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@ -32,7 +32,7 @@ An `llm` component is defined by two main settings:
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- A [**task**](#tasks), defining the prompt to send to the LLM as well as the
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functionality to parse the resulting response back into structured fields on
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the [Doc](https://spacy.io/api/doc) objects.
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the [Doc](/api/doc) objects.
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- A [**model**](#models) defining the model and how to connect to it. Note that
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`spacy-llm` supports both access to external APIs (such as OpenAI) as well as
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access to self-hosted open-source LLMs (such as using Dolly through Hugging
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@ -187,7 +187,7 @@ the following parameters:
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case variances in the LLM's output.
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- The `alignment_mode` argument is used to match entities as returned by the LLM
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to the tokens from the original `Doc` - specifically it's used as argument in
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the call to [`doc.char_span()`](https://spacy.io/api/doc#char_span). The
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the call to [`doc.char_span()`](/api/doc#char_span). The
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`"strict"` mode will only keep spans that strictly adhere to the given token
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boundaries. `"contract"` will only keep those tokens that are fully within the
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given range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will
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@ -277,7 +277,7 @@ the following parameters:
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case variances in the LLM's output.
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- The `alignment_mode` argument is used to match entities as returned by the LLM
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to the tokens from the original `Doc` - specifically it's used as argument in
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the call to [`doc.char_span()`](https://spacy.io/api/doc#char_span). The
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the call to [`doc.char_span()`](/api/doc#char_span). The
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`"strict"` mode will only keep spans that strictly adhere to the given token
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boundaries. `"contract"` will only keep those tokens that are fully within the
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given range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will
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@ -12,10 +12,9 @@ menu:
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---
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[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large
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Language Models (LLMs) into spaCy pipelines, featuring a modular
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system for **fast prototyping** and **prompting**, and turning unstructured
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responses into **robust outputs** for various NLP tasks, **no training data**
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required.
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Language Models (LLMs) into spaCy pipelines, featuring a modular system for
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**fast prototyping** and **prompting**, and turning unstructured responses into
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**robust outputs** for various NLP tasks, **no training data** required.
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- Serializable `llm` **component** to integrate prompts into your pipeline
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- **Modular functions** to define the [**task**](#tasks) (prompting and parsing)
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@ -25,11 +24,13 @@ required.
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- Access to
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**[OpenAI API](https://platform.openai.com/docs/api-reference/introduction)**,
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including GPT-4 and various GPT-3 models
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- Built-in support for various **open-source** models hosted on [Hugging Face](https://huggingface.co/)
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- Usage examples for standard NLP tasks such as **Named Entity Recognition** and **Text Classification**
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- Easy implementation of **your own functions** via
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the [registry](https://spacy.io/api/top-level#registry) for custom
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prompting, parsing and model integrations
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- Built-in support for various **open-source** models hosted on
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[Hugging Face](https://huggingface.co/)
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- Usage examples for standard NLP tasks such as **Named Entity Recognition** and
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**Text Classification**
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- Easy implementation of **your own functions** via the
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[registry](/api/top-level#registry) for custom prompting, parsing and model
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integrations
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## Motivation {id="motivation"}
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@ -38,10 +39,6 @@ capabilities. With only a few (and sometimes no) examples, an LLM can be
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prompted to perform custom NLP tasks such as text categorization, named entity
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recognition, coreference resolution, information extraction and more.
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[spaCy](https://spacy.io) is a well-established library for building systems
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that need to work with language in various ways. spaCy's built-in components are
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generally powered by supervised learning or rule-based approaches.
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Supervised learning is much worse than LLM prompting for prototyping, but for
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many tasks it's much better for production. A transformer model that runs
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comfortably on a single GPU is extremely powerful, and it's likely to be a
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@ -70,7 +67,7 @@ well-thought-out library, which is exactly what spaCy provides.
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`spacy-llm` will be installed automatically in future spaCy versions. For now,
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you can run the following in the same virtual environment where you already have
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`spacy` [installed](https://spacy.io/usage).
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`spacy` [installed](/usage).
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> ⚠️ This package is still experimental and it is possible that changes made to
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> the interface will be breaking in minor version updates.
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@ -82,9 +79,8 @@ python -m pip install spacy-llm
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## Usage {id="usage"}
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The task and the model have to be supplied to the `llm` pipeline component using
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the [config system](https://spacy.io/api/data-formats#config). This package
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provides various built-in functionality, as detailed in the [API](#-api)
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documentation.
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the [config system](/api/data-formats#config). This package provides various
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built-in functionality, as detailed in the [API](#-api) documentation.
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### Example 1: Add a text classifier using a GPT-3 model from OpenAI {id="example-1"}
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@ -173,8 +169,8 @@ to be `"databricks/dolly-v2-12b"` for better performance.
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### Example 3: Create the component directly in Python {id="example-3"}
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The `llm` component behaves as any other component does, so adding it to
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an existing pipeline follows the same pattern:
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The `llm` component behaves as any other component does, so adding it to an
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existing pipeline follows the same pattern:
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```python
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import spacy
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@ -198,16 +194,16 @@ print([(ent.text, ent.label_) for ent in doc.ents])
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```
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Note that for efficient usage of resources, typically you would use
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[`nlp.pipe(docs)`](https://spacy.io/api/language#pipe) with a batch, instead of
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calling `nlp(doc)` with a single document.
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[`nlp.pipe(docs)`](/api/language#pipe) with a batch, instead of calling
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`nlp(doc)` with a single document.
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### Example 4: Implement your own custom task {id="example-4"}
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To write a [`task`](#tasks), you need to implement two functions:
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`generate_prompts` that takes a list of [`Doc`](https://spacy.io/api/doc)
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objects and transforms them into a list of prompts, and `parse_responses` that
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transforms the LLM outputs into annotations on the
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[`Doc`](https://spacy.io/api/doc), e.g. entity spans, text categories and more.
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`generate_prompts` that takes a list of [`Doc`](/api/doc) objects and transforms
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them into a list of prompts, and `parse_responses` that transforms the LLM
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outputs into annotations on the [`Doc`](/api/doc), e.g. entity spans, text
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categories and more.
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To register your custom task, decorate a factory function using the
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`spacy_llm.registry.llm_tasks` decorator with a custom name that you can refer
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@ -325,7 +321,7 @@ An `llm` component is defined by two main settings:
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- A [**task**](#tasks), defining the prompt to send to the LLM as well as the
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functionality to parse the resulting response back into structured fields on
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the [Doc](https://spacy.io/api/doc) objects.
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the [Doc](/api/doc) objects.
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- A [**model**](#models) defining the model to use and how to connect to it.
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Note that `spacy-llm` supports both access to external APIs (such as OpenAI)
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as well as access to self-hosted open-source LLMs (such as using Dolly through
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@ -350,8 +346,7 @@ want to disable this behavior.
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A _task_ defines an NLP problem or question, that will be sent to the LLM via a
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prompt. Further, the task defines how to parse the LLM's responses back into
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structured information. All tasks are registered in the `llm_tasks`
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registry.
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structured information. All tasks are registered in the `llm_tasks` registry.
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Practically speaking, a task should adhere to the `Protocol` `LLMTask` defined
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in [`ty.py`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/ty.py).
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@ -363,10 +358,10 @@ function.
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| [`task.generate_prompts`](/api/large-language-models#task-generate-prompts) | Takes a collection of documents, and returns a collection of "prompts", which can be of type `Any`. |
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| [`task.parse_responses`](/api/large-language-models#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. |
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Moreover, the task may define an optional
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[`scorer` method](https://spacy.io/api/scorer#score). It should accept an
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iterable of `Example`s as input and return a score dictionary. If the `scorer`
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method is defined, `spacy-llm` will call it to evaluate the component.
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Moreover, the task may define an optional [`scorer` method](/api/scorer#score).
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It should accept an iterable of `Example`s as input and return a score
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dictionary. If the `scorer` method is defined, `spacy-llm` will call it to
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evaluate the component.
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| Component | Description |
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| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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@ -409,11 +404,10 @@ The supplied file has to conform to the format expected by the required task
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##### (2) Initializing the `llm` component with a `get_examples()` callback
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Alternatively, you can initialize your `nlp` pipeline by providing a
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`get_examples` callback for
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[`nlp.initialize`](https://spacy.io/api/language#initialize) and setting
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`n_prompt_examples` to a positive number to automatically fetch a few examples
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for few-shot learning. Set `n_prompt_examples` to `-1` to use all examples as
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part of the few-shot learning prompt.
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`get_examples` callback for [`nlp.initialize`](/api/language#initialize) and
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setting `n_prompt_examples` to a positive number to automatically fetch a few
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examples for few-shot learning. Set `n_prompt_examples` to `-1` to use all
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examples as part of the few-shot learning prompt.
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```ini
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[initialize.components.llm]
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