Add empty and null check for the vefctorQuery.matches[0], output necessary log (#17478)

Co-authored-by: daisyfaithauma <[email protected]>

Author: xqliu

src/content/docs/workers-ai/tutorials/build-a-retrieval-augmented-generation-ai.mdx

@@ -280,47 +280,46 @@ const app = new Hono();
 // Existing post route...
 // app.post('/notes', async (c) => { ... })
 
-app.get("/", async (c) => {
-	const question = c.req.query("text") || "What is the square root of 9?";
-
-	const embeddings = await c.env.AI.run("@cf/baai/bge-base-en-v1.5", {
-		text: question,
-	});
-	const vectors = embeddings.data[0];
-
-	const vectorQuery = await c.env.VECTOR_INDEX.query(vectors, { topK: 1 });
-	let vecId;
-	if (vectorQuery?.matches?.length) {
-		vecId = vectorQuery.matches[0].id;
-	} else {
-		console.log("No matching vector found or vectorQuery.matches is empty");
-	}
-
-	let notes = [];
-	if (vecId) {
-		const query = `SELECT * FROM notes WHERE id = ?`;
-		const { results } = await c.env.DB.prepare(query).bind(vecId).all();
-		if (results) notes = results.map((vec) => vec.text);
-	}
-
-	const contextMessage = notes.length
-		? `Context:\n${notes.map((note) => `- ${note}`).join("\n")}`
-		: "";
-
-	const systemPrompt = `When answering the question or responding, use the context provided, if it is provided and relevant.`;
-
-	const { response: answer } = await c.env.AI.run(
-		"@cf/meta/llama-3-8b-instruct",
-		{
-			messages: [
-				...(notes.length ? [{ role: "system", content: contextMessage }] : []),
-				{ role: "system", content: systemPrompt },
-				{ role: "user", content: question },
-			],
-		},
-	);
+app.get('/', async (c) => {
+  const question = c.req.query('text') || "What is the square root of 9?"
+
+  const embeddings = await c.env.AI.run('@cf/baai/bge-base-en-v1.5', { text: question })
+  const vectors = embeddings.data[0]
+
+  const vectorQuery = await c.env.VECTOR_INDEX.query(vectors, { topK: 1 });
+  let vecId;
+  if (vectorQuery.matches && vectorQuery.matches.length > 0 && vectorQuery.matches[0]) {
+    vecId = vectorQuery.matches[0].id;
+  } else {
+    console.log("No matching vector found or vectorQuery.matches is empty");
+  }
+
+  let notes = []
+  if (vecId) {
+    const query = `SELECT * FROM notes WHERE id = ?`
+    const { results } = await c.env.DB.prepare(query).bind(vecId).all()
+    if (results) notes = results.map(vec => vec.text)
+  }
+
+  const contextMessage = notes.length
+    ? `Context:\n${notes.map(note => `- ${note}`).join("\n")}`
+    : ""
+
+  const systemPrompt = `When answering the question or responding, use the context provided, if it is provided and relevant.`
+
+  const { response: answer } = await c.env.AI.run(
+    '@cf/meta/llama-3-8b-instruct',
+    {
+      messages: [
+        ...(notes.length ? [{ role: 'system', content: contextMessage }] : []),
+        { role: 'system', content: systemPrompt },
+        { role: 'user', content: question }
+      ]
+    }
+  )
+
+  return c.text(answer);
 
-	return c.text(answer);
 });
 
 app.onError((err, c) => {