> ## 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.

# Connect Nodes together

## Tutorial Overview

The next step in our tutorial is connecting our `Node`s together to build a DAG.

## Building a DAG

All we need to do is instantiate an instance of a DAG, add instances of our `Node`s, and add edges between them.

### Instantiate the DAG

First, we need to import the `DAG` class.

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

Then, we can instantiate a `DAG`.

```python example_dag.py theme={null}
dag = DAG()
```

`DAG` takes no arguments. We'll set everything up through function calls to it.

### Import our cat fact tool

We need to import our `CatFactsAPITool` class from the previous tutorial.

```python example_dag.py theme={null}
from cat_fact_tool import CatFactsAPITool

cat_fact_tool = CatFactsAPITool("cat_fact_tool")
```

### Add Nodes

Next, we'll add our `Node`s using the `add_node` function.

```python example_dag.py theme={null}
dag.add_node(cat_fact_tool)
dag.add_node(generate_cat_fact_llm)
dag.add_node(distinguish_cat_fact_llm)
```

### Add Edges

Finally, we'll add edges between our `Node`s using the `add_edge` function.

```python example_dag.py theme={null}
dag.add_edge(cat_fact_tool, distinguish_cat_fact_llm)
```

The cat\_fact\_tool -> `distinguish_cat_fact_llm` edge is easy, because we already changed the output name of `cat_fact_tool` to `cat_fact`.

```python example_dag.py theme={null}
dag.add_edge(
    generate_cat_fact_llm,
    distinguish_cat_fact_llm,
    fn=lambda x: {"cat_fact_2": x["choices"][0]["message"]["content"]},
)
```

The generate\_cat\_fact\_llm -> distinguish\_cat\_fact\_llm edge is a little more complicated. We need to specify a function that transforms and renames
the output of the generate\_cat\_fact\_llm into the input of the distinguish\_cat\_fact\_llm. In this case, we'll just take the first choice
and rename it to `cat_fact_2`.

Every edge in Trellis allows you to optionally transform data between nodes when you add an edge. We designed it this way to reduce the amount
of code you'd have to rewrite.

### Putting it all together

That's it! We've put our `DAG` together. In the next and final tutorial, we'll run it. Visit the [DAG reference](../reference/dag) to learn more. Here's the full code for this tutorial.

```python example_dag.py theme={null}
from cat_fact_tool import CatFactsAPITool
from trellis_dag import DAG

cat_fact_tool = CatFactsAPITool("cat_fact_tool")

dag = DAG()
dag.add_node(cat_fact_tool)
dag.add_node(generate_cat_fact_llm)
dag.add_node(distinguish_cat_fact_llm)

dag.add_edge(cat_fact_tool, distinguish_cat_fact_llm)
dag.add_edge(
    generate_cat_fact_llm,
    distinguish_cat_fact_llm,
    fn=lambda x: {"cat_fact_2": x["choices"][0]["message"]["content"]},
)
```