spacy-llm: Integrating LLMs into structured NLP pipelines

This package 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.

• Serializable llm component to integrate prompts into your pipeline
• Modular functions to define the task (prompting and parsing) and model
• Support for hosted APIs and self-hosted open-source models
• Integration with LangChain
• Access to OpenAI API, including GPT-4 and various GPT-3 models
• Built-in support for open-source Dolly models hosted on Hugging Face
• Usage examples for Named Entity Recognition and Text Classification
• Easy implementation of your own functions via spaCy's registry for custom prompting, parsing and model integrations

🧠 Motivation

Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more.

spaCy is a well-established library for building systems that need to work with language in various ways. spaCy's built-in components are generally powered by supervised learning or rule-based approaches.

Supervised learning is much worse than LLM prompting for prototyping, but for many tasks it's much better for production. A transformer model that runs comfortably on a single GPU is extremely powerful, and it's likely to be a better choice for any task for which you have a well-defined output. You train the model with anything from a few hundred to a few thousand labelled examples, and it will learn to do exactly that. Efficiency, reliability and control are all better with supervised learning, and accuracy will generally be higher than LLM prompting as well.

spacy-llm lets you have the best of both worlds. You can quickly initialize a pipeline with components powered by LLM prompts, and freely mix in components powered by other approaches. As your project progresses, you can look at replacing some or all of the LLM-powered components as you require.

Of course, there can be components in your system for which the power of an LLM is fully justified. If you want a system that can synthesize information from multiple documents in subtle ways and generate a nuanced summary for you, bigger is better. However, even if your production system needs an LLM for some of the task, that doesn't mean you need an LLM for all of it. Maybe you want to use a cheap text classification model to help you find the texts to summarize, or maybe you want to add a rule-based system to sanity check the output of the summary. These before-and-after tasks are much easier with a mature and well-thought-out library, which is exactly what spaCy provides.

⏳ Install

spacy-llm will be installed automatically in future spaCy versions. For now, you can run the following in the same virtual environment where you already have spacy installed.

python -m pip install spacy-llm

⚠️ This package is still experimental and it is possible that changes made to the interface will be breaking in minor version updates.

🐍 Usage

The task and the model have to be supplied to the llm pipeline component using spaCy's config system. This package provides various built-in functionality, as detailed in the API documentation.

Example 1: Add a text classifier using a GPT-3 model from OpenAI

Create a new API key from openai.com or fetch an existing one, and ensure the keys are set as environmental variables. For more background information, see the OpenAI section.

Create a config file config.cfg containing at least the following (or see the full example here):

[nlp]
lang = "en"
pipeline = ["llm"]

[components]

[components.llm]
factory = "llm"

[components.llm.task]
@llm_tasks = "spacy.TextCat.v2"
labels = ["COMPLIMENT", "INSULT"]

[components.llm.model]
@llm_models = "spacy.GPT-3-5.v1"
config = {"temperature": 0.3}

Now run:

from spacy_llm.util import assemble

nlp = assemble("config.cfg")
doc = nlp("You look gorgeous!")
print(doc.cats)

Example 2: Add NER using an open-source model through Hugging Face

To run this example, ensure that you have a GPU enabled, and transformers, torch and CUDA installed. For more background information, see the DollyHF section.

Create a config file config.cfg containing at least the following (or see the full example here):

[nlp]
lang = "en"
pipeline = ["llm"]

[components]

[components.llm]
factory = "llm"

[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = ["PERSON", "ORGANISATION", "LOCATION"]

[components.llm.model]
@llm_models = "spacy.Dolly.v1"
# For better performance, use dolly-v2-12b instead
name = "dolly-v2-3b"

Now run:

from spacy_llm.util import assemble

nlp = assemble("config.cfg")
doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes")
print([(ent.text, ent.label_) for ent in doc.ents])

Note that Hugging Face will download the "databricks/dolly-v2-3b" model the first time you use it. You can define the cached directory by setting the environmental variable HF_HOME. Also, you can upgrade the model to be "databricks/dolly-v2-12b" for better performance.

Example 3: Create the component directly in Python

The llm component behaves as any other spaCy component does, so adding it to an existing pipeline follows the same pattern:

import spacy

nlp = spacy.blank("en")
nlp.add_pipe(
    "llm",
    config={
        "task": {
            "@llm_tasks": "spacy.NER.v2",
            "labels": ["PERSON", "ORGANISATION", "LOCATION"]
        },
        "model": {
            "@llm_models": "spacy.GPT-3-5.v1",
        },
    },
)
nlp.initialize()
doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes")
print([(ent.text, ent.label_) for ent in doc.ents])

Note that for efficient usage of resources, typically you would use nlp.pipe(docs) with a batch, instead of calling nlp(doc) with a single document.

Example 4: Implement your own custom task

To write a task, you need to implement two functions: generate_prompts that takes a list of spaCy Doc objects and transforms them into a list of prompts, and parse_responses that transforms the LLM outputs into annotations on the Doc, e.g. entity spans, text categories and more.

To register your custom task with spaCy, decorate a factory function using the spacy_llm.registry.llm_tasks decorator with a custom name that you can refer to in your config.

📖 For more details, see the usage example on writing your own task

from typing import Iterable, List
from spacy.tokens import Doc
from spacy_llm.registry import registry
from spacy_llm.util import split_labels


@registry.llm_tasks("my_namespace.MyTask.v1")
def make_my_task(labels: str, my_other_config_val: float) -> "MyTask":
    labels_list = split_labels(labels)
    return MyTask(labels=labels_list, my_other_config_val=my_other_config_val)


class MyTask:
    def __init__(self, labels: List[str], my_other_config_val: float):
        ...

    def generate_prompts(self, docs: Iterable[Doc]) -> Iterable[str]:
        ...

    def parse_responses(
        self, docs: Iterable[Doc], responses: Iterable[str]
    ) -> Iterable[Doc]:
        ...

# config.cfg (excerpt)
[components.llm.task]
@llm_tasks = "my_namespace.MyTask.v1"
labels = LABEL1,LABEL2,LABEL3
my_other_config_val = 0.3

Logging

spacy-llm has a built-in logger that can log the prompt sent to the LLM as well as its raw response. This logger uses the debug level and by default has a logging.NullHandler() configured.

In order to use this logger, you can setup a simple handler like this:

import logging
import spacy_llm


spacy_llm.logger.addHandler(logging.StreamHandler())
spacy_llm.logger.setLevel(logging.DEBUG)

NOTE: Any logging handler will work here so you probably want to use some sort of rotating FileHandler as the generated prompts can be quite long, especially for tasks with few-shot examples.

Then when using the pipeline you'll be able to view the prompt and response.

E.g. with the config and code from Example 1 above:

from spacy_llm.util import assemble


nlp = assemble("config.cfg")
doc = nlp("You look gorgeous!")
print(doc.cats)

You will see logging output similar to:

Generated prompt for doc: You look gorgeous!

You are an expert Text Classification system. Your task is to accept Text as input
and provide a category for the text based on the predefined labels.

Classify the text below to any of the following labels: COMPLIMENT, INSULT
The task is non-exclusive, so you can provide more than one label as long as
they're comma-delimited. For example: Label1, Label2, Label3.
Do not put any other text in your answer, only one or more of the provided labels with nothing before or after.
If the text cannot be classified into any of the provided labels, answer `==NONE==`.

Here is the text that needs classification


Text:
'''
You look gorgeous!
'''

Model response for doc: You look gorgeous!
COMPLIMENT

print(doc.cats) to standard output should look like:

{'COMPLIMENT': 1.0, 'INSULT': 0.0}

📓 API

spacy-llm exposes a llm factory that accepts the following configuration options:

Argument	Type	Description
task	Optional[LLMTask]	An LLMTask can generate prompts and parse LLM responses. See docs.
model	Callable[[Iterable[Any]], Iterable[Any]]]	Callable querying a specific LLM API. See docs.
cache	Cache	Cache to use for caching prompts and responses per doc (batch). See docs.
save_io	bool	Whether to save prompts/responses within Doc.user_data["llm_io"].
validate_types	bool	Whether to check if signatures of configured model and task are consistent.

An llm component is defined by two main settings:

• A task, defining the prompt to send to the LLM as well as the functionality to parse the resulting response back into structured fields on spaCy's Doc objects.
• A model 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 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 spaCy 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

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 spaCy's llm_tasks registry.

Practically speaking, a task should adhere to the Protocol LLMTask defined in ty.py. It needs to define a generate_prompts function and a parse_responses function.

Moreover, the task may define an optional scorer method. It should accept an iterable of Examples as input and return a score dictionary. If the scorer method is defined, spacy-llm will call it to evaluate the component.

Providing examples for few-shot prompts

All built-in tasks support few-shot prompts, i. e. including examples in a prompt. Examples can be supplied in two ways: (1) as a separate file containing only examples or (2) by initializing llm with a get_examples() callback (like any other spaCy pipeline component).

(1) Few-shot example file

A file containing examples for few-shot prompting can be configured like this:

[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = PERSON,ORGANISATION,LOCATION
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "ner_examples.yml"

The supplied file has to conform to the format expected by the required task (see the task documentation further down).

(2) Initializing the llm component with a get_examples() callback

Alternatively, you can initialize your nlp pipeline by providing a get_examples callback for nlp.initialize and setting n_prompt_examples to a positive number to automatically fetch a few examples for few-shot learning. Set n_prompt_examples to -1 to use all examples as part of the few-shot learning prompt.

[initialize.components.llm]
n_prompt_examples = 3

function 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	Type	Description
docs	Iterable[Doc]	The input documents.
RETURNS	Iterable[Any]	The generated prompts.

function 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.

Argument	Type	Description
docs	Iterable[Doc]	The input documents.
responses	Iterable[Any]	The generated prompts.
RETURNS	Iterable[Doc]	The annotated documents.

spacy.Summarization.v1

The spacy.Summarization.v1 task supports both zero-shot and few-shot prompting.

[components.llm.task]
@llm_tasks = "spacy.Summarization.v1"
examples = null
max_n_words = null

Argument	Type	Default	Description
template	str	summarization.jinja	Custom prompt template to send to LLM backend. Default templates for each task are located in the spacy_llm/tasks/templates directory.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
max_n_words	Optional[int]	None	Maximum number of words to be used in summary. Note that this should not expected to work exactly.
field	str	summary	Name of extension attribute to store summary in (i. e. the summary will be available in doc._.{field}).

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, 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.

- 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."

[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

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.

[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = ["PERSON", "ORGANISATION", "LOCATION"]
examples = null

Argument	Type	Default	Description
labels	Union[List[str], str]		List of labels or str of comma-separated list of labels.
template	str	ner.v2.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
label_definitions	Optional[Dict[str, str]]	None	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.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, defaults to spacy.LowercaseNormalizer.v1.
alignment_mode	str	"contract"	Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are "strict", "contract" or "expand".
case_sensitive_matching	bool	False	Whether to search without case sensitivity.
single_match	bool	False	Whether to match an entity in the LLM's response only once (the first hit) or multiple times.

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(). 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, 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.

- 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

[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = PERSON,ORGANISATION,LOCATION
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "ner_examples.yml"

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.

[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"

Label descriptions can also be used with explicit examples to give as much info to the LLM model as possible.

spacy.NER.v1

The original version of the built-in NER task supports both zero-shot and few-shot prompting.

[components.llm.task]
@llm_tasks = "spacy.NER.v1"
labels = PERSON,ORGANISATION,LOCATION
examples = null

Argument	Type	Default	Description
labels	str		Comma-separated list of labels.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, defaults to spacy.LowercaseNormalizer.v1.
alignment_mode	str	"contract"	Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are "strict", "contract" or "expand".
case_sensitive_matching	bool	False	Whether to search without case sensitivity.
single_match	bool	False	Whether to match an entity in the LLM's response only once (the first hit) or multiple times.

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(). 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, 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.

- 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

[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

The built-in SpanCat task is a simple adaptation of the NER task to support overlapping entities and store its annotations in doc.spans.

[components.llm.task]
@llm_tasks = "spacy.SpanCat.v2"
labels = ["PERSON", "ORGANISATION", "LOCATION"]
examples = null

Argument	Type	Default	Description
labels	Union[List[str], str]		List of labels or str of comma-separated list of labels.
template	str	spancat.v2.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
label_definitions	Optional[Dict[str, str]]	None	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.
spans_key	str	"sc"	Key of the Doc.spans dict to save the spans under.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, defaults to spacy.LowercaseNormalizer.v1.
alignment_mode	str	"contract"	Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are "strict", "contract" or "expand".
case_sensitive_matching	bool	False	Whether to search without case sensitivity.
single_match	bool	False	Whether to match an entity in the LLM's response only once (the first hit) or multiple times.

Except for the spans_key parameter, the SpanCat task reuses the configuration from the NER task. Refer to its documentation for more insight.

spacy.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.

[components.llm.task]
@llm_tasks = "spacy.SpanCat.v1"
labels = PERSON,ORGANISATION,LOCATION
examples = null

Argument	Type	Default	Description
labels	str		Comma-separated list of labels.
spans_key	str	"sc"	Key of the Doc.spans dict to save the spans under.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, defaults to spacy.LowercaseNormalizer.v1.
alignment_mode	str	"contract"	Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are "strict", "contract" or "expand".
case_sensitive_matching	bool	False	Whether to search without case sensitivity.
single_match	bool	False	Whether to match an entity in the LLM's response only once (the first hit) or multiple times.

Except for the spans_key parameter, the SpanCat task reuses the configuration from the NER task. Refer to its documentation for more insight.

spacy.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.

[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	Type	Default	Description
labels	Union[List[str], str]		List of labels or str of comma-separated list of labels.
label_definitions	Optional[Dict[str, str]]	None	Dictionary of label definitions. Included in the prompt, if set.
template	str	textcat.jinja	Custom prompt template to send to LLM backend. Default templates for each task are located in the spacy_llm/tasks/templates directory.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, falls back to spacy.LowercaseNormalizer.v1.
exclusive_classes	bool	False	If set to True, only one label per document should be valid. If set to False, one document can have multiple labels.
allow_none	bool	True	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.
verbose	bool	False	If set to True, warnings will be generated when the LLM returns invalid responses.

To perform few-shot learning, 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.

[
  {
    "text": "You look great!",
    "answer": "Compliment"
  },
  {
    "text": "You are not very clever at all.",
    "answer": "Insult"
  }
]

[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

Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template.

[components.llm.task]
@llm_tasks = "spacy.TextCat.v2"
labels = ["COMPLIMENT", "INSULT"]
examples = null

Argument	Type	Default	Description
labels	Union[List[str], str]		List of labels or str of comma-separated list of labels.
template	str	textcat.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, falls back to spacy.LowercaseNormalizer.v1.
exclusive_classes	bool	False	If set to True, only one label per document should be valid. If set to False, one document can have multiple labels.
allow_none	bool	True	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.
verbose	bool	False	If set to True, warnings will be generated when the LLM returns invalid responses.

To perform few-shot learning, 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.

[
  {
    "text": "You look great!",
    "answer": "Compliment"
  },
  {
    "text": "You are not very clever at all.",
    "answer": "Insult"
  }
]

[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

Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting.

[components.llm.task]
@llm_tasks = "spacy.TextCat.v1"
labels = COMPLIMENT,INSULT
examples = null

Argument	Type	Default	Description
labels	str		Comma-separated list of labels.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, falls back to spacy.LowercaseNormalizer.v1.
exclusive_classes	bool	False	If set to True, only one label per document should be valid. If set to False, one document can have multiple labels.
allow_none	bool	True	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.
verbose	bool	False	If set to True, warnings will be generated when the LLM returns invalid responses.

To perform few-shot learning, 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.

[
  {
    "text": "You look great!",
    "answer": "Compliment"
  },
  {
    "text": "You are not very clever at all.",
    "answer": "Insult"
  }
]

[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

The built-in REL task supports both zero-shot and few-shot prompting. It relies on an upstream NER component for entities extraction.

[components.llm.task]
@llm_tasks = "spacy.REL.v1"
labels = ["LivesIn", "Visits"]

Argument	Type	Default	Description
labels	Union[List[str], str]		List of labels or str of comma-separated list of labels.
template	str	rel.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
label_description	Optional[Dict[str, str]]	None	Dictionary providing a description for each relation label.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
normalizer	Optional[Callable[[str], str]]	None	Function that normalizes the labels as returned by the LLM. If None, falls back to spacy.LowercaseNormalizer.v1.
verbose	bool	False	If set to True, warnings will be generated when the LLM returns invalid responses.

To perform few-shot learning, 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.

{"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"}]}

[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

The Lemma.v1 task lemmatizes the provided text and updates the lemma_ attribute in the doc's tokens accordingly.

[components.llm.task]
@llm_tasks = "spacy.Lemma.v1"
examples = null

Argument	Type	Default	Description
template	str	lemma.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.

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 recognized by spaCy, 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, 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.

- 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"
    - ".": "."

[components.llm.task]
@llm_tasks = "spacy.Lemma.v1"
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "lemma_examples.yml"

spacy.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.

[components.llm.task]
@llm_tasks = "spacy.Sentiment.v1"
examples = null

Argument	Type	Default	Description
template	str	sentiment.jinja	Custom prompt template to send to LLM model. Default templates for each task are located in the spacy_llm/tasks/templates directory.
examples	Optional[Callable[[], Iterable[Any]]]	None	Optional function that generates examples for few-shot learning.
field	str	sentiment	Name of extension attribute to store summary in (i. e. the summary will be available in doc._.{field}).

To perform few-shot learning, 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.

- 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

[components.llm.task]
@llm_tasks = "spacy.Sentiment.v1"
[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "sentiment_examples.yml"

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.

[components.llm.task]
@llm_tasks = "spacy.NoOp.v1"

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]].

All built-in models are registered in llm_models. If no model is specified, the repo currently connects to the OpenAI API by default using REST, and accesses the "gpt-3.5-turbo" model.

Currently three different approaches to use LLMs are supported:

1. spacy-llms native REST backend. This is the default for all hosted models (e. g. OpenAI, Cohere, Anthropic, ...).
2. A HuggingFace integration that allows to run a limited set of HF models locally.
3. A LangChain integration that allows to run any model supported by LangChain (hosted or locally).

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.

❓ Why LangChain if there are also are a native REST and a HuggingFace backend? 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.

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:

export OPENAI_API_KEY="sk-..."
export OPENAI_API_ORG="org-..."

For Cohere it's

export CO_API_KEY="..."

and for Anthropic

export ANTHROPIC_API_KEY="..."

spacy.GPT-4.v1

OpenAI's gpt-4 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.GPT-4.v1"
name = "gpt-4"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]	"gpt-4"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.GPT-3-5.v1

OpenAI's gpt-3-5 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.GPT-3-5.v1"
name = "gpt-3.5-turbo"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]	"gpt-3.5-turbo"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Text-Davinci.v1

OpenAI's text-davinci model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Text-Davinci.v1"
name = "text-davinci-003"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["text-davinci-002", "text-davinci-003"]	"text-davinci-003"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Code-Davinci.v1

OpenAI's code-davinci model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Code-Davinci.v1"
name = "code-davinci-002"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["code-davinci-002"]	"code-davinci-002"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Text-Curie.v1

OpenAI's text-curie model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Text-Curie.v1"
name = "text-curie-001"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["text-curie-001"]	"text-curie-001"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Text-Babbage.v1

OpenAI's text-babbage model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Text-Babbage.v1"
name = "text-babbage-001"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["text-babbage-001"]	"text-babbage-001"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Text-Ada.v1

OpenAI's text-ada model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Text-Ada.v1"
name = "text-ada-001"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["text-ada-001"]	"text-ada-001"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Davinci.v1

OpenAI's davinci model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Davinci.v1 "
name = "davinci"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["davinci"]	"davinci"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Curie.v1

OpenAI's curie model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Curie.v1 "
name = "curie"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["curie"]	"curie"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Babbage.v1

OpenAI's babbage model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Babbage.v1 "
name = "babbage"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["babbage"]	"babbage"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Ada.v1

OpenAI's ada model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Ada.v1 "
name = "ada"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["ada"]	"ada"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Command.v1

Cohere's command model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Command.v1 "
name = "command"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["command", "command-light", "command-light-nightly", "command-nightly"]	"command"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-1.v1

Anthropic's claude-1 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-1.v1 "
name = "claude-1"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-1", "claude-1-100k"]	"claude-1"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-instant-1.v1

Anthropic's claude-instant-1 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-instant-1.v1 "
name = "claude-instant-1"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-instant-1", "claude-instant-1-100k"]	"claude-instant-1"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-instant-1-1.v1

Anthropic's claude-instant-1.1 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-instant-1-1.v1 "
name = "claude-instant-1.1"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-instant-1.1", "claude-instant-1.1-100k"]	"claude-instant-1.1"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-1-0.v1

Anthropic's claude-1.0 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-1-0.v1 "
name = "claude-1.0"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-1.0"]	"claude-1.0"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-1-2.v1

Anthropic's claude-1.2 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-1-2.v1 "
name = "claude-1.2"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-1.2"]	"claude-1.2"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Claude-1-3.v1

Anthropic's claude-1.3 model family.

Example config:

[components.llm.model]
@llm_models = "spacy.Claude-1-3.v1 "
name = "claude-1.3"
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	Literal["claude-1.3", "claude-1.3-100k"]	"claude-1.3"	Model name, i. e. any supported variant for this particular model.
config	Dict[Any, Any]	{}	Further configuration passed on to the model.
strict	bool	True	If True, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is.
max_tries	int	3	Max. number of tries for API request.
timeout	int	30	Timeout for API request in seconds.

spacy.Dolly.v1

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

python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"

If you don't have access to a GPU, you can install accelerate and setdevice_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.

python -m pip install "accelerate>=0.16.0,<1.0"

Example config block:

[components.llm.model]
@llm_models = "spacy.Dolly.v1"
name = "dolly-v2-3b"

Argument	Type	Default	Description
name	Literal["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]		The name of a Dolly model that is supported (e. g. "dolly-v2-3b" or "dolly-v2-12b").
config_init	Dict[str, Any]	{}	Further configuration passed on to the construction of the model with transformers.pipeline().
config_run	Dict[str, Any]	{}	Further configuration used during model inference.

Supported models (see the Databricks models page 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 by setting the environmental variable HF_HOME.

spacy.Falcon.v1

To use this backend, 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

python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"

If you don't have access to a GPU, you can install accelerate and setdevice_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.

python -m pip install "accelerate>=0.16.0,<1.0"

Example config block:

[components.llm.model]
@llm_models = "spacy.Falcon.v1"
name = "falcon-7b"

Argument	Type	Default	Description
name	Literal["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]	"7b-instruct"	The name of a Falcon model variant that is supported.
config_init	Dict[str, Any]	{}	Further configuration passed on to the construction of the model with transformers.pipeline().
config_run	Dict[str, Any]	{}	Further configuration used during model inference.

Note that Hugging Face will download this model the first time you use it - you can define the cache directory by setting the environmental variable HF_HOME.

spacy.StableLM.v1

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

python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"

If you don't have access to a GPU, you can install accelerate and setdevice_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.

python -m pip install "accelerate>=0.16.0,<1.0"

Example config block:

[components.llm.model]
@llm_models = "spacy.StableLM.v1"
name = "stablelm-tuned-alpha-7b"

Argument	Type	Default	Description
name	Literal["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]		The name of a StableLM model that is supported (e. g. "stablelm-tuned-alpha-7b").
config_init	Dict[str, Any]	{}	Further configuration passed on to the construction of the model with transformers.AutoModelForCausalLM.from_pretrained().
config_run	Dict[str, Any]	{}	Further configuration used during model inference.

See the Stability AI StableLM GitHub repo for details.

Note that Hugging Face will download this model the first time you use it - you can define the cached directory by setting the environmental variable HF_HOME.

spacy.OpenLLaMA.v1

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

python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]"

If you don't have access to a GPU, you can install accelerate and setdevice_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.

python -m pip install "accelerate>=0.16.0,<1.0"

Example config block:

[components.llm.model]
@llm_models = "spacy.OpenLLaMA.v1"
name = "open_llama_3b"

Argument	Type	Default	Description
name	Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]		The name of a OpenLLaMA model that is supported.
config_init	Dict[str, Any]	{}	Further configuration passed on to the construction of the model with transformers.AutoModelForCausalLM.from_pretrained().
config_run	Dict[str, Any]	{}	Further configuration used during model inference.

See the OpenLM Research OpenLLaMA GitHub repo for details.

Note that Hugging Face will download this model the first time you use it - you can define the cached directory by setting the environmental variable HF_HOME.

LangChain models

To use LangChain for the API retrieval part, make sure you have installed it first:

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 block:

[components.llm.model]
@llm_models = "langchain.OpenAI.v1"
name = "gpt-3.5-turbo"
query = {"@llm_queries": "spacy.CallLangChain.v1"}
config = {"temperature": 0.3}

Argument	Type	Default	Description
name	str		The name of a mdodel supported by LangChain for this API.
config	Dict[Any, Any]	{}	Configuration passed on to the LangChain model.
query	Optional[Callable[["langchain.llms.BaseLLM", Iterable[Any]], Iterable[Any]]]	None	Function that executes the prompts. If None, defaults to spacy.CallLangChain.v1.

The default query (spacy.CallLangChain.v1) executes the prompts by running model(text) for each given textual prompt.

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.

The cache implementation also ensures that documents in one cache directory were all produced using the same prompt template. This is only possible however if the specified task implements

@property
def prompt_template() -> str:
    ...

which returns the raw prompt template as string. If prompt_template() isn't implemented, the cache will emit a warning and not check for prompt template consistency.

Example config block:

[components.llm.cache]
@llm_misc = "spacy.BatchCache.v1"
path = "path/to/cache"
batch_size = 64
max_batches_in_mem = 4

Argument	Type	Default	Description
path	Optional[Union[str, Path]]	None	Cache directory. If None, no caching is performed, and this component will act as a NoOp.
batch_size	int	64	Number of docs in one batch (file). Once a batch is full, it will be persisted to disk.
max_batches_in_mem	int	4	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.

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

spacy.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 to read in these files and parses them depending on the file extension.

[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "ner_examples.yml"

Argument	Type	Description
path	Union[str, Path]	Path to an examples file with suffix .yml, .yaml, .json or .jsonl.

spacy.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 files containing the prompt template.

[components.llm.task.template]
@misc = "spacy.FileReader.v1"
path = "ner_template.jinja2"

Argument	Type	Description
path	Union[str, Path]	Path to the file to be read.

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.

🚀 Ongoing work

In the near future, we will

• Add more example tasks
• Support a broader range of models
• Provide more example use-cases and tutorials
• Make the built-in tasks easier to customize via Jinja templates to define the instructions & examples

PRs are always welcome!

📝️ Reporting issues

If you have questions regarding the usage of spacy-llm, or want to give us feedback after giving it a spin, please use the discussion board. Bug reports can be filed on the spaCy issue tracker. Thank you!

Migration guides

Please refer to our migration guide.