Update README.md

README.md

@@ -11,6 +11,10 @@ Use your own data with large language models (LLMs, OpenAI ChatGPT and others) i
 
 Documentation: https://ts.llamaindex.ai/
 
+Try examples online:
+
+[![Open in Stackblitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples)
+
 ## 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.

examples/README.md

@@ -1,29 +1,19 @@
-# Simple Examples
+# LlamaIndexTS Examples
 
-Before running any of the examples, make sure to set your OpenAI environment variable:
+Before running any of the code examples,
+make sure you have basic knowledge of the [LlamaIndexTS](https://ts.llamaindex.ai/).
 
-```bash
+## Usage
+
+```shell
+# export your API key
 export OPENAI_API_KEY="sk-..."
+
+npx ts-node ./chatEngine.ts
 ```
 
-There are two ways to run the examples, using the latest published version of `llamaindex` or using a local build.
+## Build your own RAG app
 
-## Using the latest published version
-
-Make sure to call `npm install` before running these examples:
-
-```bash
-npm install
+```shell
+npx create llama
 ```
-
-Then run the examples with `ts-node`, for example `npx ts-node vectorIndex.ts`
-
-## Using the local build
-
-```bash
-pnpm install
-pnpm --filter llamaindex build
-pnpm link ../packages/core
-```
-
-Then run the examples with `ts-node`, for example `pnpx ts-node vectorIndex.ts`

examples/chatEngine.ts

@@ -10,7 +10,7 @@ import {
   VectorStoreIndex,
 } from "llamaindex";
 
-import essay from "./essay.js";
+import essay from "./essay";
 
 async function main() {
   const document = new Document({ text: essay });

examples/keywordIndex.ts

@@ -3,7 +3,7 @@ import {
   KeywordTableIndex,
   KeywordTableRetrieverMode,
 } from "llamaindex";
-import essay from "./essay.js";
+import essay from "./essay";
 
 async function main() {
   const document = new Document({ text: essay, id_: "essay" });

examples/package.json

@@ -1,7 +1,6 @@
 {
-  "version": "0.0.3",
-  "private": true,
   "name": "examples",
+  "private": true,
   "dependencies": {
     "@datastax/astra-db-ts": "^0.1.2",
     "@notionhq/client": "^2.2.14",

examples/sentenceWindow.ts

@@ -6,7 +6,7 @@ import {
   VectorStoreIndex,
   serviceContextFromDefaults,
 } from "llamaindex";
-import essay from "./essay.js";
+import essay from "./essay";
 
 async function main() {
   const document = new Document({ text: essay, id_: "essay" });

examples/storageContext.ts

@@ -3,7 +3,7 @@ import {
   storageContextFromDefaults,
   VectorStoreIndex,
 } from "llamaindex";
-import essay from "./essay.js";
+import essay from "./essay";
 
 async function main() {
   // Create Document object with essay

examples/subquestion.ts

@@ -1,6 +1,6 @@
 import { Document, SubQuestionQueryEngine, VectorStoreIndex } from "llamaindex";
 
-import essay from "./essay.js";
+import essay from "./essay";
 
 (async () => {
   const document = new Document({ text: essay, id_: essay });

examples/summaryIndex.ts

@@ -5,7 +5,7 @@ import {
   SummaryRetrieverMode,
   serviceContextFromDefaults,
 } from "llamaindex";
-import essay from "./essay.js";
+import essay from "./essay";
 
 async function main() {
   const serviceContext = serviceContextFromDefaults({

examples/tsconfig.json

@@ -1,5 +1,17 @@
 {
-  "extends": "../tsconfig.json",
+  "compilerOptions": {
+    "target": "es2016",
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "esModuleInterop": true,
+    "forceConsistentCasingInFileNames": true,
+    "strict": true,
+    "skipLibCheck": true,
+    "outDir": "./lib",
+    "tsBuildInfoFile": "./lib/.tsbuildinfo",
+    "incremental": true,
+    "composite": true,
+  },
   "ts-node": {
     "files": true,
     "compilerOptions": {

examples/vectorIndexCustomize.ts

@@ -6,7 +6,7 @@ import {
   SimilarityPostprocessor,
   VectorStoreIndex,
 } from "llamaindex";
-import essay from "./essay.js";
+import essay from "./essay";
 
 // Customize retrieval and query args
 async function main() {