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14
website/docs/api/large-language-models.mdx
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@ -26,7 +26,7 @@ options:
| `model` | Callable querying a specific LLM API. See [docs](#models). ~~Callable[[Iterable[Any]], Iterable[Any]]~~ | | `model` | Callable querying a specific LLM API. See [docs](#models). ~~Callable[[Iterable[Any]], Iterable[Any]]~~ |
| `cache` | Cache to use for caching prompts and responses per doc (batch). See [docs](#cache). ~~Cache~~ | | `cache` | Cache to use for caching prompts and responses per doc (batch). See [docs](#cache). ~~Cache~~ |
| `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~bool~~ | | `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~bool~~ |
| `validate_types` | Whether to check if signatures of configured backend and task are consistent. ~~bool~~ | | `validate_types` | Whether to check if signatures of configured model and task are consistent. ~~bool~~ |
An `llm` component is defined by two main settings: An `llm` component is defined by two main settings:
@ -49,7 +49,7 @@ component within the spaCy 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
signatures of the `backend` and `task` callables are consistent with each other signatures of the `model` and `task` callables are consistent with each other
and emits a warning if they don't. `validate_types` can be set to `False` if you and emits a warning if they don't. `validate_types` can be set to `False` if you
want to disable this behavior. want to disable this behavior.
@ -150,7 +150,7 @@ prompting.
| Argument | Description | | Argument | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.jinja](./spacy_llm/tasks/templates/summarization.jinja). ~~str~~ | | `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.jinja](./spacy_llm/tasks/templates/summarization.jinja). ~~str~~ |
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | | `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ | | `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ |
| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ | | `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ |
@ -437,7 +437,7 @@ definitions are included in the prompt.
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | | `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
| `label_definitions` | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | | `label_definitions` | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | | `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ |
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | | `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | | `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | | `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
@ -759,7 +759,7 @@ accesses the `"gpt-3.5-turbo"` model.
Currently three different approaches to use LLMs are supported: Currently three different approaches to use LLMs are supported:
1. `spacy-llm`s native REST backend. This is the default for all hosted models 1. `spacy-llm`s native REST interface. This is the default for all hosted models
(e. g. OpenAI, Cohere, Anthropic, ...). (e. g. OpenAI, Cohere, Anthropic, ...).
2. A HuggingFace integration that allows to run a limited set of HF models 2. A HuggingFace integration that allows to run a limited set of HF models
locally. locally.
@ -771,7 +771,7 @@ respectively. Alternatively you can use LangChain to access hosted or local
models by specifying one of the models registered with the `langchain.` prefix. models by specifying one of the models registered with the `langchain.` prefix.
<Infobox> <Infobox>
_Why LangChain if there are also are a native REST and a HuggingFace backend? When should I use what?_ _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 Third-party libraries like `langchain` focus on prompt management, integration
of many different LLM APIs, and other related features such as conversational of many different LLM APIs, and other related features such as conversational
@ -1244,7 +1244,7 @@ by setting the environmental variable `HF_HOME`.
#### spacy.Falcon.v1 {id="falcon"} #### spacy.Falcon.v1 {id="falcon"}
To use this backend, ideally you have a GPU enabled and have installed To use this model, ideally you have a GPU enabled and have installed
`transformers`, `torch` and CUDA in your virtual environment. This allows you to `transformers`, `torch` and CUDA in your virtual environment. This allows you to
have the setting `device=cuda:0` in your config, which ensures that the model is have the setting `device=cuda:0` in your config, which ensures that the model is
loaded entirely on the GPU (and fails otherwise). loaded entirely on the GPU (and fails otherwise).

10
website/docs/usage/large-language-models.mdx
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@ -8,7 +8,7 @@ menu:
- ['Logging', 'logging'] - ['Logging', 'logging']
- ['API', 'api'] - ['API', 'api']
- ['Tasks', 'tasks'] - ['Tasks', 'tasks']
- ['Backends', 'backends'] - ['Models', 'models']
- ['Ongoing work', 'ongoing-work'] - ['Ongoing work', 'ongoing-work']
- ['Issues', 'issues'] - ['Issues', 'issues']
--- ---
@ -328,7 +328,7 @@ 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. spaCy's [Doc](https://spacy.io/api/doc) objects.
- A [**backend**](#backends) defining the model to use and how to connect to it. - A [**model**](#models) defining the model to use and how to connect to it.
Note that `spacy-llm` supports both access to external APIs (such as OpenAI) Note that `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 as well as access to self-hosted open-source LLMs (such as using Dolly through
Hugging Face). Hugging Face).
@ -344,7 +344,7 @@ component within the spaCy 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
signatures of the `backend` and `task` callables are consistent with each other signatures of the `model` and `task` callables are consistent with each other
and emits a warning if they don't. `validate_types` can be set to `False` if you and emits a warning if they don't. `validate_types` can be set to `False` if you
want to disable this behavior. want to disable this behavior.
@ -397,7 +397,7 @@ accesses the `"gpt-3.5-turbo"` model.
Currently three different approaches to use LLMs are supported: Currently three different approaches to use LLMs are supported:
1. `spacy-llm`s native REST backend. This is the default for all hosted models 1. `spacy-llm`s native REST interface. This is the default for all hosted models
(e. g. OpenAI, Cohere, Anthropic, ...). (e. g. OpenAI, Cohere, Anthropic, ...).
2. A HuggingFace integration that allows to run a limited set of HF models 2. A HuggingFace integration that allows to run a limited set of HF models
locally. locally.
@ -409,7 +409,7 @@ respectively. Alternatively you can use LangChain to access hosted or local
models by specifying one of the models registered with the `langchain.` prefix. models by specifying one of the models registered with the `langchain.` prefix.
<Infobox> <Infobox>
_Why LangChain if there are also are a native REST and a HuggingFace backend? When should I use what?_ _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 Third-party libraries like `langchain` focus on prompt management, integration
of many different LLM APIs, and other related features such as conversational of many different LLM APIs, and other related features such as conversational