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Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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website/docs/api/large-language-models.mdx
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@ -10,7 +10,7 @@ menu:
--- ---
[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large [The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large
Language Models (LLMs) into [spaCy](https://spacy.io), featuring a modular Language Models (LLMs) into spaCy, featuring a modular
system for **fast prototyping** and **prompting**, and turning unstructured system for **fast prototyping** and **prompting**, and turning unstructured
responses into **robust outputs** for various NLP tasks, **no training data** responses into **robust outputs** for various NLP tasks, **no training data**
required. required.
@ -32,7 +32,7 @@ An `llm` component is defined by two main settings:
- A [**task**](#tasks), defining the prompt to send to the LLM as well as the - A [**task**](#tasks), defining the prompt to send to the LLM as well as the
functionality to parse the resulting response back into structured fields on functionality to parse the resulting response back into structured fields on
spaCy's [Doc](https://spacy.io/api/doc) objects. the [Doc](https://spacy.io/api/doc) objects.
- A [**model**](#models) defining the model and how to connect to it. Note that - A [**model**](#models) defining the model and how to connect to it. Note that
`spacy-llm` supports both access to external APIs (such as OpenAI) as well as `spacy-llm` supports both access to external APIs (such as OpenAI) as well as
access to self-hosted open-source LLMs (such as using Dolly through Hugging access to self-hosted open-source LLMs (such as using Dolly through Hugging
@ -45,7 +45,7 @@ through a REST API) more than once.
Finally, you can choose to save a stringified version of LLM prompts/responses Finally, you can choose to save a stringified version of LLM prompts/responses
within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`.
`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM `Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM
component within the spaCy pipeline. Each entry is itself a dictionary, with two component within the `nlp` pipeline. Each entry is itself a dictionary, with two
keys: `prompt` and `response`. keys: `prompt` and `response`.
A note on `validate_types`: by default, `spacy-llm` checks whether the A note on `validate_types`: by default, `spacy-llm` checks whether the
@ -57,7 +57,7 @@ want to disable this behavior.
A _task_ defines an NLP problem or question, that will be sent to the LLM via a A _task_ defines an NLP problem or question, that will be sent to the LLM via a
prompt. Further, the task defines how to parse the LLM's responses back into prompt. Further, the task defines how to parse the LLM's responses back into
structured information. All tasks are registered in spaCy's `llm_tasks` structured information. All tasks are registered in the `llm_tasks`
registry. registry.
#### task.generate_prompts {id="task-generate-prompts"} #### task.generate_prompts {id="task-generate-prompts"}
@ -611,7 +611,7 @@ friends: friend
``` ```
If for any given text/doc instance the number of lemmas returned by the LLM If for any given text/doc instance the number of lemmas returned by the LLM
doesn't match the number of tokens recognized by spaCy, no lemmas are stored in doesn't match the number of tokens from the pipeline's tokenizer, no lemmas are stored in
the corresponding doc's tokens. Otherwise the tokens `.lemma_` property is the corresponding doc's tokens. Otherwise the tokens `.lemma_` property is
updated with the lemma suggested by the LLM. updated with the lemma suggested by the LLM.