset for each PR:
+
+### Changesets

 We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
 changeset, run in the root folder:

-```
+```shell
 pnpm changeset
 ```

-Please send a descriptive changeset for each PR.
-
 ## Publishing (maintainers only)

 The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
@@ -1,5 +1,128 @@
 # @llamaindex/doc

+## 0.2.9
+
+### Patch Changes
+
+- Updated dependencies [d365eb2]
+  - @llamaindex/openai@0.3.2
+  - llamaindex@0.9.19
+
+## 0.2.8
+
+### Patch Changes
+
+- 2ffdb27: docs: correct the CondenseQuestionChatEngine path
+- Updated dependencies [88b7046]
+  - @llamaindex/openai@0.3.1
+  - llamaindex@0.9.18
+
+## 0.2.7
+
+### Patch Changes
+
+- 3ffee26: feat: enhance config params for LlamaIndexServer
+
+## 0.2.6
+
+### Patch Changes
+
+- Updated dependencies [3534c37]
+- Updated dependencies [41191d0]
+  - llamaindex@0.9.17
+  - @llamaindex/workflow@1.0.3
+  - @llamaindex/cloud@4.0.3
+
+## 0.2.5
+
+### Patch Changes
+
+- 4999df1: bump nextjs
+- Updated dependencies [f5e4d09]
+  - llamaindex@0.9.16
+
+## 0.2.4
+
+### Patch Changes
+
+- 9c63f3f: Add support for openai responses api
+- Updated dependencies [9c63f3f]
+- Updated dependencies [c515a32]
+  - @llamaindex/openai@0.3.0
+  - @llamaindex/core@0.6.2
+  - @llamaindex/workflow@1.0.2
+  - llamaindex@0.9.15
+  - @llamaindex/cloud@4.0.2
+  - @llamaindex/node-parser@2.0.2
+  - @llamaindex/readers@3.0.2
+
+## 0.2.3
+
+### Patch Changes
+
+- 648cfb5: Add support for supabase vector store
+  Added doc for the supbase vector store
+- Updated dependencies [1b6f368]
+- Updated dependencies [eaf326e]
+- Updated dependencies [9d951b2]
+  - @llamaindex/core@0.6.1
+  - llamaindex@0.9.14
+  - @llamaindex/cloud@4.0.1
+  - @llamaindex/node-parser@2.0.1
+  - @llamaindex/openai@0.2.1
+  - @llamaindex/readers@3.0.1
+  - @llamaindex/workflow@1.0.1
+
+## 0.2.2
+
+### Patch Changes
+
+- e98033e: docs: correct the number of indexes
+
+## 0.2.1
+
+### Patch Changes
+
+- Updated dependencies [75d6e29]
+  - llamaindex@0.9.13
+
+## 0.2.0
+
+### Minor Changes
+
+- f1db9b3: Adding an options parameter to vercel tool to tailor responses
+
+### Patch Changes
+
+- 21bebfc: Expose more content to fix the issue with unavailable documentation links, and adjust the documentation based on the latest code.
+- 2b39cef: Added documentation for structured output in openai and ollama
+- Updated dependencies [21bebfc]
+- Updated dependencies [93bc0ff]
+- Updated dependencies [91a18e7]
+- Updated dependencies [bf56fc0]
+- Updated dependencies [f8a86e4]
+- Updated dependencies [5189b44]
+- Updated dependencies [58a9446]
+  - @llamaindex/readers@3.0.0
+  - @llamaindex/core@0.6.0
+  - @llamaindex/openai@0.2.0
+  - @llamaindex/cloud@4.0.0
+  - @llamaindex/workflow@1.0.0
+  - llamaindex@0.9.12
+  - @llamaindex/node-parser@2.0.0
+
+## 0.1.11
+
+### Patch Changes
+
+- a8c0637: feat: simplify to provide base URL to OpenAI
+- a654f58: Added docs for using perplexity
+- 98eebf7: Add RequestOptions parameter passing to support Gemini proxy calls.
+  Add a usage example for the RequestOptions parameter.
+- Updated dependencies [a8c0637]
+  - @llamaindex/openai@0.1.61
+  - llamaindex@0.9.11
+
 ## 0.1.10

 ### Patch Changes
@@ -6,8 +6,7 @@ This is a Next.js application generated with
 Run development server:

 ```bash
-turbo run dev
-# turbo will build all required packages before running the dev server
+pnpm run dev
 ```

 ## Learn More
@@ -4,6 +4,8 @@ const withMDX = createMDX();

 /** @type {import('next').NextConfig} */
 const config = {
+  // default timeout for static generation is 60s, but we need to increase it to 10 minutes due to the large number of document pages
+  staticPageGenerationTimeout: 600,
  reactStrictMode: true,
  eslint: {
    ignoreDuringBuilds: true,
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.1.10",
+  "version": "0.2.9",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -8,8 +8,9 @@
    "build": "next build",
    "dev": "next dev",
    "start": "next start",
-    "postbuild": "tsx scripts/post-build.mts",
-    "build:docs": "cross-env NODE_OPTIONS=\"--max-old-space-size=8192\" typedoc && tsx scripts/generate-docs.mts"
+    "postbuild": "tsx scripts/post-build.mts && tsx scripts/validate-links.mts",
+    "build:docs": "cross-env NODE_OPTIONS=\"--max-old-space-size=8192\" typedoc && tsx scripts/generate-docs.mts",
+    "validate-links": "tsx scripts/validate-links.mts"
  },
  "dependencies": {
    "@icons-pack/react-simple-icons": "^10.1.0",
@@ -45,7 +46,7 @@
    "hast-util-to-jsx-runtime": "^2.3.2",
    "llamaindex": "workspace:*",
    "lucide-react": "^0.460.0",
-    "next": "^15.2.1",
+    "next": "^15.2.4",
    "next-themes": "^0.4.3",
    "react": "^19.0.0",
    "react-dom": "^19.0.0",
@@ -0,0 +1,249 @@
+import glob from "fast-glob";
+import fs from "fs";
+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"
+const INTERNAL_LINK_REGEX = /(?:(?:\]\(|\bhref=["'])\/docs\/([^")]+))/g;
+
+// Regular expression to find relative links
+// This captures relative links like [text](./path) or ![alt](../images/image.png)
+const RELATIVE_LINK_REGEX = /(?:\]\()(?:\s*)(?:\.\.?)\//g;
+
+interface LinkValidationResult {
+  file: string;
+  invalidLinks: Array<{ link: string; line: number }>;
+}
+
+interface RelativeLinkResult {
+  file: string;
+  relativeLinks: Array<{ line: number; lineContent: string }>;
+}
+
+/**
+ * Get all valid documentation routes from the content directory
+ */
+async function getValidRoutes(): Promise<Set<string>> {
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
+
+  const routes = new Set<string>();
+
+  // Add each MDX file as a valid route
+  for (const file of mdxFiles) {
+    // Remove .mdx extension and normalize to route format
+    let route = file.replace(/\.mdx$/, "");
+
+    // Handle index files
+    if (route.endsWith("/index")) {
+      route = route.replace(/\/index$/, "");
+    } else if (route === "index") {
+      route = "";
+    }
+
+    routes.add(route);
+  }
+
+  return routes;
+}
+
+/**
+ * Extract internal links from a MDX file
+ */
+function extractLinksFromFile(
+  filePath: string,
+): Array<{ link: string; line: number }> {
+  const content = fs.readFileSync(filePath, "utf-8");
+  const { content: mdxContent } = matter(content);
+
+  const lines = mdxContent.split("\n");
+  const links: Array<{ link: string; line: number }> = [];
+
+  lines.forEach((line, lineNumber) => {
+    let match;
+    while ((match = INTERNAL_LINK_REGEX.exec(line)) !== null) {
+      if (match[1]) {
+        links.push({
+          link: match[1],
+          line: lineNumber + 1, // 1-based line numbers
+        });
+      }
+    }
+  });
+
+  return links;
+}
+
+/**
+ * Check if a link is an image link
+ */
+function isImageLink(link: string): boolean {
+  // Check for image extensions
+  const imageExtensions = [".png", ".jpg", ".jpeg", ".gif", ".svg", ".webp"];
+  const hasImageExtension = imageExtensions.some((ext) =>
+    link.toLowerCase().endsWith(ext),
+  );
+
+  // Check for markdown image syntax: ![alt](./path)
+  const isMarkdownImage = link.trim().startsWith("!");
+
+  return hasImageExtension || isMarkdownImage;
+}
+
+/**
+ * Extract relative links from a MDX file
+ */
+function findRelativeLinksInFile(
+  filePath: string,
+): Array<{ line: number; lineContent: string }> {
+  const content = fs.readFileSync(filePath, "utf-8");
+  const { content: mdxContent } = matter(content);
+
+  const lines = mdxContent.split("\n");
+  const relativeLinks: Array<{ line: number; lineContent: string }> = [];
+
+  lines.forEach((line, lineNumber) => {
+    // Check for relative links
+    if (RELATIVE_LINK_REGEX.test(line)) {
+      // Reset the regex lastIndex to start from the beginning of the line
+      RELATIVE_LINK_REGEX.lastIndex = 0;
+
+      // Skip image links
+      if (!isImageLink(line)) {
+        relativeLinks.push({
+          line: lineNumber + 1, // 1-based line numbers
+          lineContent: line.trim(),
+        });
+      }
+    }
+  });
+
+  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 results: RelativeLinkResult[] = [];
+
+  for (const file of mdxFiles) {
+    const filePath = path.join(CONTENT_DIR, file);
+    const relativeLinks = findRelativeLinksInFile(filePath);
+
+    if (relativeLinks.length > 0) {
+      results.push({
+        file,
+        relativeLinks,
+      });
+    }
+  }
+
+  return results;
+}
+
+async function validateLinks(): Promise<LinkValidationResult[]> {
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
+  const validRoutes = await getValidRoutes();
+
+  const results: LinkValidationResult[] = [];
+
+  for (const file of mdxFiles) {
+    const filePath = path.join(CONTENT_DIR, file);
+    const links = extractLinksFromFile(filePath);
+
+    const invalidLinks = links.filter(({ link }) => {
+      // 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];
+      // Remove the trailing slash if present.
+      // This works with links like "api/interfaces/MetadataFilter#operator" and "api/interfaces/MetadataFilter/#operator".
+      const normalizedLink = baseLink.endsWith("/")
+        ? baseLink.slice(0, -1)
+        : baseLink;
+
+      // Remove llamaindex/ prefix if it exists as it's the root of the docs
+      let routePath = normalizedLink;
+      if (routePath.startsWith("llamaindex/")) {
+        routePath = routePath.substring("llamaindex/".length);
+      }
+
+      return !validRoutes.has(normalizedLink) && !validRoutes.has(routePath);
+    });
+
+    if (invalidLinks.length > 0) {
+      results.push({
+        file,
+        invalidLinks,
+      });
+    }
+  }
+
+  return results;
+}
+
+/**
+ * Main function to validate links and report errors
+ */
+async function main() {
+  console.log("🔍 Validating links in documentation...");
+
+  try {
+    // Check for invalid internal links
+    const validationResults: LinkValidationResult[] = await validateLinks();
+    // Check for relative links
+    const relativeLinksResults = await findRelativeLinks();
+
+    let hasErrors = false;
+
+    // Report invalid internal links
+    if (validationResults.length > 0) {
+      console.error("❌ Found invalid internal links:");
+      hasErrors = true;
+
+      for (const result of validationResults) {
+        console.error(`\nFile: ${result.file}`);
+
+        for (const { link, line } of result.invalidLinks) {
+          console.error(`  - Line ${line}: /docs/${link}`);
+        }
+      }
+    }
+
+    // Report relative links
+    if (relativeLinksResults.length > 0) {
+      console.error("\n❌ Found relative links (use absolute paths instead):");
+      hasErrors = true;
+
+      for (const result of relativeLinksResults) {
+        console.error(`\nFile: ${result.file}`);
+
+        for (const { line, lineContent } of result.relativeLinks) {
+          console.error(`  - Line ${line}: ${lineContent}`);
+        }
+      }
+    }
+
+    if (hasErrors) {
+      // Exit with error code to fail the build
+      process.exit(1);
+    } else {
+      console.log("✅ All links are valid!");
+    }
+  } catch (error) {
+    console.error("Error validating links:", error);
+    process.exit(1);
+  }
+}
+
+main().catch((error) => {
+  console.error("Unhandled error:", error);
+  process.exit(1);
+});
@@ -60,7 +60,7 @@ export default function HomePage() {
          icon={Footprints}
          subheading="Progressive"
          heading="From the simplest to the most complex"
-          description="LlamaIndex.TS is designed to be simple to get started, but powerful enough to build complex, agentic AI applications."
+          description="LlamaIndex.TS is designed to be simple to get started, but powerful enough to build complex, agentic AI applications using multi-agents."
        >
          <Suspense
            fallback={
@@ -76,44 +76,48 @@ export default function HomePage() {
          >
            <MagicMove
              code={[
-                `import { OpenAI } from "@llamaindex/openai";
+                `import { openai } from "@llamaindex/openai";

-const llm = new OpenAI();
+const llm = openai();
 const response = await llm.complete({ prompt: "How are you?" });`,
-                `import { OpenAI } from "@llamaindex/openai";
+                `import { openai } from "@llamaindex/openai";

-const llm = new OpenAI();
+const llm = openai();
 const response = await llm.chat({
  messages: [{ content: "Tell me a joke.", role: "user" }],
 });`,
-                `import { ChatMemoryBuffer } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+                `import { agent } from "llamaindex";
+import { openai } from "@llamaindex/openai";

-const llm = new OpenAI({ model: 'gpt4o-turbo' });
-const buffer = new ChatMemoryBuffer({
-  tokenLimit: 128_000,
-})
-buffer.put({ content: "Tell me a joke.", role: "user" })
-const response = await llm.chat({
-  messages: buffer.getMessages(),
-  stream: true
-});`,
-                `import { ChatMemoryBuffer } from "llamaindex";
-import { OpenAIAgent } from "@llamaindex/openai";
-
-const agent = new OpenAIAgent({
-  llm,
-  tools: [...myTools]
+const analyseAgent = agent({
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
  systemPrompt,
 });
-const buffer = new ChatMemoryBuffer({
-  tokenLimit: 128_000,
-})
-buffer.put({ content: "Analysis the data based on the given data.", role: "user" })
-buffer.put({ content: \`\${data}\`, role: "user" })
-const response = await agent.chat({
-  message: buffer.getMessages(),
-});`,
+const response = await analyseAgent.run(\`Analyse the given data:
+\${data}\`);`,
+                `import { agent, multiAgent } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+
+const analyseAgent = agent({
+  name: "AnalyseAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
+});
+const reporterAgent = agent({
+  name: "ReporterAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [reporterTools],
+  canHandoffTo: [analyseAgent],
+});
+
+const agents = multiAgent({
+  agents: [analyseAgent, reporterAgent],
+  rootAgent: reporterAgent,
+});
+
+const response = await agents.run(\`Analyse the given data:
+\${data}\`);`,
              ]}
            />
          </Suspense>
@@ -125,20 +129,20 @@ const response = await agent.chat({
          description="Truly powerful retrieval-augmented generation applications use agentic techniques, and LlamaIndex.TS makes it easy to build them."
        >
          <CodeBlock
-            code={`import { agent } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+            code={`import { agent, SimpleDirectoryReader, VectorStoreIndex } from "llamaindex";
+import { openai } from "@llamaindex/openai";

-// using a previously created LlamaIndex index to query information from
-const queryTool = index.queryTool();
+// load documents from current directoy into an index
+const reader = new SimpleDirectoryReader();
+const documents = await reader.loadData(currentDir);
+const index = await VectorStoreIndex.fromDocuments(documents);

-const agent = agent({
-  llm: new OpenAI({
-    model: "gpt-4o",
-  }),
-  tools: [queryTool],
+const myAgent = agent({
+  llm: openai({ model: "gpt-4o" }),
+  tools: [index.queryTool()],
 });

-await agent.run('...');`}
+await myAgent.run('...');`}
            lang="ts"
          />
        </Feature>
@@ -11,8 +11,6 @@ import {
 } from "fumadocs-ui/page";
 import { notFound } from "next/navigation";

-const { AutoTypeTable } = createTypeTable();
-
 export const revalidate = false;

 export default async function Page(props: {
@@ -22,6 +20,7 @@ export default async function Page(props: {
  const page = source.getPage(params.slug);
  if (!page) notFound();

+  const { AutoTypeTable } = createTypeTable();
  const MDX = page.data.body;

  return (
@@ -1,12 +0,0 @@
---
-title: Agents
---
-
-A built-in agent that can take decisions and reasoning based on the tools provided to it.
-
-## OpenAI Agent
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/agent/openai";
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,28 +0,0 @@
---
-title: Gemini Agent
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSourceGemini from "!raw-loader!../../../../../../../examples/gemini/agent.ts";
-
-## Installation
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install llamaindex @llamaindex/google
-	```
-
-	```shell tab="yarn"
-	yarn add llamaindex @llamaindex/google
-	```
-
-	```shell tab="pnpm"
-	pnpm add llamaindex @llamaindex/google
-	```
-</Tabs>
-
-## Source 
-
-<DynamicCodeBlock lang="ts" code={CodeSourceGemini} />
@@ -1,10 +0,0 @@
---
-title: Chat Engine
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/chatEngine";
-
-Chat Engine is a class that allows you to create a chatbot from a retriever. It is a wrapper around a retriever that allows you to chat with it in a conversational manner.
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,59 +0,0 @@
---
-title: Context-Aware Agent
---
-
-The Context-Aware Agent enhances the capabilities of standard LLM agents by incorporating relevant context from a retriever for each query. This allows the agent to provide more informed and specific responses based on the available information.
-
-## Usage
-
-Here's a simple example of how to use the Context-Aware Agent:
-
-```typescript
-import {
-  Document,
-  VectorStoreIndex,
-} from "llamaindex";
-import { OpenAI, OpenAIContextAwareAgent } from "@llamaindex/openai";
-
-async function createContextAwareAgent() {
-  // Create and index some documents
-  const documents = [
-    new Document({
-      text: "LlamaIndex is a data framework for LLM applications.",
-      id_: "doc1",
-    }),
-    new Document({
-      text: "The Eiffel Tower is located in Paris, France.",
-      id_: "doc2",
-    }),
-  ];
-
-  const index = await VectorStoreIndex.fromDocuments(documents);
-  const retriever = index.asRetriever({ similarityTopK: 1 });
-
-  // Create the Context-Aware Agent
-  const agent = new OpenAIContextAwareAgent({
-    llm: new OpenAI({ model: "gpt-3.5-turbo" }),
-    contextRetriever: retriever,
-  });
-
-  // Use the agent to answer queries
-  const response = await agent.chat({
-    message: "What is LlamaIndex used for?",
-  });
-
-  console.log("Agent Response:", response.response);
-}
-
-createContextAwareAgent().catch(console.error);
-```
-
-In this example, the Context-Aware Agent uses the retriever to fetch relevant context for each query, allowing it to provide more accurate and informed responses based on the indexed documents.
-
-## Key Components
-
- `contextRetriever`: A retriever (e.g., from a VectorStoreIndex) that fetches relevant documents or passages for each query.
-
-## Available Context-Aware Agents
-
- `OpenAIContextAwareAgent`: A context-aware agent using OpenAI's models.
@@ -1,15 +0,0 @@
-{
-  "title": "Examples",
-  "pages": [
-    "more_examples",
-    "chat_engine",
-    "vector_index",
-    "summary_index",
-    "save_load_index",
-    "context_aware_agent",
-    "agent",
-    "agent_gemini",
-    "local_llm",
-    "other_llms"
-  ]
-}
@@ -1,66 +0,0 @@
---
-title: Using other LLM APIs
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/mistral";
-
-By default LlamaIndex.TS uses OpenAI's LLMs and embedding models, but we support [lots of other LLMs](../modules/llms) including models from Mistral (Mistral, Mixtral), Anthropic (Claude) and Google (Gemini).
-
-If you don't want to use an API at all you can [run a local model](../../examples/local_llm).
-
-This example runs you through the process of setting up a Mistral model:
-
-
-## Installation
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install llamaindex @llamaindex/mistral
-	```
-
-	```shell tab="yarn"
-	yarn add llamaindex @llamaindex/mistral
-	```
-
-	```shell tab="pnpm"
-	pnpm add llamaindex @llamaindex/mistral
-	```
-</Tabs>
-
-## Using another LLM
-
-You can specify what LLM LlamaIndex.TS will use on the `Settings` object, like this:
-
-```typescript
-import { MistralAI } from "@llamaindex/mistral";
-import { Settings } from "llamaindex";
-
-Settings.llm = new MistralAI({
-  model: "mistral-tiny",
-  apiKey: "<YOUR_API_KEY>",
-});
-```
-
-You can see examples of other APIs we support by checking out "Available LLMs" in the sidebar of our [LLMs section](../modules/llms).
-
-## Using another embedding model
-
-A frequent gotcha when trying to use a different API as your LLM is that LlamaIndex will also by default index and embed your data using OpenAI's embeddings. To completely switch away from OpenAI you will need to set your embedding model as well, for example:
-
-```typescript
-import { MistralAIEmbedding } from "@llamaindex/mistral";
-import { Settings } from "llamaindex";
-
-Settings.embedModel = new MistralAIEmbedding();
-```
-
-We support [many different embeddings](../modules/embeddings).
-
-## Full example
-
-This example uses Mistral's `mistral-tiny` model as the LLM and Mistral for embeddings as well.
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,8 +0,0 @@
---
-title: Save/Load an Index
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/storageContext";
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,8 +0,0 @@
---
-title: Summary Index
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/summaryIndex";
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,8 +0,0 @@
---
-title: Vector Index
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/vectorIndex";
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,11 +1,7 @@
 ---
-title: Chatbot tutorial
+title: Create-Llama
 ---

-Once you've mastered basic [retrieval-augment generation](retrieval_augmented_generation) you may want to create an interface to chat with your data. You can do this step-by-step, but we recommend getting started quickly using `create-llama`.
-
-## Using create-llama
-
 `create-llama` is a powerful but easy to use command-line tool that generates a working, full-stack web application that allows you to chat with your data. You can learn more about it on [the `create-llama` README page](https://www.npmjs.com/package/create-llama).

 Run it once and it will ask you a series of questions about the kind of application you want to generate. Then you can customize your application to suit your use-case. To get started, run:
@@ -1,10 +1,10 @@
 ---
-title: See all examples
+title: Code examples
 ---

 Our GitHub repository has a wealth of examples to explore and try out. You can check out our [examples folder](https://github.com/run-llama/LlamaIndexTS/tree/main/examples) to see them all at once, or browse the pages in this section for some selected highlights.

-## Check out all examples
+## Use examples locally

 It may be useful to check out all the examples at once so you can try them out locally. To do this into a folder called `my-new-project`, run these commands:

@@ -19,3 +19,14 @@ Then you can run any example in the folder with `tsx`, e.g.:
 ```bash npm2yarn
 npx tsx ./vectorIndex.ts
 ```
+
+## Try examples online
+
+You can also try the examples online using StackBlitz:
+
+<iframe
+  className="w-full h-[440px]"
+  aria-label="LlamaIndex.TS Examples"
+  aria-description="This is a list of examples for LlamaIndex.TS."
+  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?file=README.md"
+/>
@@ -1,56 +0,0 @@
---
-title: Installation
-description: How to install llamaindex packages.
---
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-To install llamaindex, run the following command:
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install llamaindex
-	```
-
-	```shell tab="yarn"
-	yarn add llamaindex
-	```
-
-	```shell tab="pnpm"
-	pnpm add llamaindex
-	```
-</Tabs>
-
-In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install @llamaindex/openai
-	```
-
-	```shell tab="yarn"
-	yarn add @llamaindex/openai
-	```
-
-	```shell tab="pnpm"
-	pnpm add @llamaindex/openai
-	```
-</Tabs>
-
-Go to [Using other LLM APIs](/docs/llamaindex/examples/other_llms) to find out how to use other LLMs.
-
-
-## What's next?
-
-<Cards>
-	<Card
-		title="I want to try LlamaIndex.TS"
-		description="Learn how to use LlamaIndex.TS with different JS runtime and frameworks."
-		href="/docs/llamaindex/getting_started/setup"
-	/>
-	<Card
-		title="Show me code examples"
-		description="Explore code examples using LlamaIndex.TS."
-		href="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?file=README.md"
-	/>
-</Cards>
@@ -14,7 +14,7 @@ Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure y

 <Card
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/setup/node"
+  href="/docs/llamaindex/getting_started/installation/node"
 />

 Also, you need have the basic understanding of <a href='https://developers.cloudflare.com/workers/'><SiCloudflareworkers className="inline mr-2" color="#F38020" />Cloudflare Worker</a>.
@@ -69,7 +69,7 @@ export default {
 In Cloudflare Worker and similar serverless JS environment, you need to be aware of the following differences:

 - Some Node.js modules are not available in Cloudflare Worker, such as `node:fs`, `node:child_process`, `node:cluster`...
- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, insteadof a long-running process in Node.js.
+- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, instead of a long-running process in Node.js.
 - Some of LlamaIndex.TS packages are not available in Cloudflare Worker, for example `@llamaindex/readers` and `@llamaindex/huggingface`.
 - The main `llamaindex` is designed to work in all JavaScript environment, including Cloudflare Worker. If you find any issue, please report to us.
 - `@llamaindex/env` is a JS environment binding module, which polyfill some Node.js/Modern Web API (for example, we have a memory based `fs` module, and Crypto API polyfill). It is designed to work in all JavaScript environment, including Cloudflare Worker.
@@ -0,0 +1,98 @@
+---
+title: Installation
+description: How to install llamaindex packages.
+---
+
+import {
+	SiNodedotjs,
+	SiTypescript,
+	SiNextdotjs,
+	SiCloudflareworkers,
+	SiVite
+} from "@icons-pack/react-simple-icons";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+To install llamaindex, run the following command:
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex
+	```
+</Tabs>
+
+In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/openai
+	```
+</Tabs>
+
+Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
+
+
+## Frameworks
+
+LlamaIndex supports a wide range of frameworks and runtimes. Click on the card below to learn more.
+
+<Cards>
+	<Card title={
+		<>
+			<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
+		</>
+	} href="/docs/llamaindex/getting_started/installation/node" />
+	<Card title={
+		<>
+			<SiTypescript className="inline" color="#3178C6" /> TypeScript
+		</>
+	} href="/docs/llamaindex/getting_started/installation/typescript" />
+	<Card title={
+		<>
+			<SiVite className='inline' color='#646CFF' /> Vite
+		</>
+	} href="/docs/llamaindex/getting_started/installation/vite" />
+	<Card
+		title={
+			<>
+				<SiNextdotjs className='inline' /> Next.js (React Server Component)
+			</>
+		}
+		href="/docs/llamaindex/getting_started/installation/next"
+	/>
+	<Card title={
+		<>
+			<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
+		</>
+	} href="/docs/llamaindex/getting_started/installation/cloudflare" />
+</Cards>
+
+## What's next?
+
+<Cards>
+	<Card
+		title="Learn LlamaIndex.TS"
+		description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
+		href="/docs/llamaindex/tutorials/rag"
+	/>
+	<Card
+		title="Show me code examples"
+		description="Explore code examples using LlamaIndex.TS."
+		href="/docs/llamaindex/getting_started/examples"
+	/>
+</Cards>
@@ -0,0 +1,4 @@
+{
+  "title": "Installation",
+  "pages": ["node", "typescript", "next", "vite", "cloudflare"]
+}
@@ -7,7 +7,7 @@ Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure y

 <Card
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/setup/node"
+  href="/docs/llamaindex/getting_started/installation/node"
 />

 ## Differences between Node.js and Next.js
@@ -17,9 +17,9 @@ This means that you need to be careful when using LlamaIndex.TS in Next.js.
 Don't leak the import data like API keys to the client side.

 Also, in Next.js, there is build time and runtime. Some computations can be done at build time like Document embedding could be done at build time for better performance.
-LlamaIndex.TS has lots of upstream dependencies, some of them are not compatible with Next.js.
+Where as the `llamaindex` package is working with Next.js, some provider packages like `@llamaindex/huggingface` are not working well with Next.js. This is due to the upstream dependencies used by the provider package. 

-You might need to use `withNext` to make sure that LlamaIndex.TS works well with Next.js.
+Make sure to use `withLlamaIndex` to make sure that LlamaIndex.TS works well with Next.js.

 ```js
 // next.config.mjs / next.config.ts
@@ -35,7 +35,7 @@ If you see any dependency issues, you are welcome to open an issue on the GitHub

 ## Edge Runtime

-[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](./cloudflare#difference-between-nodejs-and-cloudflare-worker),
+[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](/docs/llamaindex/getting_started/installation/cloudflare#difference-between-nodejs-and-cloudflare-worker),
 it is a serverless platform that runs your code on the edge.

 Not all features of Node.js are supported in Vercel Edge Runtime, so does LlamaIndex.TS, we are working on more compatibility with all JavaScript runtimes.
@@ -42,11 +42,11 @@ By the default, we are using `js-tiktoken` for tokenization. You can install `gp
 	```
 </Tabs>

-> Note: This only works for Node.js
+**Note**: This only works for Node.js

 ## TypeScript support

 <Card
 	title="Getting Started with LlamaIndex.TS in TypeScript"
-	href="/docs/llamaindex/getting_started/setup/typescript"
+	href="/docs/llamaindex/getting_started/installation/typescript"
 />
@@ -0,0 +1,110 @@
+---
+title: With TypeScript
+description: In this guide, you'll learn how to use LlamaIndex with TypeScript
+---
+
+LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript projects.
+
+We put a lot of work on strong typing to make sure you have a great typing experience with code completion such as:
+
+```ts twoslash
+import { PromptTemplate } from 'llamaindex'
+const promptTemplate = new PromptTemplate({
+  template: `Context information from multiple sources is below.
+---------------------
+{context}
+---------------------
+Given the information from multiple sources and not prior knowledge.
+Answer the query in the style of a Shakespeare play"
+Query: {query}
+Answer:`,
+	templateVars: ["context", "query"],
+});
+// @noErrors
+promptTemplate.format({
+	c
+//^|
+})
+```
+
+## Enable TypeScript
+
+Make sure to set [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) in your `tsconfig.json` file:
+
+```json5
+{
+  compilerOptions: {
+    // ⬇️ add this line to your tsconfig.json
+    moduleResolution: "bundler", // or "nodenext" | "node16" | "node"
+  },
+}
+```
+
+We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it, but with import path limitations.
+
+So you may encounter type errors when importing sub paths from the `llamaindex` package like:
+
+```ts
+import { Settings } from "llamaindex/Settings";
+```
+
+The simplest way to fix this without changing `moduleResolution` is to import directly from `llamaindex`:
+
+```ts
+import { Settings } from "llamaindex";
+```
+
+## Enable AsyncIterable for `Web Stream` API
+
+Some modules uses `Web Stream` API like `ReadableStream` and `WritableStream`, you need to enable `DOM.AsyncIterable` in your `tsconfig.json`.
+
+```json5
+{
+  compilerOptions: {
+    // ⬇️ add this lib to your tsconfig.json
+    lib: ["DOM.AsyncIterable"],
+  },
+}
+```
+
+```typescript
+import { agent, tool } from 'llamaindex'
+import { openai } from "@llamaindex/openai";
+
+Settings.llm = openai({
+  model: "gpt-4o-mini",
+});
+
+const addTool = tool({
+  name: "add", 
+  description: "Adds two numbers",
+  parameters: z.object({x: z.number(), y: z.number()}),
+  execute: ({ x, y }) => x + y,
+});
+
+const myAgent = agent({
+  tools: [addTool],
+});
+
+// Chat with the agent
+const context = myAgent.run("Hello, how are you?");
+
+for await (const event of context) {
+  if (event instanceof AgentStream) {
+    for (const chunk of event.data.delta) {
+      process.stdout.write(chunk); // stream response
+    }
+  } else {
+    console.log(event); // other events
+  }
+}
+
+```
+
+## Run TypeScript Script in Node.js
+
+We recommend to use [tsx](https://www.npmjs.com/package/tsx) to run TypeScript script in Node.js.
+
+```shell
+node --import tsx ./my-script.ts
+```
@@ -7,7 +7,7 @@ Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure y

 <Card
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/setup/node"
+  href="/docs/llamaindex/getting_started/installation/node"
 />

 Also, make sure you have a basic understanding of [Vite](https://vitejs.dev/).
@@ -1,4 +1,4 @@
 {
  "title": "Getting Started",
-  "pages": ["index", "setup", "starter_tutorial", "environments", "concepts"]
+  "pages": ["installation", "create_llama", "examples"]
 }
@@ -1,42 +0,0 @@
---
-title: Choose Framework
-description: We support multiple JS runtime and frameworks, bundlers.
---
-import {
-	SiNodedotjs,
-	SiTypescript,
-	SiNextdotjs,
-	SiCloudflareworkers,
-	SiVite
-} from "@icons-pack/react-simple-icons";
-
-<Cards>
-	<Card title={
-		<>
-			<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
-		</>
-	} href="/docs/llamaindex/getting_started/setup/node" />
-	<Card title={
-		<>
-			<SiTypescript className="inline" color="#3178C6" /> TypeScript
-		</>
-	} href="/docs/llamaindex/getting_started/setup/typescript" />
-	<Card title={
-		<>
-			<SiVite className='inline' color='#646CFF' /> Vite
-		</>
-	} href="/docs/llamaindex/getting_started/setup/vite" />
-	<Card
-		title={
-			<>
-				<SiNextdotjs className='inline' /> Next.js (React Server Component)
-			</>
-		}
-		href="/docs/llamaindex/getting_started/setup/next"
-	/>
-	<Card title={
-		<>
-			<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
-		</>
-	} href="/docs/llamaindex/getting_started/setup/cloudflare" />
-</Cards>
@@ -1,6 +0,0 @@
-{
-  "title": "Setup",
-  "description": "The setup guide",
-  "defaultOpen": true,
-  "pages": ["index", "next", "node", "typescript", "vite", "cloudflare"]
-}
@@ -1,132 +0,0 @@
---
-title: With TypeScript
-description: In this guide, you'll learn how to use LlamaIndex with TypeScript
---
-import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
-
-LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript projects.
-
-We do lots of work on strong typing to make sure you have a great typing experience with LlamaIndex.TS.
-
-```ts twoslash
-import { PromptTemplate } from 'llamaindex'
-const promptTemplate = new PromptTemplate({
-  template: `Context information from multiple sources is below.
---------------------
-{context}
---------------------
-Given the information from multiple sources and not prior knowledge.
-Answer the query in the style of a Shakespeare play"
-Query: {query}
-Answer:`,
-	templateVars: ["context", "query"],
-});
-// @noErrors
-promptTemplate.format({
-	c
-//^|
-})
-```
-
-```ts twoslash
-import { FunctionTool } from 'llamaindex'
-import { z } from 'zod'
-
-// ---cut-before---
-const inputSchema = z.object({
-	time: z.string(),
-	city: z.string(),
-})
-
-type Input = z.infer<typeof inputSchema>
-
-FunctionTool.from<Input>((input) => {
-// @noErrors
-	input.t
-//      ^|
-}, {
-	name: 'getWeather',
-	description: 'Get the weather information',
-	parameters: inputSchema,
-})
-```
-
-## Enable TypeScript
-
-
-```json5
-{
-  compilerOptions: {
-    // ⬇️ add this line to your tsconfig.json
-    moduleResolution: "bundler", // or "node16"
-  },
-}
-```
-
-<Accordions>
-	<Accordion
-		title="Why modify tsconfig.json"
-	>
-
-We are shipping both ESM and CJS module, and compatible with Vercel Edge, Cloudflare Workers, and other serverless platforms.
-
-So we are using [conditional exports](https://nodejs.org/api/packages.html#conditional-exports) to support all environments.
-
-This is a kind of modern way of shipping packages, but might cause TypeScript type check to fail because of legacy module resolution.
-
-Imaging you put output file into `/dist/openai.js` but you are importing `llamaindex/openai` in your code, and set `package.json` like this:
-
-```json5
-{
-	"exports": {
-		"./openai": "./dist/openai.js"
-	}
-}
-```
-
-In old module resolution, TypeScript will not be able to find the module because it is not following the file structure, even you run `node index.js` successfully. (on Node.js >=16)
-
-See more about [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) or
-[TypeScript 5.0 blog](https://devblogs.microsoft.com/typescript/announcing-typescript-5-0/#--moduleresolution-bundler7).
-
-
-	</Accordion>
-</Accordions>
-
-## Enable AsyncIterable for `Web Stream` API
-
-Some modules uses `Web Stream` API like `ReadableStream` and `WritableStream`, you need to enable `DOM.AsyncIterable` in your `tsconfig.json`.
-
-```json5
-{
-  compilerOptions: {
-    // ⬇️ add this lib to your tsconfig.json
-    lib: ["DOM.AsyncIterable"],
-  },
-}
-```
-
-```ts twoslash
-import { OpenAIAgent } from '@llamaindex/openai'
-
-const agent = new OpenAIAgent({
-  tools: []
-})
-
-const response = await agent.chat({
-  message: 'Hello, how are you?',
-  stream: true
-})
-for await (const _ of response) {
-                      //^?
-  // ...
-}
-```
-
-## Run TypeScript Script in Node.js
-
-We recommend to use [tsx](https://www.npmjs.com/package/tsx) to run TypeScript script in Node.js.
-
-```shell
-node --import tsx ./my-script.ts
-```
@@ -1,9 +0,0 @@
-{
-  "title": "Starter Tutorials",
-  "pages": [
-    "retrieval_augmented_generation",
-    "chatbot",
-    "structured_data_extraction",
-    "agent"
-  ]
-}
@@ -1,92 +0,0 @@
---
-title: Using a local model via Ollama
---
-
-If you're happy using OpenAI, you can skip this section, but many people are interested in using models they run themselves. The easiest way to do this is via the great work of our friends at [Ollama](https://ollama.com/), who provide a simple to use client that will download, install and run a [growing range of models](https://ollama.com/library) for you.
-
-### Install Ollama
-
-They provide a one-click installer for Mac, Linux and Windows on their [home page](https://ollama.com/).
-
-### Pick and run a model
-
-Since we're going to be doing agentic work, we'll need a very capable model, but the largest models are hard to run on a laptop. We think `mixtral 8x7b` is a good balance between power and resources, but `llama3` is another great option. You can run it simply by running
-
-```bash
-ollama run mixtral:8x7b
-```
-
-The first time you run it will also automatically download and install the model for you.
-
-### Switch the LLM in your code
-
-There are two changes you need to make to the code we already wrote in `1_agent` to get Mixtral 8x7b to work. First, you need to switch to that model. Replace the call to `Settings.llm` with this:
-
-```javascript
-Settings.llm = new Ollama({
-  model: "mixtral:8x7b",
-});
-```
-
-### Swap to a ReActAgent
-
-In our original code we used a specific OpenAIAgent, so we'll need to switch to a more generic agent pattern, the ReAct pattern. This is simple: change the `const agent` line in your code to read
-
-```javascript
-const agent = new ReActAgent({ tools });
-```
-
-(You will also need to bring in `Ollama` and `ReActAgent` in your imports)
-
-### Run your totally local agent
-
-Because your embeddings were already local, your agent can now run entirely locally without making any API calls.
-
-```bash
-node agent.mjs
-```
-
-Note that your model will probably run a lot slower than OpenAI, so be prepared to wait a while!
-
-**_Output_**
-
-```javascript
-{
-  response: {
-    message: {
-      role: 'assistant',
-      content: ' Thought: I need to use a tool to add the numbers 101 and 303.\n' +
-        'Action: sumNumbers\n' +
-        'Action Input: {"a": 101, "b": 303}\n' +
-        '\n' +
-        'Observation: 404\n' +
-        '\n' +
-        'Thought: I can answer without using any more tools.\n' +
-        'Answer: The sum of 101 and 303 is 404.'
-    },
-    raw: {
-      model: 'mixtral:8x7b',
-      created_at: '2024-05-09T00:24:30.339473Z',
-      message: [Object],
-      done: true,
-      total_duration: 64678371209,
-      load_duration: 57394551334,
-      prompt_eval_count: 475,
-      prompt_eval_duration: 4163981000,
-      eval_count: 94,
-      eval_duration: 3116692000
-    }
-  },
-  sources: [Getter]
-}
-```
-
-Tada! You can see all of this in the folder `1a_mixtral`.
-
-### Extending to other examples
-
-You can use a ReActAgent instead of an OpenAIAgent in any of the further examples below, but keep in mind that GPT-4 is a lot more capable than Mixtral 8x7b, so you may see more errors or failures in reasoning if you are using an entirely local setup.
-
-### Next steps
-
-Now you've got a local agent, you can [add Retrieval-Augmented Generation to your agent](4_agentic_rag).
@@ -1,16 +0,0 @@
---
-title: Cost Analysis
---
-
-This page shows how to track LLM cost using APIs.
-
-## Callback Manager
-
-The callback manager is a class that manages the callback functions.
-
-You can register `llm-start`, `llm-end`, and `llm-stream` callbacks to the callback manager for tracking the cost.
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/recipes/cost-analysis";
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
@@ -1,106 +0,0 @@
---
-title: Document and Nodes
-description: llamaindex readers is a collection of readers for different file formats.
---
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
-
-<Accordions>
-	<Accordion title="Install @llamaindex/readers">
-
-		If you want to use the reader module, you need to install `@llamaindex/readers`
-
-		<Tabs groupId="install-llamaindex" items={["npm", "yarn", "pnpm"]} persist>
-			```shell tab="npm"
-			npm install @llamaindex/readers
-			```
-
-			```shell tab="yarn"
-			yarn add @llamaindex/readers
-			```
-
-			```shell tab="pnpm"
-			pnpm add @llamaindex/readers
-			```
-
-		</Tabs>
-	</Accordion>
-</Accordions>
-
-We offer readers for different file formats.
-
-```ts twoslash 
-import { CSVReader } from '@llamaindex/readers/csv'
-import { PDFReader } from '@llamaindex/readers/pdf'
-import { JSONReader } from '@llamaindex/readers/json'
-import { MarkdownReader } from '@llamaindex/readers/markdown'
-import { HTMLReader } from '@llamaindex/readers/html'
-// you can find more readers in the documentation
-```
-
-## SimpleDirectoryReader
-
-`SimpleDirectoryReader` is the simplest way to load data from local files into LlamaIndex.
-
-```ts twoslash 
-import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-
-const reader = new SimpleDirectoryReader()
-const documents = await reader.loadData("./data")
-//    ^?
-
-
-const texts = documents.map(doc => doc.getText())
-//    ^?
-```
-
-
-## Tips when using in non-Node.js environments
-
-When using `@llamaindex/readers` in a non-Node.js environment (such as Vercel Edge, Cloudflare Workers, etc.)
-Some classes are not exported from top-level entry file.
-
-The reason is that some classes are only compatible with Node.js runtime, (e.g. `PDFReader`) which uses Node.js specific APIs (like `fs`, `child_process`, `crypto`).
-
-If you need any of those classes, you have to import them instead directly through their file path in the package.
-
-As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
-
-```typescript
-import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-import { LlamaParseReader } from "@llamaindex/cloud";
-
-export const DATA_DIR = "./data";
-
-export async function getDocuments() {
-  const reader = new SimpleDirectoryReader();
-  // Load PDFs using LlamaParseReader
-  return await reader.loadData({
-    directoryPath: DATA_DIR,
-    fileExtToReader: {
-      pdf: new LlamaParseReader({ resultType: "markdown" }),
-    },
-  });
-}
-```
-
-> _Note_: Reader classes have to be added explicitly to the `fileExtToReader` map in the Edge version of the `SimpleDirectoryReader`.
-
-You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
-
-
-## Load file natively using Node.js Customization Hooks
-
-We have a helper utility to allow you to import a file in Node.js script.
-
-```shell 
-node --import @llamaindex/readers/node ./script.js
-```
-
-```ts
-import csv from './path/to/data.csv';
-
-const text = csv.getText()
-```
@@ -1,5 +0,0 @@
-{
-  "title": "Loading Data",
-  "description": "Loading Data using LlamaIndex.TS",
-  "pages": ["index", "node-parser"]
-}
@@ -1,5 +0,0 @@
-{
-  "title": "Guide",
-  "description": "See our guide",
-  "pages": ["loading", "workflow", "chat", "agents", "cost-analysis"]
-}
@@ -16,9 +16,13 @@ The TypeScript implementation is designed for JavaScript server side application

 LlamaIndex.TS provides tools for beginners, advanced users, and everyone in between.

+Try it out with a starter example using StackBlitz:
+
 <iframe
  className="w-full h-[440px]"
  aria-label="LlamaIndex.TS Starter"
  aria-description="This is a starter example for LlamaIndex.TS, it shows the basic usage of the library."
  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?embed=1&file=starter.ts"
 />
+
+You'll need an OpenAI API key to run this example. You can retrieve it from [OpenAI](https://platform.openai.com/api-keys).
@@ -84,6 +84,7 @@ const queryTool = llamaindex({
  model: openai("gpt-4"),
  index,
  description: "Search through the documents",
+  options: { fields: ["sourceNodes", "messages"]}
 });

 // Use the tool with Vercel's AI SDK
@@ -4,13 +4,11 @@
  "root": true,
  "pages": [
    "---Guide---",
-    "what-is-llamaindex",
    "index",
    "getting_started",
-    "migration",
-    "guide",
-    "examples",
+    "tutorials",
    "modules",
-    "integration"
+    "integration",
+    "migration"
  ]
 }
@@ -47,7 +47,7 @@ import { OpenAI } from "@llamaindex/openai";
 	```
 </Tabs>

-For more details on available AI model providers and their configuration, see the [LLMs documentation](/docs/llamaindex/modules/llms) and the [Embedding Models documentation](/docs/llamaindex/modules/embeddings).
+For more details on available AI model providers and their configuration, see the [LLMs documentation](/docs/llamaindex/modules/models/llms) and the [Embedding Models documentation](/docs/llamaindex/modules/models/embeddings).

 ### 2. Storage Providers

@@ -61,7 +61,7 @@ Now:
 import { PineconeVectorStore } from "@llamaindex/pinecone";
 ```

-For more information about available storage options, refer to the [Data Stores documentation](/docs/llamaindex/modules/data_stores).
+For more information about available storage options, refer to the [Data Stores documentation](/docs/llamaindex/modules/data/stores).

 ### 3. Data Loaders

@@ -75,7 +75,7 @@ Now:
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
 ```

-For more details about available data loaders and their usage, check the [Loading Data](/docs/llamaindex/guide/loading).
+For more details about available data loaders and their usage, check the [Loading Data](/docs/llamaindex/modules/data/readers).

 ### 4. Prefer using `llamaindex` instead of `@llamaindex/core`

@@ -2,6 +2,8 @@
 title: Agents
 ---

+**Note**: Agents are deprecated, use [Agent Workflows](/docs/llamaindex/modules/agents/agent_workflow) instead.
+
 An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. The key agent components can include, but are not limited to:

 - Breaking down a complex question into smaller ones
@@ -19,11 +21,6 @@ LlamaIndex.TS comes with a few built-in agents, but you can also create your own
 - ReACT Agent
 - Meta3.1 504B via Bedrock (in `@llamaIndex/community`)

-## Examples
-
- [OpenAI Agent](/docs/llamaindex/examples/agent)
- [Gemini Agent](/docs/llamaindex/examples/agent_gemini)
-
 ## Api References

 - [OpenAIAgent](/docs/api/classes/OpenAIAgent)
@@ -1,5 +1,5 @@
 {
  "title": "Migration",
  "description": "Migration between different versions",
-  "pages": ["0.8-to-0.9"]
+  "pages": ["0.8-to-0.9", "deprecated"]
 }
@@ -1,12 +1,9 @@
 ---
-title: Agent Workflow
+title: Agent Workflows
 ---

-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/agentworkflow/blog-writer.ts";
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";

-Agent Workflows are a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base `Workflow` system and provides a streamlined interface for agent interactions.
+Agent Workflows are a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base [`Workflow`](/docs/llamaindex/modules/agents/workflows) system and provides a streamlined interface for agent interactions.

 ## Usage

@@ -15,11 +12,11 @@ Agent Workflows are a powerful system that enables you to create and orchestrate
 The simplest use case is creating a single agent with specific tools. Here's an example of creating an assistant that tells jokes:

 ```typescript
-import { agent, FunctionTool } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+import { agent, tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";

 // Define a joke-telling tool
-const jokeTool = FunctionTool.from(
+const jokeTool = tool(
  () => "Baby Llama is called cria",
  {
    name: "joke",
@@ -28,15 +25,13 @@ const jokeTool = FunctionTool.from(
 );

 // Create an single agent workflow with the tool
-const workflow = agent({
+const jokeAgent = agent({
  tools: [jokeTool],
-  llm: new OpenAI({
-    model: "gpt-4o-mini",
-  }),
+  llm: openai({ model: "gpt-4o-mini" }),
 });

 // Run the workflow
-const result = await workflow.run("Tell me something funny");
+const result = await jokeAgent.run("Tell me something funny");
 console.log(result); // Baby Llama is called cria
 ```

@@ -73,8 +68,8 @@ An Agent Workflow can orchestrate multiple agents, enabling complex interactions
 Here's an example of a multi-agent system that combines joke-telling and weather information:

 ```typescript
-import { multiAgent, agent, FunctionTool } from "llamaindex";
-import { OpenAI } from "@llamaindex/openai";
+import { multiAgent, agent, tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
 import { z } from "zod";

 // Create a weather agent
@@ -82,18 +77,18 @@ const weatherAgent = agent({
  name: "WeatherAgent",
  description: "Provides weather information for any city",
  tools: [
-    FunctionTool.from(
-      ({ city }: { city: string }) => `The weather in ${city} is sunny`,
+    tool(
      {
        name: "fetchWeather",
        description: "Get weather information for a city",
        parameters: z.object({
          city: z.string(),
        }),
+        execute: ({ city }) => `The weather in ${city} is sunny`,
      }
    ),
  ],
-  llm: new OpenAI({ model: "gpt-4o-mini" }),
+  llm: openai({ model: "gpt-4o-mini" }),
 });

 // Create a joke-telling agent
@@ -101,18 +96,18 @@ const jokeAgent = agent({
  name: "JokeAgent",
  description: "Tells jokes and funny stories",
  tools: [jokeTool], // Using the joke tool defined earlier
-  llm: new OpenAI({ model: "gpt-4o-mini" }),
+  llm: openai({ model: "gpt-4o-mini" }),
  canHandoffTo: [weatherAgent], // Can hand off to the weather agent
 });

 // Create the multi-agent workflow
-const workflow = multiAgent({
+const agents = multiAgent({
  agents: [jokeAgent, weatherAgent],
  rootAgent: jokeAgent, // Start with the joke agent
 });

 // Run the workflow
-const result = await workflow.run(
+const result = await agents.run(
  "Give me a morning greeting with a joke and the weather in San Francisco"
 );
 ```
@@ -0,0 +1,4 @@
+{
+  "title": "Agents",
+  "pages": ["tool", "agent_workflow", "workflows"]
+}
@@ -0,0 +1,107 @@
+---
+title: Tools
+---
+
+A "tool" is a utility that can be called by an agent on behalf of an LLM.
+A tool can be called to perform custom actions, or retrieve extra information based on the LLM-generated input.
+A result from a tool call can be used by subsequent steps in a workflow, or to compute a final answer.
+For example, a "weather tool" could fetch some live weather information from a geographical location.
+
+## Tool Function
+
+The `tool` function is a utility provided to define a tool that can be used by an agent. It takes a function and a configuration object as arguments. The configuration object includes the tool's name, description, and parameters.
+
+### Parameters with Zod
+
+The `parameters` field in the tool configuration is defined using `zod`, a TypeScript-first schema declaration and validation library. `zod` allows you to specify the expected structure and types of the input parameters, ensuring that the data passed to the tool is valid.
+
+Example:
+```ts
+import { agent, tool } from "llamaindex";
+import { z } from "zod";
+
+// first arg is LLM input, second is bound arg
+const queryKnowledgeBase = async ({ question }, { userToken }) => {
+  const response = await fetch(`https://knowledge-base.com?token=${userToken}&query=${question}`);
+  // ...
+};
+
+// define tool with zod validation
+const kbTool = tool(queryKnowledgeBase, {
+  name: 'queryKnowledgeBase',
+  description: 'Query knowledge base',
+  parameters: z.object({
+    question: z.string({
+      description: 'The user question',
+    }),
+  }),
+});
+
+```
+In this example, `z.object` is used to define a schema for the `parameters` where `question` is expected to be a string. This ensures that any input to the tool adheres to the specified structure, providing a layer of type safety and validation.
+
+
+## Built-in tools
+
+You can import built-in tools from the `@llamaindex/tools` package.
+
+```ts
+import { agent } from "llamaindex";
+import { wiki } from "@llamaindex/tools";
+
+const researchAgent = agent({
+  name: "WikiAgent",
+  description: "Gathering information from the internet",
+  systemPrompt: `You are a research agent. Your role is to gather information from the internet using the provided tools.`,
+  tools: [wiki()],
+});
+```
+
+## Function tool
+
+You can still use the `FunctionTool` class to define a tool.
+A `FunctionTool` is constructed from a function with signature
+```ts
+(input: T, additionalArg?: AdditionalToolArgument) => R
+```
+where
+- `input` is generated by the LLM, `T` is the type defined by the tool `parameters`
+- `additionalArg` is an optional extra argument, see "Binding" below
+- `R` is the return type
+
+### Binding
+
+An additional argument can be bound to a tool, each tool call will be passed
+- the input provided by the LLM
+- the additional argument (extends object)
+
+Note: calling the `bind` method will return a new `FunctionTool` instance, without modifying the tool which `bind` is called on.
+
+Example to pass a `userToken` as additional argument:
+```ts
+import { agent, tool } from "llamaindex";
+
+// first arg is LLM input, second is bound arg
+const queryKnowledgeBase = async ({ question }, { userToken }) => {
+  const response = await fetch(`https://knowledge-base.com?token=${userToken}&query=${question}`);
+  // ...
+};
+
+// define tool as usual
+const kbTool = tool(queryKnowledgeBase, {
+  name: 'queryKnowledgeBase',
+  description: 'Query knowledge base',
+  parameters: z.object({
+    question: z.string({
+      description: 'The user question',
+    }),
+  }),
+});
+
+// create an agent
+const additionalArg = { userToken: 'abcd1234' };
+const workflow = agent({
+  tools: [kbTool.bind(additionalArg)],
+  // llm, systemPrompt etc
+})
+```
@@ -3,7 +3,7 @@ title: Workflows
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/workflow/joke.ts";
+import CodeSource from "!raw-loader!@/examples/workflow/joke.ts";

 A `Workflow` in LlamaIndexTS is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events.

@@ -2,7 +2,8 @@
 title: Index
 ---

-An index is the basic container and organization for your data. LlamaIndex.TS supports two indexes:
+An index is the basic container for organizing your data. Besides managed indexes using [LlamaCloud](/docs/llamaindex/modules/data/data_index/managed), LlamaIndex.TS supports three indexes:
+

 - `VectorStoreIndex` - will send the top-k `Node`s to the LLM when generating a response. The default top-k is 2.
 - `SummaryIndex` - will send every `Node` in the index to the LLM in order to generate a response
@@ -0,0 +1,36 @@
+---
+title: Managed Index
+description: Managed index using LlamaCloud
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/cloud/chat.ts";
+import CodeSource2 from "!raw-loader!@/examples/cloud/from-documents.ts";
+
+LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
+
+LlamaCloud supports
+
+- Managed Ingestion API, handling parsing and document management
+- Managed Retrieval API, configuring optimal retrieval for your RAG system
+
+## Access
+
+Visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
+
+## Create a Managed Index
+
+Here's an example of how to create a managed index by ingesting a couple of documents:
+
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
+
+## Use a Managed Index
+
+Here's an example of how to use a managed index together with a chat engine:
+
+<DynamicCodeBlock lang="ts" code={CodeSource} />
+
+## API Reference
+
+- [LlamaCloudIndex](/docs/api/classes/LlamaCloudIndex)
+- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever)
@@ -0,0 +1,17 @@
+---
+title: Documents and Nodes
+description: Data structure for storing data in LlamaIndex
+---
+
+`Document`s and `Node`s are the basic building blocks of data in LlamaIndexTS. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
+
+```typescript
+import { Document } from "llamaindex";
+
+document = new Document({ text: "text", metadata: { key: "val" } });
+```
+
+## API Reference
+
+- [Document](/docs/api/classes/Document)
+- [TextNode](/docs/api/classes/TextNode)
@@ -6,9 +6,9 @@ A transformation is something that takes a list of nodes as an input, and return

 Currently, the following components are Transformation objects:

- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
- [MetadataExtractor](/docs/llamaindex/modules/documents_and_nodes/metadata_extraction)
- [Embeddings](/docs/llamaindex/modules/embeddings/index)
+- [SentenceSplitter](/docs/llamaindex/modules/data/ingestion_pipeline/transformations/node-parser)
+- [MetadataExtractor](/docs/llamaindex/modules/data/ingestion_pipeline/transformations/metadata_extraction)
+- [Embeddings](/docs/llamaindex/modules/models/embeddings)

 ## Usage Pattern

@@ -1,5 +1,5 @@
 ---
-title: Metadata Extraction Usage Pattern
+title: Metadata Extraction 
 ---

 You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
@@ -2,17 +2,17 @@
 title: Node Parsers / Text Splitters
 description: Learn how to use Node Parsers and Text Splitters to extract data from documents.
 ---
-import { CodeNodeParserDemo } from '../../../../../components/demo/code-node-parser.tsx';
+import { CodeNodeParserDemo } from '@/components/demo/code-node-parser.tsx';
 import { Tab, Tabs } from "fumadocs-ui/components/tabs";

-Node parsers are a simple abstraction that take a list of documents, and chunk them into `Node` objects, such that each node is a specific chunk of the parent document. When a document is broken into nodes, all of it's attributes are inherited to the children nodes (i.e. `metadata`, text and metadata templates, etc.). You can read more about `Node` and `Document` properties [here](./).
-
-## NodeParser
-
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects.
+Node parsers are a simple abstraction that take a list of `Document` objects, and chunk them into `Node` objects, such that each node is a specific chunk of the parent document. When a document is broken into nodes, all of it's attributes are inherited to the children nodes (i.e. `metadata`, text and metadata templates, etc.). You can read more about `Node` and `Document` properties [here](/docs/llamaindex/modules/data).

 By default, we will use `Settings.nodeParser` to split the document into nodes. You can also assign a custom `NodeParser` to the `Settings` object.

+## SentenceSplitter
+
+The `SentenceSplitter` is the default `NodeParser` in LlamaIndex. It will split the text from a `Document` into sentences.
+
 ```ts twoslash
 import { TextFileReader } from '@llamaindex/readers/text'
 import { SentenceSplitter } from 'llamaindex';
@@ -23,8 +23,6 @@ Settings.nodeParser = nodeParser;
 //		     ^?
 ```

-## TextSplitter
-
 The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.

 ```ts twoslash
@@ -68,6 +66,46 @@ The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdow
 	```
 </Tabs>

+The output metadata will be something like:
+
+```bash
+[
+  TextNode {
+    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
+    metadata: { 'Header 1': 'Main Header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
+    text: 'Main Header\nMain content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
+    metadata: { 'Header 1': 'Header 2' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
+    text: 'Header 2\nHeader 2 content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
+    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
+    text: 'Sub-header\nSub-header content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  }
+]
+```
+
 ## CodeSplitter

 The `CodeSplitter` is a more advanced `NodeParser` that can handle code documents.
@@ -155,3 +193,9 @@ import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
 		```
 	</Accordion>
 </Accordions>
+
+## API Reference
+
+- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
+- [MarkdownNodeParser](/docs/api/classes/MarkdownNodeParser)
+- [CodeSplitter](/docs/api/classes/CodeSplitter)
@@ -0,0 +1,4 @@
+{
+  "title": "Data",
+  "pages": ["index", "readers", "data_index", "ingestion_pipeline", "stores"]
+}
@@ -3,7 +3,7 @@ title: DiscordReader
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../examples/readers/src/discord";
+import CodeSource from "!raw-loader!@/examples/readers/src/discord";

 DiscordReader is a simple data loader that reads all messages in a given Discord channel and returns them as Document objects.
 It uses the [@discordjs/rest](https://github.com/discordjs/discord.js/tree/main/packages/rest) library to fetch the messages.
@@ -0,0 +1,144 @@
+---
+title: Loading Data
+description: Loading data using Readers into Documents
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/simple-directory-reader";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/custom-simple-directory-reader";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
+Before you can start indexing your documents, you need to load them into memory.
+A reader is a module that loads data from a file into a `Document` object.
+
+To install readers call:
+
+<Accordions>
+	<Accordion title="Install @llamaindex/readers">
+
+		If you want to use the reader module, you need to install `@llamaindex/readers`
+
+		<Tabs groupId="install-llamaindex" items={["npm", "yarn", "pnpm"]} persist>
+			```shell tab="npm"
+			npm install @llamaindex/readers
+			```
+
+			```shell tab="yarn"
+			yarn add @llamaindex/readers
+			```
+
+			```shell tab="pnpm"
+			pnpm add @llamaindex/readers
+			```
+
+		</Tabs>
+	</Accordion>
+</Accordions>
+
+We offer readers for different file formats.
+
+```ts twoslash 
+import { CSVReader } from '@llamaindex/readers/csv'
+import { PDFReader } from '@llamaindex/readers/pdf'
+import { JSONReader } from '@llamaindex/readers/json'
+import { MarkdownReader } from '@llamaindex/readers/markdown'
+import { HTMLReader } from '@llamaindex/readers/html'
+// you can find more readers in the documentation
+```
+
+Additionally the following loaders exist without separate documentation:
+
+- `AssemblyAIReader` transcribes audio using [AssemblyAI](https://www.assemblyai.com/).
+  - [AudioTranscriptReader](/docs/api/classes/AudioTranscriptReader): loads entire transcript as a single document.
+  - [AudioTranscriptParagraphsReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document per paragraph.
+  - [AudioTranscriptSentencesReader](/docs/api/classes/AudioTranscriptSentencesReader): creates a document per sentence.
+  - [AudioSubtitlesReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document containing the subtitles of a transcript.
+- [NotionReader](/docs/api/classes/NotionReader) loads [Notion](https://www.notion.so/) pages.
+- [SimpleMongoReader](/docs/api/classes/SimpleMongoReader) loads data from a [MongoDB](https://www.mongodb.com/).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## SimpleDirectoryReader
+
+[Open in StackBlitz](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
+
+It is a simple reader that reads all files from a directory and its subdirectories and delegates the actual reading to the reader specified in the `fileExtToReader` map.
+
+<DynamicCodeBlock lang="ts" code={CodeSource} />
+
+Currently, the following readers are mapped to specific file types:
+
+- [TextFileReader](/docs/api/classes/TextFileReader): `.txt`
+- [PDFReader](/docs/api/classes/PDFReader): `.pdf`
+- [CSVReader](/docs/api/classes/CSVReader): `.csv`
+- [MarkdownReader](/docs/api/classes/MarkdownReader): `.md`
+- [DocxReader](/docs/api/classes/DocxReader): `.docx`
+- [HTMLReader](/docs/api/classes/HTMLReader): `.htm`, `.html`
+- [ImageReader](/docs/api/classes/ImageReader): `.jpg`, `.jpeg`, `.png`, `.gif`
+
+You can modify the reader three different ways:
+
+- `overrideReader` overrides the reader for all file types, including unsupported ones.
+- `fileExtToReader` maps a reader to a specific file type. Can override reader for existing file types or add support for new file types.
+- `defaultReader` sets a fallback reader for files with unsupported extensions. By default it is `TextFileReader`.
+
+SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
+
+### Example
+
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
+
+## Tips when using in non-Node.js environments
+
+When using `@llamaindex/readers` in a non-Node.js environment (such as Vercel Edge, Cloudflare Workers, etc.)
+Some classes are not exported from top-level entry file.
+
+The reason is that some classes are only compatible with Node.js runtime, (e.g. `PDFReader`) which uses Node.js specific APIs (like `fs`, `child_process`, `crypto`).
+
+If you need any of those classes, you have to import them instead directly through their file path in the package.
+
+As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
+
+```typescript
+import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
+import { LlamaParseReader } from "@llamaindex/cloud";
+
+export const DATA_DIR = "./data";
+
+export async function getDocuments() {
+  const reader = new SimpleDirectoryReader();
+  // Load PDFs using LlamaParseReader
+  return await reader.loadData({
+    directoryPath: DATA_DIR,
+    fileExtToReader: {
+      pdf: new LlamaParseReader({ resultType: "markdown" }),
+    },
+  });
+}
+```
+
+> _Note_: Reader classes have to be added explicitly to the `fileExtToReader` map in the Edge version of the `SimpleDirectoryReader`.
+
+You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
+
+
+## Load file natively using Node.js Customization Hooks
+
+We have a helper utility to allow you to import a file in Node.js script.
+
+```shell 
+node --import @llamaindex/readers/node ./script.js
+```
+
+```ts
+import csv from './path/to/data.csv';
+
+const text = csv.getText()
+```
+
+## API Reference
+
+- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
@@ -3,8 +3,8 @@ title: LlamaParse
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../../examples/readers/src/llamaparse";
-import CodeSource2 from "!raw-loader!../../../../../../../../../examples/readers/src/simple-directory-reader-with-llamaparse.ts";
+import CodeSource from "!raw-loader!@/examples/readers/src/llamaparse";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/simple-directory-reader-with-llamaparse.ts";

 LlamaParse is an API created by LlamaIndex to efficiently parse files, e.g. it's great at converting PDF tables into markdown.

@@ -6,11 +6,11 @@ Chat stores manage chat history by storing sequences of messages in a structured

 ## Available Chat Stores

- [SimpleChatStore](/docs/api/classes/SimpleChatStore): A simple in-memory chat store with support for [persisting](/docs/llamaindex/modules/data_stores/#local-storage) data to disk.
+- [SimpleChatStore](/docs/api/classes/SimpleChatStore): A simple in-memory chat store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

 ## API Reference

- [BaseChatStore](/docs/api/interfaces/BaseChatStore)
+- [BaseChatStore](/docs/api/classes/BaseChatStore)

@@ -2,12 +2,12 @@
 title: Document Stores
 ---

-Document stores contain ingested document chunks, i.e. [Node](/docs/llamaindex/modules/documents_and_nodes/index)s.
+Document stores contain ingested document chunks, i.e. [Node](/docs/llamaindex/modules/data)s.

 ## Available Document Stores

- [SimpleDocumentStore](/docs/api/classes/SimpleDocumentStore): A simple in-memory document store with support for [persisting](/docs/llamaindex/modules/data_stores/#local-storage) data to disk.
- [PostgresDocumentStore](/docs/api/classes/PostgresDocumentStore): A PostgreSQL document store, see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores/#postgresql-storage).
+- [SimpleDocumentStore](/docs/api/classes/SimpleDocumentStore): A simple in-memory document store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
+- [PostgresDocumentStore](/docs/api/classes/PostgresDocumentStore): A PostgreSQL document store, see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -2,12 +2,12 @@
 title: Index Stores
 ---

-Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](/docs/llamaindex/modules/data_index) itself.
+Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](/docs/llamaindex/modules/data/data_index) itself.

 ## Available Index Stores

- [SimpleIndexStore](/docs/api/classes/SimpleIndexStore): A simple in-memory index store with support for [persisting](/docs/llamaindex/modules/data_stores/#local-storage) data to disk.
- [PostgresIndexStore](/docs/api/classes/PostgresIndexStore): A PostgreSQL index store, , see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores/#postgresql-storage).
+- [SimpleIndexStore](/docs/api/classes/SimpleIndexStore): A simple in-memory index store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
+- [PostgresIndexStore](/docs/api/classes/PostgresIndexStore): A PostgreSQL index store, , see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -2,12 +2,12 @@
 title: Key-Value Stores
 ---

-Key-Value Stores represent underlying storage components used in [Document Stores](/docs/llamaindex/modules/data_stores/doc_stores/index) and [Index Stores](/docs/llamaindex/modules/data_stores/index_stores/index)
+Key-Value Stores represent underlying storage components used in [Document Stores](/docs/llamaindex/modules/data/stores/doc_stores) and [Index Stores](/docs/llamaindex/modules/data/stores/index_stores)

 ## Available Key-Value Stores

- [SimpleKVStore](/docs/api/classes/SimpleKVStore): A simple Key-Value store with support of [persisting](/docs/llamaindex/modules/data_stores/#local-storage) data to disk.
- [PostgresKVStore](/docs/api/classes/PostgresKVStore): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores/#postgresql-storage).
+- [SimpleKVStore](/docs/api/classes/SimpleKVStore): A simple Key-Value store with support of [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
+- [PostgresKVStore](/docs/api/classes/PostgresKVStore): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -8,7 +8,7 @@ Vector stores save embedding vectors of your ingested document chunks.

 Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:

- [SimpleVectorStore](/docs/api/classes/SimpleVectorStore): A simple in-memory vector store with optional [persistance](/docs/llamaindex/modules/data_stores/#local-storage) to disk.
+- [SimpleVectorStore](/docs/api/classes/SimpleVectorStore): A simple in-memory vector store with optional [persistance](/docs/llamaindex/modules/data/stores#local-storage) to disk.
 - [AstraDBVectorStore](/docs/api/classes/AstraDBVectorStore): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
 - [ChromaVectorStore](/docs/api/classes/ChromaVectorStore): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
 - [MilvusVectorStore](/docs/api/classes/MilvusVectorStore): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
@@ -19,6 +19,3 @@ Available Vector Stores are shown on the sidebar to the left. Additionally the f

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

-## API Reference
-
- [BaseVectorStore](/docs/api/classes/BaseVectorStore)
@@ -56,10 +56,10 @@ const vectorStore = new QdrantVectorStore({

 ```ts
 const document = new Document({ text: essay, id_: path });
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  vectorStore,
-});
+const storageContext = await storageContextFromDefaults({ vectorStore });
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    storageContext,
+  });
 ```

 ## Query the index
@@ -91,11 +91,11 @@ async function main() {
  });

  const document = new Document({ text: essay, id_: path });
-
+  const storageContext = await storageContextFromDefaults({ vectorStore });
  const index = await VectorStoreIndex.fromDocuments([document], {
-    vectorStore,
+    storageContext,
  });
-
+  
  const queryEngine = index.asQueryEngine();

  const response = await queryEngine.query({
@@ -0,0 +1,166 @@
+---
+title: Supabase Vector Store
+---
+
+[supabase.com](https://supabase.com/)
+
+To use this vector store, you need a Supabase project. You can create one at [supabase.com](https://supabase.com/).
+
+## Installation
+
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+  ```shell tab="npm"
+  npm install llamaindex @llamaindex/supabase
+  ```
+
+  ```shell tab="yarn"
+  yarn add llamaindex @llamaindex/supabase
+  ```
+
+  ```shell tab="pnpm"
+  pnpm add llamaindex @llamaindex/supabase
+  ```
+</Tabs>
+
+## Database Setup
+
+Before using the vector store, you need to:
+1. Enable the `pgvector` extension
+2. Create a table for storing vectors
+3. Create a vector similarity search function
+
+```sql
+create table documents (
+id uuid primary key,
+content text,
+metadata jsonb,
+embedding vector(1536)
+);
+```
+
+-- Create a function for similarity search
+```sql
+create function match_documents (
+query_embedding vector(1536),
+match_count int
+) returns table (
+id uuid,
+content text,
+metadata jsonb,
+embedding vector(1536),
+similarity float
+)
+language plpgsql
+as $$
+begin
+return query
+select
+id,
+content,
+metadata,
+embedding,
+1 - (embedding <=> query_embedding) as similarity
+from documents
+order by embedding <=> query_embedding
+limit match_count;
+end;
+$$;
+```
+
+## Importing the modules
+
+```ts
+import { Document, VectorStoreIndex } from "llamaindex";
+import { SupabaseVectorStore } from "@llamaindex/supabase";
+```
+
+## Setup Supabase
+
+```ts
+const vectorStore = new SupabaseVectorStore({
+  supabaseUrl: process.env.SUPABASE_URL,
+  supabaseKey: process.env.SUPABASE_KEY,
+  table: "documents",
+});
+```
+
+## Setup the index
+
+```ts
+const documents = [
+  new Document({ 
+    text: "Sample document text",
+    metadata: { source: "example" }
+  })
+];
+
+const storageContext = await storageContextFromDefaults({ vectorStore });
+const index = await VectorStoreIndex.fromDocuments(documents, {
+  storageContext,
+});
+```
+
+## Query the index
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const response = await queryEngine.query({
+  query: "What is in the document?",
+});
+
+// Output response
+console.log(response.toString());
+```
+
+## Full code
+
+```ts
+import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { SupabaseVectorStore } from "@llamaindex/supabase";
+
+async function main() {
+  // Initialize the vector store
+  const vectorStore = new SupabaseVectorStore({
+    supabaseUrl: process.env.SUPABASE_URL,
+    supabaseKey: process.env.SUPABASE_KEY,
+    table: "documents",
+  });
+
+  // Create sample documents
+  const documents = [
+    new Document({
+      text: "Vector search enables semantic similarity search",
+      metadata: {
+        source: "research_paper",
+        author: "Jane Smith",
+      },
+    }),
+  ];
+
+  // Create storage context
+  const storageContext = await storageContextFromDefaults({ vectorStore });
+
+  // Create and store embeddings
+  const index = await VectorStoreIndex.fromDocuments(documents, {
+    storageContext,
+  });
+
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query({
+    query: "What is vector search?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
+```
+
+## API Reference
+
+- [SupabaseVectorStore](/docs/api/classes/SupabaseVectorStore)
@@ -1,58 +0,0 @@
---
-title: Loader
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../examples/readers/src/simple-directory-reader";
-import CodeSource2 from "!raw-loader!../../../../../../../../examples/readers/src/custom-simple-directory-reader";
-
-Before you can start indexing your documents, you need to load them into memory.
-
-All "basic" data loaders can be seen below, mapped to their respective filetypes in `SimpleDirectoryReader`. More loaders are shown in the sidebar on the left.
-Additionally the following loaders exist without separate documentation:
-
- `AssemblyAIReader` transcribes audio using [AssemblyAI](https://www.assemblyai.com/).
-  - [AudioTranscriptReader](/docs/api/classes/AudioTranscriptReader): loads entire transcript as a single document.
-  - [AudioTranscriptParagraphsReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document per paragraph.
-  - [AudioTranscriptSentencesReader](/docs/api/classes/AudioTranscriptSentencesReader): creates a document per sentence.
-  - [AudioSubtitlesReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document containing the subtitles of a transcript.
- [NotionReader](/docs/api/classes/NotionReader) loads [Notion](https://www.notion.so/) pages.
- [SimpleMongoReader](/docs/api/classes/SimpleMongoReader) loads data from a [MongoDB](https://www.mongodb.com/).
-
-Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
-
-## SimpleDirectoryReader
-
-[Open in StackBlitz](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
-
-LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
-
-It is a simple reader that reads all files from a directory and its subdirectories.
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
-
-Currently, the following readers are mapped to specific file types:
-
- [TextFileReader](/docs/api/classes/TextFileReader): `.txt`
- [PDFReader](/docs/api/classes/PDFReader): `.pdf`
- [PapaCSVReader](/docs/api/classes/PapaCSVReader): `.csv`
- [MarkdownReader](/docs/api/classes/MarkdownReader): `.md`
- [DocxReader](/docs/api/classes/DocxReader): `.docx`
- [HTMLReader](/docs/api/classes/HTMLReader): `.htm`, `.html`
- [ImageReader](/docs/api/classes/ImageReader): `.jpg`, `.jpeg`, `.png`, `.gif`
-
-You can modify the reader three different ways:
-
- `overrideReader` overrides the reader for all file types, including unsupported ones.
- `fileExtToReader` maps a reader to a specific file type. Can override reader for existing file types or add support for new file types.
- `defaultReader` sets a fallback reader for files with unsupported extensions. By default it is `TextFileReader`.
-
-SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
-
-### Example
-
-<DynamicCodeBlock lang="ts" code={CodeSource2} />
-
-## API Reference
-
- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
@@ -1,16 +0,0 @@
---
-title: Documents and Nodes
---
-
-`Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
-
-```typescript
-import { Document } from "llamaindex";
-
-document = new Document({ text: "text", metadata: { key: "val" } });
-```
-
-## API Reference
-
- [Document](/docs/api/classes/Document)
- [TextNode](/docs/api/classes/TextNode)
@@ -74,4 +74,4 @@ the response is not correct with a score of 2.5

 ## API Reference

- [CorrectnessEvaluator](/docs/api/classes/CorrectnessEvaluator)
+- [CorrectnessEvaluator](/docs/api/classes/CorrectnessEvaluator)
@@ -1,34 +0,0 @@
---
-title: LlamaCloud
---
-
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/cloud/chat.ts";
-
-LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
-
-Currently, LlamaCloud supports
-
- Managed Ingestion API, handling parsing and document management
- Managed Retrieval API, configuring optimal retrieval for your RAG system
-
-## Access
-
-We are opening up a private beta to a limited set of enterprise partners for the managed ingestion and retrieval API. If you’re interested in centralizing your data pipelines and spending more time working on your actual RAG use cases, come [talk to us.](https://www.llamaindex.ai/contact)
-
-If you have access to LlamaCloud, you can visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
-
-## Create a Managed Index
-
-Currently, you can't create a managed index on LlamaCloud using LlamaIndexTS, but you can use an existing managed index for retrieval that was created by the Python version of LlamaIndex. See [the LlamaCloudIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/indexing/llama_cloud_index.html#usage) for more information on how to create a managed index.
-
-## Use a Managed Index
-
-Here's an example of how to use a managed index together with a chat engine:
-
-<DynamicCodeBlock lang="ts" code={CodeSource} />
-
-## API Reference
-
- [LlamaCloudIndex](/docs/api/classes/LlamaCloudIndex)
- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever)
@@ -1,98 +0,0 @@
---
-title: OpenAI
---
-
-## Installation
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install llamaindex @llamaindex/openai
-	```
-
-	```shell tab="yarn"
-	yarn add llamaindex @llamaindex/openai
-	```
-
-	```shell tab="pnpm"
-	pnpm add llamaindex @llamaindex/openai
-	```
-</Tabs>
-
-
-```ts
-import { OpenAI } from "@llamaindex/openai";
-import { Settings } from "llamaindex";
-
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
-```
-
-You can setup the apiKey on the environment variables, like:
-
-```bash
-export OPENAI_API_KEY="<YOUR_API_KEY>"
-```
-
-## 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
-import { Document, VectorStoreIndex } from "llamaindex";
-
-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,
-});
-```
-
-## Full Example
-
-```ts
-import { OpenAI } from "@llamaindex/openai";
-import { Document, Settings, VectorStoreIndex } from "llamaindex";
-
-// Use the OpenAI LLM
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", 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);
-}
-```
-
-## API Reference
-
- [OpenAI](/docs/api/classes/OpenAI)
@@ -0,0 +1,4 @@
+{
+  "title": "Modules",
+  "pages": ["models", "agents", "data", "rag", "ui", "evaluation"]
+}
@@ -4,7 +4,7 @@ title: Embedding

 The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI.

-This can be explicitly updated through `Settings`
+This can be explicitly updated through `Settings.embedModel`.

 ## Installation

@@ -35,7 +35,7 @@ Settings.embedModel = new OpenAIEmbedding({

 ## Local Embedding

-For local embeddings, you can use the [HuggingFace](/docs/llamaindex/modules/embeddings/available_embeddings/huggingface) embedding model.
+For local embeddings, you can use the [HuggingFace](/docs/llamaindex/modules/models/embeddings/huggingface) embedding model.

 ## Local Ollama Embeddings With Remote Host

@@ -31,6 +31,20 @@ Settings.llm = new Gemini({
 });
 ```

+## Usage with Proxy
+
+```ts
+import { Gemini, GEMINI_MODEL } from "@llamaindex/google";
+import { Settings } from "llamaindex";
+
+Settings.llm = new Gemini({
+  model: GEMINI_MODEL.GEMINI_PRO,
+  requestOptions: {
+    baseUrl: <YOUR_PROXY_URL>   // optional, but useful for custom endpoints
+  }
+});
+```
+
 ### Usage with Vertex AI

 To use Gemini via Vertex AI you can use `GeminiVertexSession`.
@@ -3,7 +3,7 @@ title: Groq
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../../examples/groq.ts";
+import CodeSource from "!raw-loader!@/examples/groq.ts";

 ## Installation

@@ -45,7 +45,7 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name

 ## Local LLM

-For local LLMs, currently we recommend the use of [Ollama](/docs/llamaindex/modules/llms/available_llms/ollama) LLM.
+For local LLMs, currently we recommend the use of [Ollama](/docs/llamaindex/modules/models/llms/ollama) LLM.

 ## Available LLMs

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions[bot]	5f3eb457e6	Release 0.9.19 (#1844 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-15 00:31:31 -07:00
Peter Goldstein	d365eb2e54	Add GPT-4.1 models to OpenAI (#1842 )	2025-04-15 09:07:38 +02:00
Thuc Pham	bb34ade6d4	feat: support cn utils for server UI (#1843 )	2025-04-15 09:06:39 +02:00
github-actions[bot]	c540df5069	Release 0.9.18 (#1836 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-14 20:52:09 +07:00
Marcus Schiesser	400b3b54bf	feat: use full-source code with import statements for custom comps (#1838 ) Co-authored-by: thucpn <thucsh2@gmail.com> Co-authored-by: Thuc Pham <51660321+thucpn@users.noreply.github.com>	2025-04-14 13:48:21 +02:00
Marcus Schiesser	88b7046c68	chore: Move zod to peer deps (#1837 )	2025-04-10 18:17:26 +07:00
Zhanghao	2ffdb274f2	docs: correct the `CondenseQuestionChatEngine` path (#1834 )	2025-04-10 16:07:07 +07:00
github-actions[bot]	139eb050f9	Release @llamaindex/server@0.1.0 (#1835 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-10 15:38:40 +07:00
Thuc Pham	3ffee26b77	feat: enhance config params for LlamaIndexServer (#1833 )	2025-04-10 15:21:51 +07:00
Marcus Schiesser	dc6e774d78	chore: remove deepresearch events (#1831 )	2025-04-09 20:45:49 +07:00
github-actions[bot]	6716188e10	Release @llamaindex/server@0.0.9 (#1830 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-09 17:44:13 +07:00
Thuc Pham	0b75bd6d92	feat: component dir in llamaindex server (#1828 )	2025-04-09 17:25:21 +07:00
github-actions[bot]	045b267d1b	Release 0.9.17 (#1823 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>	2025-04-08 17:35:55 -07:00
Alex Yang	41191d074a	fix(parse): file input (#1829 )	2025-04-08 14:15:07 -07:00
Marcus Schiesser	8b2914c8b7	docs: reorganize modules structure in docs (#1827 )	2025-04-08 22:42:00 +07:00
Marcus Schiesser	4c24dfcbce	docs: Move framework docs to installation directory and simplify gett… (#1826 )	2025-04-07 22:54:12 +07:00
r3rer3	0dfa371fc9	Add "thinking" and "thinking_signature" to chat response, and "thinking_signature" to chat stream (#1825 )	2025-04-07 21:57:55 +07:00
Peter Goldstein	0d852d6fdc	Add the Gemini 2.5 Pro Preview model (#1822 )	2025-04-07 16:08:40 +07:00
ANKIT VARSHNEY	2410527e64	feat: reader for postgres (#1813 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-04-07 15:57:14 +07:00
Marcus Schiesser	7d2be8c640	fix: mcp test	2025-04-07 10:44:54 +02:00
Thuc Pham	3534c373f2	feat: support multi-resolution compatibility (#1816 )	2025-04-05 18:41:39 +07:00
github-actions[bot]	2cbdf71669	Release 0.9.16 (#1811 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-04 11:12:27 +02:00
Huu Le	ead657aedd	feat: add MCP tools integration and example usage (#1819 )	2025-04-04 11:03:10 +02:00
Marcus Schiesser	f5e4d098b0	chore: remove gpt-tokenizer (#1815 )	2025-04-03 14:23:31 +02:00
dependabot[bot]	4d97226e50	chore(deps): bump next from 15.2.3 to 15.2.4 (#1812 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-04-03 13:57:39 +07:00
Marcus Schiesser	4999df18cc	chore: bump nextjs to "^15.2.3" (#1810 )	2025-04-02 21:24:57 +07:00
github-actions[bot]	9a27b6d94a	Release 0.9.15 (#1807 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-02 17:08:47 +07:00
Thuc Pham	8c02684f0f	fix: handle error when streaming workflow (#1808 )	2025-04-02 16:26:01 +07:00
ANKIT VARSHNEY	9c63f3f94e	feat: openai responses api (#1801 )	2025-04-02 16:21:43 +07:00
Thuc Pham	c515a324f6	feat: return raw output for agent toolcall result (#1806 )	2025-04-01 22:20:06 +07:00
github-actions[bot]	c70d7b9930	Release 0.9.14 (#1799 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-01 12:59:10 +02:00
Marcus Schiesser	1b6f368a3f	feat: Support loading from URLs for all readers extending FileReader (#1805 )	2025-04-01 17:39:59 +07:00
Thuc Pham	9d951b288f	feat: support llamacloud in @llamaindex/server (#1796 )	2025-04-01 17:39:39 +07:00
dependabot[bot]	5fe16697a2	chore(deps-dev): bump vite from 5.4.15 to 5.4.16 (#1804 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-04-01 16:49:36 +07:00
Marcus Schiesser	189d8a83ac	chore: use node 20 for examples (#1803 )	2025-03-31 17:21:19 +07:00
ANKIT VARSHNEY	648cfb5cb5	feat: supbase vector store (#1790 )	2025-03-29 15:14:28 +07:00
Marcus Schiesser	eaf326ee90	fix: passing right llm setting from SimpleChatEngine to ChatMemoryBuffer (#1798 )	2025-03-28 18:20:52 +07:00
github-actions[bot]	fc1bedf438	Release (#1794 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-03-28 15:22:44 +07:00
Thuc Pham	164cf7a6df	fix: custom next server start fail (#1795 )	2025-03-28 15:09:57 +07:00
Zhanghao	e98033e2cc	docs: correct the number of indexes (#1793 )	2025-03-27 16:33:52 +02:00
dependabot[bot]	c0ffc7b434	chore(deps-dev): bump vite from 5.4.14 to 5.4.15 (#1787 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-03-26 20:43:49 +07:00
github-actions[bot]	9cf88e9f3f	Release 0.9.13 (#1783 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-03-26 10:31:29 +02:00
Thuc Pham	75d6e29187	feat: response source nodes in query tool output (#1784 )	2025-03-26 15:24:53 +07:00
Parham Saidi	132517877e	fix: stringify all tool results for anthropic on bedrock (#1786 )	2025-03-25 21:16:44 +07:00
Thuc Pham	299008b34f	feat: copy create-llama to @llamaindex/servers (#1780 )	2025-03-25 11:55:44 +02:00
Thuc Pham	482ed67690	fix: document deployment fail due to static generation timed out (#1779 )	2025-03-24 11:12:09 +02:00
github-actions[bot]	9aeec9089b	Release 0.9.12 (#1744 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-03-24 14:34:45 +07:00
Daniel Bank	f1db9b3d48	feat: vercel tool response fields options (#1765 )	2025-03-24 14:25:26 +07:00
ANKIT VARSHNEY	25093531cf	feat: elastic search vector store (#1777 )	2025-03-24 14:18:42 +07:00
Thuc Pham	f8a86e4eff	feat: @llamaindex/server (#1759 )	2025-03-21 19:14:19 +07:00
ANKIT VARSHNEY	04f8c96caa	feat: add support for mongodb document store (#1771 )	2025-03-21 16:16:49 +07:00
Jorge Luis Middleton	43053f9e16	Update qdrant.mdx documentation (#1770 )	2025-03-21 10:06:25 +02:00
Parham Saidi	93bc0ffd21	fix: context engine additional options not being passed (#1772 )	2025-03-21 01:49:43 +07:00
Huu Le	58a9446220	Fix wrong multi-agent setup (#1767 )	2025-03-20 14:55:25 +03:00
Parham Saidi	da06e4550b	fix: include inline data check for GoogleStudio (#1769 )	2025-03-20 14:50:47 +03:00
Parham Saidi	3fd4cc383e	feat: google multimodal output using their new gen ai library (#1762 )	2025-03-19 21:43:39 +02:00
ANKIT VARSHNEY	2b39ceffa6	docs: doc for structured output (#1761 )	2025-03-19 14:15:11 +07:00
Thuc Pham	77e24cec65	fix: crypto is not defined when running on node18 (#1763 )	2025-03-19 08:38:16 +02:00
ANKIT VARSHNEY	2a0a899d66	chore: added saftey setting as parameter for gemini (#1760 )	2025-03-18 23:05:47 +07:00
ANKIT VARSHNEY	050cd53450	fix: delete by id in pinecone vector store (#1758 )	2025-03-18 23:03:42 +07:00
George He	5189b446f4	fix: add retry handling logic to parser reader and fix lint issues (#1757 ) Co-authored-by: Alex Yang <himself65@outlook.com>	2025-03-17 14:50:38 -07:00
Thuc Pham	c7ff3233fe	feat: @llamaindex/tools (#1755 )	2025-03-17 21:20:24 +07:00
Jack Qian	21bebfcaa6	docs: add missing links (#1754 )	2025-03-16 13:32:19 +07:00
ANKIT VARSHNEY	91a18e7057	feat: add support for structured output with zod schema. (#1749 )	2025-03-16 11:58:28 +08:00
ANKIT VARSHNEY	d1c1f99e06	feat: function calling support in mistral provider (#1756 )	2025-03-15 12:18:36 +07:00
Marcus Schiesser	8be84aeb5e	chore: add gemini streaming example (#782 )	2025-03-14 09:25:09 +07:00
Thuc Pham	5b7b314b25	fix: unhandled server error when accessing not found pages (#1753 )	2025-03-13 18:31:55 +07:00
Marcus Schiesser	4a51c9b48e	docs: add link checker and fix links (#1750 )	2025-03-13 15:46:05 +07:00
Alex Yang	bf56fc08ad	chore(cloud): update openapi.json (#1746 )	2025-03-12 15:19:40 +00:00
Marcus Schiesser	fa40b36516	docs: cleanup (#1745 )	2025-03-12 17:54:20 +07:00
yangqiao	da8068e9e0	fix: Add fromConnectionString method for Azure vCore storages (#1743 ) Co-authored-by: yangqiao <yangqiao@microsoft.com>	2025-03-12 15:58:30 +07:00
github-actions[bot]	37dcf37625	Release 0.9.11 (#1734 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-03-12 12:44:55 +07:00
Stefan Edberg	a8c0637d11	feat: make it possible to provide base URL to OpenAI (#1740 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-03-12 12:17:38 +07:00
Marcus Schiesser	387a19284d	fix: Update mistral package for mistral API 1.5.1 (#1741 )	2025-03-12 11:23:42 +07:00
ANKIT VARSHNEY	a654f580cf	docs: add doc for perplexity (#1738 )	2025-03-12 11:17:23 +07:00
Thuc Pham	68ea7ec6a5	chore: use agent workflow for examples (#1726 )	2025-03-11 19:05:00 +07:00
Marcus Schiesser	2d11ffbaea	docs: update contrib (#1736 )	2025-03-11 18:06:06 +07:00
ANKIT VARSHNEY	1587e48a14	Feat/perplexity (#1719 )	2025-03-11 17:21:57 +07:00
Marcus Schiesser	bd239aaf2d	docs: update main agent docs (#1735 )	2025-03-11 17:18:38 +07:00
Jack Qian	98eebf7277	feat: add request options for gemini (#1733 )	2025-03-11 15:56:10 +07:00