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update usage page

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Victoria Slocum 2023-07-14 10:22:21 +02:00
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website/docs/usage/large-language-models.mdx
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@ -408,27 +408,6 @@ Approaches 1. and 2 are the default for hosted model and local models,
respectively. Alternatively you can use LangChain to access hosted or local
models by specifying one of the models registered with the `langchain.` prefix.
<Infobox>
_Why LangChain if there are also are a native REST and a HuggingFace interface? When should I use what?_
Third-party libraries like `langchain` focus on prompt management, integration
of many different LLM APIs, and other related features such as conversational
memory or agents. `spacy-llm` on the other hand emphasizes features we consider
useful in the context of NLP pipelines utilizing LLMs to process documents
(mostly) independent from each other. It makes sense that the feature sets of
such third-party libraries and `spacy-llm` aren't identical - and users might
want to take advantage of features not available in `spacy-llm`.
The advantage of implementing our own REST and HuggingFace integrations is that
we can ensure a larger degree of stability and robustness, as we can guarantee
backwards-compatibility and more smoothly integrated error handling.
If however there are features or APIs not natively covered by `spacy-llm`, it's
trivial to utilize LangChain to cover this - and easy to customize the prompting
mechanism, if so required.
</Infobox>
Note that when using hosted services, you have to ensure that the proper API
keys are set as environment variables as described by the corresponding
provider's documentation.