Create serious-jokes-whisper.md

.changeset/fuzzy-snails-applaud.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-feat: add filtering of metadata to PGVectorStore

.changeset/olive-wolves-own.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-feat(reranker): cohere reranker

.changeset/serious-jokes-whisper.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+revert: export conditions not working with moduleResolution `node`

.changeset/tame-ways-applaud.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-feat: use batching in vector store index

.changeset/ten-pears-study.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-update fastapi for CVE-2024-24762

.changeset/violet-cows-fix.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-Add reader for LlamaParse

.devcontainer/devcontainer.json

@@ -4,6 +4,6 @@
     "ghcr.io/devcontainers/features/node:1": {},
     "ghcr.io/devcontainers-contrib/features/turborepo-npm:1": {},
     "ghcr.io/devcontainers-contrib/features/typescript:2": {},
-    "ghcr.io/devcontainers-contrib/features/pnpm:2": {}
-  }
+    "ghcr.io/devcontainers-contrib/features/pnpm:2": {},
+  },
 }

.gitattributes

@@ -1,2 +0,0 @@
-examples/readers/data/** binary
-examples/data/** binary

.github/workflows/e2e.yml

@@ -49,12 +49,6 @@ jobs:
       - name: Build create-llama
         run: pnpm run build
         working-directory: ./packages/create-llama
-      - name: Pack
-        run: pnpm pack --pack-destination ./output
-        working-directory: ./packages/create-llama
-      - name: Extract Pack
-        run: tar -xvzf ./output/*.tgz -C ./output
-        working-directory: ./packages/create-llama
       - name: Run Playwright tests
         run: pnpm exec playwright test
         env:

.github/workflows/test.yml

@@ -32,41 +32,7 @@ jobs:
       - name: Install dependencies
         run: pnpm install
       - name: Build
-        run: pnpm run build --filter llamaindex
+        run: pnpm run build
+        working-directory: ./packages/core
       - name: Run Type Check
         run: pnpm run type-check
-      - name: Run Circular Dependency Check
-        run: pnpm run circular-check
-        working-directory: ./packages/core
-      - uses: actions/upload-artifact@v3
-        if: failure()
-        with:
-          name: typecheck-build-dist
-          path: ./packages/core/dist
-          if-no-files-found: error
-  typecheck-examples:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v2
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: ".nvmrc"
-          cache: "pnpm"
-      - name: Install dependencies
-        run: pnpm install
-      - name: Build
-        run: pnpm run build --filter llamaindex
-      - name: Copy examples
-        run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/core
-      - name: Install llamaindex
-        run: npm add ${{ runner.temp }}/*.tgz
-        working-directory: ${{ runner.temp }}/examples
-      - name: Run Type Check
-        run: npx tsc --project ./tsconfig.json
-        working-directory: ${{ runner.temp }}/examples

.husky/pre-commit

@@ -1,3 +1,6 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
 pnpm format
 pnpm lint
 npx lint-staged

.husky/pre-push

@@ -1 +1,4 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
 pnpm test

.npmrc

@@ -1,2 +1 @@
 auto-install-peers = true
-enable-pre-post-scripts = true

.vscode/settings.json

@@ -8,11 +8,5 @@
   "jest.rootPath": "./packages/core",
   "[python]": {
     "editor.defaultFormatter": "ms-python.black-formatter"
-  },
-  "[jsonc]": {
-    "editor.defaultFormatter": "esbenp.prettier-vscode"
-  },
-  "[json]": {
-    "editor.defaultFormatter": "esbenp.prettier-vscode"
   }
 }

CONTRIBUTING.md

@@ -78,15 +78,3 @@ pnpm start
 That should start a webserver which will serve the docs on https://localhost:3000
 
 Any changes you make should be reflected in the browser. If you need to regenerate the API docs and find that your TSDoc isn't getting the updates, feel free to remove apps/docs/api. It will automatically regenerate itself when you run pnpm start again.
-
-## Publishing
-
-To publish a new version of the library, run
-
-```shell
-pnpm new-llamaindex
-pnpm new-create-llama
-pnpm release
-git push # push to the main branch
-git push --tags
-```

README.md

@@ -70,7 +70,7 @@ main();
 Then you can run it using
 
 ```bash
-pnpm dlx ts-node example.ts
+pnpx ts-node example.ts
 ```
 
 ## Playground
@@ -105,9 +105,6 @@ export const runtime = "nodejs"; // default
 // next.config.js
 /** @type {import('next').NextConfig} */
 const nextConfig = {
-  experimental: {
-    serverComponentsExternalPackages: ["pdf2json"],
-  },
   webpack: (config) => {
     config.resolve.alias = {
       ...config.resolve.alias,

apps/docs/CHANGELOG.md

@@ -1,13 +0,0 @@
-# docs
-
-## 0.0.2
-
-### Patch Changes
-
-- 0f64084: docs: update API references
-
-## 0.0.1
-
-### Patch Changes
-
-- 3154f52: chore: add qdrant readme

apps/docs/docs/examples/agent.mdx

@@ -1,85 +0,0 @@
-# Agents
-
-A built-in agent that can take decisions and reasoning based on the tools provided to it.
-
-## OpenAI Agent
-
-```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 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");
-});
-```

apps/docs/docs/getting_started/concepts.md

@@ -32,12 +32,12 @@ LlamaIndex.TS help you prepare the knowledge base with a suite of data connector
 
 ![](../_static/concepts/indexing.jpg)
 
-[**Data Loaders**](../modules/data_loader.md):
+[**Data Loaders**](./modules/high_level/data_loader.md):
 A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).
 
-[**Documents / Nodes**](../modules/documents_and_nodes/index.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
+[**Documents / Nodes**](./modules/high_level/documents_and_nodes.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
 
-[**Data Indexes**](../modules/data_index.md):
+[**Data Indexes**](./modules/high_level/data_index.md):
 Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.
 
 Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and stores your data in-memory or to disk.
@@ -60,19 +60,19 @@ These building blocks can be customized to reflect ranking preferences, as well
 
 #### Building Blocks
 
-[**Retrievers**](../modules/retriever.md):
+[**Retrievers**](./modules/low_level/retriever.md):
 A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
 The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
 
-[**Response Synthesizers**](../modules/response_synthesizer.md):
+[**Response Synthesizers**](./modules/low_level/response_synthesizer.md):
 A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.
 
 #### Pipelines
 
-[**Query Engines**](../modules/query_engines):
+[**Query Engines**](./modules/high_level/query_engine.md):
 A query engine is an end-to-end pipeline that allow you to ask question over your data.
 It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.
 
-[**Chat Engines**](../modules/chat_engine.md):
+[**Chat Engines**](./modules/high_level/chat_engine.md):
 A chat engine is an end-to-end pipeline for having a conversation with your data
 (multiple back-and-forth instead of a single question & answer).

apps/docs/docs/getting_started/installation.mdx

@@ -58,6 +58,6 @@ Our examples use OpenAI by default. You'll need to set up your Open AI key like
 export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
 ```
 
-If you want to have it automatically loaded every time, add it to your `.zshrc/.bashrc`.
+If you want to have it automatically loaded every time, add it to your .zshrc/.bashrc.
 
 WARNING: do not check in your OpenAI key into version control.

apps/docs/docs/getting_started/starter.md

@@ -36,9 +36,9 @@ async function main() {
 
   // Query the index
   const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
-    query: "What did the author do in college?",
-  });
+  const response = await queryEngine.query(
+    "What did the author do in college?",
+  );
 
   // Output response
   console.log(response.toString());

apps/docs/docs/introduction.md

@@ -37,9 +37,9 @@ For more complex applications, our lower-level APIs allow advanced users to cust
 
 `npm install llamaindex`
 
-Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter.md) to build your first application.
+Our documentation includes [Installation Instructions](./installation.mdx) and a [Starter Tutorial](./starter.md) to build your first application.
 
-Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our Examples section on the sidebar.
+Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our [End-to-End Tutorials](./end_to_end.md).
 
 ## 🗺️ Ecosystem
 

apps/docs/docs/modules/agent/_category_.yml

@@ -1 +0,0 @@
-label: "Agents"

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

@@ -1,14 +0,0 @@
-# Agents
-
-An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. The key agent components can include, but are not limited to:
-
-- Breaking down a complex question into smaller ones
-- Choosing an external Tool to use + coming up with parameters for calling the Tool
-- Planning out a set of tasks
-- Storing previously completed tasks in a memory module
-
-## Getting Started
-
-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)

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

@@ -1,183 +0,0 @@
-# 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 worker = new OpenAIAgent({
-  tools: [sumFunctionTool, divideFunctionTool],
-  verbose: true,
-});
-```
-
-## Chat with the agent
-
-Now we can chat with the agent.
-
-```ts
-const response = await worker.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

@@ -1,128 +0,0 @@
-# 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/chat_engine.md

@@ -25,5 +25,5 @@ for await (const chunk of stream) {
 
 ## Api References
 
-- [ContextChatEngine](../api/classes/ContextChatEngine.md)
-- [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)
+- [ContextChatEngine](../../api/classes/ContextChatEngine.md)
+- [CondenseQuestionChatEngine](../../api/classes/ContextChatEngine.md)

apps/docs/docs/modules/data_index.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 2
 ---
 
 # Index
@@ -19,5 +19,5 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 
 ## API Reference
 
-- [SummaryIndex](../api/classes/SummaryIndex.md)
-- [VectorStoreIndex](../api/classes/VectorStoreIndex.md)
+- [SummaryIndex](../../api/classes/SummaryIndex.md)
+- [VectorStoreIndex](../../api/classes/VectorStoreIndex.md)

apps/docs/docs/modules/data_loader.md

@@ -0,0 +1,17 @@
+---
+sidebar_position: 1
+---
+
+# Reader / Loader
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class. Currently, `.txt`, `.pdf`, `.csv`, `.md` and `.docx` files are supported, with more planned in the future!
+
+```typescript
+import { SimpleDirectoryReader } from "llamaindex";
+
+documents = new SimpleDirectoryReader().loadData("./data");
+```
+
+## API Reference
+
+- [SimpleDirectoryReader](../../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/data_loader.mdx

@@ -1,35 +0,0 @@
----
-sidebar_position: 4
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/readers/src/simple-directory-reader";
-import CodeSource2 from "!raw-loader!../../../../examples/readers/src/custom-simple-directory-reader";
-
-# Loader
-
-Before you can start indexing your documents, you need to load them into memory.
-
-### SimpleDirectoryReader
-
-[![Open in StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
-
-LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
-
-It is a simple reader that reads all files from a directory and its subdirectories.
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>
-
-Currently, it supports reading `.csv`, `.docx`, `.html`, `.md` and `.pdf` files,
-but support for other file types is planned.
-
-Also, you can provide a `defaultReader` as a fallback for files with unsupported extensions.
-Or pass new readers for `fileExtToReader` to support more file types.
-
-<CodeBlock language="ts" showLineNumbers metastring="{8-12,17-21}">
-  {CodeSource2}
-</CodeBlock>
-
-## API Reference
-
-- [SimpleDirectoryReader](../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/documents_and_nodes.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 1
+sidebar_position: 0
 ---
 
 # Documents and Nodes
@@ -14,5 +14,5 @@ document = new Document({ text: "text", metadata: { key: "val" } });
 
 ## API Reference
 
-- [Document](../api/classes/Document.md)
-- [TextNode](../api/classes/TextNode.md)
+- [Document](../../api/classes/Document.md)
+- [TextNode](../../api/classes/TextNode.md)

apps/docs/docs/modules/documents_and_nodes/_category_.yml

@@ -1,2 +0,0 @@
-label: "Document / Nodes"
-position: 0

apps/docs/docs/modules/documents_and_nodes/metadata_extraction.md

@@ -1,45 +0,0 @@
-# Metadata Extraction Usage Pattern
-
-You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
-
-Our metadata extractor modules include the following "feature extractors":
-
-- `SummaryExtractor` - automatically extracts a summary over a set of Nodes
-- `QuestionsAnsweredExtractor` - extracts a set of questions that each Node can answer
-- `TitleExtractor` - extracts a title over the context of each Node by document and combine them
-- `KeywordExtractor` - extracts keywords over the context of each Node
-
-Then you can chain the `Metadata Extractors` with the `IngestionPipeline` to extract metadata from a set of documents.
-
-```ts
-import {
-  IngestionPipeline,
-  TitleExtractor,
-  QuestionsAnsweredExtractor,
-  Document,
-  OpenAI,
-} from "llamaindex";
-
-async function main() {
-  const pipeline = new IngestionPipeline({
-    transformations: [
-      new TitleExtractor(),
-      new QuestionsAnsweredExtractor({
-        questions: 5,
-      }),
-    ],
-  });
-
-  const nodes = await pipeline.run({
-    documents: [
-      new Document({ text: "I am 10 years old. John is 20 years old." }),
-    ],
-  });
-
-  for (const node of nodes) {
-    console.log(node.metadata);
-  }
-}
-
-main().then(() => console.log("done"));
-```

apps/docs/docs/modules/embedding.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 1
 ---
 
 # Embedding
@@ -18,5 +18,5 @@ const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
 
 ## API Reference
 
-- [OpenAIEmbedding](../api/classes/OpenAIEmbedding.md)
-- [ServiceContext](../api/interfaces//ServiceContext.md)
+- [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)
+- [ServiceContext](../../api/interfaces/ServiceContext.md)

apps/docs/docs/modules/index.md

@@ -0,0 +1,31 @@
+# Core Modules
+
+LlamaIndex.TS offers several core modules, seperated into high-level modules for quickly getting started, and low-level modules for customizing key components as you need.
+
+## High-Level Modules
+
+- [**Document**](./high_level/documents_and_nodes.md): A document represents a text file, PDF file or other contiguous piece of data.
+
+- [**Node**](./high_level/documents_and_nodes.md): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
+
+- [**Reader/Loader**](./high_level/data_loader.md): A reader or loader is something that takes in a document in the real world and transforms into a Document class that can then be used in your Index and queries. We currently support plain text files and PDFs with many many more to come.
+
+- [**Indexes**](./high_level/data_index.md): indexes store the Nodes and the embeddings of those nodes.
+
+- [**QueryEngine**](./high_level/query_engine.md): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected nodes from your Index to give the LLM the context it needs to answer your query.
+
+- [**ChatEngine**](./high_level/chat_engine.md): A ChatEngine helps you build a chatbot that will interact with your Indexes.
+
+## Low Level Module
+
+- [**LLM**](./low_level/llm.md): The LLM class is a unified interface over a large language model provider such as OpenAI GPT-4, Anthropic Claude, or Meta LLaMA. You can subclass it to write a connector to your own large language model.
+
+- [**Embedding**](./low_level/embedding.md): An embedding is represented as a vector of floating point numbers. OpenAI's text-embedding-ada-002 is our default embedding model and each embedding it generates consists of 1,536 floating point numbers. Another popular embedding model is BERT which uses 768 floating point numbers to represent each Node. We provide a number of utilities to work with embeddings including 3 similarity calculation options and Maximum Marginal Relevance
+
+- [**TextSplitter/NodeParser**](./low_level/node_parser.md): Text splitting strategies are incredibly important to the overall efficacy of the embedding search. Currently, while we do have a default, there's no one size fits all solution. Depending on the source documents, you may want to use different splitting sizes and strategies. Currently we support spliltting by fixed size, splitting by fixed size with overlapping sections, splitting by sentence, and splitting by paragraph. The text splitter is used by the NodeParser when splitting `Document`s into `Node`s.
+
+- [**Retriever**](./low_level/retriever.md): The Retriever is what actually chooses the Nodes to retrieve from the index. Here, you may wish to try retrieving more or fewer Nodes per query, changing your similarity function, or creating your own retriever for each individual use case in your application. For example, you may wish to have a separate retriever for code content vs. text content.
+
+- [**ResponseSynthesizer**](./low_level/response_synthesizer.md): The ResponseSynthesizer is responsible for taking a query string, and using a list of `Node`s to generate a response. This can take many forms, like iterating over all the context and refining an answer, or building a tree of summaries and returning the root summary.
+
+- [**Storage**](./low_level/storage.md): At some point you're going to want to store your indexes, data and vectors instead of re-running the embedding models every time. IndexStore, DocStore, VectorStore, and KVStore are abstractions that let you do that. Combined, they form the StorageContext. Currently, we allow you to persist your embeddings in files on the filesystem (or a virtual in memory file system), but we are also actively adding integrations to Vector Databases.

apps/docs/docs/modules/ingestion_pipeline/_category_.yml

@@ -1,2 +0,0 @@
-label: "Ingestion Pipeline"
-position: 2

apps/docs/docs/modules/ingestion_pipeline/index.md

@@ -1,99 +0,0 @@
-# Ingestion Pipeline
-
-An `IngestionPipeline` uses a concept of `Transformations` that are applied to input data.
-These `Transformations` are applied to your input data, and the resulting nodes are either returned or inserted into a vector database (if given).
-
-## Usage Pattern
-
-The simplest usage is to instantiate an IngestionPipeline like so:
-
-```ts
-import fs from "node:fs/promises";
-
-import {
-  Document,
-  IngestionPipeline,
-  MetadataMode,
-  OpenAIEmbedding,
-  TitleExtractor,
-  SimpleNodeParser,
-} from "llamaindex";
-
-async function main() {
-  // Load essay from abramov.txt in Node
-  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 pipeline = new IngestionPipeline({
-    transformations: [
-      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
-      new TitleExtractor(),
-      new OpenAIEmbedding(),
-    ],
-  });
-
-  // run the pipeline
-  const nodes = await pipeline.run({ documents: [document] });
-
-  // print out the result of the pipeline run
-  for (const node of nodes) {
-    console.log(node.getContent(MetadataMode.NONE));
-  }
-}
-
-main().catch(console.error);
-```
-
-## Connecting to Vector Databases
-
-When running an ingestion pipeline, you can also chose to automatically insert the resulting nodes into a remote vector store.
-
-Then, you can construct an index from that vector store later on.
-
-```ts
-import fs from "node:fs/promises";
-
-import {
-  Document,
-  IngestionPipeline,
-  MetadataMode,
-  OpenAIEmbedding,
-  TitleExtractor,
-  SimpleNodeParser,
-  QdrantVectorStore,
-  VectorStoreIndex,
-} from "llamaindex";
-
-async function main() {
-  // Load essay from abramov.txt in Node
-  const path = "node_modules/llamaindex/examples/abramov.txt";
-
-  const essay = await fs.readFile(path, "utf-8");
-
-  const vectorStore = new QdrantVectorStore({
-    host: "http://localhost:6333",
-  });
-
-  // Create Document object with essay
-  const document = new Document({ text: essay, id_: path });
-  const pipeline = new IngestionPipeline({
-    transformations: [
-      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
-      new TitleExtractor(),
-      new OpenAIEmbedding(),
-    ],
-    vectorStore,
-  });
-
-  // run the pipeline
-  const nodes = await pipeline.run({ documents: [document] });
-
-  // create an index
-  const index = VectorStoreIndex.fromVectorStore(vectorStore);
-}
-
-main().catch(console.error);
-```

apps/docs/docs/modules/ingestion_pipeline/transformations.md

@@ -1,77 +0,0 @@
-# Transformations
-
-A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
-
-Currently, the following components are Transformation objects:
-
-- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
-- [MetadataExtractor](../documents_and_nodes/metadata_extraction.md)
-- Embeddings
-
-## Usage Pattern
-
-While transformations are best used with with an IngestionPipeline, they can also be used directly.
-
-```ts
-import { SimpleNodeParser, TitleExtractor, Document } from "llamaindex";
-
-async function main() {
-  let nodes = new SimpleNodeParser().getNodesFromDocuments([
-    new Document({ text: "I am 10 years old. John is 20 years old." }),
-  ]);
-
-  const titleExtractor = new TitleExtractor();
-
-  nodes = await titleExtractor.transform(nodes);
-
-  for (const node of nodes) {
-    console.log(node.getContent(MetadataMode.NONE));
-  }
-}
-
-main().catch(console.error);
-```
-
-## Custom Transformations
-
-You can implement any transformation yourself by implementing the `TransformerComponent`.
-
-The following custom transformation will remove any special characters or punctutaion in text.
-
-```ts
-import { TransformerComponent, Node } from "llamaindex";
-
-class RemoveSpecialCharacters extends TransformerComponent {
-  async transform(nodes: Node[]): Promise<Node[]> {
-    for (const node of nodes) {
-      node.text = node.text.replace(/[^\w\s]/gi, "");
-    }
-
-    return nodes;
-  }
-}
-```
-
-These can then be used directly or in any IngestionPipeline.
-
-```ts
-import { IngestionPipeline, Document } from "llamaindex";
-
-async function main() {
-  const pipeline = new IngestionPipeline({
-    transformations: [new RemoveSpecialCharacters()],
-  });
-
-  const nodes = await pipeline.run({
-    documents: [
-      new Document({ text: "I am 10 years old. John is 20 years old." }),
-    ],
-  });
-
-  for (const node of nodes) {
-    console.log(node.getContent(MetadataMode.NONE));
-  }
-}
-
-main().catch(console.error);
-```

apps/docs/docs/modules/llm.md

@@ -0,0 +1,22 @@
+---
+sidebar_position: 0
+---
+
+# LLM
+
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
+
+The LLM can be explicitly set in the `ServiceContext` object.
+
+```typescript
+import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+
+const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+```
+
+## API Reference
+
+- [OpenAI](../../api/classes/OpenAI.md)
+- [ServiceContext](../../api/interfaces/ServiceContext.md)

apps/docs/docs/modules/llms/_category_.yml

@@ -1,2 +0,0 @@
-label: "LLMs"
-position: 3

apps/docs/docs/modules/llms/available_llms/_category_.yml

@@ -1 +0,0 @@
-label: "Available LLMs"

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

@@ -1,80 +0,0 @@
-# Anthropic
-
-## Usage
-
-```ts
-import { Anthropic, serviceContextFromDefaults } from "llamaindex";
-
-const anthropicLLM = new Anthropic({
-  apiKey: "<YOUR_API_KEY>",
-});
-
-const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the Anthropic LLM
-  const anthropicLLM = new Anthropic({
-    apiKey: "<YOUR_API_KEY>",
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/azure.md

@@ -1,88 +0,0 @@
-# Azure OpenAI
-
-To use Azure OpenAI, you only need to set a few environment variables together with the `OpenAI` class.
-
-For example:
-
-## Environment Variables
-
-```
-export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
-export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
-export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
-```
-
-## Usage
-
-```ts
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
-
-const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
-
-const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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

@@ -1,97 +0,0 @@
-# LLama2
-
-## Usage
-
-```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
-
-const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
-
-const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
-```
-
-## Usage with Replication
-
-```ts
-import {
-  Ollama,
-  ReplicateSession,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-const replicateSession = new ReplicateSession({
-  replicateKey,
-});
-
-const llama2LLM = new LlamaDeuce({
-  chatStrategy: DeuceChatStrategy.META,
-  replicateSession,
-});
-
-const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/mistral.md

@@ -1,79 +0,0 @@
-# Mistral
-
-## Usage
-
-```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
-
-const mistralLLM = new MistralAI({
-  model: "mistral-tiny",
-  apiKey: "<YOUR_API_KEY>",
-});
-
-const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const mistralLLM = new MistralAI({ model: "mistral-tiny" });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/ollama.md

@@ -1,76 +0,0 @@
-# Ollama
-
-## Usage
-
-```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
-
-const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
-
-const serviceContext = serviceContextFromDefaults({ llm: ollamaLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: ollamaLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/openai.md

@@ -1,80 +0,0 @@
-# OpenAI
-
-```ts
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
-
-const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
-
-const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
-```
-
-You can setup the apiKey on the environment variables, like:
-
-```bash
-export OPENAI_API_KEY="<YOUR_API_KEY>"
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/portkey.md

@@ -1,80 +0,0 @@
-# Portkey LLM
-
-## Usage
-
-```ts
-import { Portkey, serviceContextFromDefaults } from "llamaindex";
-
-const portkeyLLM = new Portkey({
-  apiKey: "<YOUR_API_KEY>",
-});
-
-const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const portkeyLLM = new Portkey({
-    apiKey: "<YOUR_API_KEY>",
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/together.md

@@ -1,80 +0,0 @@
-# Together LLM
-
-## Usage
-
-```ts
-import { TogetherLLM, serviceContextFromDefaults } from "llamaindex";
-
-const togetherLLM = new TogetherLLM({
-  apiKey: "<YOUR_API_KEY>",
-});
-
-const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
-```
-
-## 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], {
-  serviceContext,
-});
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const togetherLLM = new TogetherLLM({
-    apiKey: "<YOUR_API_KEY>",
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
-
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // 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/index.md

@@ -1,34 +0,0 @@
----
-sidebar_position: 3
----
-
-# Large Language Models (LLMs)
-
-The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
-
-The LLM can be explicitly set in the `ServiceContext` object.
-
-```typescript
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
-
-const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
-```
-
-## Azure OpenAI
-
-To use Azure OpenAI, you only need to set a few environment variables.
-
-For example:
-
-```
-export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
-export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
-export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
-```
-
-## API Reference
-
-- [OpenAI](../api/classes/OpenAI.md)
-- [ServiceContext](../api/interfaces//ServiceContext.md)

apps/docs/docs/modules/node_parser.md

@@ -1,10 +1,10 @@
 ---
-sidebar_position: 4
+sidebar_position: 3
 ---
 
 # NodeParser
 
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
+The `NodeParser` in LlamaIndex is responbile for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
 
 ```typescript
 import { Document, SimpleNodeParser } from "llamaindex";
@@ -29,5 +29,5 @@ const textSplits = splitter.splitText("Hello World");
 
 ## API Reference
 
-- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
-- [SentenceSplitter](../api/classes/SentenceSplitter.md)
+- [SimpleNodeParser](../../api/classes/SimpleNodeParser.md)
+- [SentenceSplitter](../../api/classes/SentenceSplitter.md)

apps/docs/docs/modules/node_postprocessors/_category_.yml

@@ -1,2 +0,0 @@
-label: "Node Postprocessors"
-position: 3

apps/docs/docs/modules/node_postprocessors/cohere_reranker.md

@@ -1,71 +0,0 @@
-# Cohere Reranker
-
-The Cohere Reranker is a postprocessor that uses the Cohere 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 [Cohere](https://cohere.ai/). Once you have your API key you can import the necessary modules and create a new instance of the `CohereRerank` class.
-
-```ts
-import {
-  CohereRerank,
-  Document,
-  OpenAI,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} 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" });
-
-const serviceContext = serviceContextFromDefaults({
-  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-});
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
-```
-
-## 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 CohereRerank class
-
-Then you can create a new instance of the `CohereRerank` class and pass in your API key and the number of results you want to return.
-
-```ts
-const nodePostprocessor = new CohereRerank({
-  apiKey: "<COHERE_API_KEY>",
-  topN: 4,
-});
-```
-
-## 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/modules/node_postprocessors/index.md

@@ -1,110 +0,0 @@
-# Node Postprocessors
-
-## Concept
-
-Node postprocessors are a set of modules that take a set of nodes, and apply some kind of transformation or filtering before returning them.
-
-In LlamaIndex, node postprocessors are most commonly applied within a query engine, after the node retrieval step and before the response synthesis step.
-
-LlamaIndex offers several node postprocessors for immediate use, while also providing a simple API for adding your own custom postprocessors.
-
-## Usage Pattern
-
-An example of using a node postprocessors is below:
-
-```ts
-import {
-  Node,
-  NodeWithScore,
-  SimilarityPostprocessor,
-  CohereRerank,
-} from "llamaindex";
-
-const nodes: NodeWithScore[] = [
-  {
-    node: new TextNode({ text: "hello world" }),
-    score: 0.8,
-  },
-  {
-    node: new TextNode({ text: "LlamaIndex is the best" }),
-    score: 0.6,
-  },
-];
-
-// similarity postprocessor: filter nodes below 0.75 similarity score
-const processor = new SimilarityPostprocessor({
-  similarityCutoff: 0.7,
-});
-
-const filteredNodes = processor.postprocessNodes(nodes);
-
-// cohere rerank: rerank nodes given query using trained model
-const reranker = new CohereRerank({
-  apiKey: "<COHERE_API_KEY>",
-  topN: 2,
-});
-
-const rerankedNodes = await reranker.postprocessNodes(nodes, "<user_query>");
-
-console.log(filteredNodes, rerankedNodes);
-```
-
-Now you can use the `filteredNodes` and `rerankedNodes` in your application.
-
-## Using Node Postprocessors in LlamaIndex
-
-Most commonly, node-postprocessors will be used in a query engine, where they are applied to the nodes returned from a retriever, and before the response synthesis step.
-
-### Using Node Postprocessors in a Query Engine
-
-```ts
-import { Node, NodeWithScore, SimilarityPostprocessor, CohereRerank } from "llamaindex";
-
-const nodes: NodeWithScore[] = [
-  {
-    node: new TextNode({ text: "hello world" }),
-    score: 0.8,
-  },
-  {
-    node: new TextNode({ text: "LlamaIndex is the best" }),
-    score: 0.6,
-  }
-];
-
-// cohere rerank: rerank nodes given query using trained model
-const reranker = new CohereRerank({
-  apiKey: "<COHERE_API_KEY>,
-  topN: 2,
-})
-
-const document = new Document({ text: "essay", id_: "essay" });
-
-const serviceContext = serviceContextFromDefaults({
-  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-});
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
-
-const queryEngine = index.asQueryEngine({
-  nodePostprocessors: [processor, reranker],
-});
-
-// all node post-processors will be applied during each query
-const response = await queryEngine.query("<user_query>");
-```
-
-### Using with retrieved nodes
-
-```ts
-import { SimilarityPostprocessor } from "llamaindex";
-
-nodes = await index.asRetriever().retrieve("test query str");
-
-const processor = new SimilarityPostprocessor({
-  similarityCutoff: 0.7,
-});
-
-const filteredNodes = processor.postprocessNodes(nodes);
-```

apps/docs/docs/modules/query_engine.md

@@ -1,3 +1,7 @@
+---
+sidebar_position: 3
+---
+
 # QueryEngine
 
 A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.

apps/docs/docs/modules/query_engines/_category_.yml

@@ -1,2 +0,0 @@
-label: "Query Engines"
-position: 2

apps/docs/docs/modules/query_engines/metadata_filtering.md

@@ -1,152 +0,0 @@
-# Metadata Filtering
-
-Metadata filtering is a way to filter the documents that are returned by a query based on the metadata associated with the documents. This is useful when you want to filter the documents based on some metadata that is not part of the document text.
-
-You can also check our multi-tenancy blog post to see how metadata filtering can be used in a multi-tenant environment. [https://blog.llamaindex.ai/building-multi-tenancy-rag-system-with-llamaindex-0d6ab4e0c44b] (the article uses the Python version of LlamaIndex, but the concepts are the same).
-
-## Setup
-
-Firstly if you haven't already, you need to install the `llamaindex` package:
-
-```bash
-pnpm i llamaindex
-```
-
-Then you can import the necessary modules from `llamaindex`:
-
-```ts
-import {
-  ChromaVectorStore,
-  Document,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
-
-const collectionName = "dog_colors";
-```
-
-## Creating documents with metadata
-
-You can create documents with metadata using the `Document` class:
-
-```ts
-const docs = [
-  new Document({
-    text: "The dog is brown",
-    metadata: {
-      color: "brown",
-      dogId: "1",
-    },
-  }),
-  new Document({
-    text: "The dog is red",
-    metadata: {
-      color: "red",
-      dogId: "2",
-    },
-  }),
-];
-```
-
-## Creating a ChromaDB vector store
-
-You can create a `ChromaVectorStore` to store the documents:
-
-```ts
-const chromaVS = new ChromaVectorStore({ collectionName });
-const serviceContext = await storageContextFromDefaults({
-  vectorStore: chromaVS,
-});
-
-const index = await VectorStoreIndex.fromDocuments(docs, {
-  storageContext: serviceContext,
-});
-```
-
-## Querying the index with metadata filtering
-
-Now you can query the index with metadata filtering using the `preFilters` option:
-
-```ts
-const queryEngine = index.asQueryEngine({
-  preFilters: {
-    filters: [
-      {
-        key: "dogId",
-        value: "2",
-        filterType: "ExactMatch",
-      },
-    ],
-  },
-});
-
-const response = await queryEngine.query({
-  query: "What is the color of the dog?",
-});
-
-console.log(response.toString());
-```
-
-## Full Code
-
-```ts
-import {
-  ChromaVectorStore,
-  Document,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
-
-const collectionName = "dog_colors";
-
-async function main() {
-  try {
-    const docs = [
-      new Document({
-        text: "The dog is brown",
-        metadata: {
-          color: "brown",
-          dogId: "1",
-        },
-      }),
-      new Document({
-        text: "The dog is red",
-        metadata: {
-          color: "red",
-          dogId: "2",
-        },
-      }),
-    ];
-
-    console.log("Creating ChromaDB vector store");
-    const chromaVS = new ChromaVectorStore({ collectionName });
-    const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
-
-    console.log("Embedding documents and adding to index");
-    const index = await VectorStoreIndex.fromDocuments(docs, {
-      storageContext: ctx,
-    });
-
-    console.log("Querying index");
-    const queryEngine = index.asQueryEngine({
-      preFilters: {
-        filters: [
-          {
-            key: "dogId",
-            value: "2",
-            filterType: "ExactMatch",
-          },
-        ],
-      },
-    });
-    const response = await queryEngine.query({
-      query: "What is the color of the dog?",
-    });
-    console.log(response.toString());
-  } catch (e) {
-    console.error(e);
-  }
-}
-
-main();
-```

apps/docs/docs/modules/query_engines/router_query_engine.md

@@ -1,189 +0,0 @@
-# Router Query Engine
-
-In this tutorial, we define a custom router query engine that selects one out of several candidate query engines to execute a query.
-
-## Setup
-
-First, we need to install import the necessary modules from `llamaindex`:
-
-```bash
-pnpm i lamaindex
-```
-
-```ts
-import {
-  OpenAI,
-  RouterQueryEngine,
-  SimpleDirectoryReader,
-  SimpleNodeParser,
-  SummaryIndex,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-```
-
-## Loading Data
-
-Next, we need to load some data. We will use the `SimpleDirectoryReader` to load documents from a directory:
-
-```ts
-const documents = await new SimpleDirectoryReader().loadData({
-  directoryPath: "node_modules/llamaindex/examples",
-});
-```
-
-## Service Context
-
-Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `ServiceContext` to define the rules (eg. LLM API key, chunk size, etc.):
-
-```ts
-const nodeParser = new SimpleNodeParser({
-  chunkSize: 1024,
-});
-
-const serviceContext = serviceContextFromDefaults({
-  nodeParser,
-  llm: new OpenAI(),
-});
-```
-
-## Creating Indices
-
-Next, we need to create some indices. We will create a `VectorStoreIndex` and a `SummaryIndex`:
-
-```ts
-const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-  serviceContext,
-});
-
-const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-  serviceContext,
-});
-```
-
-## Creating Query Engines
-
-Next, we need to create some query engines. We will create a `VectorStoreQueryEngine` and a `SummaryQueryEngine`:
-
-```ts
-const vectorQueryEngine = vectorIndex.asQueryEngine();
-const summaryQueryEngine = summaryIndex.asQueryEngine();
-```
-
-## Creating a Router Query Engine
-
-Next, we need to create a router query engine. We will use the `RouterQueryEngine` to create a router query engine:
-
-We're defining two query engines, one for summarization and one for retrieving specific context. The router query engine will select the most appropriate query engine based on the query.
-
-```ts
-const queryEngine = RouterQueryEngine.fromDefaults({
-  queryEngineTools: [
-    {
-      queryEngine: vectorQueryEngine,
-      description: "Useful for summarization questions related to Abramov",
-    },
-    {
-      queryEngine: summaryQueryEngine,
-      description: "Useful for retrieving specific context from Abramov",
-    },
-  ],
-  serviceContext,
-});
-```
-
-## Querying the Router Query Engine
-
-Finally, we can query the router query engine:
-
-```ts
-const summaryResponse = await queryEngine.query({
-  query: "Give me a summary about his past experiences?",
-});
-
-console.log({
-  answer: summaryResponse.response,
-  metadata: summaryResponse?.metadata?.selectorResult,
-});
-```
-
-## Full code
-
-```ts
-import {
-  OpenAI,
-  RouterQueryEngine,
-  SimpleDirectoryReader,
-  SimpleNodeParser,
-  SummaryIndex,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Load documents from a directory
-  const documents = await new SimpleDirectoryReader().loadData({
-    directoryPath: "node_modules/llamaindex/examples",
-  });
-
-  // Parse the documents into nodes
-  const nodeParser = new SimpleNodeParser({
-    chunkSize: 1024,
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser,
-    llm: new OpenAI(),
-  });
-
-  // Create indices
-  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  // Create query engines
-  const vectorQueryEngine = vectorIndex.asQueryEngine();
-  const summaryQueryEngine = summaryIndex.asQueryEngine();
-
-  // Create a router query engine
-  const queryEngine = RouterQueryEngine.fromDefaults({
-    queryEngineTools: [
-      {
-        queryEngine: vectorQueryEngine,
-        description: "Useful for summarization questions related to Abramov",
-      },
-      {
-        queryEngine: summaryQueryEngine,
-        description: "Useful for retrieving specific context from Abramov",
-      },
-    ],
-    serviceContext,
-  });
-
-  // Query the router query engine
-  const summaryResponse = await queryEngine.query({
-    query: "Give me a summary about his past experiences?",
-  });
-
-  console.log({
-    answer: summaryResponse.response,
-    metadata: summaryResponse?.metadata?.selectorResult,
-  });
-
-  const specificResponse = await queryEngine.query({
-    query: "Tell me about abramov first job?",
-  });
-
-  console.log({
-    answer: specificResponse.response,
-    metadata: specificResponse.metadata.selectorResult,
-  });
-}
-
-main().then(() => console.log("Done"));
-```

apps/docs/docs/modules/response_synthesizer.md

@@ -57,8 +57,8 @@ for await (const chunk of stream) {
 
 ## API Reference
 
-- [ResponseSynthesizer](../api/classes/ResponseSynthesizer.md)
-- [Refine](../api/classes/Refine.md)
-- [CompactAndRefine](../api/classes/CompactAndRefine.md)
-- [TreeSummarize](../api/classes/TreeSummarize.md)
-- [SimpleResponseBuilder](../api/classes/SimpleResponseBuilder.md)
+- [ResponseSynthesizer](../../api/classes/ResponseSynthesizer.md)
+- [Refine](../../api/classes/Refine.md)
+- [CompactAndRefine](../../api/classes/CompactAndRefine.md)
+- [TreeSummarize](../../api/classes/TreeSummarize.md)
+- [SimpleResponseBuilder](../../api/classes/SimpleResponseBuilder.md)

apps/docs/docs/modules/retriever.md

@@ -16,6 +16,6 @@ const nodesWithScore = await retriever.retrieve("query string");
 
 ## API Reference
 
-- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md)
-- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md)
-- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md)
+- [SummaryIndexRetriever](../../api/classes/SummaryIndexRetriever.md)
+- [SummaryIndexLLMRetriever](../../api/classes/SummaryIndexLLMRetriever.md)
+- [VectorIndexRetriever](../../api/classes/VectorIndexRetriever.md)

apps/docs/docs/modules/storage.md

@@ -23,4 +23,4 @@ const index = await VectorStoreIndex.fromDocuments([document], {
 
 ## API Reference
 
-- [StorageContext](../api/interfaces//StorageContext.md)
+- [StorageContext](../../api/interfaces/StorageContext.md)

apps/docs/docs/modules/vector_stores/_category_.yml

@@ -1,2 +0,0 @@
-label: "Vector Stores"
-position: 1

apps/docs/docs/modules/vector_stores/qdrant.md

@@ -1,86 +0,0 @@
-# Qdrant Vector Store
-
-To run this example, you need to have a Qdrant instance running. You can run it with Docker:
-
-```bash
-docker pull qdrant/qdrant
-docker run -p 6333:6333 qdrant/qdrant
-```
-
-## Importing the modules
-
-```ts
-import fs from "node:fs/promises";
-import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
-```
-
-## Load the documents
-
-```ts
-const path = "node_modules/llamaindex/examples/abramov.txt";
-const essay = await fs.readFile(path, "utf-8");
-```
-
-## Setup Qdrant
-
-```ts
-const vectorStore = new QdrantVectorStore({
-  url: "http://localhost:6333",
-});
-```
-
-## Setup the index
-
-```ts
-const document = new Document({ text: essay, id_: path });
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  vectorStore,
-});
-```
-
-## Query the index
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const response = await queryEngine.query({
-  query: "What did the author do in college?",
-});
-
-// Output response
-console.log(response.toString());
-```
-
-## Full code
-
-```ts
-import fs from "node:fs/promises";
-import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
-
-async function main() {
-  const path = "node_modules/llamaindex/examples/abramov.txt";
-  const essay = await fs.readFile(path, "utf-8");
-
-  const vectorStore = new QdrantVectorStore({
-    url: "http://localhost:6333",
-  });
-
-  const document = new Document({ text: essay, id_: path });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    vectorStore,
-  });
-
-  const queryEngine = index.asQueryEngine();
-
-  const response = await queryEngine.query({
-    query: "What did the author do in college?",
-  });
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);
-```

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.2",
+  "version": "0.0.0",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -15,8 +15,8 @@
     "typecheck": "tsc"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.1.1",
-    "@docusaurus/remark-plugin-npm2yarn": "^3.1.1",
+    "@docusaurus/core": "^3.1.0",
+    "@docusaurus/remark-plugin-npm2yarn": "^3.1.0",
     "@mdx-js/react": "^3.0.0",
     "clsx": "^2.1.0",
     "postcss": "^8.4.33",
@@ -27,11 +27,11 @@
   },
   "devDependencies": {
     "@docusaurus/module-type-aliases": "3.1.0",
-    "@docusaurus/preset-classic": "^3.1.1",
-    "@docusaurus/theme-classic": "^3.1.1",
-    "@docusaurus/types": "^3.1.1",
+    "@docusaurus/preset-classic": "^3.1.0",
+    "@docusaurus/theme-classic": "^3.1.0",
+    "@docusaurus/types": "^3.1.0",
     "@tsconfig/docusaurus": "^2.0.2",
-    "@types/node": "^18.19.10",
+    "@types/node": "^18.19.6",
     "docusaurus-plugin-typedoc": "^0.22.0",
     "typedoc": "^0.25.7",
     "typedoc-plugin-markdown": "^3.17.1",

apps/docs/tsconfig.json

@@ -6,6 +6,6 @@
     "composite": true,
     "incremental": true,
     "outDir": "./lib",
-    "tsBuildInfoFile": "./lib/.tsbuildinfo"
-  }
+    "tsBuildInfoFile": "./lib/.tsbuildinfo",
+  },
 }

examples/CHANGELOG.md

@@ -0,0 +1,22 @@
+# simple
+
+## 0.0.3
+
+### Patch Changes
+
+- Updated dependencies [5a765aa]
+  - llamaindex@0.0.5
+
+## 0.0.2
+
+### Patch Changes
+
+- Updated dependencies [c65d671]
+  - llamaindex@0.0.4
+
+## 0.0.1
+
+### Patch Changes
+
+- Updated dependencies [ca9410f]
+  - llamaindex@0.0.3

examples/agent/openai.ts

@@ -1,76 +0,0 @@
-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, {
-    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, {
-    name: "divideNumbers",
-    description: "Use this function to divide two numbers",
-    parameters: divideJSON,
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [functionTool, functionTool2],
-    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");
-});

examples/agent/queryOpenAIAgent.ts

@@ -1,46 +0,0 @@
-import {
-  OpenAIAgent,
-  QueryEngineTool,
-  SimpleDirectoryReader,
-  VectorStoreIndex,
-} 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");
-});

examples/anthropic.ts

@@ -1,9 +1,7 @@
 import { Anthropic } from "llamaindex";
 
 (async () => {
-  const anthropic = new Anthropic({
-    apiKey: process.env.ANTHROPIC_API_KEY,
-  });
+  const anthropic = new Anthropic();
   const result = await anthropic.chat({
     messages: [
       { content: "You want to talk in rhymes.", role: "system" },

examples/astradb/README.md

@@ -14,27 +14,18 @@ Here are two sample scripts which work well with the sample data in the Astra Po
 
 - `ASTRA_DB_APPLICATION_TOKEN`: The generated app token for your Astra database
 - `ASTRA_DB_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
 
 2. `cd` Into the `examples` directory
 3. run `npm i`
 
-## Example load and query
-
-Loads and queries a simple vectorstore with some documents about Astra DB
-
-run `ts-node astradb/example`
-
-## Movie Reviews Example
-
-### Load the data
+## Load the data
 
 This sample loads the same dataset of movie reviews as the Astra Portal sample dataset. (Feel free to load the data in your the Astra Data Explorer to compare)
 
 run `ts-node astradb/load`
 
-### Use RAG to Query the data
+## Use RAG to Query the data
 
 Check out your data in the Astra Data Explorer and change the sample query as you see fit.
 

examples/astradb/example.ts

@@ -1,58 +0,0 @@
-import {
-  AstraDBVectorStore,
-  Document,
-  storageContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
-
-const collectionName = "test_collection";
-
-async function main() {
-  try {
-    const docs = [
-      new Document({
-        text: "AstraDB is built on Apache Cassandra",
-        metadata: {
-          id: 123,
-          foo: "bar",
-        },
-      }),
-      new Document({
-        text: "AstraDB is a NoSQL DB",
-        metadata: {
-          id: 456,
-          foo: "baz",
-        },
-      }),
-      new Document({
-        text: "AstraDB supports vector search",
-        metadata: {
-          id: 789,
-          foo: "qux",
-        },
-      }),
-    ];
-
-    const astraVS = new AstraDBVectorStore();
-    await astraVS.create(collectionName, {
-      vector: { dimension: 1536, metric: "cosine" },
-    });
-    await astraVS.connect(collectionName);
-
-    const ctx = await storageContextFromDefaults({ vectorStore: astraVS });
-    const index = await VectorStoreIndex.fromDocuments(docs, {
-      storageContext: ctx,
-    });
-
-    const queryEngine = index.asQueryEngine();
-    const response = await queryEngine.query({
-      query: "Describe AstraDB.",
-    });
-
-    console.log(response.toString());
-  } catch (e) {
-    console.error(e);
-  }
-}
-
-main();

examples/astradb/load.ts

@@ -10,9 +10,9 @@ const collectionName = "movie_reviews";
 async function main() {
   try {
     const reader = new PapaCSVReader(false);
-    const docs = await reader.loadData("./data/movie_reviews.csv");
+    const docs = await reader.loadData("../data/movie_reviews.csv");
 
-    const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
+    const astraVS = new AstraDBVectorStore();
     await astraVS.create(collectionName, {
       vector: { dimension: 1536, metric: "cosine" },
     });

examples/astradb/query.ts

@@ -8,7 +8,7 @@ const collectionName = "movie_reviews";
 
 async function main() {
   try {
-    const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
+    const astraVS = new AstraDBVectorStore();
     await astraVS.connect(collectionName);
 
     const ctx = serviceContextFromDefaults();
@@ -19,7 +19,7 @@ async function main() {
     const queryEngine = await index.asQueryEngine({ retriever });
 
     const results = await queryEngine.query({
-      query: 'How was "La Sapienza" reviewed?',
+      query: "What is the best reviewed movie?",
     });
 
     console.log(results.response);

examples/chatEngine.ts

@@ -1,4 +1,6 @@
 import { stdin as input, stdout as output } from "node:process";
+// readline/promises is still experimental so not in @types/node yet
+// @ts-ignore
 import readline from "node:readline/promises";
 
 import {

examples/chromadb/README.md

@@ -6,7 +6,7 @@ Export your OpenAI API Key using `export OPEN_API_KEY=insert your api key here`
 
 If you haven't installed chromadb, run `pip install chromadb`. Start the server using `chroma run`.
 
-Now, open a new terminal window and inside `examples`, run `pnpm dlx ts-node chromadb/test.ts`.
+Now, open a new terminal window and inside `examples`, run `pnpx ts-node chromadb/test.ts`.
 
 Here's the output for the input query `Tell me about Godfrey Cheshire's rating of La Sapienza.`:
 

examples/extractors/keywordExtractor.ts

@@ -16,10 +16,7 @@ import {
 
   console.log(nodes);
 
-  const keywordExtractor = new KeywordExtractor({
-    llm: openaiLLM,
-    keywords: 5,
-  });
+  const keywordExtractor = new KeywordExtractor(openaiLLM, 5);
 
   const nodesWithKeywordMetadata = await keywordExtractor.processNodes(nodes);
 

examples/extractors/questionsAnsweredExtractor.ts

@@ -19,10 +19,10 @@ import {
     }),
   ]);
 
-  const questionsAnsweredExtractor = new QuestionsAnsweredExtractor({
-    llm: openaiLLM,
-    questions: 5,
-  });
+  const questionsAnsweredExtractor = new QuestionsAnsweredExtractor(
+    openaiLLM,
+    5,
+  );
 
   const nodesWithQuestionsMetadata =
     await questionsAnsweredExtractor.processNodes(nodes);

examples/extractors/summaryExtractor.ts

@@ -16,9 +16,7 @@ import {
     }),
   ]);
 
-  const summaryExtractor = new SummaryExtractor({
-    llm: openaiLLM,
-  });
+  const summaryExtractor = new SummaryExtractor(openaiLLM);
 
   const nodesWithSummaryMetadata = await summaryExtractor.processNodes(nodes);
 

examples/extractors/titleExtractor.ts

@@ -11,10 +11,7 @@ import { Document, OpenAI, SimpleNodeParser, TitleExtractor } from "llamaindex";
     }),
   ]);
 
-  const titleExtractor = new TitleExtractor({
-    llm: openaiLLM,
-    nodes: 5,
-  });
+  const titleExtractor = new TitleExtractor(openaiLLM, 1);
 
   const nodesWithTitledMetadata = await titleExtractor.processNodes(nodes);
 

examples/gptllama.ts

@@ -1,4 +1,6 @@
 import { stdin as input, stdout as output } from "node:process";
+// readline/promises is still experimental so not in @types/node yet
+// @ts-ignore
 import readline from "node:readline/promises";
 
 import { ChatMessage, LlamaDeuce, OpenAI } from "llamaindex";

examples/package.json

@@ -1,21 +1,19 @@
 {
   "name": "examples",
   "private": true,
-  "version": "0.0.3",
   "dependencies": {
-    "@datastax/astra-db-ts": "^0.1.4",
+    "@datastax/astra-db-ts": "^0.1.2",
     "@notionhq/client": "^2.2.14",
-    "@pinecone-database/pinecone": "^1.1.3",
-    "chromadb": "^1.8.1",
+    "@pinecone-database/pinecone": "^1.1.2",
+    "chromadb": "^1.7.3",
     "commander": "^11.1.0",
-    "dotenv": "^16.4.1",
+    "dotenv": "^16.3.1",
     "llamaindex": "latest",
     "mongodb": "^6.2.0"
   },
   "devDependencies": {
-    "@types/node": "^18.19.10",
-    "ts-node": "^10.9.2",
-    "typescript": "^5.3.3"
+    "@types/node": "^18.18.6",
+    "ts-node": "^10.9.1"
   },
   "scripts": {
     "lint": "eslint ."

examples/pipeline/ingestion.ts

@@ -19,6 +19,7 @@ async function main() {
   const pipeline = new IngestionPipeline({
     transformations: [
       new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      // new TitleExtractor(llm),
       new OpenAIEmbedding(),
     ],
   });

examples/readers/README.md

@@ -1,5 +1,61 @@
-## LlamaIndex Reader Examples
+## Reader Examples
 
-```shell
-npm run start
+These examples show how to use a specific reader class by loading a document and running a test query.
+
+1. Make sure you are in `examples` directory
+
+```bash
+cd ./examples
+```
+
+2. Prepare `OPENAI_API_KEY` environment variable:
+
+```bash
+export OPENAI_API_KEY=your_openai_api_key
+```
+
+3. Run the following command to load documents and test query:
+
+- MarkdownReader Example
+
+```bash
+npx ts-node readers/load-md.ts
+```
+
+- DocxReader Example
+
+```bash
+npx ts-node readers/load-docx.ts
+```
+
+- PdfReader Example
+
+```bash
+npx ts-node readers/load-pdf.ts
+```
+
+- HtmlReader Example
+
+```bash
+npx ts-node readers/load-html.ts
+```
+
+- CsvReader Example
+
+```bash
+npx ts-node readers/load-csv.ts
+```
+
+- NotionReader Example
+
+```bash
+export NOTION_TOKEN=your_notion_token
+npx ts-node readers/load-notion.ts
+```
+
+- AssemblyAI Example
+
+```bash
+export ASSEMBLYAI_API_KEY=your_assemblyai_api_key
+npx ts-node readers/load-assemblyai.ts
 ```

examples/readers/load-assemblyai.ts

@@ -1,8 +1,13 @@
 import { program } from "commander";
-import { TranscribeParams, VectorStoreIndex } from "llamaindex";
-import { AudioTranscriptReader } from "llamaindex/readers/AssemblyAIReader";
+import {
+  AudioTranscriptReader,
+  TranscribeParams,
+  VectorStoreIndex,
+} from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";
-import { createInterface } from "node:readline/promises";
+// readline/promises is still experimental so not in @types/node yet
+// @ts-ignore
+import readline from "node:readline/promises";
 
 program
   .option("-a, --audio [string]", "URL or path of the audio file to transcribe")
@@ -35,7 +40,7 @@ program
     // Create query engine
     const queryEngine = index.asQueryEngine();
 
-    const rl = createInterface({ input, output });
+    const rl = readline.createInterface({ input, output });
     while (true) {
       const query = await rl.question("Ask a question: ");
 

examples/readers/load-csv.ts

@@ -1,16 +1,16 @@
 import {
   CompactAndRefine,
   OpenAI,
+  PapaCSVReader,
   ResponseSynthesizer,
   serviceContextFromDefaults,
   VectorStoreIndex,
 } from "llamaindex";
-import { PapaCSVReader } from "llamaindex/readers/CSVReader";
 
 async function main() {
   // Load CSV
   const reader = new PapaCSVReader();
-  const path = "../data/titanic_train.csv";
+  const path = "data/titanic_train.csv";
   const documents = await reader.loadData(path);
 
   const serviceContext = serviceContextFromDefaults({

examples/readers/load-docx.ts

@@ -1,7 +1,6 @@
-import { VectorStoreIndex } from "llamaindex";
-import { DocxReader } from "llamaindex/readers/DocxReader";
+import { DocxReader, VectorStoreIndex } from "llamaindex";
 
-const FILE_PATH = "../data/stars.docx";
+const FILE_PATH = "./data/stars.docx";
 const SAMPLE_QUERY = "Information about Zodiac";
 
 async function main() {

examples/readers/load-html.ts

@@ -1,10 +1,9 @@
-import { VectorStoreIndex } from "llamaindex";
-import { HTMLReader } from "llamaindex/readers/HTMLReader";
+import { HTMLReader, VectorStoreIndex } from "llamaindex";
 
 async function main() {
   // Load page
   const reader = new HTMLReader();
-  const documents = await reader.loadData("../data/llamaindex.html");
+  const documents = await reader.loadData("data/18-1_Changelog.html");
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
   const index = await VectorStoreIndex.fromDocuments(documents);
@@ -12,7 +11,7 @@ async function main() {
   // Query the index
   const queryEngine = index.asQueryEngine();
   const response = await queryEngine.query({
-    query: "What can I do with LlamaIndex?",
+    query: "What were the notable changes in 18.1?",
   });
 
   // Output response

examples/readers/load-md.ts

@@ -1,7 +1,6 @@
-import { VectorStoreIndex } from "llamaindex";
-import { MarkdownReader } from "llamaindex/readers/MarkdownReader";
+import { MarkdownReader, VectorStoreIndex } from "llamaindex";
 
-const FILE_PATH = "../data/planets.md";
+const FILE_PATH = "./data/planets.md";
 const SAMPLE_QUERY = "List all planets";
 
 async function main() {

examples/readers/load-notion.ts

@@ -1,9 +1,10 @@
 import { Client } from "@notionhq/client";
 import { program } from "commander";
-import { VectorStoreIndex } from "llamaindex";
-import { NotionReader } from "llamaindex/readers/NotionReader";
+import { NotionReader, VectorStoreIndex } from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";
-import { createInterface } from "node:readline/promises";
+// readline/promises is still experimental so not in @types/node yet
+// @ts-ignore
+import readline from "node:readline/promises";
 
 program
   .argument("[page]", "Notion page id (must be provided)")
@@ -70,7 +71,7 @@ program
     // Create query engine
     const queryEngine = index.asQueryEngine();
 
-    const rl = createInterface({ input, output });
+    const rl = readline.createInterface({ input, output });
     while (true) {
       const query = await rl.question("Query: ");
 

examples/readers/load-pdf.ts

@@ -1,10 +1,9 @@
-import { VectorStoreIndex } from "llamaindex";
-import { PDFReader } from "llamaindex/readers/PDFReader";
+import { PDFReader, VectorStoreIndex } from "llamaindex";
 
 async function main() {
   // Load PDF
   const reader = new PDFReader();
-  const documents = await reader.loadData("../data/brk-2022.pdf");
+  const documents = await reader.loadData("data/brk-2022.pdf");
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
   const index = await VectorStoreIndex.fromDocuments(documents);
@@ -12,7 +11,7 @@ async function main() {
   // Query the index
   const queryEngine = index.asQueryEngine();
   const response = await queryEngine.query({
-    query: "What mistakes did Warren E. Buffett make?",
+    query: "What mistakes did they make?",
   });
 
   // Output response

examples/readers/package.json

@@ -1,22 +0,0 @@
-{
-  "name": "llamaindex-loader-example",
-  "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"
-  },
-  "dependencies": {
-    "llamaindex": "latest"
-  },
-  "devDependencies": {
-    "@types/node": "^20.11.14",
-    "ts-node": "^10.9.2",
-    "typescript": "^5.3.3"
-  }
-}

examples/readers/src/custom-simple-directory-reader.ts

@@ -1,26 +0,0 @@
-import type { BaseReader, Document, Metadata } from "llamaindex";
-import {
-  FILE_EXT_TO_READER,
-  SimpleDirectoryReader,
-  TextFileReader,
-} from "llamaindex/readers/SimpleDirectoryReader";
-
-class ZipReader implements BaseReader {
-  loadData(...args: any[]): Promise<Document<Metadata>[]> {
-    throw new Error("Implement me");
-  }
-}
-
-const reader = new SimpleDirectoryReader();
-const documents = await reader.loadData({
-  directoryPath: "../data",
-  defaultReader: new TextFileReader(),
-  fileExtToReader: {
-    ...FILE_EXT_TO_READER,
-    zip: new ZipReader(),
-  },
-});
-
-documents.forEach((doc) => {
-  console.log(`document (${doc.id_}):`, doc.getText());
-});

examples/readers/src/llamaparse.ts

@@ -1,21 +0,0 @@
-import { LlamaParseReader, VectorStoreIndex } from "llamaindex";
-
-async function main() {
-  // Load PDF using LlamaParse
-  const reader = new LlamaParseReader({ resultType: "markdown" });
-  const documents = await reader.loadData("../data/TOS.pdf");
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
-    query: "What is the license grant in the TOS?",
-  });
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);

examples/readers/src/simple-directory-reader.ts

@@ -1,10 +0,0 @@
-import { SimpleDirectoryReader } from "llamaindex/readers/SimpleDirectoryReader";
-// or
-// import { SimpleDirectoryReader } from 'llamaindex'
-
-const reader = new SimpleDirectoryReader();
-const documents = await reader.loadData("../data");
-
-documents.forEach((doc) => {
-  console.log(`document (${doc.id_}):`, doc.getText());
-});

examples/readers/tsconfig.json

@@ -1,11 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "es2017",
-    "module": "node16",
-    "moduleResolution": "node16",
-    "outDir": "./dist",
-    "types": ["node"],
-    "skipLibCheck": true
-  },
-  "include": ["./src/**/*.ts"]
-}

examples/rerankers/CohereReranker.ts

@@ -1,55 +0,0 @@
-import {
-  CohereRerank,
-  Document,
-  OpenAI,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-import essay from "../essay";
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-  });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  const retriever = index.asRetriever();
-
-  retriever.similarityTopK = 5;
-
-  const nodePostprocessor = new CohereRerank({
-    apiKey: "<COHERE_API_KEY>",
-    topN: 5,
-  });
-
-  const queryEngine = index.asQueryEngine({
-    retriever,
-    nodePostprocessors: [nodePostprocessor],
-  });
-
-  const baseQueryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const response = await queryEngine.query({
-    query: "What did the author do growing up?",
-  });
-
-  // cohere response
-  console.log(response.response);
-
-  const baseResponse = await baseQueryEngine.query({
-    query: "What did the author do growing up?",
-  });
-
-  // response without cohere
-  console.log(baseResponse.response);
-}
-
-main().catch(console.error);

examples/routerQueryEngine.ts

@@ -1,76 +0,0 @@
-import {
-  OpenAI,
-  RouterQueryEngine,
-  SimpleDirectoryReader,
-  SimpleNodeParser,
-  SummaryIndex,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Load documents from a directory
-  const documents = await new SimpleDirectoryReader().loadData({
-    directoryPath: "node_modules/llamaindex/examples",
-  });
-
-  // Parse the documents into nodes
-  const nodeParser = new SimpleNodeParser({
-    chunkSize: 1024,
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser,
-    llm: new OpenAI(),
-  });
-
-  // Create indices
-  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  // Create query engines
-  const vectorQueryEngine = vectorIndex.asQueryEngine();
-  const summaryQueryEngine = summaryIndex.asQueryEngine();
-
-  // Create a router query engine
-  const queryEngine = RouterQueryEngine.fromDefaults({
-    queryEngineTools: [
-      {
-        queryEngine: vectorQueryEngine,
-        description: "Useful for summarization questions related to Abramov",
-      },
-      {
-        queryEngine: summaryQueryEngine,
-        description: "Useful for retrieving specific context from Abramov",
-      },
-    ],
-    serviceContext,
-  });
-
-  // Query the router query engine
-  const summaryResponse = await queryEngine.query({
-    query: "Give me a summary about his past experiences?",
-  });
-
-  console.log({
-    answer: summaryResponse.response,
-    metadata: summaryResponse?.metadata?.selectorResult,
-  });
-
-  const specificResponse = await queryEngine.query({
-    query: "Tell me about abramov first job?",
-  });
-
-  console.log({
-    answer: specificResponse.response,
-    metadata: specificResponse.metadata.selectorResult,
-  });
-}
-
-main().then(() => console.log("Done"));

examples/subquestion.ts

@@ -1,9 +1,4 @@
-import {
-  Document,
-  QueryEngineTool,
-  SubQuestionQueryEngine,
-  VectorStoreIndex,
-} from "llamaindex";
+import { Document, SubQuestionQueryEngine, VectorStoreIndex } from "llamaindex";
 
 import essay from "./essay";
 
@@ -11,18 +6,16 @@ import essay from "./essay";
   const document = new Document({ text: essay, id_: essay });
   const index = await VectorStoreIndex.fromDocuments([document]);
 
-  const queryEngineTools = [
-    new QueryEngineTool({
-      queryEngine: index.asQueryEngine(),
-      metadata: {
-        name: "pg_essay",
-        description: "Paul Graham essay on What I Worked On",
-      },
-    }),
-  ];
-
   const queryEngine = SubQuestionQueryEngine.fromDefaults({
-    queryEngineTools,
+    queryEngineTools: [
+      {
+        queryEngine: index.asQueryEngine(),
+        metadata: {
+          name: "pg_essay",
+          description: "Paul Graham essay on What I Worked On",
+        },
+      },
+    ],
   });
 
   const response = await queryEngine.query({

examples/tsconfig.json

@@ -1,6 +1,6 @@
 {
   "compilerOptions": {
-    "target": "es2017",
+    "target": "es2016",
     "module": "esnext",
     "moduleResolution": "bundler",
     "esModuleInterop": true,
@@ -10,13 +10,13 @@
     "outDir": "./lib",
     "tsBuildInfoFile": "./lib/.tsbuildinfo",
     "incremental": true,
-    "composite": true
+    "composite": true,
   },
   "ts-node": {
     "files": true,
     "compilerOptions": {
-      "module": "commonjs"
-    }
+      "module": "commonjs",
+    },
   },
-  "include": ["./**/*.ts"]
+  "include": ["./**/*.ts"],
 }