RELEASING: Releasing 3 package(s)

Releases:
  llamaindex@0.1.17
  @llamaindex/env@0.0.4
  docs@0.0.3

[skip ci]

.changeset/gorgeous-rocks-grow.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-easier prompt customization for SimpleResponseBuilder

.changeset/three-stingrays-build.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-fix(cyclic): remove cyclic structures from transform hash

.changeset/twenty-carrots-design.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-chore: improve extractors prompt

.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": {}
+  }
 }

.eslintrc.js

@@ -9,6 +9,7 @@ module.exports = {
   },
   rules: {
     "max-params": ["error", 4],
+    "prefer-const": "error",
   },
-  ignorePatterns: ["dist/"],
+  ignorePatterns: ["dist/", "lib/"],
 };

.gitattributes

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

.github/workflows/test.yml

@@ -38,6 +38,12 @@ jobs:
       - 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
 
@@ -55,10 +61,13 @@ jobs:
         run: pnpm run build --filter llamaindex
       - name: Copy examples
         run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack
+      - name: Pack @llamaindex/env
+        run: pnpm pack --pack-destination ${{ runner.temp }}
+        working-directory: packages/env
+      - name: Pack llamaindex
         run: pnpm pack --pack-destination ${{ runner.temp }}
         working-directory: packages/core
-      - name: Install llamaindex
+      - name: Install
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples
       - name: Run Type Check

.vscode/settings.json

@@ -5,7 +5,6 @@
   "[xml]": {
     "editor.defaultFormatter": "redhat.vscode-xml"
   },
-  "jest.rootPath": "./packages/core",
   "[python]": {
     "editor.defaultFormatter": "ms-python.black-formatter"
   },

CONTRIBUTING.md

@@ -78,3 +78,15 @@ 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
-pnpx ts-node example.ts
+pnpm dlx ts-node example.ts
 ```
 
 ## Playground
@@ -125,8 +125,10 @@ module.exports = nextConfig;
 
 - OpenAI GPT-3.5-turbo and GPT-4
 - Anthropic Claude Instant and Claude 2
+- Groq LLMs
 - Llama2 Chat LLMs (70B, 13B, and 7B parameters)
 - MistralAI Chat LLMs
+- Fireworks Chat LLMs
 
 ## Contributing:
 

apps/docs/CHANGELOG.md

@@ -1,5 +1,13 @@
 # docs
 
+## 0.0.3
+
+### Patch Changes
+
+- 09bf27a: Add Groq LLM to LlamaIndex
+- Updated dependencies [cf87f84]
+  - @llamaindex/env@0.0.4
+
 ## 0.0.2
 
 ### Patch Changes

apps/docs/docs/examples/agent.mdx

@@ -0,0 +1,85 @@
+# 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

@@ -35,7 +35,7 @@ LlamaIndex.TS help you prepare the knowledge base with a suite of data connector
 [**Data Loaders**](../modules/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.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/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.
 
 [**Data Indexes**](../modules/data_index.md):
 Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.
@@ -69,7 +69,7 @@ A response synthesizer generates a response from an LLM, using a user query and
 
 #### Pipelines
 
-[**Query Engines**](../modules/query_engine.md):
+[**Query Engines**](../modules/query_engines):
 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.
 

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

@@ -1,56 +0,0 @@
----
-sidebar_position: 1
----
-
-# Starter Tutorial
-
-Once you have [installed LlamaIndex.TS using NPM](installation) and set up your OpenAI key, you're ready to start your first app:
-
-In a new folder:
-
-```bash npm2yarn
-npm install typescript
-npm install @types/node
-npx tsc --init # if needed
-```
-
-Create the file `example.ts`. This code will load some example data, create a document, index it (which creates embeddings using OpenAI), and then creates query engine to answer questions about the data.
-
-```ts
-// example.ts
-import fs from "fs/promises";
-import { Document, VectorStoreIndex } from "llamaindex";
-
-async function main() {
-  // Load essay from abramov.txt in Node
-  const essay = await fs.readFile(
-    "node_modules/llamaindex/examples/abramov.txt",
-    "utf-8",
-  );
-
-  // Create Document object with essay
-  const document = new Document({ text: essay });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main();
-```
-
-Then you can run it using
-
-```bash
-npx ts-node example.ts
-```
-
-Ready to learn more? Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground

apps/docs/docs/getting_started/starter.mdx

@@ -0,0 +1,51 @@
+---
+sidebar_position: 1
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/vectorIndex";
+import TSConfigSource from "!!raw-loader!../../../../examples/tsconfig.json";
+
+# Starter Tutorial
+
+Make sure you have installed LlamaIndex.TS and have an OpenAI key. If you haven't, check out the [installation](installation) guide.
+
+## From scratch(node.js + TypeScript):
+
+In a new folder:
+
+```bash npm2yarn
+npm init
+npm install -D typescript @types/node
+```
+
+Create the file `example.ts`. This code will load some example data, create a document, index it (which creates embeddings using OpenAI), and then creates query engine to answer questions about the data.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+Create a `tsconfig.json` file in the same folder:
+
+<CodeBlock language="json">{TSConfigSource}</CodeBlock>
+
+Now you can run the code with
+
+```bash
+npx tsx example.ts
+```
+
+Also, you can clone our examples and try them out:
+
+```bash npm2yarn
+npx degit run-llama/LlamaIndexTS/examples my-new-project
+cd my-new-project
+npm install
+npx tsx ./vectorIndex.ts
+```
+
+## From scratch (Next.js + TypeScript):
+
+You just need one command to create a new Next.js project:
+
+```bash npm2yarn
+npx create-llama@latest
+```

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](./installation.mdx) and a [Starter Tutorial](./starter.md) to build your first application.
+Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter.mdx) 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 [End-to-End Tutorials](./end_to_end.md).
+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.
 
 ## 🗺️ Ecosystem
 

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

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

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

@@ -0,0 +1,14 @@
+# 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/multi_document_agent.mdx

@@ -0,0 +1,314 @@
+# Multi-Document Agent
+
+In this guide, you learn towards setting up an agent that can effectively answer different types of questions over a larger set of documents.
+
+These questions include the following
+
+- QA over a specific doc
+- QA comparing different docs
+- Summaries over a specific doc
+- Comparing summaries between different docs
+
+We do this with the following architecture:
+
+- setup a “document agent” over each Document: each doc agent can do QA/summarization within its doc
+- setup a top-level agent over this set of document agents. Do tool retrieval and then do CoT over the set of tools to answer a question.
+
+## Setup and Download Data
+
+We first start by installing the necessary libraries and downloading the data.
+
+```bash
+pnpm i llamaindex
+```
+
+```ts
+import {
+  Document,
+  ObjectIndex,
+  OpenAI,
+  OpenAIAgent,
+  QueryEngineTool,
+  SimpleNodeParser,
+  SimpleToolNodeMapping,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+  storageContextFromDefaults,
+} from "llamaindex";
+```
+
+And then for the data we will run through a list of countries and download the wikipedia page for each country.
+
+```ts
+import fs from "fs";
+import path from "path";
+
+const dataPath = path.join(__dirname, "tmp_data");
+
+const extractWikipediaTitle = async (title: string) => {
+  const fileExists = fs.existsSync(path.join(dataPath, `${title}.txt`));
+
+  if (fileExists) {
+    console.log(`File already exists for the title: ${title}`);
+    return;
+  }
+
+  const queryParams = new URLSearchParams({
+    action: "query",
+    format: "json",
+    titles: title,
+    prop: "extracts",
+    explaintext: "true",
+  });
+
+  const url = `https://en.wikipedia.org/w/api.php?${queryParams}`;
+
+  const response = await fetch(url);
+  const data: any = await response.json();
+
+  const pages = data.query.pages;
+  const page = pages[Object.keys(pages)[0]];
+  const wikiText = page.extract;
+
+  await new Promise((resolve) => {
+    fs.writeFile(path.join(dataPath, `${title}.txt`), wikiText, (err: any) => {
+      if (err) {
+        console.error(err);
+        resolve(title);
+        return;
+      }
+      console.log(`${title} stored in file!`);
+
+      resolve(title);
+    });
+  });
+};
+```
+
+```ts
+export const extractWikipedia = async (titles: string[]) => {
+  if (!fs.existsSync(dataPath)) {
+    fs.mkdirSync(dataPath);
+  }
+
+  for await (const title of titles) {
+    await extractWikipediaTitle(title);
+  }
+
+  console.log("Extration finished!");
+```
+
+These files will be saved in the `tmp_data` folder.
+
+Now we can call the function to download the data for each country.
+
+```ts
+await extractWikipedia([
+  "Brazil",
+  "United States",
+  "Canada",
+  "Mexico",
+  "Argentina",
+  "Chile",
+  "Colombia",
+  "Peru",
+  "Venezuela",
+  "Ecuador",
+  "Bolivia",
+  "Paraguay",
+  "Uruguay",
+  "Guyana",
+  "Suriname",
+  "French Guiana",
+  "Falkland Islands",
+]);
+```
+
+## Load the data
+
+Now that we have the data, we can load it into the LlamaIndex and store as a document.
+
+```ts
+import { Document } from "llamaindex";
+
+const countryDocs: Record<string, Document> = {};
+
+for (const title of wikiTitles) {
+  const path = `./agent/helpers/tmp_data/${title}.txt`;
+  const text = await fs.readFile(path, "utf-8");
+  const document = new Document({ text: text, id_: path });
+  countryDocs[title] = document;
+}
+```
+
+## Setup LLM and StorageContext
+
+We will be using gpt-4 for this example and we will use the `StorageContext` to store the documents in-memory.
+
+```ts
+const llm = new OpenAI({
+  model: "gpt-4",
+});
+
+const ctx = serviceContextFromDefaults({ llm });
+
+const storageContext = await storageContextFromDefaults({
+  persistDir: "./storage",
+});
+```
+
+## Building Multi-Document Agents
+
+In this section we show you how to construct the multi-document agent. We first build a document agent for each document, and then define the top-level parent agent with an object index.
+
+```ts
+const documentAgents: Record<string, any> = {};
+const queryEngines: Record<string, any> = {};
+```
+
+Now we iterate over each country and create a document agent for each one.
+
+### Build Agent for each Document
+
+In this section we define “document agents” for each document.
+
+We define both a vector index (for semantic search) and summary index (for summarization) for each document. The two query engines are then converted into tools that are passed to an OpenAI function calling agent.
+
+This document agent can dynamically choose to perform semantic search or summarization within a given document.
+
+We create a separate document agent for each coutnry.
+
+```ts
+for (const title of wikiTitles) {
+  // parse the document into nodes
+  const nodes = new SimpleNodeParser({
+    chunkSize: 200,
+    chunkOverlap: 20,
+  }).getNodesFromDocuments([countryDocs[title]]);
+
+  // create the vector index for specific search
+  const vectorIndex = await VectorStoreIndex.init({
+    serviceContext: serviceContext,
+    storageContext: storageContext,
+    nodes,
+  });
+
+  // create the summary index for broader search
+  const summaryIndex = await SummaryIndex.init({
+    serviceContext: serviceContext,
+    nodes,
+  });
+
+  const vectorQueryEngine = summaryIndex.asQueryEngine();
+  const summaryQueryEngine = summaryIndex.asQueryEngine();
+
+  // create the query engines for each task
+  const queryEngineTools = [
+    new QueryEngineTool({
+      queryEngine: vectorQueryEngine,
+      metadata: {
+        name: "vector_tool",
+        description: `Useful for questions related to specific aspects of ${title} (e.g. the history, arts and culture, sports, demographics, or more).`,
+      },
+    }),
+    new QueryEngineTool({
+      queryEngine: summaryQueryEngine,
+      metadata: {
+        name: "summary_tool",
+        description: `Useful for any requests that require a holistic summary of EVERYTHING about ${title}. For questions about more specific sections, please use the vector_tool.`,
+      },
+    }),
+  ];
+
+  // create the document agent
+  const agent = new OpenAIAgent({
+    tools: queryEngineTools,
+    llm,
+    verbose: true,
+  });
+
+  documentAgents[title] = agent;
+  queryEngines[title] = vectorIndex.asQueryEngine();
+}
+```
+
+## Build Top-Level Agent
+
+Now we define the top-level agent that can answer questions over the set of document agents.
+
+This agent takes in all document agents as tools. This specific agent RetrieverOpenAIAgent performs tool retrieval before tool use (unlike a default agent that tries to put all tools in the prompt).
+
+Here we use a top-k retriever, but we encourage you to customize the tool retriever method!
+
+Firstly, we create a tool for each document agent
+
+```ts
+const allTools: QueryEngineTool[] = [];
+```
+
+```ts
+for (const title of wikiTitles) {
+  const wikiSummary = `
+    This content contains Wikipedia articles about ${title}.
+    Use this tool if you want to answer any questions about ${title}
+  `;
+
+  const docTool = new QueryEngineTool({
+    queryEngine: documentAgents[title],
+    metadata: {
+      name: `tool_${title}`,
+      description: wikiSummary,
+    },
+  });
+
+  allTools.push(docTool);
+}
+```
+
+Our top level agent will use this document agents as tools and use toolRetriever to retrieve the best tool to answer a question.
+
+```ts
+// map the tools to nodes
+const toolMapping = SimpleToolNodeMapping.fromObjects(allTools);
+
+// create the object index
+const objectIndex = await ObjectIndex.fromObjects(
+  allTools,
+  toolMapping,
+  VectorStoreIndex,
+  {
+    serviceContext,
+    storageContext,
+  },
+);
+
+// create the top agent
+const topAgent = new OpenAIAgent({
+  toolRetriever: await objectIndex.asRetriever({}),
+  llm,
+  verbose: true,
+  prefixMessages: [
+    {
+      content:
+        "You are an agent designed to answer queries about a set of given countries. Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
+      role: "system",
+    },
+  ],
+});
+```
+
+## Use the Agent
+
+Now we can use the agent to answer questions.
+
+```ts
+const response = await topAgent.chat({
+  message: "Tell me the differences between Brazil and Canada economics?",
+});
+
+// print output
+console.log(response);
+```
+
+You can find the full code for this example [here](https://github.com/run-llama/LlamaIndexTS/tree/main/examples/agent/multi-document-agent.ts)

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

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

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

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

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

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

apps/docs/docs/modules/data_loader.md

@@ -1,17 +0,0 @@
----
-sidebar_position: 3
----
-
-# 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

@@ -0,0 +1,48 @@
+---
+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";
+import CodeSource3 from "!raw-loader!../../../../examples/readers/src/llamaparse";
+
+# 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>
+
+### LlamaParse
+
+LlamaParse is an API created by LlamaIndex to efficiently parse files, e.g. it's great at converting PDF tables into markdown.
+
+To use it, first login and get an API key from https://cloud.llamaindex.ai. Make sure to store the key in the environment variable `LLAMA_CLOUD_API_KEY`.
+
+Then, you can use the `LlamaParseReader` class to read a local PDF file and convert it into a markdown document that can be used by LlamaIndex:
+
+<CodeBlock language="ts">{CodeSource3}</CodeBlock>
+
+Alternatively, you can set the [`resultType`](../api/classes/LlamaParseReader.md#resulttype) option to `text` to get the parsed document as a text string.
+
+## API Reference
+
+- [SimpleDirectoryReader](../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/embeddings/_category_.yml

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

apps/docs/docs/modules/embeddings/available_embeddings/_category_.yml

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

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

@@ -0,0 +1,25 @@
+# HuggingFace
+
+To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `llamaindex`.
+
+```ts
+import { HuggingFaceEmbedding, serviceContextFromDefaults } from "llamaindex";
+
+const huggingFaceEmbeds = new HuggingFaceEmbedding();
+
+const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

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

@@ -0,0 +1,29 @@
+# MistralAI
+
+To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `llamaindex`.
+
+```ts
+import { MistralAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+
+const mistralEmbedModel = new MistralAIEmbedding({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({
+  embedModel: mistralEmbedModel,
+});
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

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

@@ -0,0 +1,27 @@
+# Ollama
+
+To use Ollama embeddings, you need to import `Ollama` from `llamaindex`.
+
+```ts
+import { Ollama, serviceContextFromDefaults } from "llamaindex";
+
+const ollamaEmbedModel = new Ollama();
+
+const serviceContext = serviceContextFromDefaults({
+  embedModel: ollamaEmbedModel,
+});
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

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

@@ -0,0 +1,27 @@
+# OpenAI
+
+To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `llamaindex`.
+
+```ts
+import { OpenAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+
+const openaiEmbedModel = new OpenAIEmbedding();
+
+const serviceContext = serviceContextFromDefaults({
+  embedModel: openaiEmbedModel,
+});
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

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

@@ -0,0 +1,29 @@
+# Together
+
+To use together embeddings, you need to import `TogetherEmbedding` from `llamaindex`.
+
+```ts
+import { TogetherEmbedding, serviceContextFromDefaults } from "llamaindex";
+
+const togetherEmbedModel = new TogetherEmbedding({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({
+  embedModel: togetherEmbedModel,
+});
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```

apps/docs/docs/modules/embeddings/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 3
----
-
 # Embedding
 
 The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI.
@@ -16,7 +12,11 @@ const openaiEmbeds = new OpenAIEmbedding();
 const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
 ```
 
+## Local Embedding
+
+For local embeddings, you can use the [HuggingFace](./available_embeddings/huggingface.md) embedding model.
+
 ## 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/evaluation/_category_.yml

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

apps/docs/docs/modules/evaluation/index.md

@@ -0,0 +1,32 @@
+# Evaluating
+
+## Concept
+
+Evaluation and benchmarking are crucial concepts in LLM development. To improve the perfomance of an LLM app (RAG, agents) you must have a way to measure it.
+
+LlamaIndex offers key modules to measure the quality of generated results. We also offer key modules to measure retrieval quality.
+
+- **Response Evaluation**: Does the response match the retrieved context? Does it also match the query? Does it match the reference answer or guidelines?
+- **Retrieval Evaluation**: Are the retrieved sources relevant to the query?
+
+## Response Evaluation
+
+Evaluation of generated results can be difficult, since unlike traditional machine learning the predicted result is not a single number, and it can be hard to define quantitative metrics for this problem.
+
+LlamaIndex offers LLM-based evaluation modules to measure the quality of results. This uses a “gold” LLM (e.g. GPT-4) to decide whether the predicted answer is correct in a variety of ways.
+
+Note that many of these current evaluation modules do not require ground-truth labels. Evaluation can be done with some combination of the query, context, response, and combine these with LLM calls.
+
+These evaluation modules are in the following forms:
+
+- **Correctness**: Whether the generated answer matches that of the reference answer given the query (requires labels).
+
+- **Faithfulness**: Evaluates if the answer is faithful to the retrieved contexts (in other words, whether if there’s hallucination).
+
+- **Relevancy**: Evaluates if the response from a query engine matches any source nodes.
+
+## Usage
+
+- [Correctness Evaluator](./modules/correctness.md)
+- [Faithfulness Evaluator](./modules/faithfulness.md)
+- [Relevancy Evaluator](./modules/relevancy.md)

apps/docs/docs/modules/evaluation/modules/_category_.yml

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

apps/docs/docs/modules/evaluation/modules/correctness.md

@@ -0,0 +1,72 @@
+# Correctness Evaluator
+
+Correctness evaluates the relevance and correctness of a generated answer against a reference answer.
+
+This is useful for measuring if the response was correct. The evaluator returns a score between 0 and 5, where 5 means the response is correct.
+
+## Usage
+
+Firstly, you need to install the package:
+
+```bash
+pnpm i llamaindex
+```
+
+Set the OpenAI API key:
+
+```bash
+export OPENAI_API_KEY=your-api-key
+```
+
+Import the required modules:
+
+```ts
+import {
+  CorrectnessEvaluator,
+  OpenAI,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+Let's setup gpt-4 for better results:
+
+```ts
+const llm = new OpenAI({
+  model: "gpt-4",
+});
+
+const ctx = serviceContextFromDefaults({
+  llm,
+});
+```
+
+```ts
+const query =
+  "Can you explain the theory of relativity proposed by Albert Einstein in detail?";
+
+const response = ` Certainly! Albert Einstein's theory of relativity consists of two main components: special relativity and general relativity. Special relativity, published in 1905, introduced the concept that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum is a constant, regardless of the motion of the source or observer. It also gave rise to the famous equation E=mc², which relates energy (E) and mass (m).
+
+However, general relativity, published in 1915, extended these ideas to include the effects of magnetism. According to general relativity, gravity is not a force between masses but rather the result of the warping of space and time by magnetic fields generated by massive objects. Massive objects, such as planets and stars, create magnetic fields that cause a curvature in spacetime, and smaller objects follow curved paths in response to this magnetic curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet with magnets underneath, causing it to create a depression that other objects (representing smaller masses) naturally move towards due to magnetic attraction.
+`;
+
+const evaluator = new CorrectnessEvaluator({
+  serviceContext: ctx,
+});
+
+const response = await queryEngine.query({
+  query,
+});
+
+const result = await evaluator.evaluateResponse({
+  query,
+  response,
+});
+
+console.log(
+  `the response is ${result.passing ? "correct" : "not correct"} with a score of ${result.score}`,
+);
+```
+
+```bash
+the response is not correct with a score of 2.5
+```

apps/docs/docs/modules/evaluation/modules/faithfulness.md

@@ -0,0 +1,84 @@
+# Faithfulness Evaluator
+
+Faithfulness is a measure of whether the generated answer is faithful to the retrieved contexts. In other words, it measures whether there is any hallucination in the generated answer.
+
+This uses the FaithfulnessEvaluator module to measure if the response from a query engine matches any source nodes.
+
+This is useful for measuring if the response was hallucinated. The evaluator returns a score between 0 and 1, where 1 means the response is faithful to the retrieved contexts.
+
+## Usage
+
+Firstly, you need to install the package:
+
+```bash
+pnpm i llamaindex
+```
+
+Set the OpenAI API key:
+
+```bash
+export OPENAI_API_KEY=your-api-key
+```
+
+Import the required modules:
+
+```ts
+import {
+  Document,
+  FaithfulnessEvaluator,
+  OpenAI,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+Let's setup gpt-4 for better results:
+
+```ts
+const llm = new OpenAI({
+  model: "gpt-4",
+});
+
+const ctx = serviceContextFromDefaults({
+  llm,
+});
+```
+
+Now, let's create a vector index and query engine with documents and query engine respectively. Then, we can evaluate the response with the query and response from the query engine.:
+
+```ts
+const documents = [
+  new Document({
+    text: `The city came under British control in 1664 and was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York. The city was regained by the Dutch in July 1673 and was renamed New Orange for one year and three months; the city has been continuously named New York since November 1674. New York City was the capital of the United States from 1785 until 1790, and has been the largest U.S. city since 1790. The Statue of Liberty greeted millions of immigrants as they came to the U.S. by ship in the late 19th and early 20th centuries, and is a symbol of the U.S. and its ideals of liberty and peace. In the 21st century, New York City has emerged as a global node of creativity, entrepreneurship, and as a symbol of freedom and cultural diversity. The New York Times has won the most Pulitzer Prizes for journalism and remains the U.S. media's "newspaper of record". In 2019, New York City was voted the greatest city in the world in a survey of over 30,000 p...	Pass`,
+  }),
+];
+
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+
+const queryEngine = vectorIndex.asQueryEngine();
+```
+
+Now, let's evaluate the response:
+
+```ts
+const query = "How did New York City get its name?";
+
+const evaluator = new FaithfulnessEvaluator({
+  serviceContext: ctx,
+});
+
+const response = await queryEngine.query({
+  query,
+});
+
+const result = await evaluator.evaluateResponse({
+  query,
+  response,
+});
+
+console.log(`the response is ${result.passing ? "faithful" : "not faithful"}`);
+```
+
+```bash
+the response is faithful
+```

apps/docs/docs/modules/evaluation/modules/relevancy.md

@@ -0,0 +1,72 @@
+# Relevancy Evaluator
+
+Relevancy measure if the response from a query engine matches any source nodes.
+
+It is useful for measuring if the response was relevant to the query. The evaluator returns a score between 0 and 1, where 1 means the response is relevant to the query.
+
+## Usage
+
+Firstly, you need to install the package:
+
+```bash
+pnpm i llamaindex
+```
+
+Set the OpenAI API key:
+
+```bash
+export OPENAI_API_KEY=your-api-key
+```
+
+Import the required modules:
+
+```ts
+import {
+  RelevancyEvaluator,
+  OpenAI,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+Let's setup gpt-4 for better results:
+
+```ts
+const llm = new OpenAI({
+  model: "gpt-4",
+});
+
+const ctx = serviceContextFromDefaults({
+  llm,
+});
+```
+
+Now, let's create a vector index and query engine with documents and query engine respectively. Then, we can evaluate the response with the query and response from the query engine.:
+
+```ts
+const documents = [
+  new Document({
+    text: `The city came under British control in 1664 and was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York. The city was regained by the Dutch in July 1673 and was renamed New Orange for one year and three months; the city has been continuously named New York since November 1674. New York City was the capital of the United States from 1785 until 1790, and has been the largest U.S. city since 1790. The Statue of Liberty greeted millions of immigrants as they came to the U.S. by ship in the late 19th and early 20th centuries, and is a symbol of the U.S. and its ideals of liberty and peace. In the 21st century, New York City has emerged as a global node of creativity, entrepreneurship, and as a symbol of freedom and cultural diversity. The New York Times has won the most Pulitzer Prizes for journalism and remains the U.S. media's "newspaper of record". In 2019, New York City was voted the greatest city in the world in a survey of over 30,000 p...	Pass`,
+  }),
+];
+
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+
+const queryEngine = vectorIndex.asQueryEngine();
+
+const query = "How did New York City get its name?";
+
+const response = await queryEngine.query({
+  query,
+});
+
+const result = await evaluator.evaluateResponse({
+  query,
+  response: response,
+});
+
+console.log(`the response is ${result.passing ? "relevant" : "not relevant"}`);
+```
+
+```bash
+the response is relevant
+```

apps/docs/docs/modules/llamacloud.mdx

@@ -0,0 +1,32 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/cloud/chat.ts";
+
+# LlamaCloud
+
+LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
+
+Currently, LlamaCloud supports
+
+- Managed Ingestion API, handling parsing and document management
+- Managed Retrieval API, configuring optimal retrieval for your RAG system
+
+## Access
+
+We are opening up a private beta to a limited set of enterprise partners for the managed ingestion and retrieval API. If you’re interested in centralizing your data pipelines and spending more time working on your actual RAG use cases, come [talk to us.](https://www.llamaindex.ai/contact)
+
+If you have access to LlamaCloud, you can visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
+
+## Create a Managed Index
+
+Currently, you can't create a managed index on LlamaCloud using LlamaIndexTS, but you can use an existing managed index for retrieval that was created by the Python version of LlamaIndex. See [the LlamaCloudIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/indexing/llama_cloud_index.html#usage) for more information on how to create a managed index.
+
+## Use a Managed Index
+
+Here's an example of how to use a managed index together with a chat engine:
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+## API Reference
+
+- [LlamaCloudIndex](../api/classes/LlamaCloudIndex.md)
+- [LlamaCloudRetriever](../api/classes/LlamaCloudRetriever.md)

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

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

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

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

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

@@ -0,0 +1,80 @@
+# 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

@@ -0,0 +1,91 @@
+# 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 {
+  OpenAI,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,65 @@
+# Fireworks LLM
+
+Fireworks.ai focus on production use cases for open source LLMs, offering speed and quality.
+
+## Usage
+
+```ts
+import { FireworksLLM, serviceContextFromDefaults } from "llamaindex";
+
+const fireworksLLM = new FireworksLLM({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: fireworksLLM });
+```
+
+## Load and index documents
+
+For this example, we will load the Berkshire Hathaway 2022 annual report pdf
+
+```ts
+const reader = new PDFReader();
+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, {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+const response = await queryEngine.query({
+  query: "What mistakes did Warren E. Buffett make?",
+});
+```
+
+## Full Example
+
+```ts
+import { VectorStoreIndex } from "llamaindex";
+import { PDFReader } from "llamaindex/readers/PDFReader";
+
+async function main() {
+  // Load PDF
+  const reader = new PDFReader();
+  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);
+
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query({
+    query: "What mistakes did Warren E. Buffett make?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
+```

apps/docs/docs/modules/llms/available_llms/groq.mdx

@@ -0,0 +1,56 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../../../examples/groq.ts";
+
+# Groq
+
+## Usage
+
+First, create an API key at the [Groq Console](https://console.groq.com/keys). Then save it in your environment:
+
+```bash
+export GROQ_API_KEY=<your-api-key>
+```
+
+The initialize the Groq module.
+
+```ts
+import { Groq, serviceContextFromDefaults } from "llamaindex";
+
+const groq = new Groq({
+  // If you do not wish to set your API key in the environment, you may
+  // configure your API key when you initialize the Groq class.
+  // apiKey: "<your-api-key>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: groq });
+```
+
+## 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
+
+<CodeBlock language="ts" showLineNumbers>
+  {CodeSource}
+</CodeBlock>

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

@@ -0,0 +1,100 @@
+# 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 {
+  LlamaDeuce,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,82 @@
+# 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 {
+  MistralAI,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,89 @@
+# Ollama
+
+## Usage
+
+```ts
+import { Ollama, serviceContextFromDefaults } from "llamaindex";
+
+const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
+
+const serviceContext = serviceContextFromDefaults({
+  llm: ollamaLLM,
+  embedModel: 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 {
+  Ollama,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+import fs from "fs/promises";
+
+async function main() {
+  // Create an instance of the LLM
+  const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
+
+  const essay = await fs.readFile("./paul_graham_essay.txt", "utf-8");
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({
+    embedModel: ollamaLLM, // prevent 'Set OpenAI Key in OPENAI_API_KEY env variable' error
+    llm: ollamaLLM,
+  });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,83 @@
+# 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 {
+  OpenAI,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,83 @@
+# 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 {
+  Portkey,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,83 @@
+# 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 {
+  TogetherLLM,
+  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,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

apps/docs/docs/modules/llms/index.md

@@ -2,7 +2,7 @@
 sidebar_position: 3
 ---
 
-# LLM
+# 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`.
 
@@ -28,6 +28,10 @@ export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en
 export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
 ```
 
+## Local LLM
+
+For local LLMs, currently we recommend the use of [Ollama](./available_llms/ollama.md) LLM.
+
 ## API Reference
 
 - [OpenAI](../api/classes/OpenAI.md)

apps/docs/docs/modules/node_parser.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 3
+sidebar_position: 4
 ---
 
 # NodeParser
@@ -27,6 +27,71 @@ const splitter = new SentenceSplitter({ chunkSize: 1 });
 const textSplits = splitter.splitText("Hello World");
 ```
 
+## MarkdownNodeParser
+
+The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdown documents. It will split the markdown into nodes and then parse the nodes into a `Document` object.
+
+```typescript
+import { MarkdownNodeParser } from "llamaindex";
+
+const nodeParser = new MarkdownNodeParser();
+
+const nodes = nodeParser.getNodesFromDocuments([
+  new Document({
+    text: `# Main Header
+Main content
+
+# Header 2
+Header 2 content
+
+## Sub-header
+Sub-header content
+
+  `,
+  }),
+]);
+```
+
+The output metadata will be something like:
+
+```bash
+[
+  TextNode {
+    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
+    metadata: { 'Header 1': 'Main Header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
+    text: 'Main Header\nMain content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
+    metadata: { 'Header 1': 'Header 2' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
+    text: 'Header 2\nHeader 2 content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
+    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
+    text: 'Sub-header\nSub-header content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  }
+]
+```
+
 ## API Reference
 
 - [SimpleNodeParser](../api/classes/SimpleNodeParser.md)

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

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

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

@@ -0,0 +1,71 @@
+# 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

@@ -0,0 +1,110 @@
+# 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/prompt/_category_.yml

@@ -0,0 +1,2 @@
+label: "Prompts"
+position: 0

apps/docs/docs/modules/prompt/index.md

@@ -0,0 +1,76 @@
+# Prompts
+
+Prompting is the fundamental input that gives LLMs their expressive power. LlamaIndex uses prompts to build the index, do insertion, perform traversal during querying, and to synthesize the final answer.
+
+Users may also provide their own prompt templates to further customize the behavior of the framework. The best method for customizing is copying the default prompt from the link above, and using that as the base for any modifications.
+
+## Usage Pattern
+
+Currently, there are two ways to customize prompts in LlamaIndex:
+
+For both methods, you will need to create an function that overrides the default prompt.
+
+```ts
+// Define a custom prompt
+const newTextQaPrompt: TextQaPrompt = ({ context, query }) => {
+  return `Context information is below.
+---------------------
+${context}
+---------------------
+Given the context information and not prior knowledge, answer the query.
+Answer the query in the style of a Sherlock Holmes detective novel.
+Query: ${query}
+Answer:`;
+};
+```
+
+### 1. Customizing the default prompt on initialization
+
+The first method is to create a new instance of `ResponseSynthesizer` (or the module you would like to update the prompt) and pass the custom prompt to the `responseBuilder` parameter. Then, pass the instance to the `asQueryEngine` method of the index.
+
+```ts
+// Create an instance of response synthesizer
+const responseSynthesizer = new ResponseSynthesizer({
+  responseBuilder: new CompactAndRefine(serviceContext, newTextQaPrompt),
+});
+
+// Create index
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+// Query the index
+const queryEngine = index.asQueryEngine({ responseSynthesizer });
+
+const response = await queryEngine.query({
+  query: "What did the author do in college?",
+});
+```
+
+### 2. Customizing submodules prompt
+
+The second method is that most of the modules in LlamaIndex have a `getPrompts` and a `updatePrompt` method that allows you to override the default prompt. This method is useful when you want to change the prompt on the fly or in submodules on a more granular level.
+
+```ts
+// Create index
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+// Query the index
+const queryEngine = index.asQueryEngine();
+
+// Get a list of prompts for the query engine
+const prompts = queryEngine.getPrompts();
+
+// output: { "responseSynthesizer:textQATemplate": defaultTextQaPrompt, "responseSynthesizer:refineTemplate": defaultRefineTemplatePrompt }
+
+// Now, we can override the default prompt
+queryEngine.updatePrompt({
+  "responseSynthesizer:textQATemplate": newTextQaPrompt,
+});
+
+const response = await queryEngine.query({
+  query: "What did the author do in college?",
+});
+```

apps/docs/docs/modules/query_engines/index.md

@@ -36,6 +36,6 @@ You can learn more about Tools by taking a look at the LlamaIndex Python documen
 
 ## API Reference
 
-- [RetrieverQueryEngine](../api/classes/RetrieverQueryEngine.md)
-- [SubQuestionQueryEngine](../api/classes/SubQuestionQueryEngine.md)
-- [QueryEngineTool](../api/interfaces//QueryEngineTool.md)
+- [RetrieverQueryEngine](../../api/classes/RetrieverQueryEngine.md)
+- [SubQuestionQueryEngine](../../api/classes/SubQuestionQueryEngine.md)
+- [QueryEngineTool](../../api/interfaces/QueryEngineTool.md)

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

@@ -0,0 +1,152 @@
+# 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/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.2",
+  "version": "0.0.3",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -16,6 +16,7 @@
   },
   "dependencies": {
     "@docusaurus/core": "^3.1.1",
+    "@llamaindex/env": "workspace:*",
     "@docusaurus/remark-plugin-npm2yarn": "^3.1.1",
     "@mdx-js/react": "^3.0.0",
     "clsx": "^2.1.0",

apps/docs/tsconfig.json

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

examples/.gitignore

@@ -1,2 +1,3 @@
 package-lock.json
 storage
+tmp_data

examples/agent/helpers/extractWikipedia.ts

@@ -0,0 +1,55 @@
+import fs from "fs";
+import path from "path";
+
+const dataPath = path.join(__dirname, "tmp_data");
+
+const extractWikipediaTitle = async (title: string) => {
+  const fileExists = fs.existsSync(path.join(dataPath, `${title}.txt`));
+
+  if (fileExists) {
+    console.log(`Arquivo já existe para o título: ${title}`);
+    return;
+  }
+
+  const queryParams = new URLSearchParams({
+    action: "query",
+    format: "json",
+    titles: title,
+    prop: "extracts",
+    explaintext: "true",
+  });
+
+  const url = `https://en.wikipedia.org/w/api.php?${queryParams}`;
+
+  const response = await fetch(url);
+  const data: any = await response.json();
+
+  const pages = data.query.pages;
+  const page = pages[Object.keys(pages)[0]];
+  const wikiText = page.extract;
+
+  await new Promise((resolve) => {
+    fs.writeFile(path.join(dataPath, `${title}.txt`), wikiText, (err: any) => {
+      if (err) {
+        console.error(err);
+        resolve(title);
+        return;
+      }
+      console.log(`${title} stored!`);
+
+      resolve(title);
+    });
+  });
+};
+
+export const extractWikipedia = async (titles: string[]) => {
+  if (!fs.existsSync(dataPath)) {
+    fs.mkdirSync(dataPath);
+  }
+
+  for await (const title of titles) {
+    await extractWikipediaTitle(title);
+  }
+
+  console.log("Extration finished!");
+};

examples/agent/multi_document_agent.ts

@@ -0,0 +1,156 @@
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  ObjectIndex,
+  OpenAI,
+  OpenAIAgent,
+  QueryEngineTool,
+  SimpleNodeParser,
+  SimpleToolNodeMapping,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+import { extractWikipedia } from "./helpers/extractWikipedia";
+
+const wikiTitles = ["Brazil", "Canada"];
+
+async function main() {
+  await extractWikipedia(wikiTitles);
+
+  const countryDocs: Record<string, Document> = {};
+
+  for (const title of wikiTitles) {
+    const path = `./agent/helpers/tmp_data/${title}.txt`;
+    const text = await fs.readFile(path, "utf-8");
+    const document = new Document({ text: text, id_: path });
+    countryDocs[title] = document;
+  }
+
+  const llm = new OpenAI({
+    model: "gpt-4",
+  });
+
+  const serviceContext = serviceContextFromDefaults({ llm });
+  const storageContext = await storageContextFromDefaults({
+    persistDir: "./storage",
+  });
+
+  // TODO: fix any
+  const documentAgents: any = {};
+  const queryEngines: any = {};
+
+  for (const title of wikiTitles) {
+    console.log(`Processing ${title}`);
+
+    const nodes = new SimpleNodeParser({
+      chunkSize: 200,
+      chunkOverlap: 20,
+    }).getNodesFromDocuments([countryDocs[title]]);
+
+    console.log(`Creating index for ${title}`);
+
+    const vectorIndex = await VectorStoreIndex.init({
+      serviceContext: serviceContext,
+      storageContext: storageContext,
+      nodes,
+    });
+
+    const summaryIndex = await SummaryIndex.init({
+      serviceContext: serviceContext,
+      nodes,
+    });
+
+    console.log(`Creating query engines for ${title}`);
+
+    const vectorQueryEngine = summaryIndex.asQueryEngine();
+    const summaryQueryEngine = summaryIndex.asQueryEngine();
+
+    const queryEngineTools = [
+      new QueryEngineTool({
+        queryEngine: vectorQueryEngine,
+        metadata: {
+          name: "vector_tool",
+          description: `Useful for questions related to specific aspects of ${title} (e.g. the history, arts and culture, sports, demographics, or more).`,
+        },
+      }),
+      new QueryEngineTool({
+        queryEngine: summaryQueryEngine,
+        metadata: {
+          name: "summary_tool",
+          description: `Useful for any requests that require a holistic summary of EVERYTHING about ${title}. For questions about more specific sections, please use the vector_tool.`,
+        },
+      }),
+    ];
+
+    console.log(`Creating agents for ${title}`);
+
+    const agent = new OpenAIAgent({
+      tools: queryEngineTools,
+      llm,
+      verbose: true,
+    });
+
+    documentAgents[title] = agent;
+    queryEngines[title] = vectorIndex.asQueryEngine();
+  }
+
+  const allTools: QueryEngineTool[] = [];
+
+  console.log(`Creating tools for all countries`);
+
+  for (const title of wikiTitles) {
+    const wikiSummary = `This content contains Wikipedia articles about ${title}. Use this tool if you want to answer any questions about ${title}`;
+
+    console.log(`Creating tool for ${title}`);
+
+    const docTool = new QueryEngineTool({
+      queryEngine: documentAgents[title],
+      metadata: {
+        name: `tool_${title}`,
+        description: wikiSummary,
+      },
+    });
+
+    allTools.push(docTool);
+  }
+
+  console.log("creating tool mapping");
+
+  const toolMapping = SimpleToolNodeMapping.fromObjects(allTools);
+
+  const objectIndex = await ObjectIndex.fromObjects(
+    allTools,
+    toolMapping,
+    VectorStoreIndex,
+    {
+      serviceContext,
+    },
+  );
+
+  const topAgent = new OpenAIAgent({
+    toolRetriever: await objectIndex.asRetriever({}),
+    llm,
+    verbose: true,
+    prefixMessages: [
+      {
+        content:
+          "You are an agent designed to answer queries about a set of given countries. Please always use the tools provided to answer a question. Do not rely on prior knowledge.",
+        role: "system",
+      },
+    ],
+  });
+
+  const response = await topAgent.chat({
+    message: "Tell me the differences between Brazil and Canada economics?",
+  });
+
+  console.log({
+    capitalOfBrazil: response,
+  });
+}
+
+main();

examples/agent/openai.ts

@@ -0,0 +1,76 @@
+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

@@ -0,0 +1,46 @@
+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/agent/query_openai_agent.ts

@@ -0,0 +1,46 @@
+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/agent/react_agent.ts

@@ -0,0 +1,76 @@
+import { FunctionTool, ReActAgent } from "llamaindex";
+
+// Define a function to sum two numbers
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+
+// Define the parameters of the sum function as a JSON schema
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The dividend",
+    },
+    b: {
+      type: "number",
+      description: "The divisor",
+    },
+  },
+  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 ReActAgent({
+    tools: [functionTool, functionTool2],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "Divide 16 by 2 then add 20",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});

examples/anthropic.ts

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

examples/astradb/README.md

@@ -14,18 +14,27 @@ 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`
 
-## Load the data
+## 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
 
 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

@@ -0,0 +1,58 @@
+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();
+    const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
     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();
+    const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
     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: "What is the best reviewed movie?",
+      query: 'How was "La Sapienza" reviewed?',
     });
 
     console.log(results.response);

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 `pnpx ts-node chromadb/test.ts`.
+Now, open a new terminal window and inside `examples`, run `pnpm dlx ts-node chromadb/test.ts`.
 
 Here's the output for the input query `Tell me about Godfrey Cheshire's rating of La Sapienza.`:
 

examples/cloud/README.md

@@ -0,0 +1,33 @@
+# LlamaCloud Integration
+
+## Getting started
+
+To start the examples call them from the `examples` folder:
+
+And make sure, you're setting your `LLAMA_CLOUD_API_KEY` in your environment variable:
+
+```shell
+export LLAMA_CLOUD_API_KEY=your-api-key
+```
+
+For using another environment, also set the `LLAMA_CLOUD_BASE_URL` environment variable:
+
+```shell
+export LLAMA_CLOUD_BASE_URL="https://api.staging.llamaindex.ai"
+```
+
+## Chat Engine
+
+This example is using the managed index named `test` from the project `default` to create a chat engine.
+
+```shell
+pnpx ts-node cloud/chat.ts
+```
+
+## Query Engine
+
+This example shows how to use the managed index with a query engine.
+
+```shell
+pnpx ts-node cloud/query.ts
+```

examples/cloud/chat.ts

@@ -0,0 +1,29 @@
+import { stdin as input, stdout as output } from "node:process";
+import readline from "node:readline/promises";
+
+import { ContextChatEngine, LlamaCloudIndex } from "llamaindex";
+
+async function main() {
+  const index = new LlamaCloudIndex({
+    name: "test",
+    projectName: "default",
+    baseUrl: process.env.LLAMA_CLOUD_BASE_URL,
+    apiKey: process.env.LLAMA_CLOUD_API_KEY,
+  });
+  const retriever = index.asRetriever({
+    similarityTopK: 5,
+  });
+  const chatEngine = new ContextChatEngine({ retriever });
+  const rl = readline.createInterface({ input, output });
+
+  while (true) {
+    const query = await rl.question("User: ");
+    const stream = await chatEngine.chat({ message: query, stream: true });
+    console.log();
+    for await (const chunk of stream) {
+      process.stdout.write(chunk.response);
+    }
+  }
+}
+
+main().catch(console.error);

examples/cloud/query.ts

@@ -0,0 +1,31 @@
+import { stdin as input, stdout as output } from "node:process";
+import readline from "node:readline/promises";
+
+import { LlamaCloudIndex } from "llamaindex";
+
+async function main() {
+  const index = new LlamaCloudIndex({
+    name: "test",
+    projectName: "default",
+    baseUrl: process.env.LLAMA_CLOUD_BASE_URL,
+    apiKey: process.env.LLAMA_CLOUD_API_KEY,
+  });
+  const queryEngine = index.asQueryEngine({
+    denseSimilarityTopK: 5,
+  });
+  const rl = readline.createInterface({ input, output });
+
+  while (true) {
+    const query = await rl.question("Query: ");
+    const stream = await queryEngine.query({
+      query,
+      stream: true,
+    });
+    console.log();
+    for await (const chunk of stream) {
+      process.stdout.write(chunk.response);
+    }
+  }
+}
+
+main().catch(console.error);

examples/evaluation/correctness.ts

@@ -0,0 +1,36 @@
+import {
+  CorrectnessEvaluator,
+  OpenAI,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({
+    model: "gpt-4",
+  });
+
+  const ctx = serviceContextFromDefaults({
+    llm,
+  });
+
+  const evaluator = new CorrectnessEvaluator({
+    serviceContext: ctx,
+  });
+
+  const query =
+    "Can you explain the theory of relativity proposed by Albert Einstein in detail?";
+
+  const response = `
+Certainly! Albert Einstein's theory of relativity consists of two main components: special relativity and general relativity. Special relativity, published in 1905, introduced the concept that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum is a constant, regardless of the motion of the source or observer. It also gave rise to the famous equation E=mc², which relates energy (E) and mass (m).
+However, general relativity, published in 1915, extended these ideas to include the effects of magnetism. According to general relativity, gravity is not a force between masses but rather the result of the warping of space and time by magnetic fields generated by massive objects. Massive objects, such as planets and stars, create magnetic fields that cause a curvature in spacetime, and smaller objects follow curved paths in response to this magnetic curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet with magnets underneath, causing it to create a depression that other objects (representing smaller masses) naturally move towards due to magnetic attraction.
+`;
+
+  const result = await evaluator.evaluate({
+    query: query,
+    response: response,
+  });
+
+  console.log(result);
+}
+
+main();

examples/evaluation/faithfulness.ts

@@ -0,0 +1,46 @@
+import {
+  Document,
+  FaithfulnessEvaluator,
+  OpenAI,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({
+    model: "gpt-4",
+  });
+
+  const ctx = serviceContextFromDefaults({
+    llm,
+  });
+
+  const evaluator = new FaithfulnessEvaluator({
+    serviceContext: ctx,
+  });
+
+  const documents = [
+    new Document({
+      text: `The city came under British control in 1664 and was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York. The city was regained by the Dutch in July 1673 and was renamed New Orange for one year and three months; the city has been continuously named New York since November 1674. New York City was the capital of the United States from 1785 until 1790, and has been the largest U.S. city since 1790. The Statue of Liberty greeted millions of immigrants as they came to the U.S. by ship in the late 19th and early 20th centuries, and is a symbol of the U.S. and its ideals of liberty and peace. In the 21st century, New York City has emerged as a global node of creativity, entrepreneurship, and as a symbol of freedom and cultural diversity. The New York Times has won the most Pulitzer Prizes for journalism and remains the U.S. media's "newspaper of record". In 2019, New York City was voted the greatest city in the world in a survey of over 30,000 p...	Pass`,
+    }),
+  ];
+
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+
+  const queryEngine = vectorIndex.asQueryEngine();
+
+  const query = "How did New York City get its name?";
+
+  const response = await queryEngine.query({
+    query,
+  });
+
+  const result = await evaluator.evaluateResponse({
+    query,
+    response,
+  });
+
+  console.log(result);
+}
+
+main();

examples/evaluation/relevancy.ts

@@ -0,0 +1,46 @@
+import {
+  Document,
+  OpenAI,
+  RelevancyEvaluator,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({
+    model: "gpt-4",
+  });
+
+  const ctx = serviceContextFromDefaults({
+    llm,
+  });
+
+  const evaluator = new RelevancyEvaluator({
+    serviceContext: ctx,
+  });
+
+  const documents = [
+    new Document({
+      text: `The city came under British control in 1664 and was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York. The city was regained by the Dutch in July 1673 and was renamed New Orange for one year and three months; the city has been continuously named New York since November 1674. New York City was the capital of the United States from 1785 until 1790, and has been the largest U.S. city since 1790. The Statue of Liberty greeted millions of immigrants as they came to the U.S. by ship in the late 19th and early 20th centuries, and is a symbol of the U.S. and its ideals of liberty and peace. In the 21st century, New York City has emerged as a global node of creativity, entrepreneurship, and as a symbol of freedom and cultural diversity. The New York Times has won the most Pulitzer Prizes for journalism and remains the U.S. media's "newspaper of record". In 2019, New York City was voted the greatest city in the world in a survey of over 30,000 p...	Pass`,
+    }),
+  ];
+
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+
+  const queryEngine = vectorIndex.asQueryEngine();
+
+  const query = "How did New York City get its name?";
+
+  const response = await queryEngine.query({
+    query,
+  });
+
+  const result = await evaluator.evaluateResponse({
+    query,
+    response: response,
+  });
+
+  console.log(result);
+}
+
+main();

examples/groq.ts

@@ -0,0 +1,48 @@
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  Groq,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const groq = new Groq({
+    apiKey: process.env.GROQ_API_KEY,
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: groq });
+
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+
+await main();

examples/nodeParser/MarkdownNodeParser.ts

@@ -0,0 +1,24 @@
+import { Document, MarkdownNodeParser } from "llamaindex";
+
+async function main() {
+  const markdownParser = new MarkdownNodeParser();
+
+  const splits = markdownParser.getNodesFromDocuments([
+    new Document({
+      text: `# Main Header
+Main content
+
+# Header 2
+Header 2 content
+
+## Sub-header
+Sub-header content
+
+`,
+    }),
+  ]);
+
+  console.log(splits);
+}
+
+main();

examples/ollama.ts

@@ -1,4 +1,4 @@
-import { Ollama } from "llamaindex";
+import { Ollama } from "llamaindex/llm/ollama";
 
 (async () => {
   const llm = new Ollama({ model: "llama2", temperature: 0.75 });

examples/prompts/promptMixin.ts

@@ -0,0 +1,51 @@
+import {
+  Document,
+  ResponseSynthesizer,
+  TreeSummarize,
+  TreeSummarizePrompt,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+const treeSummarizePrompt: TreeSummarizePrompt = ({ context, query }) => {
+  return `Context information from multiple sources is below.
+---------------------
+${context}
+---------------------
+Given the information from multiple sources and not prior knowledge.
+Answer the query in the style of a Shakespeare play"
+Query: ${query}
+Answer:`;
+};
+
+async function main() {
+  const documents = new Document({
+    text: "The quick brown fox jumps over the lazy dog",
+  });
+
+  const index = await VectorStoreIndex.fromDocuments([documents]);
+
+  const query = "The quick brown fox jumps over the lazy dog";
+
+  const ctx = serviceContextFromDefaults({});
+
+  const responseSynthesizer = new ResponseSynthesizer({
+    responseBuilder: new TreeSummarize(ctx),
+  });
+
+  const queryEngine = index.asQueryEngine({
+    responseSynthesizer,
+  });
+
+  console.log({
+    promptsToUse: queryEngine.getPrompts(),
+  });
+
+  queryEngine.updatePrompts({
+    "responseSynthesizer:summaryTemplate": treeSummarizePrompt,
+  });
+
+  await queryEngine.query({ query });
+}
+
+main();

examples/readers/README.md

@@ -1,61 +1,5 @@
-## Reader Examples
+## LlamaIndex Reader Examples
 
-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
+```shell
+npm run start
 ```

examples/readers/package.json

@@ -0,0 +1,22 @@
+{
+  "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/assemblyai.ts

@@ -2,7 +2,7 @@ import { program } from "commander";
 import { TranscribeParams, VectorStoreIndex } from "llamaindex";
 import { AudioTranscriptReader } from "llamaindex/readers/AssemblyAIReader";
 import { stdin as input, stdout as output } from "node:process";
-import readline from "node:readline/promises";
+import { createInterface } from "node:readline/promises";
 
 program
   .option("-a, --audio [string]", "URL or path of the audio file to transcribe")
@@ -35,7 +35,7 @@ program
     // Create query engine
     const queryEngine = index.asQueryEngine();
 
-    const rl = readline.createInterface({ input, output });
+    const rl = createInterface({ input, output });
     while (true) {
       const query = await rl.question("Ask a question: ");
 

examples/readers/src/csv.ts

@@ -10,7 +10,7 @@ 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/src/custom-simple-directory-reader.ts

@@ -0,0 +1,26 @@
+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/docx.ts

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

examples/readers/src/html.ts

@@ -4,7 +4,7 @@ import { HTMLReader } from "llamaindex/readers/HTMLReader";
 async function main() {
   // Load page
   const reader = new HTMLReader();
-  const documents = await reader.loadData("data/18-1_Changelog.html");
+  const documents = await reader.loadData("../data/llamaindex.html");
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
   const index = await VectorStoreIndex.fromDocuments(documents);
@@ -12,7 +12,7 @@ async function main() {
   // Query the index
   const queryEngine = index.asQueryEngine();
   const response = await queryEngine.query({
-    query: "What were the notable changes in 18.1?",
+    query: "What can I do with LlamaIndex?",
   });
 
   // Output response

examples/readers/src/llamaparse.ts

@@ -0,0 +1,21 @@
+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/markdown.ts

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

examples/readers/src/notion.ts

@@ -3,7 +3,7 @@ import { program } from "commander";
 import { VectorStoreIndex } from "llamaindex";
 import { NotionReader } from "llamaindex/readers/NotionReader";
 import { stdin as input, stdout as output } from "node:process";
-import readline from "node:readline/promises";
+import { createInterface } from "node:readline/promises";
 
 program
   .argument("[page]", "Notion page id (must be provided)")
@@ -70,7 +70,7 @@ program
     // Create query engine
     const queryEngine = index.asQueryEngine();
 
-    const rl = readline.createInterface({ input, output });
+    const rl = createInterface({ input, output });
     while (true) {
       const query = await rl.question("Query: ");
 

examples/readers/src/pdf.ts

@@ -1,13 +1,10 @@
 import { VectorStoreIndex } from "llamaindex";
 import { PDFReader } from "llamaindex/readers/PDFReader";
-import { resolve } from "node:path";
 
 async function main() {
   // Load PDF
   const reader = new PDFReader();
-  const documents = await reader.loadData(
-    resolve(__dirname, "../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);

examples/readers/src/pdf_fw.ts

@@ -0,0 +1,36 @@
+import { FireworksEmbedding, FireworksLLM, VectorStoreIndex } from "llamaindex";
+import { PDFReader } from "llamaindex/readers/PDFReader";
+
+import { serviceContextFromDefaults } from "llamaindex";
+
+const embedModel = new FireworksEmbedding({
+  model: "nomic-ai/nomic-embed-text-v1.5",
+});
+
+const llm = new FireworksLLM({
+  model: "accounts/fireworks/models/mixtral-8x7b-instruct",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm, embedModel });
+
+async function main() {
+  // Load PDF
+  const reader = new PDFReader();
+  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, {
+    serviceContext,
+  });
+
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query({
+    query: "What mistakes did Warren E. Buffett make?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);

examples/readers/src/pdf_fw_openai.ts

@@ -0,0 +1,36 @@
+import { OpenAI, OpenAIEmbedding, VectorStoreIndex } from "llamaindex";
+import { PDFReader } from "llamaindex/readers/PDFReader";
+
+import { serviceContextFromDefaults } from "llamaindex";
+
+const embedModel = new OpenAIEmbedding({
+  model: "nomic-ai/nomic-embed-text-v1.5",
+});
+
+const llm = new OpenAI({
+  model: "accounts/fireworks/models/mixtral-8x7b-instruct",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm, embedModel });
+
+async function main() {
+  // Load PDF
+  const reader = new PDFReader();
+  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, {
+    serviceContext,
+  });
+
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query({
+    query: "What mistakes did Warren E. Buffett make?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);