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---
title: Large Language Models
teaser: Integrating LLMs into structured NLP pipelines
menu:
- ['Config', 'config']
- ['Tasks', 'tasks']
- ['Models', 'models']
- ['Cache', 'cache']
- ['Various Functions', 'various-functions']
---
[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large
Language Models (LLMs) into spaCy, featuring a modular system for **fast
prototyping** and **prompting**, and turning unstructured responses into
**robust outputs** for various NLP tasks, **no training data** required.
## Config {id="config"}
`spacy-llm` exposes a `llm` factory that accepts the following configuration
options:
| Argument | Description |
| ---------------- | ------------------------------------------------------------------------------------------------------- |
| `task` | An LLMTask can generate prompts and parse LLM responses. See [docs](#tasks). ~~Optional[LLMTask]~~ |
| `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~~ |
| `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~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:
- 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
the [Doc](/api/doc) objects.
- 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
access to self-hosted open-source LLMs (such as using Dolly through Hugging
Face).
Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality
to avoid running the same document through an LLM service (be it local or
through a REST API) more than once.
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`.
`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM
component within the `nlp` pipeline. Each entry is itself a dictionary, with two
keys: `prompt` and `response`.
A note on `validate_types`: by default, `spacy-llm` checks whether the
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
want to disable this behavior.
### Tasks {id="tasks"}
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
structured information. All tasks are registered in the `llm_tasks` registry.
#### task.generate_prompts {id="task-generate-prompts"}
Takes a collection of documents, and returns a collection of "prompts", which
can be of type `Any`. Often, prompts are of type `str` - but this is not
enforced to allow for maximum flexibility in the framework.
| Argument | Description |
| ----------- | ---------------------------------------- |
| `docs` | The input documents. ~~Iterable[Doc]~~ |
| **RETURNS** | The generated prompts. ~~Iterable[Any]~~ |
#### task.parse_responses {id="task-parse-responses"}
Takes a collection of LLM responses and the original documents, parses the
responses into structured information, and sets the annotations on the
documents. The `parse_responses` function is free to set the annotations in any
way, including `Doc` fields like `ents`, `spans` or `cats`, or using custom
defined fields.
The `responses` are of type `Iterable[Any]`, though they will often be `str`
objects. This depends on the return type of the [model](#models).
| Argument | Description |
| ----------- | ------------------------------------------ |
| `docs` | The input documents. ~~Iterable[Doc]~~ |
| `responses` | The generated prompts. ~~Iterable[Any]~~ |
| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~ |
#### spacy.Summarization.v1 {id="summarization-v1"}
The `spacy.Summarization.v1` task supports both zero-shot and few-shot
prompting.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.Summarization.v1"
> examples = null
> max_n_words = null
> ```
| Argument | Description |
| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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]]]~~ |
| `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~~ |
The summarization task prompts the model for a concise summary of the provided
text. It optionally allows to limit the response to a certain number of tokens -
note that this requirement will be included in the prompt, but the task doesn't
perform a hard cut-off. It's hence possible that your summary exceeds
`max_n_words`.
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```yaml
- text: >
The United Nations, referred to informally as the UN, is an
intergovernmental organization whose stated purposes are to maintain
international peace and security, develop friendly relations among nations,
achieve international cooperation, and serve as a centre for harmonizing the
actions of nations. It is the world's largest international organization.
The UN is headquartered on international territory in New York City, and the
organization has other offices in Geneva, Nairobi, Vienna, and The Hague,
where the International Court of Justice is headquartered.\n\n The UN was
established after World War II with the aim of preventing future world wars,
and succeeded the League of Nations, which was characterized as
ineffective.
summary:
'The UN is an international organization that promotes global peace,
cooperation, and harmony. Established after WWII, its purpose is to prevent
future world wars.'
```
```ini
[components.llm.task]
@llm_tasks = "spacy.Summarization.v1"
max_n_words = 20
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "summarization_examples.yml"
```
#### spacy.NER.v2 {id="ner-v2"}
The built-in NER task supports both zero-shot and few-shot prompting. This
version also supports explicitly defining the provided labels with custom
descriptions.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.NER.v2"
> labels = ["PERSON", "ORGANISATION", "LOCATION"]
> examples = null
> ```
| Argument | Description |
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], 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 [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ |
| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
| `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`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
The NER task implementation doesn't currently ask the LLM for specific offsets,
but simply expects a list of strings that represent the enties in the document.
This means that a form of string matching is required. This can be configured by
the following parameters:
- The `single_match` parameter is typically set to `False` to allow for multiple
matches. For instance, the response from the LLM might only mention the entity
"Paris" once, but you'd still want to mark it every time it occurs in the
document.
- The case-sensitive matching is typically set to `False` to be robust against
case variances in the LLM's output.
- The `alignment_mode` argument is used to match entities as returned by the LLM
to the tokens from the original `Doc` - specifically it's used as argument in
the call to [`doc.char_span()`](/api/doc#char_span). The `"strict"` mode will
only keep spans that strictly adhere to the given token boundaries.
`"contract"` will only keep those tokens that are fully within the given
range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will expand the
span to the next token boundaries, e.g. expanding `"New Y"` out to
`"New York"`.
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```yaml
- text: Jack and Jill went up the hill.
entities:
PERSON:
- Jack
- Jill
LOCATION:
- hill
- text: Jack fell down and broke his crown.
entities:
PERSON:
- Jack
```
```ini
[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = PERSON,ORGANISATION,LOCATION
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "ner_examples.yml"
```
> Label descriptions can also be used with explicit examples to give as much
> info to the LLM model as possible.
You can also write definitions for each label and provide them via the
`label_definitions` argument. This lets you tell the LLM exactly what you're
looking for rather than relying on the LLM to interpret its task given just the
label name. Label descriptions are freeform so you can write whatever you want
here, but through some experiments a brief description along with some examples
and counter examples seems to work quite well.
```ini
[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = PERSON,SPORTS_TEAM
[components.llm.task.label_definitions]
PERSON = "Extract any named individual in the text."
SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid"
```
#### spacy.NER.v1 {id="ner-v1"}
The original version of the built-in NER task supports both zero-shot and
few-shot prompting.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.NER.v1"
> labels = PERSON,ORGANISATION,LOCATION
> examples = null
> ```
| Argument | Description |
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `labels` | Comma-separated list of labels. ~~str~~ |
| `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`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
The NER task implementation doesn't currently ask the LLM for specific offsets,
but simply expects a list of strings that represent the enties in the document.
This means that a form of string matching is required. This can be configured by
the following parameters:
- The `single_match` parameter is typically set to `False` to allow for multiple
matches. For instance, the response from the LLM might only mention the entity
"Paris" once, but you'd still want to mark it every time it occurs in the
document.
- The case-sensitive matching is typically set to `False` to be robust against
case variances in the LLM's output.
- The `alignment_mode` argument is used to match entities as returned by the LLM
to the tokens from the original `Doc` - specifically it's used as argument in
the call to [`doc.char_span()`](/api/doc#char_span). The `"strict"` mode will
only keep spans that strictly adhere to the given token boundaries.
`"contract"` will only keep those tokens that are fully within the given
range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will expand the
span to the next token boundaries, e.g. expanding `"New Y"` out to
`"New York"`.
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```yaml
- text: Jack and Jill went up the hill.
entities:
PERSON:
- Jack
- Jill
LOCATION:
- hill
- text: Jack fell down and broke his crown.
entities:
PERSON:
- Jack
```
```ini
[components.llm.task]
@llm_tasks = "spacy.NER.v1"
labels = PERSON,ORGANISATION,LOCATION
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "ner_examples.yml"
```
#### spacy.SpanCat.v2 {id="spancat-v2"}
The built-in SpanCat task is a simple adaptation of the NER task to support
overlapping entities and store its annotations in `doc.spans`.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.SpanCat.v2"
> labels = ["PERSON", "ORGANISATION", "LOCATION"]
> examples = null
> ```
| Argument | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], 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 [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ |
| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
| `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`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
Except for the `spans_key` parameter, the SpanCat task reuses the configuration
from the NER task. Refer to [its documentation](#ner-v2) for more insight.
#### spacy.SpanCat.v1 {id="spancat-v1"}
The original version of the built-in SpanCat task is a simple adaptation of the
v1 NER task to support overlapping entities and store its annotations in
`doc.spans`.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.SpanCat.v1"
> labels = PERSON,ORGANISATION,LOCATION
> examples = null
> ```
| Argument | Description |
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `labels` | Comma-separated list of labels. ~~str~~ |
| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
| `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`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
Except for the `spans_key` parameter, the SpanCat task reuses the configuration
from the NER task. Refer to [its documentation](#ner-v1) for more insight.
#### spacy.TextCat.v3 {id="textcat-v3"}
Version 3 (the most recent) of the built-in TextCat task supports both zero-shot
and few-shot prompting. It allows setting definitions of labels. Those
definitions are included in the prompt.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.TextCat.v3"
> labels = ["COMPLIMENT", "INSULT"]
> label_definitions = {
> "COMPLIMENT": "a polite expression of praise or admiration.",
> "INSULT": "a disrespectful or scornfully abusive remark or act."
> }
> examples = null
> ```
| Argument | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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]]~~ |
| `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/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ |
| `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]]~~ |
| `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~~ |
| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```json
[
{
"text": "You look great!",
"answer": "Compliment"
},
{
"text": "You are not very clever at all.",
"answer": "Insult"
}
]
```
```ini
[components.llm.task]
@llm_tasks = "spacy.TextCat.v3"
labels = ["COMPLIMENT", "INSULT"]
label_definitions = {
"COMPLIMENT": "a polite expression of praise or admiration.",
"INSULT": "a disrespectful or scornfully abusive remark or act."
}
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "textcat_examples.json"
```
#### spacy.TextCat.v2 {id="textcat-v2"}
Version 2 of the built-in TextCat task supports both zero-shot and few-shot
prompting and includes an improved prompt template.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.TextCat.v2"
> labels = ["COMPLIMENT", "INSULT"]
> examples = null
> ```
| Argument | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], 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/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ |
| `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`. ~~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~~ |
| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```json
[
{
"text": "You look great!",
"answer": "Compliment"
},
{
"text": "You are not very clever at all.",
"answer": "Insult"
}
]
```
```ini
[components.llm.task]
@llm_tasks = "spacy.TextCat.v2"
labels = ["COMPLIMENT", "INSULT"]
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "textcat_examples.json"
```
#### spacy.TextCat.v1 {id="textcat-v1"}
Version 1 of the built-in TextCat task supports both zero-shot and few-shot
prompting.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.TextCat.v1"
> labels = COMPLIMENT,INSULT
> examples = null
> ```
| Argument | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | Comma-separated list of labels. ~~str~~ |
| `examples` | Optional function that generates examples for few-shot learning. Deafults 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`. ~~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. Deafults to `False`. ~~bool~~ |
| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Deafults to `True`. ~~bool~~ |
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ |
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```json
[
{
"text": "You look great!",
"answer": "Compliment"
},
{
"text": "You are not very clever at all.",
"answer": "Insult"
}
]
```
```ini
[components.llm.task]
@llm_tasks = "spacy.TextCat.v2"
labels = COMPLIMENT,INSULT
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "textcat_examples.json"
```
#### spacy.REL.v1 {id="rel-v1"}
The built-in REL task supports both zero-shot and few-shot prompting. It relies
on an upstream NER component for entities extraction.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.REL.v1"
> labels = ["LivesIn", "Visits"]
> ```
| Argument | Description |
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], 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 [`rel.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.jinja). ~~str~~ |
| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
| `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]]~~ |
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```json
{"text": "Laura bought a house in Boston with her husband Mark.", "ents": [{"start_char": 0, "end_char": 5, "label": "PERSON"}, {"start_char": 24, "end_char": 30, "label": "GPE"}, {"start_char": 48, "end_char": 52, "label": "PERSON"}], "relations": [{"dep": 0, "dest": 1, "relation": "LivesIn"}, {"dep": 2, "dest": 1, "relation": "LivesIn"}]}
{"text": "Michael travelled through South America by bike.", "ents": [{"start_char": 0, "end_char": 7, "label": "PERSON"}, {"start_char": 26, "end_char": 39, "label": "LOC"}], "relations": [{"dep": 0, "dest": 1, "relation": "Visits"}]}
```
```ini
[components.llm.task]
@llm_tasks = "spacy.REL.v1"
labels = ["LivesIn", "Visits"]
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "rel_examples.jsonl"
```
Note: the REL task relies on pre-extracted entities to make its prediction.
Hence, you'll need to add a component that populates `doc.ents` with recognized
spans to your spaCy pipeline and put it _before_ the REL component.
#### spacy.Lemma.v1 {id="lemma-v1"}
The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_`
attribute in the doc's tokens accordingly.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.Lemma.v1"
> examples = null
> ```
| Argument | Description |
| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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 [lemma.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.jinja). ~~str~~ |
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
`Lemma.v1` prompts the LLM to lemmatize the passed text and return the
lemmatized version as a list of tokens and their corresponding lemma. E. g. the
text `I'm buying ice cream for my friends` should invoke the response
```
I: I
'm: be
buying: buy
ice: ice
cream: cream
for: for
my: my
friends: friend
.: .
```
If for any given text/doc instance the number of lemmas returned by the LLM
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 updated with the lemma suggested by the LLM.
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```yaml
- text: I'm buying ice cream.
lemmas:
- 'I': 'I'
- "'m": 'be'
- 'buying': 'buy'
- 'ice': 'ice'
- 'cream': 'cream'
- '.': '.'
- text: I've watered the plants.
lemmas:
- 'I': 'I'
- "'ve": 'have'
- 'watered': 'water'
- 'the': 'the'
- 'plants': 'plant'
- '.': '.'
```
```ini
[components.llm.task]
@llm_tasks = "spacy.Lemma.v1"
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "lemma_examples.yml"
```
#### spacy.Sentiment.v1 {id="sentiment-v1"}
Performs sentiment analysis on provided texts. Scores between 0 and 1 are stored
in `Doc._.sentiment` - the higher, the more positive. Note in cases of parsing
issues (e. g. in case of unexpected LLM responses) the value might be `None`.
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.Sentiment.v1"
> examples = null
> ```
| Argument | Description |
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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 [sentiment.jinja](./spacy_llm/tasks/templates/sentiment.jinja). ~~str~~ |
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ |
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
you can write down a few examples in a separate file, and provide these to be
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
```yaml
- text: 'This is horrifying.'
score: 0
- text: 'This is underwhelming.'
score: 0.25
- text: 'This is ok.'
score: 0.5
- text: "I'm looking forward to this!"
score: 1.0
```
```ini
[components.llm.task]
@llm_tasks = "spacy.Sentiment.v1"
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "sentiment_examples.yml"
```
#### spacy.NoOp.v1 {id="noop-v1"}
> #### Example config
>
> ```ini
> [components.llm.task]
> @llm_tasks = "spacy.NoOp.v1"
> ```
This task is only useful for testing - it tells the LLM to do nothing, and does
not set any fields on the `docs`.
### Models {id="models"}
A _model_ defines which LLM model to query, and how to query it. It can be a
simple function taking a collection of prompts (consistent with the output type
of `task.generate_prompts()`) and returning a collection of responses
(consistent with the expected input of `parse_responses`). Generally speaking,
it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific
implementations can have other signatures, like
`Callable[[Iterable[str]], Iterable[str]]`.
#### API Keys {id="api-keys"}
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.
E. g. when using OpenAI, you have to get an API key from openai.com, and ensure
that the keys are set as environmental variables:
```shell
export OPENAI_API_KEY="sk-..."
export OPENAI_API_ORG="org-..."
```
For Cohere it's
```shell
export CO_API_KEY="..."
```
and for Anthropic
```shell
export ANTHROPIC_API_KEY="..."
```
#### spacy.GPT-4.v1 {id="gpt-4"}
OpenAI's `gpt-4` model family.
> #### Example config:
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.GPT-4.v1"
> name = "gpt-4"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-4"`. ~~Literal["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.GPT-3-5.v1 {id="gpt-3-5"}
OpenAI's `gpt-3-5` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.GPT-3-5.v1"
> name = "gpt-3.5-turbo"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-3.5-turbo"`. ~~Literal["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Text-Davinci.v1 {id="text-davinci"}
OpenAI's `text-davinci` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Text-Davinci.v1"
> name = "text-davinci-003"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-davinci-003"`. ~~Literal["text-davinci-002", "text-davinci-003"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Code-Davinci.v1 {id="code-davinci"}
OpenAI's `code-davinci` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Code-Davinci.v1"
> name = "code-davinci-002"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"code-davinci-002"`. ~~Literal["code-davinci-002"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Text-Curie.v1 {id="text-curie"}
OpenAI's `text-curie` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Text-Curie.v1"
> name = "text-curie-001"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-curie-001"`. ~~Literal["text-curie-001"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Text-Babbage.v1 {id="text-babbage"}
OpenAI's `text-babbage` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Text-Babbage.v1"
> name = "text-babbage-001"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-babbage-001"`. ~~Literal["text-babbage-001"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Text-Ada.v1 {id="text-ada"}
OpenAI's `text-ada` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Text-Ada.v1"
> name = "text-ada-001"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-ada-001"`. ~~Literal["text-ada-001"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Davinci.v1 {id="davinci"}
OpenAI's `davinci` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Davinci.v1"
> name = "davinci"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"davinci"`. ~~Literal["davinci"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Curie.v1 {id="curie"}
OpenAI's `curie` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Curie.v1"
> name = "curie"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"curie"`. ~~Literal["curie"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Babbage.v1 {id="babbage"}
OpenAI's `babbage` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Babbage.v1"
> name = "babbage"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"babbage"`. ~~Literal["babbage"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Ada.v1 {id="ada"}
OpenAI's `ada` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Ada.v1"
> name = "ada"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"ada"`. ~~Literal["ada"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Command.v1 {id="command"}
Cohere's `command` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Command.v1"
> name = "command"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"command"`. ~~Literal["command", "command-light", "command-light-nightly", "command-nightly"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-2.v1 {id="claude-2"}
Anthropic's `claude-2` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-2.v1"
> name = "claude-2"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-2"`. ~~Literal["claude-2", "claude-2-100k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-1.v1 {id="claude-1"}
Anthropic's `claude-1` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-1.v1"
> name = "claude-1"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1"`. ~~Literal["claude-1", "claude-1-100k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-instant-1.v1 {id="claude-instant-1"}
Anthropic's `claude-instant-1` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-instant-1.v1"
> name = "claude-instant-1"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1"`. ~~Literal["claude-instant-1", "claude-instant-1-100k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-instant-1-1.v1 {id="claude-instant-1-1"}
Anthropic's `claude-instant-1.1` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-instant-1-1.v1"
> name = "claude-instant-1.1"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1.1"`. ~~Literal["claude-instant-1.1", "claude-instant-1.1-100k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-1-0.v1 {id="claude-1-0"}
Anthropic's `claude-1.0` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-1-0.v1"
> name = "claude-1.0"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.0"`. ~~Literal["claude-1.0"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-1-2.v1 {id="claude-1-2"}
Anthropic's `claude-1.2` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-1-2.v1 "
> name = "claude-1.2"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.2"`. ~~Literal["claude-1.2"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Claude-1-3.v1 {id="claude-1-3"}
Anthropic's `claude-1.3` model family.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Claude-1-3.v1"
> name = "claude-1.3"
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.3"`. ~~Literal["claude-1.3", "claude-1.3-100k"]~~ |
| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ |
| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ |
#### spacy.Dolly.v1 {id="dolly"}
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
have the setting `device=cuda:0` in your config, which ensures that the model is
loaded entirely on the GPU (and fails otherwise).
You can do so with
```shell
python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"
```
If you don't have access to a GPU, you can install `accelerate` and
set`device_map=auto` instead, but be aware that this may result in some layers
getting distributed to the CPU or even the hard drive, which may ultimately
result in extremely slow queries.
```shell
python -m pip install "accelerate>=0.16.0,<1.0"
```
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Dolly.v1"
> name = "dolly-v2-3b"
> ```
| Argument | Description |
| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of a Dolly model that is supported (e. g. "dolly-v2-3b" or "dolly-v2-12b"). ~~Literal["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]~~ |
| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ |
| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ |
Supported models (see the
[Databricks models page](https://huggingface.co/databricks) on Hugging Face for
details):
- `"databricks/dolly-v2-3b"`
- `"databricks/dolly-v2-7b"`
- `"databricks/dolly-v2-12b"`
Note that Hugging Face will download this model the first time you use it - you
can
[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
by setting the environmental variable `HF_HOME`.
#### spacy.Llama2.v1 {id="llama2"}
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
have the setting `device=cuda:0` in your config, which ensures that the model is
loaded entirely on the GPU (and fails otherwise).
You can do so with
```shell
python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"
```
If you don't have access to a GPU, you can install `accelerate` and
set`device_map=auto` instead, but be aware that this may result in some layers
getting distributed to the CPU or even the hard drive, which may ultimately
result in extremely slow queries.
```shell
python -m pip install "accelerate>=0.16.0,<1.0"
```
Note that the chat models variants of Llama 2 are currently not supported. This
is because they need a particular prompting setup and don't add any discernible
benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared the
completion model variants.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Llama2.v1"
> name = "llama2-7b-hf"
> ```
| Argument | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | The name of a Llama 2 model variant that is supported. Defaults to `"Llama-2-7b-hf"`. ~~Literal["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]~~ |
| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ |
| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ |
Note that Hugging Face will download this model the first time you use it - you
can
[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
by setting the environmental variable `HF_HOME`.
#### spacy.Falcon.v1 {id="falcon"}
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
have the setting `device=cuda:0` in your config, which ensures that the model is
loaded entirely on the GPU (and fails otherwise).
You can do so with
```shell
python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"
```
If you don't have access to a GPU, you can install `accelerate` and
set`device_map=auto` instead, but be aware that this may result in some layers
getting distributed to the CPU or even the hard drive, which may ultimately
result in extremely slow queries.
```shell
python -m pip install "accelerate>=0.16.0,<1.0"
```
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.Falcon.v1"
> name = "falcon-7b"
> ```
| Argument | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | The name of a Falcon model variant that is supported. Defaults to `"7b-instruct"`. ~~Literal["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]~~ |
| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ |
| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ |
Note that Hugging Face will download this model the first time you use it - you
can
[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
by setting the environmental variable `HF_HOME`.
#### spacy.StableLM.v1 {id="stablelm"}
To use this model, ideally you have a GPU enabled and have installed
`transformers`, `torch` and CUDA in your virtual environment.
You can do so with
```shell
python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"
```
If you don't have access to a GPU, you can install `accelerate` and
set`device_map=auto` instead, but be aware that this may result in some layers
getting distributed to the CPU or even the hard drive, which may ultimately
result in extremely slow queries.
```shell
python -m pip install "accelerate>=0.16.0,<1.0"
```
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.StableLM.v1"
> name = "stablelm-tuned-alpha-7b"
> ```
| Argument | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of a StableLM model that is supported (e. g. "stablelm-tuned-alpha-7b"). ~~Literal["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]~~ |
| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ |
| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ |
See the
[Stability AI StableLM GitHub repo](https://github.com/Stability-AI/StableLM/#stablelm-alpha)
for details.
Note that Hugging Face will download this model the first time you use it - you
can
[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
by setting the environmental variable `HF_HOME`.
#### spacy.OpenLLaMA.v1 {id="openllama"}
To use this model, ideally you have a GPU enabled and have installed
- `transformers[sentencepiece]`
- `torch`
- CUDA in your virtual environment.
You can do so with
```shell
python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"
```
If you don't have access to a GPU, you can install `accelerate` and
set`device_map=auto` instead, but be aware that this may result in some layers
getting distributed to the CPU or even the hard drive, which may ultimately
result in extremely slow queries.
```shell
python -m pip install "accelerate>=0.16.0,<1.0"
```
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "spacy.OpenLLaMA.v1"
> name = "open_llama_3b"
> ```
| Argument | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of a OpenLLaMA model that is supported. ~~Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]~~ |
| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ |
| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ |
See the
[OpenLM Research OpenLLaMA GitHub repo](https://github.com/openlm-research/open_llama)
for details.
Note that Hugging Face will download this model the first time you use it - you
can
[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
by setting the environmental variable `HF_HOME`.
#### LangChain models {id="langchain-models"}
To use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval
part, make sure you have installed it first:
```shell
python -m pip install "langchain==0.0.191"
# Or install with spacy-llm directly
python -m pip install "spacy-llm[extras]"
```
Note that LangChain currently only supports Python 3.9 and beyond.
LangChain models in `spacy-llm` work slightly differently. `langchain`'s models
are parsed automatically, each LLM class in `langchain` has one entry in
`spacy-llm`'s registry. As `langchain`'s design has one class per API and not
per model, this results in registry entries like `langchain.OpenAI.v1` - i. e.
there is one registry entry per API and not per model (family), as for the REST-
and HuggingFace-based entries.
The name of the model to be used has to be passed in via the `name` attribute.
> #### Example config
>
> ```ini
> [components.llm.model]
> @llm_models = "langchain.OpenAI.v1"
> name = "gpt-3.5-turbo"
> query = {"@llm_queries": "spacy.CallLangChain.v1"}
> config = {"temperature": 0.3}
> ```
| Argument | Description |
| -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of a mdodel supported by LangChain for this API. ~~str~~ |
| `config` | Configuration passed on to the LangChain model. Defaults to `{}`. ~~Dict[Any, Any]~~ |
| `query` | Function that executes the prompts. If `None`, defaults to `spacy.CallLangChain.v1`. ~~Optional[Callable[["langchain.llms.BaseLLM", Iterable[Any]], Iterable[Any]]]~~ |
The default `query` (`spacy.CallLangChain.v1`) executes the prompts by running
`model(text)` for each given textual prompt.
### Cache {id="cache"}
Interacting with LLMs, either through an external API or a local instance, is
costly. Since developing an NLP pipeline generally means a lot of exploration
and prototyping, `spacy-llm` implements a built-in cache to avoid reprocessing
the same documents at each run that keeps batches of documents stored on disk.
> #### Example config
>
> ```ini
> [components.llm.cache]
> @llm_misc = "spacy.BatchCache.v1"
> path = "path/to/cache"
> batch_size = 64
> max_batches_in_mem = 4
> ```
| Argument | Description |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | Cache directory. If `None`, no caching is performed, and this component will act as a NoOp. Defaults to `None`. ~~Optional[Union[str, Path]]~~ |
| `batch_size` | Number of docs in one batch (file). Once a batch is full, it will be peristed to disk. Defaults to 64. ~~int~~ |
| `max_batches_in_mem` | Max. number of batches to hold in memory. Allows you to limit the effect on your memory if you're handling a lot of docs. Defaults to 4. ~~int~~ |
When retrieving a document, the `BatchCache` will first figure out what batch
the document belongs to. If the batch isn't in memory it will try to load the
batch from disk and then move it into memory.
Note that since the cache is generated by a registered function, you can also
provide your own registered function returning your own cache implementation. If
you wish to do so, ensure that your cache object adheres to the `Protocol`
defined in `spacy_llm.ty.Cache`.
### Various functions {id="various-functions"}
#### spacy.FewShotReader.v1 {id="fewshotreader-v1"}
This function is registered in spaCy's `misc` registry, and reads in examples
from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses
[`srsly`](https://github.com/explosion/srsly) to read in these files and parses
them depending on the file extension.
> #### Example config
>
> ```ini
> [components.llm.task.examples]
> @misc = "spacy.FewShotReader.v1"
> path = "ner_examples.yml"
> ```
| Argument | Description |
| -------- | ----------------------------------------------------------------------------------------------- |
| `path` | Path to an examples file with suffix `.yml`, `.yaml`, `.json` or `.jsonl`. ~~Union[str, Path]~~ |
#### spacy.FileReader.v1 {id="filereader-v1"}
This function is registered in spaCy's `misc` registry, and reads a file
provided to the `path` to return a `str` representation of its contents. This
function is typically used to read
[Jinja](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt
template.
> #### Example config
>
> ```ini
> [components.llm.task.template]
> @misc = "spacy.FileReader.v1"
> path = "ner_template.jinja2"
> ```
| Argument | Description |
| -------- | ------------------------------------------------- |
| `path` | Path to the file to be read. ~~Union[str, Path]~~ |
#### Normalizer functions {id="normalizer-functions"}
These functions provide simple normalizations for string comparisons, e.g.
between a list of specified labels and a label given in the raw text of the LLM
response. They are registered in spaCy's `misc` registry and have the signature
`Callable[[str], str]`.
- `spacy.StripNormalizer.v1`: only apply `text.strip()`
- `spacy.LowercaseNormalizer.v1`: applies `text.strip().lower()` to compare
strings in a case-insensitive way.
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