feat: add vercel chat example (#1532)

Co-authored-by: Marcus Schiesser <[email protected]>

.changeset/clever-monkeys-switch.md

@@ -0,0 +1,7 @@
+---
+"@llamaindex/vercel": patch
+"@llamaindex/doc": patch
+"@llamaindex/examples": patch
+---
+
+Add vercel tool adapter to use query engine tool

apps/next/src/content/docs/llamaindex/integration/meta.json

@@ -1,5 +1,5 @@
 {
   "title": "Integration",
   "description": "See our integrations",
-  "pages": ["open-llm-metry", "lang-trace"]
+  "pages": ["open-llm-metry", "lang-trace", "vercel"]
 }

apps/next/src/content/docs/llamaindex/integration/vercel.mdx

@@ -0,0 +1,80 @@
+---
+title: Vercel
+description: Integrate LlamaIndex with Vercel's AI SDK
+---
+
+LlamaIndex provides integration with Vercel's AI SDK, allowing you to create powerful search and retrieval applications. Below are examples of how to use LlamaIndex with `streamText` from the Vercel AI SDK.
+
+## Setup
+
+First, install the required dependencies:
+
+```bash
+npm install @llamaindex/vercel ai
+```
+
+## Using Local Vector Store
+
+Here's how to create a simple vector store index and query it using Vercel's AI SDK:
+
+```typescript
+import { openai } from "@ai-sdk/openai";
+import { llamaindex } from "@llamaindex/vercel";
+import { streamText } from "ai";
+import { Document, VectorStoreIndex } from "llamaindex";
+
+// Create an index from your documents
+const document = new Document({ text: yourText, id_: "unique-id" });
+const index = await VectorStoreIndex.fromDocuments([document]);
+
+// Create a query tool
+const queryTool = llamaindex({
+  index,
+  description: "Search through the documents", // optional
+});
+
+// Use the tool with Vercel's AI SDK
+streamText({
+  tools: { queryTool },
+  prompt: "Your question here",
+  model: openai("gpt-4"),
+  onFinish({ response }) {
+    console.log("Response:", response.messages); // log the response
+  },
+}).toDataStream();
+```
+
+## Using LlamaCloud
+
+For production deployments, you can use LlamaCloud to store and manage your documents:
+
+```typescript
+import { LlamaCloudIndex } from "llamaindex";
+
+// Create a LlamaCloud index
+const index = await LlamaCloudIndex.fromDocuments({
+  documents: [document],
+  name: "your-index-name",
+  projectName: "your-project",
+  apiKey: process.env.LLAMA_CLOUD_API_KEY,
+});
+
+// Use it the same way as VectorStoreIndex
+const queryTool = llamaindex({
+  index,
+  description: "Search through the documents",
+});
+
+// Use the tool with Vercel's AI SDK
+streamText({
+  tools: { queryTool },
+  prompt: "Your question here",
+  model: openai("gpt-4"),
+}).toDataStream();
+```
+
+## Next Steps
+
+1. Explore [LlamaCloud](https://cloud.llamaindex.ai/) for managed document storage and retrieval
+2. Join our [Discord community](https://discord.gg/llamaindex) for support and discussions
+

examples/package.json

@@ -3,17 +3,20 @@
   "private": true,
   "version": "0.0.16",
   "dependencies": {
+    "@ai-sdk/openai": "^1.0.5",
     "@aws-crypto/sha256-js": "^5.2.0",
     "@azure/cosmos": "^4.1.1",
     "@azure/identity": "^4.4.1",
     "@datastax/astra-db-ts": "^1.4.1",
     "@llamaindex/core": "^0.4.10",
     "@llamaindex/readers": "^1.0.11",
     "@llamaindex/workflow": "^0.0.6",
+    "@llamaindex/vercel": "^0.0.1",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^4.0.0",
     "@vercel/postgres": "^0.10.0",
     "@zilliz/milvus2-sdk-node": "^2.4.6",
+    "ai": "^4.0.0",
     "chromadb": "^1.8.1",
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",

examples/vercel/README.md

@@ -0,0 +1,50 @@
+# Vercel Examples
+
+These examples demonstrate how to integrate LlamaIndexTS with Vercel's AI SDK. The examples show how to use LlamaIndex for search and retrieval in both local vector store and LlamaCloud environments.
+
+## Setup
+
+To run these examples, first install the required dependencies from the parent folder `examples`:
+
+```bash
+npm i
+```
+
+## Running the Examples
+
+Make sure to run the examples from the parent folder called `examples`. The following examples are available:
+
+### Vector Store Example
+
+Run the local vector store example with:
+
+```bash
+npx tsx vercel/vector-store.ts
+```
+
+This example demonstrates:
+
+- Creating a vector store index from one document
+- Using Vercel's AI SDK with LlamaIndex for streaming responses
+
+### LlamaCloud Example
+
+To run the LlamaCloud example:
+
+```bash
+npx tsx vercel/llamacloud.ts
+```
+
+This example requires a LlamaCloud API key set in your environment and an embedding model set in the `EMBEDDING_MODEL` environment variable:
+
+```bash
+export LLAMA_CLOUD_API_KEY=your_api_key_here
+export EMBEDDING_MODEL="text-embedding-3-small"
+```
+
+The example demonstrates:
+
+- Creating a LlamaCloud index from one document
+- Streaming responses using Vercel's AI SDK
+
+For more detailed information about the Vercel integration, check out [the documentation](https://ts.llamaindex.ai/docs/llamaindex/integration/vercel).

examples/vercel/llamacloud.ts

@@ -0,0 +1,38 @@
+import { openai } from "@ai-sdk/openai";
+import { llamaindex } from "@llamaindex/vercel";
+import { streamText } from "ai";
+import { Document, LlamaCloudIndex } from "llamaindex";
+import fs from "node:fs/promises";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await LlamaCloudIndex.fromDocuments({
+    documents: [document],
+    name: "test-pipeline",
+    projectName: "Default",
+    apiKey: process.env.LLAMA_CLOUD_API_KEY,
+  });
+  console.log("Successfully created index");
+
+  const result = streamText({
+    model: openai("gpt-4o"),
+    prompt: "Cost of moving cat from Russia to UK?",
+    tools: {
+      queryTool: llamaindex({
+        index,
+        description:
+          "get information from your knowledge base to answer questions.", // optional description
+      }),
+    },
+    maxSteps: 5,
+  });
+
+  for await (const textPart of result.textStream) {
+    process.stdout.write(textPart);
+  }
+}
+
+main().catch(console.error);

examples/vercel/vector-store.ts

@@ -0,0 +1,34 @@
+import { openai } from "@ai-sdk/openai";
+import { llamaindex } from "@llamaindex/vercel";
+import { streamText } from "ai";
+import { Document, VectorStoreIndex } from "llamaindex";
+
+import fs from "node:fs/promises";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await VectorStoreIndex.fromDocuments([document]);
+  console.log("Successfully created index");
+
+  const result = streamText({
+    model: openai("gpt-4o"),
+    prompt: "Cost of moving cat from Russia to UK?",
+    tools: {
+      queryTool: llamaindex({
+        index,
+        description:
+          "get information from your knowledge base to answer questions.", // optional description
+      }),
+    },
+    maxSteps: 5,
+  });
+
+  for await (const textPart of result.textStream) {
+    process.stdout.write(textPart);
+  }
+}
+
+main().catch(console.error);

packages/providers/vercel/package.json

@@ -0,0 +1,50 @@
+{
+  "name": "@llamaindex/vercel",
+  "description": "Vercel Adapter for LlamaIndex",
+  "version": "0.0.1",
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "exports": {
+    ".": {
+      "edge-light": {
+        "types": "./dist/index.edge-light.d.ts",
+        "default": "./dist/index.edge-light.js"
+      },
+      "workerd": {
+        "types": "./dist/index.edge-light.d.ts",
+        "default": "./dist/index.edge-light.js"
+      },
+      "require": {
+        "types": "./dist/index.d.cts",
+        "default": "./dist/index.cjs"
+      },
+      "import": {
+        "types": "./dist/index.d.ts",
+        "default": "./dist/index.js"
+      }
+    }
+  },
+  "files": [
+    "dist"
+  ],
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/run-llama/LlamaIndexTS.git",
+    "directory": "packages/providers/vercel"
+  },
+  "scripts": {
+    "build": "bunchee",
+    "dev": "bunchee --watch"
+  },
+  "devDependencies": {
+    "bunchee": "5.6.1"
+  },
+  "dependencies": {
+    "@llamaindex/core": "workspace:*",
+    "zod": "^3.23.8"
+  },
+  "peerDependencies": {
+    "ai": "^4.0.0"
+  }
+}

packages/providers/vercel/src/index.ts

@@ -0,0 +1 @@
+export { llamaindex } from "./tool";

packages/providers/vercel/src/tool.ts

@@ -0,0 +1,29 @@
+import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
+import { type CoreTool, tool } from "ai";
+import { z } from "zod";
+
+interface DatasourceIndex {
+  asQueryEngine: () => BaseQueryEngine;
+}
+
+export function llamaindex({
+  index,
+  description,
+}: {
+  index: DatasourceIndex;
+  description?: string;
+}): CoreTool {
+  const queryEngine = index.asQueryEngine();
+  return tool({
+    description: description ?? "Get information about your documents.",
+    parameters: z.object({
+      query: z
+        .string()
+        .describe("The query to get information about your documents."),
+    }),
+    execute: async ({ query }) => {
+      const result = await queryEngine?.query({ query });
+      return result?.message.content ?? "No result found in documents.";
+    },
+  });
+}

packages/providers/vercel/tsconfig.json

@@ -0,0 +1,19 @@
+{
+  "extends": "../../../tsconfig.json",
+  "compilerOptions": {
+    "target": "ESNext",
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "outDir": "./lib",
+    "tsBuildInfoFile": "./lib/.tsbuildinfo"
+  },
+  "include": ["./src"],
+  "references": [
+    {
+      "path": "../../core/tsconfig.json"
+    },
+    {
+      "path": "../../env/tsconfig.json"
+    }
+  ]
+}