It only says about neuron which seems to only related to LLM space.

const response = await env.AI.run('@cf/meta/llama-2-7b-chat-int8', {
  prompt: "tell me a joke about cloudflare";
});

const response = await env.AI.run('@cf/meta/llama-2-7b-chat-int8', {
  prompt: "tell me a joke about cloudflare";
});

const response = await ai.run(model || "@cf/stabilityai/stable-diffusion-xl-base-1.0", requestInput);
// Store the image name in R2, in background        
ctx.waitUntil(updateRecentImages(imageName, (await generateThumbs(imageName, '400x', true)), input, env, response));
return new Response(response, {
    headers: {
        "content-type": "image/png",
    },
});

const response = await ai.run(model || "@cf/stabilityai/stable-diffusion-xl-base-1.0", requestInput);
// Store the image name in R2, in background        
ctx.waitUntil(updateRecentImages(imageName, (await generateThumbs(imageName, '400x', true)), input, env, response));
return new Response(response, {
    headers: {
        "content-type": "image/png",
    },
});

{
  "errors": [
    {
      "message": "Server Error",
      "code": 6001
    }
  ],
  "success": false,
  "result": {},
  "messages": []
}

{
  "errors": [
    {
      "message": "Server Error",
      "code": 6001
    }
  ],
  "success": false,
  "result": {},
  "messages": []
}

VECTOR_UPSERT_ERROR (code = 40012): invalid vector for id="sdfksd", expected 768 dimensions, and got 1024 dimensions

VECTOR_UPSERT_ERROR (code = 40012): invalid vector for id="sdfksd", expected 768 dimensions, and got 1024 dimensions

const embeddingResult = await env.AI.run('@cf/baai/bge-large-en-v1.5', {
      text:  value,      
    });
    const embeddingBatch: number[][] = embeddingResult.data;

    await env.VECTORIZE.upsert(
      embeddingBatch.map((embedding, index) => ({
        id: sourceId,
        values: embedding,
        namespace: 'default',
        metadata: {
          id: sessionId
        },
      }))
    );

const embeddingResult = await env.AI.run('@cf/baai/bge-large-en-v1.5', {
      text:  value,      
    });
    const embeddingBatch: number[][] = embeddingResult.data;

    await env.VECTORIZE.upsert(
      embeddingBatch.map((embedding, index) => ({
        id: sourceId,
        values: embedding,
        namespace: 'default',
        metadata: {
          id: sessionId
        },
      }))
    );