> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neutrinoapp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Optimized Inference Engines

> Inference Engines are designed to generate optimal LLM inference for their respective use-case. They have access to a carefully curated model selection and intelligently route queries to the best-suited LLM for each prompt. Maximize response quality while optimizing for cost and latency.

## Supported Engines

* **Chat Preview**
* **Code Preview**

### 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

### 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

***

## 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'.

```python theme={null}
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

```python theme={null}
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="")

```