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Update EL task docs.

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Raphael Mitsch 2023-11-17 12:34:02 +01:00
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website/docs/api/large-language-models.mdx
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@ -305,14 +305,14 @@ path = "summarization_examples.yml"
### EL (Entity Linking) {id="nel"} ### EL (Entity Linking) {id="nel"}
The EL links recognized entities (see [NER](#ner)) to those in a knowledge The EL links recognized entities (see [NER](#ner)) to those in a knowledge base
base (KB). The EL task prompts the LLM to select the most likely (KB). The EL task prompts the LLM to select the most likely candidates from the
candidates from the KB, whose structure can be arbitrary. KB, whose structure can be arbitrary.
Note that the documents processed by the entity linking task are expected to have Note that the documents processed by the entity linking task are expected to
recognized entities in their `.ents` attribute. This can be achieved by either running the have recognized entities in their `.ents` attribute. This can be achieved by
[NER task](#ner), using a trained spaCy NER model or setting the entities manually prior either running the [NER task](#ner), using a trained spaCy NER model or setting
to running the EL task. the entities manually prior to running the EL task.
In order to be able to pull data from the KB, an object implementing the 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: `CandidateSelector` protocol has to be provided. This requires two functions:
@ -322,18 +322,25 @@ fetch descriptions for any given entity ID. Descriptions can be empty, but
ideally provide more context for entities stored in the KB. ideally provide more context for entities stored in the KB.
`spacy-llm` provides a `CandidateSelector` implementation `spacy-llm` provides a `CandidateSelector` implementation
(`spacy.CandidateSelector.v1`) that leverages a spaCy pipeline with an (`spacy.CandidateSelector.v1`) that leverages a a spaCy knowledge base -as used
`entity_linking` component to select candidates. Note that this pipeline doesn't in an `entity_linking` component - to select candidates. This knowledge base can
have to provide a trained EL model but merely its default (or custom) candidate be loaded from an existing spaCy pipeline (note that the pipeline's EL component
selection capabilities. doesn't have to be trained) or from a separate .yaml file.
#### spacy.EntityLinker.v1 {id="el-v1"} #### spacy.EntityLinker.v1 {id="el-v1"}
Supports zero- and few-shot prompting. Supports zero- and few-shot prompting.
> #### Example config > #### Example config (loading a knowledge base from a spaCy pipeline)
> >
> ```ini > ```ini
> [paths]
> el_nlp = null
> el_kb = null
> el_desc = null
>
> ...
>
> [components.llm.task] > [components.llm.task]
> @llm_tasks = "spacy.EntityLinker.v1" > @llm_tasks = "spacy.EntityLinker.v1"
> >
@ -342,12 +349,42 @@ Supports zero- and few-shot prompting.
> [initialize.components.llm] > [initialize.components.llm]
> [initialize.components.llm.candidate_selector] > [initialize.components.llm.candidate_selector]
> @llm_misc = "spacy.CandidateSelector.v1" > @llm_misc = "spacy.CandidateSelector.v1"
>
> [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} > nlp_path = ${paths.el_nlp}
> # Path to file with descriptions for entity.
> desc_path = ${paths.el_desc} > desc_path = ${paths.el_desc}
> ``` > ```
> #### Example config (loading a knowledge base from a knowledge base file)
>
> ```ini
> [paths]
> el_kb = 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"
>
> [initialize.components.llm.candidate_selector.kb_loader]
> @llm_misc = "spacy.KBFileLoader.v1"
> # Path to knowledge base .yaml file.
> path = ${paths.el_kb}
> ```
| Argument | Description | | Argument | Description |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `template` | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~ | | `template` | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~ |
| `parse_responses` | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[EntityLinkerTask]]~~ | | `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]]~~ | | `prompt_example_type` | Type to use for fewshot examples. Defaults to `ELExample`. ~~Optional[Type[FewshotExample]]~~ |
@ -358,11 +395,14 @@ Supports zero- and few-shot prompting.
`spacy.CandidateSelector.v1` is an implementation of the `CandidateSelector` `spacy.CandidateSelector.v1` is an implementation of the `CandidateSelector`
protocol required by [`spacy.EntityLinker.v1`](#el-v1). The built-in candidate protocol required by [`spacy.EntityLinker.v1`](#el-v1). The built-in candidate
selector method leverages a spaCy pipeline with an entity linking component. The selector method allows loading existing knowledge bases in several ways, e. g.
EL component's candidate selection capabilities are used to select loading from a spaCy pipeline with a (not necessarily trained) entity linking
the most likely entity candidates for the specified mentions. 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 > #### Example config (loading a knowledge base from a spaCy pipeline)
> >
> ```ini > ```ini
> [initialize] > [initialize]
@ -370,18 +410,102 @@ the most likely entity candidates for the specified mentions.
> [initialize.components.llm] > [initialize.components.llm]
> [initialize.components.llm.candidate_selector] > [initialize.components.llm.candidate_selector]
> @llm_misc = "spacy.CandidateSelector.v1" > @llm_misc = "spacy.CandidateSelector.v1"
>
> [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} > nlp_path = ${paths.el_nlp}
> # Path to file with descriptions for entity.
> desc_path = ${paths.el_desc} > desc_path = ${paths.el_desc}
> top_n = 3 > ```
> #### Example config (loading a knowledge base from a knowledge base file)
>
> ```ini
> [initialize]
> [initialize.components]
> [initialize.components.llm]
> [initialize.components.llm.candidate_selector]
> @llm_misc = "spacy.CandidateSelector.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 | | Argument | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ----------- | ----------------------------------------------------------------- |
| `nlp_path` | Path to stored spaCy pipeline. ~~Union[Path, str]~~ | | `kb_loader` | KB loader object. ~~InMemoryLookupKBLoader~~ |
| `desc_path` | Path to `.csv` file with descriptions for entities. Must have two columns: entity ID and description. The entity ID has to match with the entity ID in the stored knowledge base. ~~Union[Path, str]~~ |
| `el_component_name` | Name of the EL component in the pipeline loaded from `nlp_path`. Defaults to `entity_linker`. ~~str~~ |
| `top_n` | Top-n candidates to include in the prompt. Defaults to 5. ~~int~~ | | `top_n` | Top-n candidates to include in the prompt. Defaults to 5. ~~int~~ |
| `ent_desc_reader` | Entity description reader. Defaults to an internal method that expects a CSV file in the following format: No header row, ";" as delimiters, two columns - one for the entitys' IDs and one for their descriptions. ~~Optional[Scorer]~~ |
##### spacy.KBObjectLoader.v1 {id="kb-object-loader-v1"}
Adheres to the `InMemoryLookupKBLoader` interface required by
`spacy.CandidateSelector.v1`. Loads a knowledge base from an existing spaCy
pipeline.
> #### Example config
>
> ```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` | Reader function for entity description file. Defaults to a reader expecting a CSV with two columns: entity ID and decsription. ~~EntDescReader~~ |
##### spacy.KBFileLoader.v1 {id="kb-file-loader-v1"}
Adheres to the `InMemoryLookupKBLoader` interface required by
`spacy.CandidateSelector.v1`. Loads a knowledge base from a knowledge base file.
The KB .yaml file has to stick to the following format:
```yaml
entities:
ID1: # This can be whatever ID identifies this entity in your knowledge base.
name: "..."
desc: "..."
ID2:
...
aliases: # Aliases in your knowledge base - e. g. "Apple" for the entity "Apple Inc.".
- alias: "..."
entities: ["ID1", "ID2", ...] # List of all entities that this alias refers to.
probabilities: [0.5, 0.2, ...] # Prior probabilities that this alias refers to the n-th entity in the "entities" attribute. This is optional.
- alias: "..."
entities: [...]
probabilities: [...]
...
```
See
[here](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tests/tasks/misc/el_kb_data.yml)
for a toy example of how such a KB file might look like.
> #### Example config
>
> ```ini
> [initialize.components.llm.candidate_selector.kb_loader]
> @llm_misc = "spacy.KBObjectLoader.v1"
> # Path to knowledge base file.
> path = ${paths.el_kb}
> ```
| Argument | Description |
| -------- | ------------------------------------- |
| `path` | Path to KB file. ~~Union[str, Path]~~ |
### NER {id="ner"} ### NER {id="ner"}