Model Access: LLM Inference Through Nonhumans

const response = await agent.models.chat({
  model: 'claude-3-5-sonnet',
  messages: [
    { role: 'system', content: 'You are a helpful scheduling assistant.' },
    { role: 'user', content: 'Find a time for Alice and Bob to meet this week.' },
  ],
});

console.log(response.content);

response = await agent.models.chat(
    model="claude-3-5-sonnet",
    messages=[
        {"role": "system", "content": "You are a helpful scheduling assistant."},
        {"role": "user", "content": "Find a time for Alice and Bob to meet this week."},
    ],
)

print(response.content)

const stream = await agent.models.chat({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Write me a project brief.' }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.delta);
}

stream = await agent.models.chat(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write me a project brief."}],
    stream=True,
)

async for chunk in stream:
    print(chunk.delta, end="", flush=True)

const embedding = await agent.models.embed({
  model: 'text-embedding-3-small',
  input: 'Alice prefers morning meetings and async communication.',
});

// embedding.vector is a Float32Array ready to store
console.log('Dimensions:', embedding.vector.length);

embedding = await agent.models.embed(
    model="text-embedding-3-small",
    input="Alice prefers morning meetings and async communication.",
)

# embedding.vector is a list of floats ready to store
print("Dimensions:", len(embedding.vector))