diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index 2b6b7d7e0..6deea336a 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -60,52 +60,6 @@ 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`](https://github.com/spacy-llm/spacy_llm/ty.py). It needs to define a -`generate_prompts` function and a `parse_responses` function. - -Moreover, the task may define an optional -[`scorer` method](https://spacy.io/api/scorer#score). It should accept an -iterable of `Example`s 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 {id="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: - -```ini -[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`](https://spacy.io/api/language#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. - -```ini -[initialize.components.llm] -n_prompt_examples = 3 -``` - #### task.generate_prompts {id="task-generate-prompts"} Takes a collection of documents, and returns a collection of "prompts", which @@ -161,9 +115,10 @@ 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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```yaml - text: > @@ -239,9 +194,10 @@ the following parameters: 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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```yaml - text: Jack and Jill went up the hill. @@ -328,9 +284,10 @@ the following parameters: 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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```yaml - text: Jack and Jill went up the hill. @@ -442,9 +399,10 @@ definitions are included in the prompt. | `allow_none` | 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. Defaults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```json [ @@ -496,9 +454,10 @@ prompting and includes an improved prompt template. | `allow_none` | 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. Defaults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```json [ @@ -545,9 +504,10 @@ prompting. | `allow_none` | 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. Deafults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ | -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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```json [ @@ -593,9 +553,10 @@ on an upstream NER component for entities extraction. | `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```json {"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"}]} @@ -654,9 +615,10 @@ 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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```yaml - text: I'm buying ice cream. @@ -706,9 +668,10 @@ issues (e. g. in case of unexpected LLM responses) the value might be `None`. | `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | | `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ | -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`. +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +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`. ```yaml - text: 'This is horrifying.' @@ -751,43 +714,7 @@ 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-llm`s native REST interface. 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 interface? 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. - - +#### API Keys {id="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 @@ -1376,11 +1303,11 @@ python -m pip install "accelerate>=0.16.0,<1.0" > name = "open_llama_3b" > ``` -| Argument | Description | -| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `name` | The name of a OpenLLaMA model that is supported. ~~Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | +| Argument | Description | +| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | The name of a OpenLLaMA model that is supported. ~~Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | See the [OpenLM Research OpenLLaMA GitHub repo](https://github.com/openlm-research/open_llama) diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx index 964aeddaf..8ec3907f8 100644 --- a/website/docs/usage/large-language-models.mdx +++ b/website/docs/usage/large-language-models.mdx @@ -9,8 +9,6 @@ menu: - ['API', 'api'] - ['Tasks', 'tasks'] - ['Models', 'models'] - - ['Ongoing work', 'ongoing-work'] - - ['Issues', 'issues'] --- [The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large @@ -359,27 +357,67 @@ Practically speaking, a task should adhere to the `Protocol` `LLMTask` defined in [`ty.py`](https://github.com/spacy-llm/spacy_llm/ty.py). It needs to define a `generate_prompts` function and a `parse_responses` function. +| Task | Description | +| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [`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`. | +| [`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. | + Moreover, the task may define an optional [`scorer` method](https://spacy.io/api/scorer#score). It should accept an iterable of `Example`s as input and return a score dictionary. If the `scorer` method is defined, `spacy-llm` will call it to evaluate the component. -| Component | Description | -| --------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`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`. | -| [`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. | -| [`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.v2`](/api/large-language-models#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. | -| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. | -| [`spacy.SpanCat.v2`](/api/large-language-models#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`. | -| [`spacy.SpanCat.v1`](/api/large-language-models#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`. | -| [`spacy.TextCat.v3`](/api/large-language-models#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. | -| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template. | -| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. | -| [`spacy.REL.v1`](/api/large-language-models#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. | -| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` attribute in the doc's tokens accordingly. | -| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. | -| [`spacy.NoOp.v1`](/api/large-language-models#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`. | +| Component | Description | +| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`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.v2`](/api/large-language-models#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. | +| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. | +| [`spacy.SpanCat.v2`](/api/large-language-models#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`. | +| [`spacy.SpanCat.v1`](/api/large-language-models#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`. | +| [`spacy.TextCat.v3`](/api/large-language-models#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. | +| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template. | +| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. | +| [`spacy.REL.v1`](/api/large-language-models#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. | +| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` attribute in the doc's tokens accordingly. | +| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. | +| [`spacy.NoOp.v1`](/api/large-language-models#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`. | + +#### Providing examples for few-shot prompts {id="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: + +```ini +[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`](https://spacy.io/api/language#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. + +```ini +[initialize.components.llm] +n_prompt_examples = 3 +``` ### Model {id="models"} @@ -408,29 +446,33 @@ 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. -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 + +_Why LangChain if there are also are a native REST and a HuggingFace interface? 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](/api/large-language-models#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: - -```shell -export OPENAI_API_KEY="sk-..." -export OPENAI_API_ORG="org-..." -``` - -For Cohere it's - -```shell -export CO_API_KEY="..." -``` - -and for Anthropic - -```shell -export ANTHROPIC_API_KEY="..." -``` + | Component | Description | | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ | @@ -473,23 +515,3 @@ documents at each run that keeps batches of documents stored on disk. | [`spacy.FewShotReader.v1`](/api/large-language-models#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`](https://github.com/explosion/srsly) to read in these files and parses them depending on the file extension. | | [`spacy.FileReader.v1`](/api/large-language-models#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](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt template. | | [Normalizer functions](/api/large-language-models#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. | - -## Ongoing work {id="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 {id="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](https://github.com/explosion/spaCy/discussions). Bug reports -can be filed on the -[spaCy issue tracker](https://github.com/explosion/spaCy/issues). Thank you! diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index ebd302855..0adccb494 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -37,7 +37,11 @@ { "text": "spaCy Projects", "url": "/usage/projects", "tag": "new" }, { "text": "Saving & Loading", "url": "/usage/saving-loading" }, { "text": "Visualizers", "url": "/usage/visualizers" }, - { "text": "Large Language Models", "url": "/usage/large-language-models", "tag": "new" } + { + "text": "Large Language Models", + "url": "/usage/large-language-models", + "tag": "new" + } ] }, { @@ -102,6 +106,7 @@ { "text": "EntityLinker", "url": "/api/entitylinker" }, { "text": "EntityRecognizer", "url": "/api/entityrecognizer" }, { "text": "EntityRuler", "url": "/api/entityruler" }, + { "text": "Large Language Models", "url": "/api/large-language-models" }, { "text": "Lemmatizer", "url": "/api/lemmatizer" }, { "text": "Morphologizer", "url": "/api/morphologizer" }, { "text": "SentenceRecognizer", "url": "/api/sentencerecognizer" }, @@ -134,7 +139,6 @@ { "text": "Corpus", "url": "/api/corpus" }, { "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" }, { "text": "KnowledgeBase", "url": "/api/kb" }, - { "text": "Large Language Models", "url": "/api/large-language-models" }, { "text": "Lookups", "url": "/api/lookups" }, { "text": "MorphAnalysis", "url": "/api/morphology#morphanalysis" }, { "text": "Morphology", "url": "/api/morphology" },