Release 0.2.13 (#773)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

.eslintrc.cjs

@@ -0,0 +1,76 @@
+module.exports = {
+  root: true,
+  extends: [
+    "turbo",
+    "prettier",
+    "plugin:@typescript-eslint/recommended-type-checked-only",
+  ],
+  parserOptions: {
+    project: true,
+    __tsconfigRootDir: __dirname,
+  },
+  settings: {
+    react: {
+      version: "999.999.999",
+    },
+  },
+  rules: {
+    "max-params": ["error", 4],
+    "prefer-const": "error",
+    "@typescript-eslint/no-floating-promises": [
+      "error",
+      {
+        ignoreIIFE: true,
+      },
+    ],
+    "@typescript-eslint/await-thenable": "off",
+    "@typescript-eslint/ban-ts-comment": "off",
+    "@typescript-eslint/ban-types": "off",
+    "no-array-constructor": "off",
+    "@typescript-eslint/no-array-constructor": "off",
+    "@typescript-eslint/no-base-to-string": "off",
+    "@typescript-eslint/no-duplicate-enum-values": "off",
+    "@typescript-eslint/no-duplicate-type-constituents": "off",
+    "@typescript-eslint/no-explicit-any": "off",
+    "@typescript-eslint/no-extra-non-null-assertion": "off",
+    "@typescript-eslint/no-for-in-array": "off",
+    "no-implied-eval": "off",
+    "@typescript-eslint/no-implied-eval": "off",
+    "no-loss-of-precision": "off",
+    "@typescript-eslint/no-loss-of-precision": "off",
+    "@typescript-eslint/no-misused-new": "off",
+    "@typescript-eslint/no-misused-promises": "off",
+    "@typescript-eslint/no-namespace": "off",
+    "@typescript-eslint/no-non-null-asserted-optional-chain": "off",
+    "@typescript-eslint/no-redundant-type-constituents": "off",
+    "@typescript-eslint/no-this-alias": "off",
+    "@typescript-eslint/no-unnecessary-type-assertion": "off",
+    "@typescript-eslint/no-unnecessary-type-constraint": "off",
+    "@typescript-eslint/no-unsafe-argument": "off",
+    "@typescript-eslint/no-unsafe-assignment": "off",
+    "@typescript-eslint/no-unsafe-call": "off",
+    "@typescript-eslint/no-unsafe-declaration-merging": "off",
+    "@typescript-eslint/no-unsafe-enum-comparison": "off",
+    "@typescript-eslint/no-unsafe-member-access": "off",
+    "@typescript-eslint/no-unsafe-return": "off",
+    "no-unused-vars": "off",
+    "@typescript-eslint/no-unused-vars": "off",
+    "@typescript-eslint/no-var-requires": "off",
+    "@typescript-eslint/prefer-as-const": "off",
+    "require-await": "off",
+    "@typescript-eslint/require-await": "off",
+    "@typescript-eslint/restrict-plus-operands": "off",
+    "@typescript-eslint/restrict-template-expressions": "off",
+    "@typescript-eslint/triple-slash-reference": "off",
+    "@typescript-eslint/unbound-method": "off",
+  },
+  overrides: [
+    {
+      files: ["examples/**/*.ts"],
+      rules: {
+        "turbo/no-undeclared-env-vars": "off",
+      },
+    },
+  ],
+  ignorePatterns: ["dist/", "lib/", "deps/"],
+};

.eslintrc.js

@@ -1,23 +0,0 @@
-module.exports = {
-  root: true,
-  // This tells ESLint to load the config from the package `eslint-config-custom`
-  extends: ["custom"],
-  settings: {
-    next: {
-      rootDir: ["apps/*/"],
-    },
-  },
-  rules: {
-    "max-params": ["error", 4],
-    "prefer-const": "error",
-  },
-  overrides: [
-    {
-      files: ["examples/**/*.ts"],
-      rules: {
-        "turbo/no-undeclared-env-vars": "off",
-      },
-    },
-  ],
-  ignorePatterns: ["dist/", "lib/"],
-};

.github/workflows/lint_on_push_or_pull.yml

@@ -13,9 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
-        with:
-          version: latest
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.github/workflows/publish.yml

@@ -14,7 +14,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.github/workflows/publish_github.yml

@@ -0,0 +1,37 @@
+name: Publish to GitHub Releases
+
+on:
+  push:
+    tags:
+      - "llamaindex@*"
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+
+      - name: Install dependencies
+        run: pnpm install
+
+      - name: Build tarball
+        run: |
+          pnpm pack
+        working-directory: packages/core
+
+      - name: Create release
+        uses: ncipollo/release-action@v1
+        with:
+          artifacts: "packages/core/llamaindex-*.tgz"
+          name: Release ${{ github.ref }}
+          bodyFile: "packages/core/CHANGELOG.md"
+          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release.yml

@@ -0,0 +1,57 @@
+name: Release
+
+on:
+  push:
+    branches:
+      - main
+
+concurrency: ${{ github.workflow }}-${{ github.ref }}
+
+jobs:
+  release:
+    name: Release
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+
+      - name: Install dependencies
+        run: pnpm install
+
+      - name: Add auth token to .npmrc file
+        run: |
+          cat << EOF >> ".npmrc"
+            //registry.npmjs.org/:_authToken=$NPM_TOKEN
+          EOF
+        env:
+          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+      - name: Get changeset status
+        id: get-changeset-status
+        run: |
+          pnpm changeset status --output .changeset/status.json
+          new_version=$(jq -r '.releases[] | select(.name == "llamaindex") | .newVersion' < .changeset/status.json)
+          rm -v .changeset/status.json
+          echo "new-version=${new_version}" >> "$GITHUB_OUTPUT"
+
+      - name: Create Release Pull Request or Publish to npm
+        id: changesets
+        uses: changesets/action@v1
+        with:
+          commit: Release ${{ steps.get-changeset-status.outputs.new-version }}
+          title: Release ${{ steps.get-changeset-status.outputs.new-version }}
+          # update version PR with the latest changesets
+          version: pnpm new-version
+          # build package and call changeset publish
+          publish: pnpm release
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}

.github/workflows/test.yml

@@ -1,18 +1,55 @@
 name: Run Tests
 
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
 
 jobs:
+  e2e:
+    strategy:
+      fail-fast: false
+      matrix:
+        node-version: [18.x, 20.x, 22.x]
+    name: E2E on Node.js ${{ matrix.node-version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Run E2E Tests
+        run: pnpm run e2e
+
   test:
+    strategy:
+      fail-fast: false
+      matrix:
+        node-version: [18.x, 20.x, 22.x]
+    name: Test on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
-          node-version-file: ".nvmrc"
+          node-version: ${{ matrix.node-version }}
           cache: "pnpm"
       - name: Install dependencies
         run: pnpm install
@@ -23,7 +60,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -33,6 +70,9 @@ jobs:
         run: pnpm install
       - name: Build
         run: pnpm run build --filter llamaindex
+      - name: Use Build For Examples
+        run: pnpm link ../packages/core/
+        working-directory: ./examples
       - name: Run Type Check
         run: pnpm run type-check
       - name: Run Circular Dependency Check
@@ -49,7 +89,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -67,7 +107,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
+      - uses: pnpm/action-setup@v3
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.npmrc

@@ -1,2 +1,5 @@
 auto-install-peers = true
 enable-pre-post-scripts = true
+prefer-workspace-packages = true
+save-workspace-protocol = true
+link-workspace-packages = true

.vscode/launch.json

@@ -10,8 +10,9 @@
       "name": "Debug Example",
       "skipFiles": ["<node_internals>/**"],
       "runtimeExecutable": "pnpm",
+      "console": "integratedTerminal",
       "cwd": "${workspaceFolder}/examples",
-      "runtimeArgs": ["ts-node", "${fileBasename}"]
+      "runtimeArgs": ["npx", "tsx", "${file}"]
     }
   ]
 }

CONTRIBUTING.md

@@ -91,16 +91,10 @@ Please send a descriptive changeset for each PR.
 
 ## Publishing (maintainers only)
 
-To publish a new version of the library, first create a new version:
+The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
+PR called "Release {version}".
 
-```shell
-pnpm new-version
-```
+This PR will update the `package.json` and `CHANGELOG.md` files of each package according to
+the current changesets in the [.changeset](.changeset/) folder.
 
-If everything looks good, commit the generated files and release the new version:
-
-```shell
-pnpm release
-git push # push to the main branch
-git push --tags
-```
+If this PR is merged it will automatically add version tags to the repository and publish the updated packages to NPM.

README-turborepo.md

@@ -1,81 +0,0 @@
-# Turborepo starter
-
-This is an official starter Turborepo.
-
-## Using this example
-
-Run the following command:
-
-```sh
-npx create-turbo@latest
-```
-
-## What's inside?
-
-This Turborepo includes the following packages/apps:
-
-### Apps and Packages
-
-- `docs`: a [Next.js](https://nextjs.org/) app
-- `web`: another [Next.js](https://nextjs.org/) app
-- `ui`: a stub React component library shared by both `web` and `docs` applications
-- `eslint-config-custom`: `eslint` configurations (includes `eslint-config-next` and `eslint-config-prettier`)
-- `tsconfig`: `tsconfig.json`s used throughout the monorepo
-
-Each package/app is 100% [TypeScript](https://www.typescriptlang.org/).
-
-### Utilities
-
-This Turborepo has some additional tools already setup for you:
-
-- [TypeScript](https://www.typescriptlang.org/) for static type checking
-- [ESLint](https://eslint.org/) for code linting
-- [Prettier](https://prettier.io) for code formatting
-
-### Build
-
-To build all apps and packages, run the following command:
-
-```
-cd my-turborepo
-pnpm build
-```
-
-### Develop
-
-To develop all apps and packages, run the following command:
-
-```
-cd my-turborepo
-pnpm dev
-```
-
-### Remote Caching
-
-Turborepo can use a technique known as [Remote Caching](https://turbo.build/repo/docs/core-concepts/remote-caching) to share cache artifacts across machines, enabling you to share build caches with your team and CI/CD pipelines.
-
-By default, Turborepo will cache locally. To enable Remote Caching you will need an account with Vercel. If you don't have an account you can [create one](https://vercel.com/signup), then enter the following commands:
-
-```
-cd my-turborepo
-npx turbo login
-```
-
-This will authenticate the Turborepo CLI with your [Vercel account](https://vercel.com/docs/concepts/personal-accounts/overview).
-
-Next, you can link your Turborepo to your Remote Cache by running the following command from the root of your Turborepo:
-
-```
-npx turbo link
-```
-
-## Useful Links
-
-Learn more about the power of Turborepo:
-
-- [Tasks](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks)
-- [Caching](https://turbo.build/repo/docs/core-concepts/caching)
-- [Remote Caching](https://turbo.build/repo/docs/core-concepts/remote-caching)
-- [Filtering](https://turbo.build/repo/docs/core-concepts/monorepos/filtering)
-- [Configuration Options](https://turbo.build/repo/docs/reference/configuration)
-- [CLI Usage](https://turbo.build/repo/docs/reference/command-line-reference)

README.md

@@ -114,14 +114,21 @@ Add the following config to your `next.config.js` to ignore specific packages in
 /** @type {import('next').NextConfig} */
 const nextConfig = {
   experimental: {
-    serverComponentsExternalPackages: ["pdf2json", "@zilliz/milvus2-sdk-node"],
+    serverComponentsExternalPackages: [
+      "pdf2json",
+      "@zilliz/milvus2-sdk-node",
+      "sharp",
+      "onnxruntime-node",
+    ],
   },
   webpack: (config) => {
-    config.resolve.alias = {
-      ...config.resolve.alias,
-      sharp$: false,
-      "onnxruntime-node$": false,
-    };
+    config.externals.push({
+      pdf2json: "commonjs pdf2json",
+      "@zilliz/milvus2-sdk-node": "commonjs @zilliz/milvus2-sdk-node",
+      sharp: "commonjs sharp",
+      "onnxruntime-node": "commonjs onnxruntime-node",
+    });
+
     return config;
   },
 };
@@ -154,7 +161,7 @@ If you need any of those classes, you have to import them instead directly. Here
 import { PineconeVectorStore } from "@llamaindex/edge/storage/vectorStore/PineconeVectorStore";
 ```
 
-As the `PDFReader` is not with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
+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/edge/readers/SimpleDirectoryReader";
@@ -183,7 +190,7 @@ You'll find a complete example of using the Edge runtime with LlamaIndexTS here:
 - OpenAI GPT-3.5-turbo and GPT-4
 - Anthropic Claude 3 (Opus, Sonnet, and Haiku) and the legacy models (Claude 2 and Instant)
 - Groq LLMs
-- Llama2 Chat LLMs (70B, 13B, and 7B parameters)
+- Llama2/3 Chat LLMs (70B, 13B, and 7B parameters)
 - MistralAI Chat LLMs
 - Fireworks Chat LLMs
 

apps/docs/CHANGELOG.md

@@ -1,5 +1,28 @@
 # docs
 
+## 0.0.7
+
+### Patch Changes
+
+- Updated dependencies [6277105]
+  - llamaindex@0.2.13
+
+## 0.0.6
+
+### Patch Changes
+
+- Updated dependencies [d8d952d]
+  - llamaindex@0.2.12
+
+## 0.0.5
+
+### Patch Changes
+
+- Updated dependencies [87142b2]
+- Updated dependencies [5a6cc0e]
+- Updated dependencies [87142b2]
+  - llamaindex@0.2.11
+
 ## 0.0.4
 
 ### Patch Changes

apps/docs/docs/examples/agent.mdx

@@ -4,82 +4,7 @@ A built-in agent that can take decisions and reasoning based on the tools provid
 
 ## OpenAI Agent
 
-```ts
-import { FunctionTool, OpenAIAgent } from "llamaindex";
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/agent/openai";
 
-// Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-
-// Define the parameters of the sum function as a JSON schema
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-// Define the parameters of the divide function as a JSON schema
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The dividend to divide",
-    },
-    b: {
-      type: "number",
-      description: "The divisor to divide by",
-    },
-  },
-  required: ["a", "b"],
-};
-
-async function main() {
-  // Create a function tool from the sum function
-  const sumFunctionTool = new FunctionTool(sumNumbers, {
-    name: "sumNumbers",
-    description: "Use this function to sum two numbers",
-    parameters: sumJSON,
-  });
-
-  // Create a function tool from the divide function
-  const divideFunctionTool = new FunctionTool(divideNumbers, {
-    name: "divideNumbers",
-    description: "Use this function to divide two numbers"
-    parameters: divideJSON,
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [sumFunctionTool, divideFunctionTool],
-    verbose: true,
-  });
-
-  // Chat with the agent
-  const response = await agent.chat({
-    message: "How much is 5 + 5? then divide by 2",
-  });
-
-  // Print the response
-  console.log(String(response));
-}
-
-main().then(() => {
-  console.log("Done");
-});
-```
+<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/modules/agent/index.md

@@ -11,4 +11,10 @@ An “agent” is an automated reasoning and decision engine. It takes in a user
 
 LlamaIndex.TS comes with a few built-in agents, but you can also create your own. The built-in agents include:
 
-- [OpenAI Agent](./openai.mdx)
+- OpenAI Agent
+- Anthropic Agent
+- ReACT Agent
+
+## Examples
+
+- [OpenAI Agent](../../examples/agent.mdx)

apps/docs/docs/modules/agent/multi_document_agent.mdx

@@ -1,309 +0,0 @@
-# Multi-Document Agent
-
-In this guide, you learn towards setting up an agent that can effectively answer different types of questions over a larger set of documents.
-
-These questions include the following
-
-- QA over a specific doc
-- QA comparing different docs
-- Summaries over a specific doc
-- Comparing summaries between different docs
-
-We do this with the following architecture:
-
-- setup a “document agent” over each Document: each doc agent can do QA/summarization within its doc
-- setup a top-level agent over this set of document agents. Do tool retrieval and then do CoT over the set of tools to answer a question.
-
-## Setup and Download Data
-
-We first start by installing the necessary libraries and downloading the data.
-
-```bash
-pnpm i llamaindex
-```
-
-```ts
-import {
-  Document,
-  ObjectIndex,
-  OpenAI,
-  OpenAIAgent,
-  QueryEngineTool,
-  SimpleNodeParser,
-  SimpleToolNodeMapping,
-  SummaryIndex,
-  VectorStoreIndex,
-  Settings,
-  storageContextFromDefaults,
-} from "llamaindex";
-```
-
-And then for the data we will run through a list of countries and download the wikipedia page for each country.
-
-```ts
-import fs from "fs";
-import path from "path";
-
-const dataPath = path.join(__dirname, "tmp_data");
-
-const extractWikipediaTitle = async (title: string) => {
-  const fileExists = fs.existsSync(path.join(dataPath, `${title}.txt`));
-
-  if (fileExists) {
-    console.log(`File already exists for the title: ${title}`);
-    return;
-  }
-
-  const queryParams = new URLSearchParams({
-    action: "query",
-    format: "json",
-    titles: title,
-    prop: "extracts",
-    explaintext: "true",
-  });
-
-  const url = `https://en.wikipedia.org/w/api.php?${queryParams}`;
-
-  const response = await fetch(url);
-  const data: any = await response.json();
-
-  const pages = data.query.pages;
-  const page = pages[Object.keys(pages)[0]];
-  const wikiText = page.extract;
-
-  await new Promise((resolve) => {
-    fs.writeFile(path.join(dataPath, `${title}.txt`), wikiText, (err: any) => {
-      if (err) {
-        console.error(err);
-        resolve(title);
-        return;
-      }
-      console.log(`${title} stored in file!`);
-
-      resolve(title);
-    });
-  });
-};
-```
-
-```ts
-export const extractWikipedia = async (titles: string[]) => {
-  if (!fs.existsSync(dataPath)) {
-    fs.mkdirSync(dataPath);
-  }
-
-  for await (const title of titles) {
-    await extractWikipediaTitle(title);
-  }
-
-  console.log("Extration finished!");
-```
-
-These files will be saved in the `tmp_data` folder.
-
-Now we can call the function to download the data for each country.
-
-```ts
-await extractWikipedia([
-  "Brazil",
-  "United States",
-  "Canada",
-  "Mexico",
-  "Argentina",
-  "Chile",
-  "Colombia",
-  "Peru",
-  "Venezuela",
-  "Ecuador",
-  "Bolivia",
-  "Paraguay",
-  "Uruguay",
-  "Guyana",
-  "Suriname",
-  "French Guiana",
-  "Falkland Islands",
-]);
-```
-
-## Load the data
-
-Now that we have the data, we can load it into the LlamaIndex and store as a document.
-
-```ts
-import { Document } from "llamaindex";
-
-const countryDocs: Record<string, Document> = {};
-
-for (const title of wikiTitles) {
-  const path = `./agent/helpers/tmp_data/${title}.txt`;
-  const text = await fs.readFile(path, "utf-8");
-  const document = new Document({ text: text, id_: path });
-  countryDocs[title] = document;
-}
-```
-
-## Setup LLM and StorageContext
-
-We will be using gpt-4 for this example and we will use the `StorageContext` to store the documents in-memory.
-
-```ts
-Settings.llm = new OpenAI({
-  model: "gpt-4",
-});
-
-const storageContext = await storageContextFromDefaults({
-  persistDir: "./storage",
-});
-```
-
-## Building Multi-Document Agents
-
-In this section we show you how to construct the multi-document agent. We first build a document agent for each document, and then define the top-level parent agent with an object index.
-
-```ts
-const documentAgents: Record<string, any> = {};
-const queryEngines: Record<string, any> = {};
-```
-
-Now we iterate over each country and create a document agent for each one.
-
-### Build Agent for each Document
-
-In this section we define “document agents” for each document.
-
-We define both a vector index (for semantic search) and summary index (for summarization) for each document. The two query engines are then converted into tools that are passed to an OpenAI function calling agent.
-
-This document agent can dynamically choose to perform semantic search or summarization within a given document.
-
-We create a separate document agent for each coutnry.
-
-```ts
-for (const title of wikiTitles) {
-  // parse the document into nodes
-  const nodes = new SimpleNodeParser({
-    chunkSize: 200,
-    chunkOverlap: 20,
-  }).getNodesFromDocuments([countryDocs[title]]);
-
-  // create the vector index for specific search
-  const vectorIndex = await VectorStoreIndex.init({
-    storageContext: storageContext,
-    nodes,
-  });
-
-  // create the summary index for broader search
-  const summaryIndex = await SummaryIndex.init({
-    nodes,
-  });
-
-  const vectorQueryEngine = summaryIndex.asQueryEngine();
-  const summaryQueryEngine = summaryIndex.asQueryEngine();
-
-  // create the query engines for each task
-  const queryEngineTools = [
-    new QueryEngineTool({
-      queryEngine: vectorQueryEngine,
-      metadata: {
-        name: "vector_tool",
-        description: `Useful for questions related to specific aspects of ${title} (e.g. the history, arts and culture, sports, demographics, or more).`,
-      },
-    }),
-    new QueryEngineTool({
-      queryEngine: summaryQueryEngine,
-      metadata: {
-        name: "summary_tool",
-        description: `Useful for any requests that require a holistic summary of EVERYTHING about ${title}. For questions about more specific sections, please use the vector_tool.`,
-      },
-    }),
-  ];
-
-  // create the document agent
-  const agent = new OpenAIAgent({
-    tools: queryEngineTools,
-    llm,
-    verbose: true,
-  });
-
-  documentAgents[title] = agent;
-  queryEngines[title] = vectorIndex.asQueryEngine();
-}
-```
-
-## Build Top-Level Agent
-
-Now we define the top-level agent that can answer questions over the set of document agents.
-
-This agent takes in all document agents as tools. This specific agent RetrieverOpenAIAgent performs tool retrieval before tool use (unlike a default agent that tries to put all tools in the prompt).
-
-Here we use a top-k retriever, but we encourage you to customize the tool retriever method!
-
-Firstly, we create a tool for each document agent
-
-```ts
-const allTools: QueryEngineTool[] = [];
-```
-
-```ts
-for (const title of wikiTitles) {
-  const wikiSummary = `
-    This content contains Wikipedia articles about ${title}.
-    Use this tool if you want to answer any questions about ${title}
-  `;
-
-  const docTool = new QueryEngineTool({
-    queryEngine: documentAgents[title],
-    metadata: {
-      name: `tool_${title}`,
-      description: wikiSummary,
-    },
-  });
-
-  allTools.push(docTool);
-}
-```
-
-Our top level agent will use this document agents as tools and use toolRetriever to retrieve the best tool to answer a question.
-
-```ts
-// map the tools to nodes
-const toolMapping = SimpleToolNodeMapping.fromObjects(allTools);
-
-// create the object index
-const objectIndex = await ObjectIndex.fromObjects(
-  allTools,
-  toolMapping,
-  VectorStoreIndex,
-  {
-    storageContext,
-  },
-);
-
-// create the top agent
-const topAgent = new OpenAIAgent({
-  toolRetriever: await objectIndex.asRetriever({}),
-  llm,
-  verbose: true,
-  prefixMessages: [
-    {
-      content:
-        "You are an agent designed to answer queries about a set of given countries. Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
-      role: "system",
-    },
-  ],
-});
-```
-
-## Use the Agent
-
-Now we can use the agent to answer questions.
-
-```ts
-const response = await topAgent.chat({
-  message: "Tell me the differences between Brazil and Canada economics?",
-});
-
-// print output
-console.log(response);
-```
-
-You can find the full code for this example [here](https://github.com/run-llama/LlamaIndexTS/tree/main/examples/agent/multi-document-agent.ts)

apps/docs/docs/modules/agent/openai.mdx

@@ -1,187 +0,0 @@
----
-sidebar_position: 0
----
-
-# OpenAI Agent
-
-OpenAI API that supports function calling, it’s never been easier to build your own agent!
-
-In this notebook tutorial, we showcase how to write your own OpenAI agent
-
-## Setup
-
-First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
-
-```bash
-pnpm i llamaindex
-```
-
-Then we can define a function to sum two numbers and another function to divide two numbers.
-
-```ts
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-```
-
-## Create a function tool
-
-Now we can create a function tool from the sum function and another function tool from the divide function.
-
-For the parameters of the sum function, we can define a JSON schema.
-
-### JSON Schema
-
-```ts
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The dividend a to divide",
-    },
-    b: {
-      type: "number",
-      description: "The divisor b to divide by",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const sumFunctionTool = new FunctionTool(sumNumbers, {
-  name: "sumNumbers",
-  description: "Use this function to sum two numbers",
-  parameters: sumJSON,
-});
-
-const divideFunctionTool = new FunctionTool(divideNumbers, {
-  name: "divideNumbers",
-  description: "Use this function to divide two numbers",
-  parameters: divideJSON,
-});
-```
-
-## Create an OpenAIAgent
-
-Now we can create an OpenAIAgent with the function tools.
-
-```ts
-const agent = new OpenAIAgent({
-  tools: [sumFunctionTool, divideFunctionTool],
-  verbose: true,
-});
-```
-
-## Chat with the agent
-
-Now we can chat with the agent.
-
-```ts
-const response = await agent.chat({
-  message: "How much is 5 + 5? then divide by 2",
-});
-
-console.log(String(response));
-```
-
-## Full code
-
-```ts
-import { FunctionTool, OpenAIAgent } from "llamaindex";
-
-// Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-
-// Define the parameters of the sum function as a JSON schema
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-// Define the parameters of the divide function as a JSON schema
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The argument a to divide",
-    },
-    b: {
-      type: "number",
-      description: "The argument b to divide",
-    },
-  },
-  required: ["a", "b"],
-};
-
-async function main() {
-  // Create a function tool from the sum function
-  const sumFunctionTool = new FunctionTool(sumNumbers, {
-    name: "sumNumbers",
-    description: "Use this function to sum two numbers",
-    parameters: sumJSON,
-  });
-
-  // Create a function tool from the divide function
-  const divideFunctionTool = new FunctionTool(divideNumbers, {
-    name: "divideNumbers",
-    description: "Use this function to divide two numbers",
-    parameters: divideJSON,
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [sumFunctionTool, divideFunctionTool],
-    verbose: true,
-  });
-
-  // Chat with the agent
-  const response = await agent.chat({
-    message: "How much is 5 + 5? then divide by 2",
-  });
-
-  // Print the response
-  console.log(String(response));
-}
-
-main().then(() => {
-  console.log("Done");
-});
-```

apps/docs/docs/modules/agent/query_engine_tool.mdx

@@ -1,132 +0,0 @@
----
-sidebar_position: 1
----
-
-# OpenAI Agent + QueryEngineTool
-
-QueryEngineTool is a tool that allows you to query a vector index. In this example, we will create a vector index from a set of documents and then create a QueryEngineTool from the vector index. We will then create an OpenAIAgent with the QueryEngineTool and chat with the agent.
-
-## Setup
-
-First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
-
-```bash
-pnpm i llamaindex
-```
-
-Then you can import the necessary classes and functions.
-
-```ts
-import {
-  OpenAIAgent,
-  SimpleDirectoryReader,
-  VectorStoreIndex,
-  QueryEngineTool,
-} from "llamaindex";
-```
-
-## Create a vector index
-
-Now we can create a vector index from a set of documents.
-
-```ts
-// Load the documents
-const documents = await new SimpleDirectoryReader().loadData({
-  directoryPath: "node_modules/llamaindex/examples/",
-});
-
-// Create a vector index from the documents
-const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
-```
-
-## Create a QueryEngineTool
-
-Now we can create a QueryEngineTool from the vector index.
-
-```ts
-// Create a query engine from the vector index
-const abramovQueryEngine = vectorIndex.asQueryEngine();
-
-// Create a QueryEngineTool with the query engine
-const queryEngineTool = new QueryEngineTool({
-  queryEngine: abramovQueryEngine,
-  metadata: {
-    name: "abramov_query_engine",
-    description: "A query engine for the Abramov documents",
-  },
-});
-```
-
-## Create an OpenAIAgent
-
-```ts
-// Create an OpenAIAgent with the query engine tool tools
-
-const agent = new OpenAIAgent({
-  tools: [queryEngineTool],
-  verbose: true,
-});
-```
-
-## Chat with the agent
-
-Now we can chat with the agent.
-
-```ts
-const response = await agent.chat({
-  message: "What was his salary?",
-});
-
-console.log(String(response));
-```
-
-## Full code
-
-```ts
-import {
-  OpenAIAgent,
-  SimpleDirectoryReader,
-  VectorStoreIndex,
-  QueryEngineTool,
-} from "llamaindex";
-
-async function main() {
-  // Load the documents
-  const documents = await new SimpleDirectoryReader().loadData({
-    directoryPath: "node_modules/llamaindex/examples/",
-  });
-
-  // Create a vector index from the documents
-  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
-
-  // Create a query engine from the vector index
-  const abramovQueryEngine = vectorIndex.asQueryEngine();
-
-  // Create a QueryEngineTool with the query engine
-  const queryEngineTool = new QueryEngineTool({
-    queryEngine: abramovQueryEngine,
-    metadata: {
-      name: "abramov_query_engine",
-      description: "A query engine for the Abramov documents",
-    },
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [queryEngineTool],
-    verbose: true,
-  });
-
-  // Chat with the agent
-  const response = await agent.chat({
-    message: "What was his salary?",
-  });
-
-  // Print the response
-  console.log(String(response));
-}
-
-main().then(() => {
-  console.log("Done");
-});
-```

apps/docs/docs/modules/agent/react_agent.mdx

@@ -1,203 +0,0 @@
-# ReAct Agent
-
-The ReAct agent is an AI agent that can reason over the next action, construct an action command, execute the action, and repeat these steps in an iterative loop until the task is complete.
-
-In this notebook tutorial, we showcase how to write your ReAct agent using the `llamaindex` package.
-
-## Setup
-
-First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
-
-```bash
-pnpm i llamaindex
-```
-
-And then you can import the `OpenAIAgent` and `FunctionTool` from the `llamaindex` package.
-
-```ts
-import { FunctionTool, OpenAIAgent } from "llamaindex";
-```
-
-Then we can define a function to sum two numbers and another function to divide two numbers.
-
-```ts
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-```
-
-## Create a function tool
-
-Now we can create a function tool from the sum function and another function tool from the divide function.
-
-For the parameters of the sum function, we can define a JSON schema.
-
-### JSON Schema
-
-```ts
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The dividend a to divide",
-    },
-    b: {
-      type: "number",
-      description: "The divisor b to divide by",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const sumFunctionTool = new FunctionTool(sumNumbers, {
-  name: "sumNumbers",
-  description: "Use this function to sum two numbers",
-  parameters: sumJSON,
-});
-
-const divideFunctionTool = new FunctionTool(divideNumbers, {
-  name: "divideNumbers",
-  description: "Use this function to divide two numbers",
-  parameters: divideJSON,
-});
-```
-
-## Create an ReAct
-
-Now we can create an OpenAIAgent with the function tools.
-
-```ts
-const agent = new ReActAgent({
-  tools: [sumFunctionTool, divideFunctionTool],
-  verbose: true,
-});
-```
-
-## Chat with the agent
-
-Now we can chat with the agent.
-
-```ts
-const response = await agent.chat({
-  message: "How much is 5 + 5? then divide by 2",
-});
-
-console.log(String(response));
-```
-
-The output will be:
-
-```bash
-Thought: I need to use a tool to help me answer the question.
-Action: sumNumbers
-Action Input: {"a":5,"b":5}
-
-Observation: 10
-Thought: I can answer without using any more tools.
-Answer: The sum of 5 and 5 is 10, and when divided by 2, the result is 5.
-
-The sum of 5 and 5 is 10, and when divided by 2, the result is 5.
-```
-
-## Full code
-
-```ts
-import { FunctionTool, ReActAgent } from "llamaindex";
-
-// Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-
-// Define the parameters of the sum function as a JSON schema
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-// Define the parameters of the divide function as a JSON schema
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The argument a to divide",
-    },
-    b: {
-      type: "number",
-      description: "The argument b to divide",
-    },
-  },
-  required: ["a", "b"],
-};
-
-async function main() {
-  // Create a function tool from the sum function
-  const sumFunctionTool = new FunctionTool(sumNumbers, {
-    name: "sumNumbers",
-    description: "Use this function to sum two numbers",
-    parameters: sumJSON,
-  });
-
-  // Create a function tool from the divide function
-  const divideFunctionTool = new FunctionTool(divideNumbers, {
-    name: "divideNumbers",
-    description: "Use this function to divide two numbers",
-    parameters: divideJSON,
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [sumFunctionTool, divideFunctionTool],
-    verbose: true,
-  });
-
-  // Chat with the agent
-  const response = await agent.chat({
-    message: "I want to sum 5 and 5 and then divide by 2",
-  });
-
-  // Print the response
-  console.log(String(response));
-}
-
-main().then(() => {
-  console.log("Done");
-});
-```

apps/docs/docs/modules/embeddings/available_embeddings/gemini.md

@@ -0,0 +1,33 @@
+# Gemini
+
+To use Gemini embeddings, you need to import `GeminiEmbedding` from `llamaindex`.
+
+```ts
+import { GeminiEmbedding, Settings } from "llamaindex";
+
+// Update Embed Model
+Settings.embedModel = new GeminiEmbedding();
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+Per default, `GeminiEmbedding` is using the `gemini-pro` model. You can change the model by passing the `model` parameter to the constructor.
+For example:
+
+```ts
+import { GEMINI_MODEL, GeminiEmbedding } from "llamaindex";
+
+Settings.embedModel = new GeminiEmbedding({
+  model: GEMINI_MODEL.GEMINI_PRO_LATEST,
+});
+```

apps/docs/docs/modules/embeddings/available_embeddings/jinaai.md

@@ -0,0 +1,21 @@
+# Jina AI
+
+To use Jina AI embeddings, you need to import `JinaAIEmbedding` from `llamaindex`.
+
+```ts
+import { JinaAIEmbedding, Settings } from "llamaindex";
+
+Settings.embedModel = new JinaAIEmbedding();
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

apps/docs/docs/modules/embeddings/available_embeddings/ollama.md

@@ -1,11 +1,19 @@
 # Ollama
 
-To use Ollama embeddings, you need to import `Ollama` from `llamaindex`.
+To use Ollama embeddings, you need to import `OllamaEmbedding` from `llamaindex`.
+
+Note that you need to pull the embedding model first before using it.
+
+In the example below, we're using the [`nomic-embed-text`](https://ollama.com/library/nomic-embed-text) model, so you have to call:
+
+```shell
+ollama pull nomic-embed-text
+```
 
 ```ts
-import { Ollama, Settings } from "llamaindex";
+import { OllamaEmbedding, Settings } from "llamaindex";
 
-Settings.embedModel = new Ollama();
+Settings.embedModel = new OllamaEmbedding({ model: "nomic-embed-text" });
 
 const document = new Document({ text: essay, id_: "essay" });
 

apps/docs/docs/modules/llms/available_llms/gemini.md

@@ -0,0 +1,71 @@
+# Gemini
+
+## Usage
+
+```ts
+import { Gemini, Settings, GEMINI_MODEL } from "llamaindex";
+
+Settings.llm = new Gemini({
+  model: GEMINI_MODEL.GEMINI_PRO,
+});
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Gemini,
+  Document,
+  VectorStoreIndex,
+  Settings,
+  GEMINI_MODEL,
+} from "llamaindex";
+
+Settings.llm = new Gemini({
+  model: GEMINI_MODEL.GEMINI_PRO,
+});
+
+async function main() {
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document]);
+
+  // 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);
+}
+```

apps/docs/docs/modules/llms/available_llms/llama2.md

@@ -3,7 +3,7 @@
 ## Usage
 
 ```ts
-import { Ollama, Settings } from "llamaindex";
+import { Ollama, Settings, DeuceChatStrategy } from "llamaindex";
 
 Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 ```
@@ -11,7 +11,12 @@ Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 ## Usage with Replication
 
 ```ts
-import { Ollama, ReplicateSession, Settings } from "llamaindex";
+import {
+  Ollama,
+  ReplicateSession,
+  Settings,
+  DeuceChatStrategy,
+} from "llamaindex";
 
 const replicateSession = new ReplicateSession({
   replicateKey,
@@ -48,7 +53,13 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { LlamaDeuce, Document, VectorStoreIndex, Settings } from "llamaindex";
+import {
+  LlamaDeuce,
+  Document,
+  VectorStoreIndex,
+  Settings,
+  DeuceChatStrategy,
+} from "llamaindex";
 
 // Use the LlamaDeuce LLM
 Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });

apps/docs/docs/modules/node_postprocessors/jinaai_reranker.md

@@ -0,0 +1,71 @@
+# Jina AI Reranker
+
+The Jina AI Reranker is a postprocessor that uses the Jina AI Reranker API to rerank the results of a search query.
+
+## Setup
+
+Firstly, you will need to install the `llamaindex` package.
+
+```bash
+pnpm install llamaindex
+```
+
+Now, you will need to sign up for an API key at [Jina AI](https://jina.ai/reranker). Once you have your API key you can import the necessary modules and create a new instance of the `JinaAIReranker` class.
+
+```ts
+import {
+  JinaAIReranker,
+  Document,
+  OpenAI,
+  VectorStoreIndex,
+  Settings,
+} from "llamaindex";
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+## Increase similarity topK to retrieve more results
+
+The default value for `similarityTopK` is 2. This means that only the most similar document will be returned. To retrieve more results, you can increase the value of `similarityTopK`.
+
+```ts
+const retriever = index.asRetriever();
+retriever.similarityTopK = 5;
+```
+
+## Create a new instance of the JinaAIReranker class
+
+Then you can create a new instance of the `JinaAIReranker` class and pass in the number of results you want to return.
+The Jina AI Reranker API key is set in the `JINAAI_API_KEY` environment variable.
+
+```bash
+export JINAAI_API_KEY=<YOUR API KEY>
+```
+
+```ts
+const nodePostprocessor = new JinaAIReranker({
+  topN: 5,
+});
+```
+
+## Create a query engine with the retriever and node postprocessor
+
+```ts
+const queryEngine = index.asQueryEngine({
+  retriever,
+  nodePostprocessors: [nodePostprocessor],
+});
+
+// log the response
+const response = await queryEngine.query("Where did the author grown up?");
+```

apps/docs/docs/observability/index.md

@@ -14,6 +14,9 @@ Configure a variable once, and you'll be able to do things like the following:
 
 Each provider has similarities and differences. Take a look below for the full set of guides for each one!
 
+- [OpenLLMetry](#openllmetry)
+- [Langtrace](#langtrace)
+
 ## OpenLLMetry
 
 [OpenLLMetry](https://github.com/traceloop/openllmetry-js) is an open-source project based on OpenTelemetry for tracing and monitoring
@@ -33,3 +36,29 @@ traceloop.initialize({
   disableBatch: true,
 });
 ```
+
+## Langtrace
+
+Enhance your observability with Langtrace, a robust open-source tool supports OpenTelemetry and is designed to trace, evaluate, and manage LLM applications seamlessly. Langtrace integrates directly with LlamaIndex, offering detailed, real-time insights into performance metrics such as accuracy, evaluations, and latency.
+
+#### Install
+
+- Self-host or sign-up and generate an API key using [Langtrace](https://www.langtrace.ai) Cloud
+
+```bash
+npm install @langtrase/typescript-sdk
+```
+
+#### Initialize
+
+```js
+import * as Langtrace from "@langtrase/typescript-sdk";
+Langtrace.init({ api_key: "<YOUR_API_KEY>" });
+```
+
+Features:
+
+- OpenTelemetry compliant, ensuring broad compatibility with observability platforms.
+- Provides comprehensive logs and detailed traces of all components.
+- Real-time monitoring of accuracy, evaluations, usage, costs, and latency.
+- For more configuration options and details, visit [Langtrace Docs](https://docs.langtrace.ai/introduction).

apps/docs/docs/recipes/cost-analysis.mdx

@@ -6,7 +6,7 @@ This page shows how to track LLM cost using APIs.
 
 The callback manager is a class that manages the callback functions.
 
-You can register `llm-start`, and `llm-end` callbacks to the callback manager for tracking the cost.
+You can register `llm-start`, `llm-end`, and `llm-stream` callbacks to the callback manager for tracking the cost.
 
 import CodeBlock from "@theme/CodeBlock";
 import CodeSource from "!raw-loader!../../../../examples/recipes/cost-analysis";

apps/docs/docusaurus.config.js

@@ -163,7 +163,7 @@ const config = {
       "docusaurus-plugin-typedoc",
       {
         entryPoints: ["../../packages/core/src/index.ts"],
-        tsconfig: "../../packages/core/tsconfig.json",
+        tsconfig: "../../tsconfig.json",
         readme: "none",
         sourceLinkTemplate:
           "https://github.com/run-llama/LlamaIndexTS/blob/{gitRevision}/{path}#L{line}",

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.4",
+  "version": "0.0.7",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -15,12 +15,13 @@
     "typecheck": "tsc"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.2.0",
-    "@docusaurus/remark-plugin-npm2yarn": "^3.2.0",
+    "@docusaurus/core": "^3.2.1",
+    "@docusaurus/remark-plugin-npm2yarn": "^3.2.1",
     "@llamaindex/examples": "workspace:*",
-    "@mdx-js/react": "^3.0.0",
+    "@mdx-js/react": "^3.0.1",
     "clsx": "^2.1.0",
-    "postcss": "^8.4.33",
+    "llamaindex": "workspace:*",
+    "postcss": "^8.4.38",
     "prism-react-renderer": "^2.3.1",
     "raw-loader": "^4.0.2",
     "react": "^18.2.0",
@@ -28,15 +29,15 @@
   },
   "devDependencies": {
     "@docusaurus/module-type-aliases": "3.2.0",
-    "@docusaurus/preset-classic": "^3.2.0",
-    "@docusaurus/theme-classic": "^3.2.0",
-    "@docusaurus/types": "^3.2.0",
+    "@docusaurus/preset-classic": "^3.2.1",
+    "@docusaurus/theme-classic": "^3.2.1",
+    "@docusaurus/types": "^3.2.1",
     "@tsconfig/docusaurus": "^2.0.3",
-    "@types/node": "^18.19.10",
+    "@types/node": "^20.12.7",
     "docusaurus-plugin-typedoc": "^0.22.0",
-    "typedoc": "^0.25.12",
+    "typedoc": "^0.25.13",
     "typedoc-plugin-markdown": "^3.17.1",
-    "typescript": "^5.4.3"
+    "typescript": "^5.4.4"
   },
   "browserslist": {
     "production": [

examples/agent/multi_document_agent.ts

@@ -86,7 +86,6 @@ async function main() {
     const agent = new OpenAIAgent({
       tools: queryEngineTools,
       llm: new OpenAI({ model: "gpt-4" }),
-      verbose: true,
     });
 
     documentAgents[title] = agent;
@@ -126,8 +125,7 @@ async function main() {
   const topAgent = new OpenAIAgent({
     toolRetriever: await objectIndex.asRetriever({}),
     llm: new OpenAI({ model: "gpt-4" }),
-    verbose: true,
-    prefixMessages: [
+    chatHistory: [
       {
         content:
           "You are an agent designed to answer queries about a set of given countries. Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
@@ -145,4 +143,4 @@ async function main() {
   });
 }
 
-main();
+void main();

examples/agent/openai.ts

@@ -1,76 +1,61 @@
 import { FunctionTool, OpenAIAgent } from "llamaindex";
 
-// Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-
-// Define the parameters of the sum function as a JSON schema
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The dividend a to divide",
-    },
-    b: {
-      type: "number",
-      description: "The divisor b to divide by",
-    },
-  },
-  required: ["a", "b"],
-};
-
-async function main() {
-  // Create a function tool from the sum function
-  const functionTool = new FunctionTool(sumNumbers, {
+const sumNumbers = FunctionTool.from(
+  ({ a, b }: { a: number; b: number }) => `${a + b}`,
+  {
     name: "sumNumbers",
     description: "Use this function to sum two numbers",
-    parameters: sumJSON,
-  });
+    parameters: {
+      type: "object",
+      properties: {
+        a: {
+          type: "number",
+          description: "The first number",
+        },
+        b: {
+          type: "number",
+          description: "The second number",
+        },
+      },
+      required: ["a", "b"],
+    },
+  },
+);
 
-  // Create a function tool from the divide function
-  const functionTool2 = new FunctionTool(divideNumbers, {
+const divideNumbers = FunctionTool.from(
+  ({ a, b }: { a: number; b: number }) => `${a / b}`,
+  {
     name: "divideNumbers",
     description: "Use this function to divide two numbers",
-    parameters: divideJSON,
-  });
+    parameters: {
+      type: "object",
+      properties: {
+        a: {
+          type: "number",
+          description: "The dividend a to divide",
+        },
+        b: {
+          type: "number",
+          description: "The divisor b to divide by",
+        },
+      },
+      required: ["a", "b"],
+    },
+  },
+);
 
-  // Create an OpenAIAgent with the function tools
+async function main() {
   const agent = new OpenAIAgent({
-    tools: [functionTool, functionTool2],
-    verbose: true,
+    tools: [sumNumbers, divideNumbers],
   });
 
-  // Chat with the agent
   const response = await agent.chat({
     message: "How much is 5 + 5? then divide by 2",
   });
 
-  // Print the response
   console.log(String(response));
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/query_openai_agent.ts

@@ -29,7 +29,6 @@ async function main() {
   // Create an OpenAIAgent with the function tools
   const agent = new OpenAIAgent({
     tools: [queryEngineTool],
-    verbose: true,
   });
 
   // Chat with the agent
@@ -41,6 +40,6 @@ async function main() {
   console.log(String(response));
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/react_agent.ts

@@ -1,13 +1,13 @@
 import { Anthropic, FunctionTool, ReActAgent } from "llamaindex";
 
 // Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
+function sumNumbers({ a, b }: { a: number; b: number }) {
+  return `${a + b}`;
 }
 
 // Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
+function divideNumbers({ a, b }: { a: number; b: number }) {
+  return `${a / b}`;
 }
 
 // Define the parameters of the sum function as a JSON schema
@@ -24,7 +24,7 @@ const sumJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 const divideJSON = {
   type: "object",
@@ -39,7 +39,7 @@ const divideJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 async function main() {
   // Create a function tool from the sum function
@@ -65,18 +65,17 @@ async function main() {
   const agent = new ReActAgent({
     llm: anthropic,
     tools: [functionTool, functionTool2],
-    verbose: true,
   });
 
   // Chat with the agent
-  const response = await agent.chat({
+  const { response } = await agent.chat({
     message: "Divide 16 by 2 then add 20",
   });
 
   // Print the response
-  console.log(String(response));
+  console.log(response.message);
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/step_wise_openai.ts

@@ -1,13 +1,13 @@
 import { FunctionTool, OpenAIAgent } from "llamaindex";
 
 // Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
+function sumNumbers({ a, b }: { a: number; b: number }) {
+  return `${a + b}`;
 }
 
 // Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
+function divideNumbers({ a, b }: { a: number; b: number }) {
+  return `${a / b}`;
 }
 
 // Define the parameters of the sum function as a JSON schema
@@ -24,22 +24,22 @@ const sumJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 const divideJSON = {
   type: "object",
   properties: {
     a: {
       type: "number",
-      description: "The dividend a to divide",
+      description: "The dividend",
     },
     b: {
       type: "number",
-      description: "The divisor b to divide by",
+      description: "The divisor",
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 async function main() {
   // Create a function tool from the sum function
@@ -59,37 +59,25 @@ async function main() {
   // Create an OpenAIAgent with the function tools
   const agent = new OpenAIAgent({
     tools: [functionTool, functionTool2],
-    verbose: true,
   });
 
   // Create a task to sum and divide numbers
-  const task = agent.createTask("How much is 5 + 5? then divide by 2");
+  const task = await agent.createTask("How much is 5 + 5? then divide by 2");
 
   let count = 0;
 
-  while (true) {
-    const stepOutput = await agent.runStep(task.taskId);
-
+  for await (const stepOutput of task) {
     console.log(`Runnning step ${count++}`);
     console.log(`======== OUTPUT ==========`);
-    if (stepOutput.output.response) {
-      console.log(stepOutput.output.response);
-    } else {
-      console.log(stepOutput.output.sources);
-    }
+    console.log(stepOutput.output.message.content);
     console.log(`==========================`);
 
     if (stepOutput.isLast) {
-      const finalResponse = await agent.finalizeResponse(
-        task.taskId,
-        stepOutput,
-      );
-      console.log({ finalResponse });
-      break;
+      console.log(stepOutput.output.message.content);
     }
   }
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/step_wise_query_tool.ts

@@ -29,36 +29,15 @@ async function main() {
   // Create an OpenAIAgent with the function tools
   const agent = new OpenAIAgent({
     tools: [queryEngineTool],
-    verbose: true,
   });
 
-  const task = agent.createTask("What was his salary?");
+  const { response } = await agent.chat({
+    message: "What was his salary?",
+  });
 
-  let count = 0;
-
-  while (true) {
-    const stepOutput = await agent.runStep(task.taskId);
-
-    console.log(`Runnning step ${count++}`);
-    console.log(`======== OUTPUT ==========`);
-    if (stepOutput.output.response) {
-      console.log(stepOutput.output.response);
-    } else {
-      console.log(stepOutput.output.sources);
-    }
-    console.log(`==========================`);
-
-    if (stepOutput.isLast) {
-      const finalResponse = await agent.finalizeResponse(
-        task.taskId,
-        stepOutput,
-      );
-      console.log({ finalResponse });
-      break;
-    }
-  }
+  console.log(response.message.content);
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/step_wise_react.ts

@@ -1,13 +1,14 @@
-import { FunctionTool, ReActAgent } from "llamaindex";
+import { Anthropic, FunctionTool, ReActAgent } from "llamaindex";
 
 // Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
+function sumNumbers({ a, b }: { a: number; b: number }) {
+  return `${a + b}`;
 }
 
 // Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
+function divideNumbers({ a, b }: { a: number; b: number }) {
+  console.log("get input", a, b);
+  return `${a / b}`;
 }
 
 // Define the parameters of the sum function as a JSON schema
@@ -24,7 +25,7 @@ const sumJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 const divideJSON = {
   type: "object",
@@ -39,7 +40,7 @@ const divideJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 async function main() {
   // Create a function tool from the sum function
@@ -58,33 +59,24 @@ async function main() {
 
   // Create an OpenAIAgent with the function tools
   const agent = new ReActAgent({
+    llm: new Anthropic({
+      model: "claude-3-opus",
+    }),
     tools: [functionTool, functionTool2],
-    verbose: true,
   });
 
-  const task = agent.createTask("Divide 16 by 2 then add 20");
+  const task = await agent.createTask("Divide 16 by 2 then add 20");
 
   let count = 0;
 
-  while (true) {
-    const stepOutput = await agent.runStep(task.taskId);
-
+  for await (const stepOutput of task) {
     console.log(`Runnning step ${count++}`);
     console.log(`======== OUTPUT ==========`);
-    console.log(stepOutput.output);
+    console.log(stepOutput);
     console.log(`==========================`);
-
-    if (stepOutput.isLast) {
-      const finalResponse = await agent.finalizeResponse(
-        task.taskId,
-        stepOutput,
-      );
-      console.log({ finalResponse });
-      break;
-    }
   }
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("Done");
 });

examples/agent/stream_openai_agent.ts

@@ -1,13 +1,13 @@
 import { FunctionTool, OpenAIAgent } from "llamaindex";
 
 // Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
+function sumNumbers({ a, b }: { a: number; b: number }) {
+  return `${a + b}`;
 }
 
 // Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
+function divideNumbers({ a, b }: { a: number; b: number }) {
+  return `${a / b}`;
 }
 
 // Define the parameters of the sum function as a JSON schema
@@ -24,7 +24,7 @@ const sumJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 const divideJSON = {
   type: "object",
@@ -39,18 +39,18 @@ const divideJSON = {
     },
   },
   required: ["a", "b"],
-};
+} as const;
 
 async function main() {
   // Create a function tool from the sum function
-  const functionTool = new FunctionTool(sumNumbers, {
+  const functionTool = FunctionTool.from(sumNumbers, {
     name: "sumNumbers",
     description: "Use this function to sum two numbers",
     parameters: sumJSON,
   });
 
   // Create a function tool from the divide function
-  const functionTool2 = new FunctionTool(divideNumbers, {
+  const functionTool2 = FunctionTool.from(divideNumbers, {
     name: "divideNumbers",
     description: "Use this function to divide two numbers",
     parameters: divideJSON,
@@ -59,7 +59,6 @@ async function main() {
   // Create an OpenAIAgent with the function tools
   const agent = new OpenAIAgent({
     tools: [functionTool, functionTool2],
-    verbose: false,
   });
 
   const stream = await agent.chat({
@@ -72,6 +71,6 @@ async function main() {
   }
 }
 
-main().then(() => {
+void main().then(() => {
   console.log("\nDone");
 });

examples/agent/wiki.ts

@@ -0,0 +1,27 @@
+import { OpenAI, OpenAIAgent, WikipediaTool } from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({ model: "gpt-4-turbo" });
+  const wikiTool = new WikipediaTool();
+
+  // Create an OpenAIAgent with the Wikipedia tool
+  const agent = new OpenAIAgent({
+    llm,
+    tools: [wikiTool],
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "Who was Goethe?",
+    stream: true,
+  });
+
+  for await (const chunk of response.response) {
+    process.stdout.write(chunk.response);
+  }
+}
+
+(async function () {
+  await main();
+  console.log("\nDone");
+})();

examples/agent/wikipedia-tool.ts

@@ -1,23 +0,0 @@
-import { OpenAIAgent, WikipediaTool } from "llamaindex";
-
-async function main() {
-  const wikipediaTool = new WikipediaTool();
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [wikipediaTool],
-    verbose: true,
-  });
-
-  // Chat with the agent
-  const response = await agent.chat({
-    message: "Where is Ho Chi Minh City?",
-  });
-
-  // Print the response
-  console.log(response);
-}
-
-main().then(() => {
-  console.log("Done");
-});

examples/anthropic/agent.ts

@@ -0,0 +1,43 @@
+import { FunctionTool, Settings, WikipediaTool } from "llamaindex";
+import { AnthropicAgent } from "llamaindex/agent/anthropic";
+
+Settings.callbackManager.on("llm-tool-call", (event) => {
+  console.log("llm-tool-call", event.detail.payload.toolCall);
+});
+
+const agent = new AnthropicAgent({
+  tools: [
+    FunctionTool.from<{ location: string }>(
+      (query) => {
+        return `The weather in ${query.location} is sunny`;
+      },
+      {
+        name: "weather",
+        description: "Get the weather",
+        parameters: {
+          type: "object",
+          properties: {
+            location: {
+              type: "string",
+              description: "The location to get the weather for",
+            },
+          },
+          required: ["location"],
+        },
+      },
+    ),
+    new WikipediaTool(),
+  ],
+});
+
+async function main() {
+  // https://docs.anthropic.com/claude/docs/tool-use#tool-use-best-practices-and-limitations
+  const { response } = await agent.chat({
+    message:
+      "What is the weather in New York? What's the history of New York from Wikipedia in 3 sentences?",
+  });
+
+  console.log(response);
+}
+
+void main();

examples/astradb/README.md

@@ -13,7 +13,7 @@ Here are two sample scripts which work well with the sample data in the Astra Po
 1. Set your env variables:
 
 - `ASTRA_DB_APPLICATION_TOKEN`: The generated app token for your Astra database
-- `ASTRA_DB_ENDPOINT`: The API endpoint for your Astra database
+- `ASTRA_DB_API_ENDPOINT`: The API endpoint for your Astra database
 - `ASTRA_DB_NAMESPACE`: (Optional) The namespace where your collection is stored defaults to `default_keyspace`
 - `OPENAI_API_KEY`: Your OpenAI key
 

examples/astradb/example.ts

@@ -34,10 +34,9 @@ async function main() {
     ];
 
     const astraVS = new AstraDBVectorStore();
-    await astraVS.create(collectionName, {
+    await astraVS.createAndConnect(collectionName, {
       vector: { dimension: 1536, metric: "cosine" },
     });
-    await astraVS.connect(collectionName);
 
     const ctx = await storageContextFromDefaults({ vectorStore: astraVS });
     const index = await VectorStoreIndex.fromDocuments(docs, {
@@ -55,4 +54,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/astradb/load.ts

@@ -13,7 +13,7 @@ async function main() {
     const docs = await reader.loadData("./data/movie_reviews.csv");
 
     const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
-    await astraVS.create(collectionName, {
+    await astraVS.createAndConnect(collectionName, {
       vector: { dimension: 1536, metric: "cosine" },
     });
     await astraVS.connect(collectionName);
@@ -27,4 +27,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/astradb/query.ts

@@ -1,4 +1,8 @@
-import { AstraDBVectorStore, VectorStoreIndex } from "llamaindex";
+import {
+  AstraDBVectorStore,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
 
 const collectionName = "movie_reviews";
 
@@ -7,7 +11,8 @@ async function main() {
     const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
     await astraVS.connect(collectionName);
 
-    const index = await VectorStoreIndex.fromVectorStore(astraVS);
+    const ctx = serviceContextFromDefaults();
+    const index = await VectorStoreIndex.fromVectorStore(astraVS, ctx);
 
     const retriever = await index.asRetriever({ similarityTopK: 20 });
 
@@ -23,4 +28,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/chatHistory.ts

@@ -1,7 +1,18 @@
 import { stdin as input, stdout as output } from "node:process";
 import readline from "node:readline/promises";
 
-import { OpenAI, SimpleChatEngine, SummaryChatHistory } from "llamaindex";
+import {
+  OpenAI,
+  Settings,
+  SimpleChatEngine,
+  SummaryChatHistory,
+} from "llamaindex";
+
+if (process.env.NODE_ENV === "development") {
+  Settings.callbackManager.on("llm-end", (event) => {
+    console.log("callers chain", event.reason?.computedCallers);
+  });
+}
 
 async function main() {
   // Set maxTokens to 75% of the context window size of 4096

examples/chromadb/preFilters.ts

@@ -54,4 +54,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/chromadb/test.ts

@@ -37,4 +37,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/cloud/from_documents.ts

@@ -0,0 +1,44 @@
+import fs from "node:fs/promises";
+
+import { stdin as input, stdout as output } from "node:process";
+
+import readline from "node:readline/promises";
+
+import { Document, LlamaCloudIndex } from "llamaindex";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await LlamaCloudIndex.fromDocuments({
+    documents: [document],
+    name: "test",
+    projectName: "default",
+    apiKey: process.env.LLAMA_CLOUD_API_KEY,
+    baseUrl: process.env.LLAMA_CLOUD_BASE_URL,
+  });
+
+  const queryEngine = index.asQueryEngine({
+    denseSimilarityTopK: 5,
+  });
+
+  const rl = readline.createInterface({ input, output });
+
+  while (true) {
+    const query = await rl.question("Query: ");
+    const stream = await queryEngine.query({
+      query,
+      stream: true,
+    });
+    console.log();
+    for await (const chunk of stream) {
+      process.stdout.write(chunk.response);
+    }
+  }
+}
+
+main().catch(console.error);

examples/evaluation/correctness.ts

@@ -22,4 +22,4 @@ However, general relativity, published in 1915, extended these ideas to include
   console.log(result);
 }
 
-main();
+void main();

examples/evaluation/faithfulness.ts

@@ -36,4 +36,4 @@ async function main() {
   console.log(result);
 }
 
-main();
+void main();

examples/evaluation/relevancy.ts

@@ -37,4 +37,4 @@ async function main() {
   console.log(result);
 }
 
-main();
+void main();

examples/gemini.ts

@@ -0,0 +1,15 @@
+import { GEMINI_MODEL, GeminiEmbedding } from "llamaindex";
+
+async function main() {
+  if (!process.env.GOOGLE_API_KEY) {
+    throw new Error("Please set the GOOGLE_API_KEY environment variable.");
+  }
+  const embedModel = new GeminiEmbedding({
+    model: GEMINI_MODEL.GEMINI_PRO,
+  });
+  const texts = ["hello", "world"];
+  const embeddings = await embedModel.getTextEmbeddingsBatch(texts);
+  console.log(`\nWe have ${embeddings.length} embeddings`);
+}
+
+main().catch(console.error);

examples/gemini/chat.ts

@@ -0,0 +1,21 @@
+import { Gemini, GEMINI_MODEL } from "llamaindex";
+
+(async () => {
+  if (!process.env.GOOGLE_API_KEY) {
+    throw new Error("Please set the GOOGLE_API_KEY environment variable.");
+  }
+  const gemini = new Gemini({
+    model: GEMINI_MODEL.GEMINI_PRO,
+  });
+  const result = await gemini.chat({
+    messages: [
+      { content: "You want to talk in rhymes.", role: "system" },
+      {
+        content:
+          "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
+        role: "user",
+      },
+    ],
+  });
+  console.log(result);
+})();

examples/gptturbollama3.ts

@@ -0,0 +1,40 @@
+import { stdin as input, stdout as output } from "node:process";
+import readline from "node:readline/promises";
+
+import { ChatMessage, OpenAI, ReplicateLLM } from "llamaindex";
+
+(async () => {
+  const gpt4 = new OpenAI({ model: "gpt-4-turbo", temperature: 0.9 });
+  const l3 = new ReplicateLLM({
+    model: "llama-3-70b-instruct",
+    temperature: 0.9,
+  });
+
+  const rl = readline.createInterface({ input, output });
+  const start = await rl.question("Start: ");
+  const history: ChatMessage[] = [
+    {
+      content:
+        "Prefer shorter answers. Keep your response to 100 words or less.",
+      role: "system",
+    },
+    { content: start, role: "user" },
+  ];
+
+  while (true) {
+    const next = history.length % 2 === 1 ? gpt4 : l3;
+    const r = await next.chat({
+      messages: history.map(({ content, role }) => ({
+        content,
+        role: next === l3 ? role : role === "user" ? "assistant" : "user",
+      })),
+    });
+    history.push({
+      content: r.message.content,
+      role: next === l3 ? "assistant" : "user",
+    });
+    await rl.question(
+      (next === l3 ? "Llama 3: " : "GPT 4 Turbo: ") + r.message.content,
+    );
+  }
+})();

examples/jsonExtract.ts

@@ -36,9 +36,7 @@ async function main() {
     ],
   });
 
-  const json = JSON.parse(response.message.content);
-
-  console.log(json);
+  console.log(response.message.content);
 }
 
 main().catch(console.error);

examples/llama3.ts

@@ -0,0 +1,13 @@
+import { ReplicateLLM } from "llamaindex";
+
+(async () => {
+  const tres = new ReplicateLLM({ model: "llama-3-70b-instruct" });
+  const stream = await tres.chat({
+    messages: [{ content: "Hello, world!", role: "user" }],
+    stream: true,
+  });
+  for await (const chunk of stream) {
+    process.stdout.write(chunk.delta);
+  }
+  console.log("\n\ndone");
+})();

examples/milvus/load.ts

@@ -23,4 +23,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/milvus/query.ts

@@ -22,4 +22,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/mongo.ts

@@ -61,4 +61,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/mongodb/1_import.ts

@@ -31,4 +31,4 @@ async function importJsonToMongo() {
 }
 
 // Run the import function
-importJsonToMongo();
+void importJsonToMongo();

examples/mongodb/3_query.ts

@@ -27,4 +27,4 @@ async function query() {
   await client.close();
 }
 
-query();
+void query();

examples/multimodal/clip.ts

@@ -30,4 +30,4 @@ async function main() {
   console.log(`Similarity between "${text2}" and the image is ${sim2}`);
 }
 
-main();
+void main();

examples/multimodal/rag.ts

@@ -13,7 +13,7 @@ Settings.chunkSize = 512;
 Settings.chunkOverlap = 20;
 
 // Update llm
-Settings.llm = new OpenAI({ model: "gpt-4-vision-preview", maxTokens: 512 });
+Settings.llm = new OpenAI({ model: "gpt-4-turbo", maxTokens: 512 });
 
 // Update callbackManager
 Settings.callbackManager = new CallbackManager({

examples/nodeParser/MarkdownNodeParser.ts

@@ -21,4 +21,4 @@ Sub-header content
   console.log(splits);
 }
 
-main();
+void main();

examples/ollama.ts

@@ -1,7 +1,9 @@
+import { OllamaEmbedding } from "llamaindex";
 import { Ollama } from "llamaindex/llm/ollama";
 
 (async () => {
-  const llm = new Ollama({ model: "llama2", temperature: 0.75 });
+  const llm = new Ollama({ model: "llama3" });
+  const embedModel = new OllamaEmbedding({ model: "nomic-embed-text" });
   {
     const response = await llm.chat({
       messages: [{ content: "Tell me a joke.", role: "user" }],
@@ -35,7 +37,7 @@ import { Ollama } from "llamaindex/llm/ollama";
     console.log(); // newline
   }
   {
-    const embedding = await llm.getTextEmbedding("Hello world!");
+    const embedding = await embedModel.getTextEmbedding("Hello world!");
     console.log("Embedding:", embedding);
   }
 })();

examples/package.json

@@ -4,24 +4,28 @@
   "version": "0.0.4",
   "dependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
-    "@datastax/astra-db-ts": "^0.1.4",
-    "@notionhq/client": "^2.2.14",
+    "@datastax/astra-db-ts": "^1.0.1",
+    "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^1.1.3",
-    "@zilliz/milvus2-sdk-node": "^2.3.5",
+    "@zilliz/milvus2-sdk-node": "^2.4.1",
     "chromadb": "^1.8.1",
     "commander": "^11.1.0",
-    "dotenv": "^16.4.1",
-    "js-tiktoken": "^1.0.10",
-    "llamaindex": "latest",
-    "mongodb": "^6.2.0",
+    "dotenv": "^16.4.5",
+    "js-tiktoken": "^1.0.11",
+    "llamaindex": "*",
+    "mongodb": "^6.5.0",
     "pathe": "^1.1.2"
   },
   "devDependencies": {
-    "@types/node": "^18.19.10",
+    "@types/node": "^20.12.7",
     "ts-node": "^10.9.2",
-    "typescript": "^5.4.3"
+    "tsx": "^4.7.2",
+    "typescript": "^5.4.5"
   },
   "scripts": {
     "lint": "eslint ."
+  },
+  "stackblitz": {
+    "startCommand": "npm start"
   }
 }

examples/pg-vector-store/load-docs.ts

@@ -32,7 +32,7 @@ async function main(args: any) {
   console.log(`Found ${count} files`);
 
   console.log(`Importing contents from ${count} files in ${sourceDir}`);
-  var fileName = "";
+  const fileName = "";
   try {
     // Passing callback fn to the ctor here
     // will enable looging to console.
@@ -42,7 +42,7 @@ async function main(args: any) {
 
     const pgvs = new PGVectorStore();
     pgvs.setCollection(sourceDir);
-    pgvs.clearCollection();
+    await pgvs.clearCollection();
 
     const ctx = await storageContextFromDefaults({ vectorStore: pgvs });
 
@@ -65,4 +65,4 @@ async function main(args: any) {
   process.exit(0);
 }
 
-main(process.argv).catch((err) => console.error(err));
+void main(process.argv).catch((err) => console.error(err));

examples/pinecone-vector-store/load-docs.ts

@@ -32,7 +32,7 @@ async function main(args: any) {
   console.log(`Found ${count} files`);
 
   console.log(`Importing contents from ${count} files in ${sourceDir}`);
-  var fileName = "";
+  const fileName = "";
   try {
     // Passing callback fn to the ctor here
     // will enable looging to console.
@@ -63,4 +63,4 @@ async function main(args: any) {
   process.exit(0);
 }
 
-main(process.argv).catch((err) => console.error(err));
+void main(process.argv).catch((err) => console.error(err));

examples/prompts/promptMixin.ts

@@ -45,4 +45,4 @@ async function main() {
   await queryEngine.query({ query });
 }
 
-main();
+void main();

examples/qdrantdb/preFilters.ts

@@ -79,4 +79,4 @@ async function main() {
   }
 }
 
-main();
+void main();

examples/readers/package.json

@@ -3,20 +3,21 @@
   "private": true,
   "type": "module",
   "scripts": {
-    "start": "node --loader ts-node/esm ./src/simple-directory-reader.ts",
-    "start:csv": "node --loader ts-node/esm ./src/csv.ts",
-    "start:docx": "node --loader ts-node/esm ./src/docx.ts",
-    "start:html": "node --loader ts-node/esm ./src/html.ts",
-    "start:markdown": "node --loader ts-node/esm ./src/markdown.ts",
-    "start:pdf": "node --loader ts-node/esm ./src/pdf.ts",
-    "start:llamaparse": "node --loader ts-node/esm ./src/llamaparse.ts"
+    "start": "node --import tsx ./src/simple-directory-reader.ts",
+    "start:csv": "node --import tsx ./src/csv.ts",
+    "start:docx": "node --import tsx ./src/docx.ts",
+    "start:html": "node --import tsx ./src/html.ts",
+    "start:markdown": "node --import tsx ./src/markdown.ts",
+    "start:pdf": "node --import tsx ./src/pdf.ts",
+    "start:llamaparse": "node --import tsx ./src/llamaparse.ts",
+    "start:notion": "node --import tsx ./src/notion.ts"
   },
   "dependencies": {
-    "llamaindex": "latest"
+    "llamaindex": "*"
   },
   "devDependencies": {
-    "@types/node": "^20.11.14",
-    "ts-node": "^10.9.2",
-    "typescript": "^5.4.3"
+    "@types/node": "^20.12.7",
+    "tsx": "^4.7.2",
+    "typescript": "^5.4.5"
   }
 }

examples/readers/src/docx.ts

@@ -20,4 +20,4 @@ async function main() {
   console.log(`Test query > ${SAMPLE_QUERY}:\n`, response.toString());
 }
 
-main();
+void main();

examples/readers/src/markdown.ts

@@ -20,4 +20,4 @@ async function main() {
   console.log(`Test query > ${SAMPLE_QUERY}:\n`, response.toString());
 }
 
-main();
+void main();

examples/readers/src/notion.ts

@@ -7,7 +7,7 @@ import { createInterface } from "node:readline/promises";
 
 program
   .argument("[page]", "Notion page id (must be provided)")
-  .action(async (page, _options, command) => {
+  .action(async (page, _options) => {
     // Initializing a client
 
     if (!process.env.NOTION_TOKEN) {
@@ -55,7 +55,7 @@ program
           .filter((page) => page !== null);
         console.log("Found pages:");
         console.table(pages);
-        console.log(`To run, run ts-node ${command.name()} [page id]`);
+        console.log(`To run, run with [page id]`);
         return;
       }
     }

examples/recipes/cost-analysis.ts

@@ -1,37 +1,46 @@
 import { encodingForModel } from "js-tiktoken";
-import { OpenAI } from "llamaindex";
+import { ChatMessage, OpenAI, type LLMStartEvent } from "llamaindex";
 import { Settings } from "llamaindex/Settings";
+import { extractText } from "llamaindex/llm/utils";
 
 const encoding = encodingForModel("gpt-4-0125-preview");
 
 const llm = new OpenAI({
-  model: "gpt-4-0125-preview",
+  // currently is "gpt-4-turbo-2024-04-09"
+  model: "gpt-4-turbo",
 });
 
 let tokenCount = 0;
 
-Settings.callbackManager.on("llm-start", (event) => {
+Settings.callbackManager.on("llm-start", (event: LLMStartEvent) => {
   const { messages } = event.detail.payload;
-  tokenCount += messages.reduce((count, message) => {
-    return count + encoding.encode(message.content).length;
+  messages.reduce((count: number, message: ChatMessage) => {
+    return count + encoding.encode(extractText(message.content)).length;
   }, 0);
   console.log("Token count:", tokenCount);
   // https://openai.com/pricing
   // $10.00 / 1M tokens
-  console.log(`Price: $${(tokenCount / 1_000_000) * 10}`);
-});
-Settings.callbackManager.on("llm-end", (event) => {
-  const { response } = event.detail.payload;
-  tokenCount += encoding.encode(response.message.content).length;
-  console.log("Token count:", tokenCount);
-  // https://openai.com/pricing
-  // $30.00 / 1M tokens
-  console.log(`Price: $${(tokenCount / 1_000_000) * 30}`);
+  console.log(`Total Price: $${(tokenCount / 1_000_000) * 10}`);
 });
 
-const question = "Hello, how are you?";
+Settings.callbackManager.on("llm-stream", (event) => {
+  const { chunk } = event.detail.payload;
+  const { delta } = chunk;
+  tokenCount += encoding.encode(extractText(delta)).length;
+  if (tokenCount > 20) {
+    // This is just an example, you can set your own limit or handle it differently
+    throw new Error("Token limit exceeded!");
+  }
+});
+Settings.callbackManager.on("llm-end", () => {
+  // https://openai.com/pricing
+  // $30.00 / 1M tokens
+  console.log(`Total Price: $${(tokenCount / 1_000_000) * 30}`);
+});
+
+const question = "Hello, how are you? Please response about 50 tokens.";
 console.log("Question:", question);
-llm
+void llm
   .chat({
     stream: true,
     messages: [

examples/routerQueryEngine.ts

@@ -65,4 +65,4 @@ async function main() {
   });
 }
 
-main().then(() => console.log("Done"));
+void main().then(() => console.log("Done"));

examples/split.ts

@@ -13,4 +13,4 @@ async function main() {
   console.log(chunks);
 }
 
-main();
+void main();

examples/toolsStream.ts

@@ -0,0 +1,48 @@
+import { OpenAI } from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({ model: "gpt-4-turbo" });
+  const args: Parameters<typeof llm.chat>[0] = {
+    additionalChatOptions: {
+      tool_choice: "auto",
+    },
+    messages: [
+      {
+        content: "Who was Goethe?",
+        role: "user",
+      },
+    ],
+    tools: [
+      {
+        metadata: {
+          name: "wikipedia_tool",
+          description: "A tool that uses a query engine to search Wikipedia.",
+          parameters: {
+            type: "object",
+            properties: {
+              query: {
+                type: "string",
+                description: "The query to search for",
+              },
+            },
+            required: ["query"],
+          },
+        },
+      },
+    ],
+  };
+
+  const stream = await llm.chat({ ...args, stream: true });
+  for await (const chunk of stream) {
+    process.stdout.write(chunk.delta);
+    if (chunk.options && "toolCall" in chunk.options) {
+      console.log("Tool call:");
+      console.log(chunk.options.toolCall);
+    }
+  }
+}
+
+(async function () {
+  await main();
+  console.log("Done");
+})();

examples/tsconfig.json

@@ -1,12 +1,13 @@
 {
   "compilerOptions": {
-    "target": "es2017",
+    "target": "ES2022",
     "module": "esnext",
     "moduleResolution": "bundler",
     "esModuleInterop": true,
     "forceConsistentCasingInFileNames": true,
     "strict": true,
     "skipLibCheck": true,
+    "lib": ["ES2022"],
     "outDir": "./lib",
     "tsBuildInfoFile": "./lib/.tsbuildinfo",
     "incremental": true,

examples/vectorIndex.ts

@@ -1,6 +1,11 @@
 import fs from "node:fs/promises";
 
-import { Document, VectorStoreIndex } from "llamaindex";
+import {
+  Document,
+  MetadataMode,
+  NodeWithScore,
+  VectorStoreIndex,
+} from "llamaindex";
 
 async function main() {
   // Load essay from abramov.txt in Node
@@ -16,12 +21,20 @@ async function main() {
 
   // Query the index
   const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
+  const { response, sourceNodes } = await queryEngine.query({
     query: "What did the author do in college?",
   });
 
-  // Output response
-  console.log(response.toString());
+  // Output response with sources
+  console.log(response);
+
+  if (sourceNodes) {
+    sourceNodes.forEach((source: NodeWithScore, index: number) => {
+      console.log(
+        `\n${index}: Score: ${source.score} - ${source.node.getContent(MetadataMode.NONE).substring(0, 50)}...\n`,
+      );
+    });
+  }
 }
 
 main().catch(console.error);

examples/vision.ts

@@ -1,7 +1,7 @@
 import { OpenAI } from "llamaindex";
 
 (async () => {
-  const llm = new OpenAI({ model: "gpt-4-vision-preview", temperature: 0.1 });
+  const llm = new OpenAI({ model: "gpt-4-turbo", temperature: 0.1 });
 
   // complete api
   const response1 = await llm.complete({ prompt: "How are you?" });

package.json

@@ -9,31 +9,36 @@
     "format:write": "prettier --ignore-unknown --write .",
     "lint": "turbo run lint",
     "prepare": "husky",
+    "e2e": "turbo run e2e",
     "test": "turbo run test",
     "type-check": "tsc -b --diagnostics",
     "release": "pnpm run check-minor-version && pnpm run build:release && changeset publish",
     "release-snapshot": "pnpm run check-minor-version && pnpm run build:release && changeset publish --tag snapshot",
     "check-minor-version": "node ./scripts/check-minor-version",
-    "update-version": "node ./scripts/update-version",
-    "new-version": "pnpm run build:release && changeset version && pnpm run check-minor-version && pnpm run update-version",
-    "new-snapshot": "pnpm run build:release && changeset version --snapshot && pnpm run update-version"
+    "new-version": "changeset version && pnpm run check-minor-version && pnpm run build:release",
+    "new-snapshot": "pnpm run build:release && changeset version --snapshot"
   },
   "devDependencies": {
     "@changesets/cli": "^2.27.1",
-    "eslint": "^8.56.0",
-    "eslint-config-custom": "workspace:*",
-    "husky": "^9.0.10",
+    "@typescript-eslint/eslint-plugin": "^7.7.0",
+    "eslint": "^8.57.0",
+    "eslint-config-next": "^13.5.6",
+    "eslint-config-prettier": "^8.10.0",
+    "eslint-config-turbo": "^1.13.2",
+    "eslint-plugin-react": "7.28.0",
+    "husky": "^9.0.11",
     "lint-staged": "^15.2.2",
     "prettier": "^3.2.5",
     "prettier-plugin-organize-imports": "^3.2.4",
-    "turbo": "^1.12.3",
-    "typescript": "^5.4.3"
+    "turbo": "^1.13.2",
+    "typescript": "^5.4.5"
   },
-  "packageManager": "pnpm@8.15.1",
+  "packageManager": "pnpm@9.0.5",
   "pnpm": {
     "overrides": {
       "trim": "1.0.1",
-      "@babel/traverse": "7.23.2"
+      "@babel/traverse": "7.23.2",
+      "protobufjs": "7.2.6"
     }
   },
   "lint-staged": {

packages/core/.gitignore

@@ -1,3 +1,4 @@
 .turbo
-README.md
-LICENSE
+/README.md
+LICENSE
+*.tgz

packages/core/CHANGELOG.md

@@ -1,5 +1,83 @@
 # llamaindex
 
+## 0.2.13
+
+### Patch Changes
+
+- 6277105: fix: allow passing empty tools to llms
+
+## 0.2.12
+
+### Patch Changes
+
+- d8d952d: feat: add gemini llm and embedding
+
+## 0.2.11
+
+### Patch Changes
+
+- 87142b2: refactor: use ollama official sdk
+- 5a6cc0e: feat: support jina ai embedding and reranker
+- 87142b2: feat: support output to json format
+
+## 0.2.10
+
+### Patch Changes
+
+- cf70edb: Llama 3 support
+
+## 0.2.9
+
+### Patch Changes
+
+- 76c3fd6: Add score to source nodes response
+- 208282d: feat: init anthropic agent
+
+  remove the `tool` | `function` type in `MessageType`. Replace with `assistant` instead.
+  This is because these two types are only available for `OpenAI`.
+  Since `OpenAI` deprecates the function type, we support the Claude 3 tool call.
+
+## 0.2.8
+
+### Patch Changes
+
+- Add ToolsFactory to generate agent tools
+
+## 0.2.7
+
+### Patch Changes
+
+- 96f8f40: fix: agent stream
+- Updated dependencies
+  - @llamaindex/env@0.0.7
+
+## 0.2.6
+
+### Patch Changes
+
+- a3b4409: Fix agent streaming with new OpenAI models
+
+## 0.2.5
+
+### Patch Changes
+
+- 7d56cdf: Allow OpenAIAgent to be called without tools
+
+## 0.2.4
+
+### Patch Changes
+
+- 3bc77f7: gpt-4-turbo GA
+- 8d2b21e: Mistral 0.1.3
+
+## 0.2.3
+
+### Patch Changes
+
+- f0704ec: Support streaming for OpenAI agent (and OpenAI tool calls)
+- Removed 'parentEvent' - Use 'event.reason?.computedCallers' instead
+- 3cbfa98: Added LlamaCloudIndex.fromDocuments
+
 ## 0.2.2
 
 ### Patch Changes

packages/core/e2e/.gitignore

@@ -0,0 +1 @@
+logs

packages/core/e2e/README.md

@@ -0,0 +1,38 @@
+# LlamaIndexTS Core E2E Tests
+
+## Overview
+
+We are using Node.js Test Runner to run E2E tests for LlamaIndexTS Core.
+
+It supports the following features:
+
+- Run tests in parallel
+- Pure Node.js Environment
+- Switch between mock and real LLM API
+- Customizable logics
+
+## Usage
+
+- Run with mock register:
+
+```shell
+node --import tsx --import ./mock-register.js --test ./node/basic.e2e.ts
+```
+
+- Run without mock register:
+
+```shell
+node --import tsx --test ./node/basic.e2e.ts
+```
+
+- Run with specific test:
+
+```shell
+node --import tsx --import ./mock-register.js --test-name-pattern=agent --test ./node/basic.e2e.ts
+```
+
+- Run with debug logs:
+
+```shell
+CONSOLA_LEVEL=5 node --import tsx --import ./mock-register.js --test-name-pattern=agent --test ./node/basic.e2e.ts
+```

packages/core/e2e/fixtures/embeddings/OpenAIEmbedding.ts

@@ -0,0 +1,34 @@
+import { BaseNode, SimilarityType, type BaseEmbedding } from "llamaindex";
+
+export class OpenAIEmbedding implements BaseEmbedding {
+  embedBatchSize = 512;
+
+  async getQueryEmbedding(query: string) {
+    return [0];
+  }
+
+  async getTextEmbedding(text: string) {
+    return [0];
+  }
+
+  async getTextEmbeddings(texts: string[]) {
+    return [[0]];
+  }
+
+  async getTextEmbeddingsBatch(texts: string[]) {
+    return [[0]];
+  }
+
+  similarity(
+    embedding1: number[],
+    embedding2: number[],
+    mode?: SimilarityType,
+  ) {
+    return 1;
+  }
+
+  async transform(nodes: BaseNode[], _options?: any): Promise<BaseNode[]> {
+    nodes.forEach((node) => (node.embedding = [0]));
+    return nodes;
+  }
+}

packages/core/e2e/fixtures/llm/anthropic.ts

@@ -0,0 +1,3 @@
+import { OpenAI } from "./openai.js";
+
+export class Anthropic extends OpenAI {}

packages/core/e2e/fixtures/llm/openai.ts

@@ -0,0 +1,105 @@
+import type {
+  ChatResponse,
+  ChatResponseChunk,
+  CompletionResponse,
+  LLM,
+  LLMChatParamsNonStreaming,
+  LLMChatParamsStreaming,
+  LLMCompletionParamsNonStreaming,
+  LLMCompletionParamsStreaming,
+} from "llamaindex/llm/types";
+import { extractText } from "llamaindex/llm/utils";
+import { deepStrictEqual, strictEqual } from "node:assert";
+import { llmCompleteMockStorage } from "../../node/utils.js";
+
+export function getOpenAISession() {
+  return {};
+}
+
+export function isFunctionCallingModel() {
+  return true;
+}
+
+export class OpenAI implements LLM {
+  supportToolCall = true;
+  get metadata() {
+    return {
+      model: "mock-model",
+      temperature: 0.1,
+      topP: 1,
+      contextWindow: 2048,
+      tokenizer: undefined,
+      isFunctionCallingModel: true,
+    };
+  }
+  chat(
+    params: LLMChatParamsStreaming<Record<string, unknown>>,
+  ): Promise<AsyncIterable<ChatResponseChunk>>;
+  chat(
+    params: LLMChatParamsNonStreaming<Record<string, unknown>>,
+  ): Promise<ChatResponse>;
+  chat(
+    params:
+      | LLMChatParamsStreaming<Record<string, unknown>>
+      | LLMChatParamsNonStreaming<Record<string, unknown>>,
+  ): unknown {
+    if (llmCompleteMockStorage.llmEventStart.length > 0) {
+      const chatMessage =
+        llmCompleteMockStorage.llmEventStart.shift()!["messages"];
+      strictEqual(params.messages.length, chatMessage.length);
+      for (let i = 0; i < chatMessage.length; i++) {
+        strictEqual(params.messages[i].role, chatMessage[i].role);
+        deepStrictEqual(params.messages[i].content, chatMessage[i].content);
+      }
+
+      if (llmCompleteMockStorage.llmEventEnd.length > 0) {
+        const { id, response } = llmCompleteMockStorage.llmEventEnd.shift()!;
+        if (params.stream) {
+          return {
+            [Symbol.asyncIterator]: async function* () {
+              while (true) {
+                const idx = llmCompleteMockStorage.llmEventStream.findIndex(
+                  (e) => e.id === id,
+                );
+                if (idx === -1) {
+                  break;
+                }
+                const chunk = llmCompleteMockStorage.llmEventStream[idx].chunk;
+                llmCompleteMockStorage.llmEventStream.splice(idx, 1);
+                yield chunk;
+              }
+            },
+          };
+        } else {
+          return response;
+        }
+      }
+    }
+    throw new Error("Method not implemented.");
+  }
+  complete(
+    params: LLMCompletionParamsStreaming,
+  ): Promise<AsyncIterable<CompletionResponse>>;
+  complete(
+    params: LLMCompletionParamsNonStreaming,
+  ): Promise<CompletionResponse>;
+  async complete(
+    params: LLMCompletionParamsStreaming | LLMCompletionParamsNonStreaming,
+  ): Promise<AsyncIterable<CompletionResponse> | CompletionResponse> {
+    if (llmCompleteMockStorage.llmEventStart.length > 0) {
+      const chatMessage =
+        llmCompleteMockStorage.llmEventStart.shift()!["messages"];
+      strictEqual(1, chatMessage.length);
+      strictEqual("user", chatMessage[0].role);
+      strictEqual(params.prompt, chatMessage[0].content);
+    }
+    if (llmCompleteMockStorage.llmEventEnd.length > 0) {
+      const response = llmCompleteMockStorage.llmEventEnd.shift()!["response"];
+      return {
+        raw: response,
+        text: extractText(response.message.content),
+      } satisfies CompletionResponse;
+    }
+    throw new Error("Method not implemented.");
+  }
+}

packages/core/e2e/mock-module.js

@@ -0,0 +1,36 @@
+/**
+ * This script will replace the resolved module with the corresponding fixture file.
+ */
+import { stat } from "node:fs/promises";
+import { join, relative } from "node:path";
+import { fileURLToPath, pathToFileURL } from "node:url";
+const packageDistDir = fileURLToPath(new URL("../dist", import.meta.url));
+const fixturesDir = fileURLToPath(new URL("./fixtures", import.meta.url));
+
+export async function resolve(specifier, context, nextResolve) {
+  const result = await nextResolve(specifier, context);
+  if (result.format === "builtin" || result.url.startsWith("node:")) {
+    return result;
+  }
+  const targetUrl = fileURLToPath(result.url).replace(/\.js$/, ".ts");
+  const relativePath = relative(packageDistDir, targetUrl);
+  if (relativePath.startsWith(".") || relativePath.startsWith("/")) {
+    return result;
+  }
+  const url = pathToFileURL(join(fixturesDir, relativePath)).toString();
+  const exist = await stat(fileURLToPath(url))
+    .then((stat) => stat.isFile())
+    .catch((err) => {
+      if (err.code === "ENOENT") {
+        return false;
+      }
+      throw err;
+    });
+  if (!exist) {
+    return result;
+  }
+  return {
+    url,
+    format: "module",
+  };
+}

packages/core/e2e/mock-register.js

@@ -0,0 +1,3 @@
+import { register } from "node:module";
+
+register("./mock-module.js", import.meta.url);

packages/core/e2e/node/claude.e2e.ts

@@ -0,0 +1,131 @@
+import { consola } from "consola";
+import { Anthropic, FunctionTool, Settings, type LLM } from "llamaindex";
+import { AnthropicAgent } from "llamaindex/agent/anthropic";
+import { extractText } from "llamaindex/llm/utils";
+import { ok, strictEqual } from "node:assert";
+import { beforeEach, test } from "node:test";
+import { sumNumbersTool } from "./fixtures/tools.js";
+import { mockLLMEvent } from "./utils.js";
+
+let llm: LLM;
+beforeEach(async () => {
+  Settings.llm = new Anthropic({
+    model: "claude-3-opus",
+  });
+  llm = Settings.llm;
+});
+
+await test("anthropic llm", async (t) => {
+  await mockLLMEvent(t, "llm-anthropic");
+  await t.test("llm.chat", async () => {
+    const response = await llm.chat({
+      messages: [
+        {
+          content: "Hello",
+          role: "user",
+          options: {},
+        },
+      ],
+    });
+    consola.debug("response:", response);
+    ok(typeof response.message.content === "string");
+  });
+
+  await t.test("stream llm.chat", async () => {
+    const iter = await llm.chat({
+      stream: true,
+      messages: [
+        {
+          content: "hello",
+          role: "user",
+        },
+      ],
+    });
+    for await (const chunk of iter) {
+      consola.debug("chunk:", chunk);
+      ok(typeof chunk.delta === "string");
+    }
+  });
+});
+
+await test("anthropic agent", async (t) => {
+  await mockLLMEvent(t, "anthropic-agent");
+  await t.test("chat", async () => {
+    const agent = new AnthropicAgent({
+      tools: [
+        {
+          call: async () => {
+            return "35 degrees and sunny in San Francisco";
+          },
+          metadata: {
+            name: "Weather",
+            description: "Get the weather",
+            parameters: {
+              type: "object",
+              properties: {
+                location: { type: "string" },
+              },
+              required: ["location"],
+            },
+          },
+        },
+      ],
+    });
+    const { response, sources } = await agent.chat({
+      message: "What is the weather in San Francisco?",
+    });
+    consola.debug("response:", response.message.content);
+
+    strictEqual(sources.length, 1);
+    ok(extractText(response.message.content).includes("35"));
+  });
+
+  await t.test("async function", async () => {
+    const uniqueId = "123456789";
+    const showUniqueId = FunctionTool.from<{
+      firstName: string;
+      lastName: string;
+    }>(
+      async ({ firstName, lastName }) => {
+        ok(typeof firstName === "string");
+        ok(typeof lastName === "string");
+        const fullName = firstName + lastName;
+        ok(fullName.toLowerCase().includes("alex"));
+        ok(fullName.toLowerCase().includes("yang"));
+        return uniqueId;
+      },
+      {
+        name: "unique_id",
+        description: "show user unique id",
+        parameters: {
+          type: "object",
+          properties: {
+            firstName: { type: "string" },
+            lastName: { type: "string" },
+          },
+          required: ["firstName", "lastName"],
+        },
+      },
+    );
+    const agent = new AnthropicAgent({
+      tools: [showUniqueId],
+    });
+    const { response } = await agent.chat({
+      message: "My name is Alex Yang. What is my unique id?",
+    });
+    consola.debug("response:", response.message.content);
+    ok(extractText(response.message.content).includes(uniqueId));
+  });
+
+  await t.test("sum numbers", async () => {
+    const openaiAgent = new AnthropicAgent({
+      tools: [sumNumbersTool],
+    });
+
+    const { response } = await openaiAgent.chat({
+      message: "how much is 1 + 1?",
+    });
+
+    ok(extractText(response.message.content).includes("2"));
+  });
+});

packages/core/e2e/node/fixtures/data/Alex.txt

@@ -0,0 +1,2 @@
+Alex is a male.
+What's very important, Alex is not in the Brazil.

packages/core/e2e/node/fixtures/tools.ts

@@ -0,0 +1,68 @@
+import { FunctionTool } from "llamaindex";
+
+function sumNumbers({ a, b }: { a: number; b: number }) {
+  return `${a + b}`;
+}
+
+function divideNumbers({ a, b }: { a: number; b: number }) {
+  return `${a / b}`;
+}
+
+export const sumNumbersTool = FunctionTool.from(sumNumbers, {
+  name: "sumNumbers",
+  description: "Use this function to sum two numbers",
+  parameters: {
+    type: "object",
+    properties: {
+      a: {
+        type: "number",
+        description: "The first number",
+      },
+      b: {
+        type: "number",
+        description: "The second number",
+      },
+    },
+    required: ["a", "b"],
+  },
+});
+
+export const divideNumbersTool = FunctionTool.from(divideNumbers, {
+  name: "divideNumbers",
+  description: "Use this function to divide two numbers",
+  parameters: {
+    type: "object",
+    properties: {
+      a: {
+        type: "number",
+        description: "The first number",
+      },
+      b: {
+        type: "number",
+        description: "The second number",
+      },
+    },
+    required: ["a", "b"],
+  },
+});
+
+// should always return the 72 degrees
+export const getWeatherTool = FunctionTool.from(
+  async ({ city }: { city: string }) => {
+    return `The weather in ${city} is 72 degrees`;
+  },
+  {
+    name: "getWeather",
+    description: "Get the weather for a city",
+    parameters: {
+      type: "object",
+      properties: {
+        city: {
+          type: "string",
+          description: "The city to get the weather for",
+        },
+      },
+      required: ["city"],
+    },
+  },
+);

packages/core/e2e/node/openai.e2e.ts

@@ -0,0 +1,381 @@
+import { consola } from "consola";
+import {
+  Document,
+  FunctionTool,
+  ObjectIndex,
+  OpenAI,
+  OpenAIAgent,
+  QueryEngineTool,
+  Settings,
+  SimpleNodeParser,
+  SimpleToolNodeMapping,
+  SubQuestionQueryEngine,
+  SummaryIndex,
+  VectorStoreIndex,
+  type LLM,
+  type ToolOutput,
+} from "llamaindex";
+import { extractText } from "llamaindex/llm/utils";
+import { ok, strictEqual } from "node:assert";
+import { readFile } from "node:fs/promises";
+import { join } from "node:path";
+import { beforeEach, test } from "node:test";
+import {
+  divideNumbersTool,
+  getWeatherTool,
+  sumNumbersTool,
+} from "./fixtures/tools.js";
+import { mockLLMEvent, testRootDir } from "./utils.js";
+
+let llm: LLM;
+beforeEach(async () => {
+  Settings.llm = new OpenAI({
+    model: "gpt-3.5-turbo",
+  });
+  llm = Settings.llm;
+});
+
+await test("openai llm", async (t) => {
+  await mockLLMEvent(t, "llm");
+  await t.test("llm.chat", async () => {
+    const response = await llm.chat({
+      messages: [
+        {
+          content: "Hello",
+          role: "user",
+        },
+      ],
+    });
+    consola.debug("response:", response);
+    ok(typeof response.message.content === "string");
+  });
+
+  await t.test("stream llm.chat", async () => {
+    const iter = await llm.chat({
+      stream: true,
+      messages: [
+        {
+          content: "hello",
+          role: "user",
+        },
+      ],
+    });
+    for await (const chunk of iter) {
+      consola.debug("chunk:", chunk);
+      ok(typeof chunk.delta === "string");
+    }
+  });
+});
+
+await test("gpt-4-turbo", async (t) => {
+  const llm = new OpenAI({ model: "gpt-4-turbo" });
+  Settings.llm = llm;
+  await mockLLMEvent(t, "gpt-4-turbo");
+  await t.test("agent", async () => {
+    const agent = new OpenAIAgent({
+      llm,
+      tools: [
+        {
+          call: async () => {
+            return "45 degrees and sunny in San Jose";
+          },
+          metadata: {
+            name: "Weather",
+            description: "Get the weather",
+            parameters: {
+              type: "object",
+              properties: {
+                location: { type: "string" },
+              },
+              required: ["location"],
+            },
+          },
+        },
+      ],
+    });
+    const { response } = await agent.chat({
+      message: "What is the weather in San Jose?",
+    });
+    consola.debug("response:", response.message.content);
+    ok(extractText(response.message.content).includes("45"));
+  });
+});
+
+await test("agent system prompt", async (t) => {
+  await mockLLMEvent(t, "openai_agent_system_prompt");
+  await t.test("chat", async (t) => {
+    const agent = new OpenAIAgent({
+      tools: [getWeatherTool],
+      systemPrompt:
+        "You are a pirate. You MUST speak every words staring with a 'Arhgs'",
+    });
+    const { response } = await agent.chat({
+      message: "What is the weather in San Francisco?",
+    });
+    consola.debug("response:", response.message.content);
+    ok(extractText(response.message.content).includes("72"));
+    ok(extractText(response.message.content).includes("Arhg"));
+  });
+});
+
+await test("agent with object retriever", async (t) => {
+  await mockLLMEvent(t, "agent_with_object_retriever");
+
+  const alexInfoPath = join(testRootDir, "./fixtures/data/Alex.txt");
+  const alexInfoText = await readFile(alexInfoPath, "utf-8");
+  const alexDocument = new Document({ text: alexInfoText, id_: alexInfoPath });
+
+  const nodes = new SimpleNodeParser({
+    chunkSize: 200,
+    chunkOverlap: 20,
+  }).getNodesFromDocuments([alexDocument]);
+
+  const summaryIndex = await SummaryIndex.init({
+    nodes,
+  });
+
+  const summaryQueryEngine = summaryIndex.asQueryEngine();
+
+  const queryEngineTools = [
+    FunctionTool.from(
+      ({ query }: { query?: string }) => {
+        throw new Error("This tool should not be called");
+      },
+      {
+        name: "vector_tool",
+        description:
+          "This tool should not be called, never use this tool in any cases.",
+        parameters: {
+          type: "object",
+          properties: {
+            query: { type: "string", nullable: true },
+          },
+        },
+      },
+    ),
+    new QueryEngineTool({
+      queryEngine: summaryQueryEngine,
+      metadata: {
+        name: "summary_tool",
+        description: `Useful for any requests that short information about Alex.
+For questions about Alex, please use this tool.
+For questions about more specific sections, please use the vector_tool.`,
+      },
+    }),
+  ];
+
+  const originalCall = queryEngineTools[1].call.bind(queryEngineTools[1]);
+  const mockCall = t.mock.fn(({ query }: { query: string }) => {
+    return originalCall({ query });
+  });
+  queryEngineTools[1].call = mockCall;
+
+  const toolMapping = SimpleToolNodeMapping.fromObjects(queryEngineTools);
+
+  const objectIndex = await ObjectIndex.fromObjects(
+    queryEngineTools,
+    toolMapping,
+    VectorStoreIndex,
+  );
+
+  const toolRetriever = await objectIndex.asRetriever({});
+
+  const agent = new OpenAIAgent({
+    toolRetriever,
+    systemPrompt:
+      "Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
+  });
+
+  strictEqual(mockCall.mock.callCount(), 0);
+  const { response } = await agent.chat({
+    message:
+      "What's the summary of Alex? Does he live in Brazil based on the brief information? Return yes or no.",
+  });
+  strictEqual(mockCall.mock.callCount(), 1);
+
+  consola.debug("response:", response.message.content);
+  ok(extractText(response.message.content).toLowerCase().includes("no"));
+});
+
+await test("agent with object function call", async (t) => {
+  await mockLLMEvent(t, "agent_with_object_function_call");
+  await t.test("basic", async () => {
+    const agent = new OpenAIAgent({
+      tools: [
+        FunctionTool.from(
+          ({ location }: { location: string }) => ({
+            location,
+            temperature: 72,
+            weather: "cloudy",
+            rain_prediction: 0.89,
+          }),
+          {
+            name: "get_weather",
+            description: "Get the weather",
+            parameters: {
+              type: "object",
+              properties: {
+                location: { type: "string" },
+              },
+              required: ["location"],
+            },
+          },
+        ),
+      ],
+    });
+    const { response, sources } = await agent.chat({
+      message: "What is the weather in San Francisco?",
+    });
+    consola.debug("response:", response.message.content);
+
+    strictEqual(sources.length, 1);
+    ok(extractText(response.message.content).includes("72"));
+  });
+});
+
+await test("agent", async (t) => {
+  await mockLLMEvent(t, "agent");
+  await t.test("chat", async () => {
+    const agent = new OpenAIAgent({
+      tools: [
+        {
+          call: async () => {
+            return "35 degrees and sunny in San Francisco";
+          },
+          metadata: {
+            name: "Weather",
+            description: "Get the weather",
+            parameters: {
+              type: "object",
+              properties: {
+                location: { type: "string" },
+              },
+              required: ["location"],
+            },
+          },
+        },
+      ],
+    });
+    const { response, sources } = await agent.chat({
+      message: "What is the weather in San Francisco?",
+    });
+    consola.debug("response:", response.message.content);
+
+    strictEqual(sources.length, 1);
+    ok(extractText(response.message.content).includes("35"));
+  });
+
+  await t.test("async function", async () => {
+    const uniqueId = "123456789";
+    const showUniqueId = FunctionTool.from<{
+      firstName: string;
+      lastName: string;
+    }>(
+      async ({ firstName, lastName }) => {
+        ok(typeof firstName === "string");
+        ok(typeof lastName === "string");
+        const fullName = firstName + lastName;
+        ok(fullName.toLowerCase().includes("alex"));
+        ok(fullName.toLowerCase().includes("yang"));
+        return uniqueId;
+      },
+      {
+        name: "unique_id",
+        description: "show user unique id",
+        parameters: {
+          type: "object",
+          properties: {
+            firstName: { type: "string" },
+            lastName: { type: "string" },
+          },
+          required: ["firstName", "lastName"],
+        },
+      },
+    );
+    const agent = new OpenAIAgent({
+      tools: [showUniqueId],
+    });
+    const { response, sources } = await agent.chat({
+      message: "My name is Alex Yang. What is my unique id?",
+    });
+    strictEqual(sources.length, 1);
+    ok(extractText(response.message.content).includes(uniqueId));
+  });
+
+  await t.test("sum numbers", async () => {
+    const openaiAgent = new OpenAIAgent({
+      tools: [sumNumbersTool],
+    });
+
+    const { response, sources } = await openaiAgent.chat({
+      message: "how much is 1 + 1?",
+    });
+
+    strictEqual(sources.length, 1);
+    ok(extractText(response.message.content).includes("2"));
+  });
+});
+
+await test("agent stream", async (t) => {
+  await mockLLMEvent(t, "agent_stream");
+  await t.test("sum numbers stream", async (t) => {
+    const fn = t.mock.fn(() => {});
+    Settings.callbackManager.on("llm-tool-call", fn);
+
+    const agent = new OpenAIAgent({
+      tools: [sumNumbersTool, divideNumbersTool],
+    });
+
+    const stream = await agent.chat({
+      message: "Divide 16 by 2 then add 20",
+      stream: true,
+    });
+
+    let message = "";
+    let soruces: ToolOutput[] = [];
+
+    for await (const { response, sources: _sources } of stream) {
+      message += response.delta;
+      soruces = _sources;
+    }
+
+    strictEqual(fn.mock.callCount(), 2);
+    strictEqual(soruces.length, 2);
+    ok(message.includes("28"));
+    Settings.callbackManager.off("llm-tool-call", fn);
+  });
+});
+
+await test("queryEngine", async (t) => {
+  await mockLLMEvent(t, "queryEngine_subquestion");
+  await t.test("subquestion", async () => {
+    const fn = t.mock.fn(() => {});
+    Settings.callbackManager.on("llm-tool-call", fn);
+    const document = new Document({
+      text: "Bill Gates stole from Apple.\n Steve Jobs stole from Xerox.",
+    });
+    const index = await VectorStoreIndex.fromDocuments([document]);
+
+    const queryEngineTools = [
+      new QueryEngineTool({
+        queryEngine: index.asQueryEngine(),
+        metadata: {
+          name: "bill_gates_idea",
+          description: "Get what Bill Gates idea from.",
+        },
+      }),
+    ];
+
+    const queryEngine = SubQuestionQueryEngine.fromDefaults({
+      queryEngineTools,
+    });
+
+    const { response } = await queryEngine.query({
+      query: "What did Bill Gates steal from?",
+    });
+
+    ok(response.includes("Apple"));
+    strictEqual(fn.mock.callCount(), 0);
+    Settings.callbackManager.off("llm-tool-call", fn);
+  });
+});

packages/core/e2e/node/react.e2e.ts

@@ -0,0 +1,29 @@
+import { OpenAI, ReActAgent, Settings, type LLM } from "llamaindex";
+import { extractText } from "llamaindex/llm/utils";
+import { ok } from "node:assert";
+import { beforeEach, test } from "node:test";
+import { getWeatherTool } from "./fixtures/tools.js";
+import { mockLLMEvent } from "./utils.js";
+
+let llm: LLM;
+beforeEach(async () => {
+  Settings.llm = new OpenAI({
+    model: "gpt-3.5-turbo",
+  });
+  llm = Settings.llm;
+});
+
+await test("react agent", async (t) => {
+  await mockLLMEvent(t, "react-agent");
+  await t.test("get weather", async () => {
+    const agent = new ReActAgent({
+      tools: [getWeatherTool],
+    });
+    const { response } = await agent.chat({
+      stream: false,
+      message: "What is the weather like in San Francisco?",
+    });
+
+    ok(extractText(response.message.content).includes("72"));
+  });
+});

packages/core/e2e/node/snapshot/agent.snap

@@ -0,0 +1,370 @@
+{
+  "llmEventStart": [
+    {
+      "id": "PRESERVE_0",
+      "messages": [
+        {
+          "content": "What is the weather in San Francisco?",
+          "role": "user"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_1",
+      "messages": [
+        {
+          "content": "What is the weather in San Francisco?",
+          "role": "user"
+        },
+        {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "Weather",
+              "input": "{\"location\":\"San Francisco\"}"
+            }
+          }
+        },
+        {
+          "content": "35 degrees and sunny in San Francisco",
+          "role": "user",
+          "options": {
+            "toolResult": {
+              "id": "HIDDEN",
+              "isError": false
+            }
+          }
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_2",
+      "messages": [
+        {
+          "content": "My name is Alex Yang. What is my unique id?",
+          "role": "user"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_3",
+      "messages": [
+        {
+          "content": "My name is Alex Yang. What is my unique id?",
+          "role": "user"
+        },
+        {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "unique_id",
+              "input": "{\"firstName\":\"Alex\",\"lastName\":\"Yang\"}"
+            }
+          }
+        },
+        {
+          "content": "123456789",
+          "role": "user",
+          "options": {
+            "toolResult": {
+              "id": "HIDDEN",
+              "isError": false
+            }
+          }
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_4",
+      "messages": [
+        {
+          "content": "how much is 1 + 1?",
+          "role": "user"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_5",
+      "messages": [
+        {
+          "content": "how much is 1 + 1?",
+          "role": "user"
+        },
+        {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "sumNumbers",
+              "input": "{\"a\":1,\"b\":1}"
+            }
+          }
+        },
+        {
+          "content": "2",
+          "role": "user",
+          "options": {
+            "toolResult": {
+              "id": "HIDDEN",
+              "isError": false
+            }
+          }
+        }
+      ]
+    }
+  ],
+  "llmEventEnd": [
+    {
+      "id": "PRESERVE_0",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": null,
+                "tool_calls": [
+                  {
+                    "id": "HIDDEN",
+                    "type": "function",
+                    "function": {
+                      "name": "Weather",
+                      "arguments": "{\"location\":\"San Francisco\"}"
+                    }
+                  }
+                ]
+              },
+              "logprobs": null,
+              "finish_reason": "tool_calls"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 49,
+            "completion_tokens": 15,
+            "total_tokens": 64
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "Weather",
+              "input": "{\"location\":\"San Francisco\"}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": "The weather in San Francisco is currently 35 degrees and sunny."
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 78,
+            "completion_tokens": 14,
+            "total_tokens": 92
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "The weather in San Francisco is currently 35 degrees and sunny.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_2",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": null,
+                "tool_calls": [
+                  {
+                    "id": "HIDDEN",
+                    "type": "function",
+                    "function": {
+                      "name": "unique_id",
+                      "arguments": "{\"firstName\":\"Alex\",\"lastName\":\"Yang\"}"
+                    }
+                  }
+                ]
+              },
+              "logprobs": null,
+              "finish_reason": "tool_calls"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 59,
+            "completion_tokens": 18,
+            "total_tokens": 77
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "unique_id",
+              "input": "{\"firstName\":\"Alex\",\"lastName\":\"Yang\"}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_3",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": "Your unique id is 123456789."
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 88,
+            "completion_tokens": 10,
+            "total_tokens": 98
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "Your unique id is 123456789.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_4",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": null,
+                "tool_calls": [
+                  {
+                    "id": "HIDDEN",
+                    "type": "function",
+                    "function": {
+                      "name": "sumNumbers",
+                      "arguments": "{\"a\":1,\"b\":1}"
+                    }
+                  }
+                ]
+              },
+              "logprobs": null,
+              "finish_reason": "tool_calls"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 70,
+            "completion_tokens": 18,
+            "total_tokens": 88
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "HIDDEN",
+              "name": "sumNumbers",
+              "input": "{\"a\":1,\"b\":1}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_5",
+      "response": {
+        "raw": {
+          "id": "HIDDEN",
+          "object": "chat.completion",
+          "created": 114514,
+          "model": "gpt-3.5-turbo-0125",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": "1 + 1 is equal to 2."
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 97,
+            "completion_tokens": 11,
+            "total_tokens": 108
+          },
+          "system_fingerprint": "HIDDEN"
+        },
+        "message": {
+          "content": "1 + 1 is equal to 2.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    }
+  ],
+  "llmEventStream": []
+}

packages/core/e2e/node/snapshot/agent_stream.snap

@@ -0,0 +1,382 @@
+{
+  "llmEventStart": [
+    {
+      "id": "PRESERVE_0",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Divide 16 by 2 then add 20"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_1",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Divide 16 by 2 then add 20"
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "options": {
+            "toolCall": {
+              "name": "divideNumbers",
+              "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+              "input": "{\"a\": 16, \"b\": 2}"
+            }
+          }
+        },
+        {
+          "role": "user",
+          "content": "8",
+          "options": {
+            "toolResult": {
+              "result": "8",
+              "isError": false,
+              "id": "call_t0vy4M815ncAQnfRqoflW5hn"
+            }
+          }
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "options": {
+            "toolCall": {
+              "name": "sumNumbers",
+              "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+              "input": "{\"a\": 8, \"b\": 20}"
+            }
+          }
+        },
+        {
+          "role": "user",
+          "content": "28",
+          "options": {
+            "toolResult": {
+              "result": "28",
+              "isError": false,
+              "id": "call_08QNOtWYlDoqPPXHMtbvr7A2"
+            }
+          }
+        }
+      ]
+    }
+  ],
+  "llmEventEnd": [
+    {
+      "id": "PRESERVE_0",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "name": "sumNumbers",
+              "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+              "input": "{\"a\": 8, \"b\": 20}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "The result of dividing 16 by 2 and then adding 20 is 28.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    }
+  ],
+  "llmEventStream": [
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "divideNumbers",
+            "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+            "input": "{\"a\": 16, \"b\": 2}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "divideNumbers",
+            "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+            "input": "{\"a\": 16, \"b\": 2}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "divideNumbers",
+            "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+            "input": "{\"a\": 16, \"b\": 2}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "divideNumbers",
+            "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+            "input": "{\"a\": 16, \"b\": 2}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "divideNumbers",
+            "id": "call_t0vy4M815ncAQnfRqoflW5hn",
+            "input": "{\"a\": 16, \"b\": 2}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "sumNumbers",
+            "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+            "input": "{\"a\": 8, \"b\": 20}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "sumNumbers",
+            "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+            "input": "{\"a\": 8, \"b\": 20}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "sumNumbers",
+            "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+            "input": "{\"a\": 8, \"b\": 20}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "sumNumbers",
+            "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+            "input": "{\"a\": 8, \"b\": 20}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_0",
+      "chunk": {
+        "raw": null,
+        "options": {
+          "toolCall": {
+            "name": "sumNumbers",
+            "id": "call_08QNOtWYlDoqPPXHMtbvr7A2",
+            "input": "{\"a\": 8, \"b\": 20}"
+          }
+        },
+        "delta": ""
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "The"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " result"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " of"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " dividing"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " "
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "16"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " by"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " "
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "2"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " and"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " then"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " adding"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " "
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "20"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " is"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": " "
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "28"
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "chunk": {
+        "raw": null,
+        "options": {},
+        "delta": "."
+      }
+    }
+  ]
+}

packages/core/e2e/node/snapshot/agent_with_object_function_call.snap

@@ -0,0 +1,80 @@
+{
+  "llmEventStart": [
+    {
+      "id": "PRESERVE_0",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What is the weather in San Francisco?"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_1",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What is the weather in San Francisco?"
+        },
+        {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "call_lR2r0rpfqNX11jukJvEUdByv",
+              "name": "get_weather",
+              "input": "{\"location\":\"San Francisco\"}"
+            }
+          }
+        },
+        {
+          "content": "{\n  location: San Francisco,\n  temperature: 72,\n  weather: cloudy,\n  rain_prediction: 0.89\n}",
+          "role": "user",
+          "options": {
+            "toolResult": {
+              "result": {
+                "location": "San Francisco",
+                "temperature": 72,
+                "weather": "cloudy",
+                "rain_prediction": 0.89
+              },
+              "isError": false,
+              "id": "call_lR2r0rpfqNX11jukJvEUdByv"
+            }
+          }
+        }
+      ]
+    }
+  ],
+  "llmEventEnd": [
+    {
+      "id": "PRESERVE_0",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "call_lR2r0rpfqNX11jukJvEUdByv",
+              "name": "get_weather",
+              "input": "{\"location\":\"San Francisco\"}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "The weather in San Francisco is currently cloudy with a temperature of 72°F. There is a 89% chance of rain.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    }
+  ],
+  "llmEventStream": []
+}

packages/core/e2e/node/snapshot/agent_with_object_retriever.snap

@@ -0,0 +1,103 @@
+{
+  "llmEventStart": [
+    {
+      "id": "PRESERVE_0",
+      "messages": [
+        {
+          "content": "Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
+          "role": "system"
+        },
+        {
+          "role": "user",
+          "content": "What's the summary of Alex? Does he live in Brazil based on the brief information? Return yes or no."
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_1",
+      "messages": [
+        {
+          "content": "Context information is below.\n---------------------\nAlex is a male. What's very important, Alex is not in the Brazil.\n---------------------\nGiven the context information and not prior knowledge, answer the query.\nQuery: Alex\nAnswer:",
+          "role": "user"
+        }
+      ]
+    },
+    {
+      "id": "PRESERVE_2",
+      "messages": [
+        {
+          "content": "Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
+          "role": "system"
+        },
+        {
+          "role": "user",
+          "content": "What's the summary of Alex? Does he live in Brazil based on the brief information? Return yes or no."
+        },
+        {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "call_EVThrsiOylO0p6ZmGdsA31x9",
+              "name": "summary_tool",
+              "input": "{\"query\": \"Alex\"}"
+            }
+          }
+        },
+        {
+          "content": "Alex is not in Brazil.",
+          "role": "user",
+          "options": {
+            "toolResult": {
+              "result": "Alex is not in Brazil.",
+              "isError": false,
+              "id": "call_EVThrsiOylO0p6ZmGdsA31x9"
+            }
+          }
+        }
+      ]
+    }
+  ],
+  "llmEventEnd": [
+    {
+      "id": "PRESERVE_0",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "",
+          "role": "assistant",
+          "options": {
+            "toolCall": {
+              "id": "call_EVThrsiOylO0p6ZmGdsA31x9",
+              "name": "summary_tool",
+              "input": "{\"query\": \"Alex\"}"
+            }
+          }
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_1",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "Alex is not in Brazil.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    },
+    {
+      "id": "PRESERVE_2",
+      "response": {
+        "raw": null,
+        "message": {
+          "content": "No, Alex does not live in Brazil based on the brief information available.",
+          "role": "assistant",
+          "options": {}
+        }
+      }
+    }
+  ],
+  "llmEventStream": []
+}