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Each field defines the expected data type, which should match the [types defined by Fondant](https://github.com/ml6team/fondant/blob/main/src/fondant/core/schema.py), -that correespond to [Arrow data types](https://arrow.apache.org/docs/python/api/datatypes.html). +that correspond to [Arrow data types](https://arrow.apache.org/docs/python/api/datatypes.html). Note that you can always map a field from your dataset with a different name to a specific field name expected by the component provided they have the same data type. For example, suppose we have a component spec that diff --git a/docs/components/containerized_components.md b/docs/components/containerized_components.md index 49e0225a1..9c2e380b4 100644 --- a/docs/components/containerized_components.md +++ b/docs/components/containerized_components.md @@ -12,7 +12,7 @@ with a simpler [lightweight components](../components/lightweight_components.md) To make sure containerized components are reusable, they should implement a single logical data processing -step (like captioning images or removing Personal Identifiable Information [PII] from text.) +step (like captioning images or removing Personal Identifiable Information [PII] from text). If a component grows too large, consider splitting it into multiple separate components each tackling one logical part. @@ -38,7 +38,7 @@ script. Note that the `main.py` script can be split up into several Python scripts in case it would become prohibitively long. See the -[prompt based LAION retrieval component](https://github.com/ml6team/fondant/tree/main/components/prompt_based_laion_retrieval/src) +[prompt based LAION retrieval component](https://github.com/ml6team/fondant/tree/main/components/retrieve_laion_by_prompt/src) as an example: the CLIP client itself is defined in a separate script called `clip_client`, which is then imported in the `main.py` script.