deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/converters/outputadapter.mdx

---
title: "OutputAdapter"
id: outputadapter
slug: "/outputadapter"
description: "This component helps the output of one component fit smoothly into the input of another. It uses Jinja expressions to define how this adaptation occurs."
---

# OutputAdapter

This component helps the output of one component fit smoothly into the input of another. It uses Jinja expressions to define how this adaptation occurs.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Flexible |
| **Mandatory init variables** | `template`: A Jinja template string that defines how to adapt the data  <br /> <br />`output_type`: Type alias that this instance will return |
| **Mandatory run variables** | `**kwargs`: Input variables to be used in Jinja expression. See [Variables](#variables)  section for more details. |
| **Output variables** | The output is specified under the `output` key dictionary |
| **API reference** | [Converters](/reference/converters-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/output_adapter.py |

</div>

## Overview

To use `OutputAdapter`, you need to specify the adaptation rule that includes:

- `template`: A Jinja template string that defines how to adapt the input data.
- `output_type`: The type of the output data (such as `str`, `List[int]`..). This doesn't change the actual output type and is only needed to validate connection with other components.
- `custom_filters`: An optional dictionary of custom Jinja filters to be used in the template.

### Variables

The `OutputAdapter` requires all template variables to be present before running and raises an error if any template variable is missing at pipeline connect time.

```python
from haystack.components.converters import OutputAdapter

adapter = OutputAdapter(template="Hello {{name}}!", output_type=str)
```

### Unsafe behavior

The `OutputAdapter` internally renders the `template` using Jinja, and by default, this is safe behavior. However, it limits the output types to strings, bytes, numbers, tuples, lists, dicts, sets, booleans, `None`, and `Ellipsis` (`...`), as well as any combination of these structures.

If you want to use other types such as `ChatMessage`, `Document`, or `Answer`, you must enable unsafe template rendering by setting the `unsafe` init argument to `True`.

Be cautious, as enabling this can be unsafe and may lead to remote code execution if the `template` is a string customizable by the end user.

## Usage

### On its own

This component is primarily meant to be used in pipelines.

In this example, `OutputAdapter` simply outputs the content field of the first document in the arrays of documents:

```python
from haystack import Document
from haystack.components.converters import OutputAdapter

adapter = OutputAdapter(template="{{ documents[0].content }}", output_type=str)
input_data = {"documents": [Document(content="Test content")]}
expected_output = {"output": "Test content"}
assert adapter.run(**input_data) == expected_output
```

### In a pipeline

The example below demonstrates a straightforward pipeline that uses the `OutputAdapter` to capitalize the first document in the list. If needed, you can also utilize the predefined Jinja [filters](https://jinja.palletsprojects.com/en/3.1.x/templates/#builtin-filters).

```python
from haystack import Pipeline, component, Document
from haystack.components.converters import OutputAdapter


@component
class DocumentProducer:
    @component.output_types(documents=dict)
    def run(self):
        return {"documents": [Document(content="haystack")]}


pipe = Pipeline()
pipe.add_component(
    name="output_adapter",
    instance=OutputAdapter(
        template="{{ documents[0].content | capitalize}}",
        output_type=str,
    ),
)
pipe.add_component(name="document_producer", instance=DocumentProducer())
pipe.connect("document_producer", "output_adapter")
result = pipe.run(data={})

assert result["output_adapter"]["output"] == "Haystack"
```

You can also define your own custom filters, which can then be added to an `OutputAdapter` instance through its init method and used in templates. Here’s an example of this approach:

```python

from haystack import Pipeline, component, Document
from haystack.components.converters import OutputAdapter


def reverse_string(s):
    return s[::-1]


@component
class DocumentProducer:
    @component.output_types(documents=dict)
    def run(self):
        return {"documents": [Document(content="haystack")]}


pipe = Pipeline()
pipe.add_component(
    name="output_adapter",
    instance=OutputAdapter(
        template="{{ documents[0].content | reverse_string}}",
        output_type=str,
        custom_filters={"reverse_string": reverse_string},
    ),
)

pipe.add_component(name="document_producer", instance=DocumentProducer())
pipe.connect("document_producer", "output_adapter")
result = pipe.run(data={})

assert result["output_adapter"]["output"] == "kcatsyah"
```