diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index f8404cb2e..730ef5054 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -20,7 +20,8 @@ An LLM component is implemented through the `LLMWrapper` class. It is accessible through a generic `llm` [component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories) as well as through task-specific component factories: `llm_ner`, `llm_spancat`, -`llm_rel`, `llm_textcat`, `llm_sentiment` and `llm_summarization`. +`llm_rel`, `llm_textcat`, `llm_sentiment`, `llm_summarization` and +`llm_entity_linker`. ### LLMWrapper.\_\_init\_\_ {id="init",tag="method"} @@ -302,6 +303,171 @@ max_n_words = 20 path = "summarization_examples.yml" ``` +### EL (Entity Linking) {id="nel"} + +The EL links recognized entities (see [NER](#ner)) to those in a knowledge base +(KB). The EL task prompts the LLM to select the most likely candidate from the +KB, whose structure can be arbitrary. + +Note that the documents processed by the entity linking task are expected to +have recognized entities in their `.ents` attribute. This can be achieved by +either running the [NER task](#ner), using a trained spaCy NER model or setting +the entities manually prior to running the EL task. + +In order to be able to pull data from the KB, an object implementing the +`CandidateSelector` protocol has to be provided. This requires two functions: +(1) `__call__()` to fetch candidate entities for entity mentions in the text +(assumed to be available in `Doc.ents`) and (2) `get_entity_description()` to +fetch descriptions for any given entity ID. Descriptions can be empty, but +ideally provide more context for entities stored in the KB. + +`spacy-llm` provides a `CandidateSelector` implementation +(`spacy.CandidateSelector.v1`) that leverages a spaCy knowledge base - as used +in an `entity_linking` component - to select candidates. This knowledge base can +be loaded from an existing spaCy pipeline (note that the pipeline's EL component +doesn't have to be trained) or from a separate .yaml file. + +#### spacy.EntityLinker.v1 {id="el-v1"} + +Supports zero- and few-shot prompting. Relies on a configurable component +suggesting viable entities before letting the LLM pick the most likely +candidate. + +> #### Example config for spacy.EntityLinker.v1 +> +> ```ini +> [paths] +> el_nlp = null +> +> ... +> +> [components.llm.task] +> @llm_tasks = "spacy.EntityLinker.v1" +> +> [initialize] +> [initialize.components] +> [initialize.components.llm] +> [initialize.components.llm.candidate_selector] +> @llm_misc = "spacy.CandidateSelector.v1" +> +> # Load a KB from a KB file. For loading KBs from spaCy pipelines see spacy.KBObjectLoader.v1. +> [initialize.components.llm.candidate_selector.kb_loader] +> @llm_misc = "spacy.KBFileLoader.v1" +> # Path to knowledge base .yaml file. +> path = ${paths.el_kb} +> ``` + +| Argument | Description | +| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Defaults to [entity_linker.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/entity_linker.v1.jinja). ~~str~~ | +| `parse_responses` | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[EntityLinkerTask]]~~ | +| `prompt_example_type` | Type to use for fewshot examples. Defaults to `ELExample`. ~~Optional[Type[FewshotExample]]~~ | +| `examples` | Optional callable that reads a file containing task examples for few-shot learning. If `None` is passed, zero-shot learning will be used. Defaults to `None`. ~~ExamplesConfigType~~ | +| `scorer` | Scorer function. Defaults to the metric used by spaCy to evaluate entity linking performance. ~~Optional[Scorer]~~ | + +##### spacy.CandidateSelector.v1 {id="candidate-selector-v1"} + +`spacy.CandidateSelector.v1` is an implementation of the `CandidateSelector` +protocol required by [`spacy.EntityLinker.v1`](#el-v1). The built-in candidate +selector method allows loading existing knowledge bases in several ways, e. g. +loading from a spaCy pipeline with a (not necessarily trained) entity linking +component, and loading from a file describing the knowlege base as a .yaml file. +Either way the loaded data will be converted to a spaCy `InMemoryLookupKB` +instance. The KB's selection capabilities are used to select the most likely +entity candidates for the specified mentions. + +> #### Example config for spacy.CandidateSelector.v1 +> +> ```ini +> [initialize] +> [initialize.components] +> [initialize.components.llm] +> [initialize.components.llm.candidate_selector] +> @llm_misc = "spacy.CandidateSelector.v1" +> +> # Load a KB from a KB file. For loading KBs from spaCy pipelines see spacy.KBObjectLoader.v1. +> [initialize.components.llm.candidate_selector.kb_loader] +> @llm_misc = "spacy.KBFileLoader.v1" +> # Path to knowledge base .yaml file. +> path = ${paths.el_kb} +> ``` + +| Argument | Description | +| ----------- | ----------------------------------------------------------------- | +| `kb_loader` | KB loader object. ~~InMemoryLookupKBLoader~~ | +| `top_n` | Top-n candidates to include in the prompt. Defaults to 5. ~~int~~ | + +##### spacy.KBObjectLoader.v1 {id="kb-object-loader-v1"} + +Adheres to the `InMemoryLookupKBLoader` interface required by +[`spacy.CandidateSelector.v1`](#candidate-selector-v1). Loads a knowledge base +from an existing spaCy pipeline. + +> #### Example config for spacy.KBObjectLoader.v1 +> +> ```ini +> [initialize.components.llm.candidate_selector.kb_loader] +> @llm_misc = "spacy.KBObjectLoader.v1" +> # Path to knowledge base directory in serialized spaCy pipeline. +> path = ${paths.el_kb} +> # Path to spaCy pipeline. If this is not specified, spacy-llm tries to determine this automatically (but may fail). +> nlp_path = ${paths.el_nlp} +> # Path to file with descriptions for entity. +> desc_path = ${paths.el_desc} +> ``` + +| Argument | Description | +| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `path` | Path to KB file. ~~Union[str, Path]~~ | +| `nlp_path` | Path to serialized NLP pipeline. If None, path will be guessed. ~~Optional[Union[Path, str]]~~ | +| `desc_path` | Path to file with descriptions for entities. ~~int~~ | +| `ent_desc_reader` | Entity description reader. Defaults to an internal method expecting a CSV file without header row, with ";" as delimiters, and with two columns - one for the entitys' IDs, one for their descriptions. ~~Optional[EntDescReader]~~ | + +##### spacy.KBFileLoader.v1 {id="kb-file-loader-v1"} + +Adheres to the `InMemoryLookupKBLoader` interface required by +[`spacy.CandidateSelector.v1`](#candidate-selector-v1). Loads a knowledge base +from a knowledge base file. The KB .yaml file has to stick to the following +format: + +```yaml +entities: + # The key should be whatever ID identifies this entity uniquely in your knowledge base. + ID1: + name: "..." + desc: "..." + ID2: + ... +# Data on aliases in your knowledge base - e. g. "Apple" for the entity "Apple Inc.". +aliases: + - alias: "..." + # List of all entities that this alias refers to. + entities: ["ID1", "ID2", ...] + # Optional: prior probabilities that this alias refers to the n-th entity in the "entities" attribute. + probabilities: [0.5, 0.2, ...] + - alias: "..." + entities: [...] + probabilities: [...] + ... +``` + +See +[here](https://github.com/explosion/spacy-llm/blob/main/usage_examples/el_openai/el_kb_data.yml) +for a toy example of how such a KB file might look like. + +> #### Example config for spacy.KBFileLoader.v1 +> +> ```ini +> [initialize.components.llm.candidate_selector.kb_loader] +> @llm_misc = "spacy.KBFileLoader.v1" +> # Path to knowledge base file. +> path = ${paths.el_kb} +> ``` + +| Argument | Description | +| -------- | ------------------------------------- | +| `path` | Path to KB file. ~~Union[str, Path]~~ | + ### NER {id="ner"} The NER task identifies non-overlapping entities in text. diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx index 94494b4e1..43b22ce07 100644 --- a/website/docs/usage/large-language-models.mdx +++ b/website/docs/usage/large-language-models.mdx @@ -357,6 +357,7 @@ evaluate the component. | Component | Description | | ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | +| [`spacy.EntityLinker.v1`](/api/large-language-models#el-v1) | The entity linking task prompts the model to link all entities in a given text to entries in a knowledge base. | | [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. | | [`spacy.NER.v3`](/api/large-language-models#ner-v3) | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2. | | [`spacy.NER.v2`](/api/large-language-models#ner-v2) | Builds on v1 and additionally supports defining the provided labels with explicit descriptions. |