82 lines
2.1 KiB
TypeScript
82 lines
2.1 KiB
TypeScript
#!/usr/bin/env bun
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/**
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* Minimal example: Send an image to a Letta agent
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*
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* Usage:
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* bun examples/send-image.ts <agent-id> <image-path>
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*
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* Example:
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* bun examples/send-image.ts agent-123abc screenshot.png
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*/
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import { readFileSync } from "node:fs";
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import { getClient } from "../src/agent/client";
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import { sendMessageStream } from "../src/agent/message";
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async function main() {
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const agentId = process.argv[2];
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const imagePath = process.argv[3];
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if (!agentId || !imagePath) {
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console.error("Usage: bun send-image.ts <agent-id> <image-path>");
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process.exit(1);
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}
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// Step 1: Read image file and convert to base64
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const imageBuffer = readFileSync(imagePath);
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const base64Data = imageBuffer.toString("base64");
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// Determine media type from file extension
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const ext = imagePath.toLowerCase();
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const mediaType = ext.endsWith(".png")
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? "image/png"
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: ext.endsWith(".jpg") || ext.endsWith(".jpeg")
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? "image/jpeg"
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: ext.endsWith(".gif")
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? "image/gif"
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: ext.endsWith(".webp")
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? "image/webp"
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: "image/jpeg"; // default
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console.log(`Sending ${imagePath} to agent ${agentId}...`);
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// Step 2: Create message content with text and image
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const messageContent = [
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{ type: "text" as const, text: "What do you see in this image?" },
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{
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type: "image" as const,
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source: {
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type: "base64" as const,
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mediaType,
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data: base64Data,
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},
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},
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];
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// Step 3: Send message to agent
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const stream = await sendMessageStream(agentId, [
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{
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role: "user",
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content: messageContent,
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},
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]);
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// Step 4: Process the streaming response
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console.log("\nAgent response:");
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for await (const chunk of stream) {
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if (chunk.messageType === "assistant_message") {
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process.stdout.write(chunk.content || "");
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} else if (chunk.messageType === "reasoning_message") {
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// Optionally show internal monologue
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// console.log(`[thinking] ${chunk.reasoning}`);
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}
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}
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console.log("\n");
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}
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main().catch((err) => {
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console.error("Error:", err);
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process.exit(1);
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});
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