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

# Instantiate LLM Nodes

## Tutorial Overview

The next step in our tutorial is preparing our `LLM` calls.

## Using the LLM class

Trellis comes with a [pre-built LLM tool](../reference/llm) which already handles rate limits and errors, so we'll be using that.
Trellis currently only supports OpenAI, so if you want to use a different provider you'll have to extend `Node` and write your own tool for it.
Each Trellis `LLM` node is effectively one call to the OpenAI API, so we'll need two `LLM` nodes for our DAG.

### Imports

First, we'll import the `LLM` class from the `trellis` package.

```python example_dag.py theme={null}
from trellis_dag import LLM
```

### Initializing the LLM generating the cat fact

Next, we'll initialize the LLM that's generating the cat fact.

```python example_dag.py theme={null}
generate_cat_fact_llm_msgs = [
    {
        "role": "user",
        "content": "Tell me a random cat fact, as a sentence.",
    }
]
generate_cat_fact_llm = LLM(
    "generate_cat_fact_llm", messages=generate_cat_fact_llm_msgs
)
```

The `LLM` class only requires a `name` to be initialized. `messages` is also very important, but
Trellis lets you set it using `set_messages` or through the constructor. In this case, we'll use the constructor,
and we'll use `set_messages` for the next LLM call.
Since our prompt doesn't have any variadic input, we can leave the input schema `input_s` blank.
Other than `stream`, you can set any other arguments that you'd expect within the OpenAI API spec for chat completions.

### Initializing the LLM judging the cat fact

Now, we'll initialize the last Node needed for our DAG, the LLM that's judging the cat fact.

```python example_dag.py theme={null}
distinguish_cat_fact_llm_msgs = [
    {
        "role": "user",
        "content": "Which of these was generated by an LLM? 1. {cat_fact_1} 2. {cat_fact_2} Give your answer as 1 or 2.",
    }
]
distinguish_cat_fact_llm = LLM("distinguish_cat_fact_llm")
distinguish_cat_fact_llm.set_messages(distinguish_cat_fact_llm_msgs)
```

We'll use `set_messages` this time to set the messages. In the messages, we're using `{cat_fact_1}` and `{cat_fact_2}` to reference the outputs of the previous `Node`s.
These will be filled in when we connect the nodes together through edges in the next section.

### Putting it all together

That's it for the LLM code! Visit the [LLM reference](../reference/llm) to learn more. Here's the full code for this tutorial:

```python example_dag.py theme={null}
from trellis_dag import LLM

generate_cat_fact_llm_msgs = [
    {
        "role": "user",
        "content": "Tell me a random cat fact, as a sentence.",
    }
]
generate_cat_fact_llm = LLM(
    "generate_cat_fact_llm", messages=generate_cat_fact_llm_msgs
)

distinguish_cat_fact_llm_msgs = [
    {
        "role": "user",
        "content": "Which of these was generated by an LLM? 1. {cat_fact_1} 2. {cat_fact_2} Give your answer as 1 or 2.",
    }
]
distinguish_cat_fact_llm = LLM("distinguish_cat_fact_llm")
distinguish_cat_fact_llm.set_messages(distinguish_cat_fact_llm_msgs)
```

Move onto the next section to connect the `Node`s together in a `DAG`.