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Supported Engines

  • Chat Preview
  • Code Preview

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Chat Engine

The chat engine is general-purpose chat-interactions such as chatbots, support assistants, etc. It intelligently routes each query to one of the below models:

model selection:

  • GPT-4-Turbo
  • Claude 3 Sonnet
  • Claude 3 Haiku

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Code Engine

The code engine is optimized for coding-related use-cases such as code generation, coding copilots, code explanation, etc. It intelligently routes each query to one of the below models:

model selection:

  • GPT-4-Turbo
  • Claude 3 Sonnet
  • Claude 3 Haiku

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Usage

An engine is a collection of LLMs with a routing function that identifies the optimal model for each given query. You can treat an engine as a sort of ‘meta LLM’.

from openai import OpenAI

client = OpenAI(
    base_url="https://router.neutrinoapp.com/api/engines",
    api_key="<Neutrino-API-key>"
)

response = client.chat.completions.create(
    # Instead of a specific model, set this to the Neutrino engine of choice
    model="chat-preview",  # options: "chat-preview", "code-preview"
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant. Your job is to be helpful and respond to user requests."},
        {"role": "user", "content": "What is a Neutrino?"},
    ],
)

print(f"Optimal model: {response.model}")
print(response.choices[0].message.content)

​
Streaming Responses

from openai import OpenAI

client = OpenAI(
    base_url="https://router.neutrinoapp.com/api/engines",
    api_key="<Neutrino-API-key>"
)

response = client.chat.completions.create(
    # Instead of a specific model, set this to the Neutrino engine of choice
    model="chat-preview",  # options: "chat-preview", "code-preview"
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant. Your job is to be helpful and respond to user requests."},
        {"role": "user", "content": "Does a Neutrino have mass?"},
    ],
    stream=True
)

for i, chunk in enumerate(response):
    if i == 0:
        print(f"Optimal model: {chunk.model}")
    print(chunk.choices[0].delta.content, end="")