LlamaIndex.TS

LlamaIndex is a data framework for your LLM application.

Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

Documentation: https://ts.llamaindex.ai/

What is LlamaIndex.TS?

LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.

Getting started with an example:

LlamaIndex.TS requries Node v18 or higher. You can download it from https://nodejs.org or use https://nvm.sh (our preferred option).

In a new folder:

export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
pnpm init
pnpm install typescript
pnpm exec tsc –-init # if needed
pnpm install llamaindex
pnpm install @types/node

Create the file example.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

pnpm dlx ts-node example.ts

Core concepts for getting started:

Document: A document represents a text file, PDF file or other contiguous piece of data.
Node: The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
Embedding: Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton.
Indices: Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
QueryEngine: Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query.
ChatEngine: A ChatEngine helps you build a chatbot that will interact with your Indices.
SimplePrompt: A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.

Playground

Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground

Contributing:

We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our contributing guide

Bugs? Questions?

Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

sweepai-dev / LlamaIndexTS

readme