fix: simplify examples

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

@@ -15,20 +15,24 @@ async function main() {
     projectName: "Default",
     apiKey: process.env.LLAMA_CLOUD_API_KEY,
   });
-  const queryTool = llamaindex({
-    index,
-    description: "Search through the documents", // optional description
-  });
-  console.log("Successfully created index and queryTool");
+  console.log("Successfully created index");
 
-  streamText({
-    tools: { queryTool },
-    prompt: "Cost of moving cat from Russia to UK?",
+  const result = streamText({
     model: openai("gpt-4o"),
-    onFinish({ response }) {
-      console.log("Response:", JSON.stringify(response.messages, null, 2));
+    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
+      }),
     },
-  }).toDataStream();
+    maxSteps: 5,
+  });
+
+  for await (const textPart of result.textStream) {
+    process.stdout.write(textPart);
+  }
 }
 
 main().catch(console.error);

examples/vercel/vector-store.ts

@@ -2,6 +2,7 @@ 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() {
@@ -10,20 +11,24 @@ async function main() {
   const document = new Document({ text: essay, id_: path });
 
   const index = await VectorStoreIndex.fromDocuments([document]);
-  const queryTool = llamaindex({
-    index,
-    description: "Search through the documents", // optional description
-  });
-  console.log("Successfully created index and queryTool");
+  console.log("Successfully created index");
 
-  streamText({
-    tools: { queryTool },
-    prompt: "Cost of moving cat from Russia to UK?",
+  const result = streamText({
     model: openai("gpt-4o"),
-    onFinish({ response }) {
-      console.log("Response:", JSON.stringify(response.messages, null, 2));
+    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
+      }),
     },
-  }).toDataStream();
+    maxSteps: 5,
+  });
+
+  for await (const textPart of result.textStream) {
+    process.stdout.write(textPart);
+  }
 }
 
 main().catch(console.error);

packages/providers/vercel/src/tool.ts

@@ -1,5 +1,5 @@
 import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
-import type { CoreTool } from "ai";
+import { type CoreTool, tool } from "ai";
 import { z } from "zod";
 
 interface DatasourceIndex {
@@ -14,7 +14,7 @@ export function llamaindex({
   description?: string;
 }): CoreTool {
   const queryEngine = index.asQueryEngine();
-  return {
+  return tool({
     description: description ?? "Get information about your documents.",
     parameters: z.object({
       query: z
@@ -25,5 +25,5 @@ export function llamaindex({
       const result = await queryEngine?.query({ query });
       return result?.message.content ?? "No result found in documents.";
     },
-  };
+  });
 }