The spacy-llm 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.

Config and implementation

An LLM component is implemented through the LLMWrapper class. It is accessible through a generic llm component factory as well as through task-specific component factories: llm_ner, llm_spancat, llm_rel, llm_textcat, llm_sentiment and llm_summarization.

LLMWrapper.__init__ method

Create a new pipeline instance. In your application, you would normally use a shortcut for this and instantiate the component using its string name and nlp.add_pipe.

LLMWrapper.__call__ method

Apply the pipe to one document. The document is modified in place and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order.

LLMWrapper.pipe method

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order.

LLMWrapper.add_label method

Add a new label to the pipe’s task. Alternatively, provide the labels upon the task definition, or through the [initialize] block of the config.

LLMWrapper.to_disk method

Serialize the pipe to disk.

LLMWrapper.from_disk method

Load the pipe from disk. Modifies the object in place and returns it.

LLMWrapper.to_bytes method

Serialize the pipe to a bytestring.

LLMWrapper.from_bytes method

Load the pipe from a bytestring. Modifies the object in place and returns it.

LLMWrapper.labels property

The labels currently added to the component. Empty tuple if the LLM’s task does not require labels.

Tasks

Task implementation

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

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.

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.

Summarization

A summarization task takes a document as input and generates a summary that is stored in an extension attribute.

spacy.Summarization.v1

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

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.

NER

The NER task identifies non-overlapping entities in text.

spacy.NER.v3

Version 3 is fundamentally different to v1 and v2, as it implements Chain-of-Thought prompting, based on the PromptNER paper by Ashok and Lipton (2023). On an internal use-case, we have found this implementation to obtain significant better accuracy - with an increase of F-score of up to 15 percentage points.

When no examples are specified, the v3 implementation will use a dummy example in the prompt. Technically this means that the task will always perform few-shot prompting under the hood.

Note that the single_match parameter, used in v1 and v2, is not supported anymore, as the CoT parsing algorithm takes care of this automatically.

New to v3 is the fact that you can provide an explicit description of what entities should look like. You can use this feature in addition to label_definitions.

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.

While not required, this task works best when both positive and negative examples are provided. The format is different than the files required for v1 and v2, as additional fields such as is_entity and reason should now be provided.

For a fully working example, see this usage example.

spacy.NER.v2

This version supports explicitly defining the provided labels with custom descriptions, and further supports zero-shot and few-shot prompting just like v1.

The parameters alignment_mode, case_sensitive_matching and single_match are identical to the v1 implementation. The format of few-shot examples are also the same.

New to v2 is the fact that you can 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 a brief description along with some examples and counter examples seems to work quite well.

For a fully working example, see this usage example.

spacy.NER.v1

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

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.

SpanCat

The SpanCat task identifies potentially overlapping entities in text.

spacy.SpanCat.v3

The built-in SpanCat v3 task is a simple adaptation of the NER v3 task to support overlapping entities and store its annotations in doc.spans.

Note that the single_match parameter, used in v1 and v2, is not supported anymore, as the CoT parsing algorithm takes care of this automatically.

spacy.SpanCat.v2

The built-in SpanCat v2 task is a simple adaptation of the NER v2 task to support overlapping entities and store its annotations in doc.spans.

Except for the spans_key parameter, the SpanCat v2 task reuses the configuration from the NER v2 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.

Except for the spans_key parameter, the SpanCat v1 task reuses the configuration from the NER v1 task. Refer to its documentation for more insight.

TextCat

The TextCat task labels documents with relevant categories.

spacy.TextCat.v3

On top of the functionality from v2, version 3 of the built-in TextCat tasks allows setting definitions of labels. Those definitions are included in the prompt.

The formatting of few-shot examples is the same as those for the v1 implementation.

spacy.TextCat.v2

V2 includes all v1 functionality, with an improved prompt template.

The formatting of few-shot examples is the same as those for the v1 implementation.

spacy.TextCat.v1

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

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.

REL

The REL task extracts relations between named entities.

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.

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.

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.

For a fully working example, see this usage example.

Lemma

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

spacy.Lemma.v1

This task supports both zero-shot and few-shot prompting.

The task 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

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, 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.

Sentiment

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.

spacy.Sentiment.v1

This task supports both zero-shot and few-shot prompting.

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.

NoOp

This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the docs.

spacy.NoOp.v1

This task needs no further configuration.

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]].

Models via REST API

These models all take the same parameters, but note that the config should contain provider-specific keys and values, as it will be passed onwards to the provider’s API.

Currently, these models are provided as part of the core library:

To use these models, make sure that you’ve set the relevant API keys as environment variables.

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:

For Cohere:

For Anthropic:

Models via HuggingFace

These models all take the same parameters:

Currently, these models are provided as part of the core library:

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

Installation with HuggingFace

To use models from HuggingFace, 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

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.

LangChain models

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

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.

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.

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.

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.

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.