Yes, Haystack can work with custom NLP models. Haystack is designed to be flexible and allows developers to integrate various models into its pipeline. This flexibility makes it a suitable choice for those looking to leverage their own models in a search or question-answering system without having to reshape their architecture.
To use a custom NLP model with Haystack, you typically need to implement a class that adheres to Haystack's interface for models. This involves defining how your model processes input text and returns outputs in the format that Haystack expects. For example, if you create a custom model that performs named entity recognition, you would need to ensure your model's output contains information about entities in the required format that Haystack can parse.
A practical way to integrate your custom NLP model would be to use the CustomModel class provided by Haystack. This allows you to wrap your model and pass it through the different components of the Haystack framework, such as the retriever or the reader. Additionally, you can leverage the existing components of Haystack to manage documents and queries, thus enabling you to optimize the search process without extensive reworking of your application pipeline. This approach provides developers with the ability to utilize existing models alongside custom solutions seamlessly.