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Merge pull request #12965 from explosion/docs/llm_develop

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Consolidate spacy-llm docs for v0.5.0
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@ -108,7 +108,7 @@ labels = ["COMPLIMENT", "INSULT"]
[components.llm.model] [components.llm.model]
@llm_models = "spacy.GPT-3-5.v1" @llm_models = "spacy.GPT-3-5.v1"
config = {"temperature": 0.3} config = {"temperature": 0.0}
``` ```
Now run: Now run:
@ -142,7 +142,7 @@ pipeline = ["llm"]
factory = "llm" factory = "llm"
[components.llm.task] [components.llm.task]
@llm_tasks = "spacy.NER.v2" @llm_tasks = "spacy.NER.v3"
labels = ["PERSON", "ORGANISATION", "LOCATION"] labels = ["PERSON", "ORGANISATION", "LOCATION"]
[components.llm.model] [components.llm.model]
@ -359,22 +359,24 @@ function.
| [`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. | | [`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](/api/scorer#score). Moreover, the task may define an optional [`scorer` method](/api/scorer#score).
It should accept an iterable of `Example`s as input and return a score It should accept an iterable of `Example` objects as input and return a score
dictionary. If the `scorer` method is defined, `spacy-llm` will call it to dictionary. If the `scorer` method is defined, `spacy-llm` will call it to
evaluate the component. evaluate the component.
| Component | Description | | 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.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.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. |
| [`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.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.v3`](/api/large-language-models#spancat-v3) | Adaptation of the v3 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.SpanCat.v2`](/api/large-language-models#spancat-v2) | Adaptation of the v2 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.SpanCat.v1`](/api/large-language-models#spancat-v1) | Adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. |
| [`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.REL.v1`](/api/large-language-models#rel-v1) | Relation Extraction task supporting both zero-shot and few-shot prompting. |
| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 builds on v2 and allows setting definitions of labels. |
| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 builds on v1 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.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) | Lemmatizes the provided text and updates the `lemma_` attribute of the tokens accordingly. |
| [`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.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`. | | [`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`. |
@ -469,32 +471,39 @@ provider's documentation.
</Infobox> </Infobox>
| Component | Description | | Model | Description |
| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ | | ----------------------------------------------------------------------- | ---------------------------------------------- |
| [`spacy.GPT-4.v1`](/api/large-language-models#gpt-4) | OpenAI’s `gpt-4` model family. | | [`spacy.GPT-4.v1`](/api/large-language-models#models-rest) | OpenAI’s `gpt-4` model family. |
| [`spacy.GPT-3-5.v1`](/api/large-language-models#gpt-3-5) | OpenAI’s `gpt-3-5` model family. | | [`spacy.GPT-3-5.v1`](/api/large-language-models#models-rest) | OpenAI’s `gpt-3-5` model family. |
| [`spacy.Text-Davinci.v1`](/api/large-language-models#text-davinci) | OpenAI’s `text-davinci` model family. | | [`spacy.Text-Davinci.v1`](/api/large-language-models#models-rest) | OpenAI’s `text-davinci` model family. |
| [`spacy.Code-Davinci.v1`](/api/large-language-models#code-davinci) | OpenAI’s `code-davinci` model family. | | [`spacy.Code-Davinci.v1`](/api/large-language-models#models-rest) | OpenAI’s `code-davinci` model family. |
| [`spacy.Text-Curie.v1`](/api/large-language-models#text-curie) | OpenAI’s `text-curie` model family. | | [`spacy.Text-Curie.v1`](/api/large-language-models#models-rest) | OpenAI’s `text-curie` model family. |
| [`spacy.Text-Babbage.v1`](/api/large-language-models#text-babbage) | OpenAI’s `text-babbage` model family. | | [`spacy.Text-Babbage.v1`](/api/large-language-models#models-rest) | OpenAI’s `text-babbage` model family. |
| [`spacy.Text-Ada.v1`](/api/large-language-models#text-ada) | OpenAI’s `text-ada` model family. | | [`spacy.Text-Ada.v1`](/api/large-language-models#models-rest) | OpenAI’s `text-ada` model family. |
| [`spacy.Davinci.v1`](/api/large-language-models#davinci) | OpenAI’s `davinci` model family. | | [`spacy.Davinci.v1`](/api/large-language-models#models-rest) | OpenAI’s `davinci` model family. |
| [`spacy.Curie.v1`](/api/large-language-models#curie) | OpenAI’s `curie` model family. | | [`spacy.Curie.v1`](/api/large-language-models#models-rest) | OpenAI’s `curie` model family. |
| [`spacy.Babbage.v1`](/api/large-language-models#babbage) | OpenAI’s `babbage` model family. | | [`spacy.Babbage.v1`](/api/large-language-models#models-rest) | OpenAI’s `babbage` model family. |
| [`spacy.Ada.v1`](/api/large-language-models#ada) | OpenAI’s `ada` model family. | | [`spacy.Ada.v1`](/api/large-language-models#models-rest) | OpenAI’s `ada` model family. |
| [`spacy.Command.v1`](/api/large-language-models#command) | Cohere’s `command` model family. | | [`spacy.Command.v1`](/api/large-language-models#models-rest) | Cohere’s `command` model family. |
| [`spacy.Claude-1.v1`](/api/large-language-models#claude-1) | Anthropic’s `claude-1` model family. | | [`spacy.Claude-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-2` model family. |
| [`spacy.Claude-instant-1.v1`](/api/large-language-models#claude-instant-1) | Anthropic’s `claude-instant-1` model family. | | [`spacy.Claude-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1` model family. |
| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#claude-instant-1-1) | Anthropic’s `claude-instant-1.1` model family. | | [`spacy.Claude-instant-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1` model family. |
| [`spacy.Claude-1-0.v1`](/api/large-language-models#claude-1-0) | Anthropic’s `claude-1.0` model family. | | [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1.1` model family. |
| [`spacy.Claude-1-2.v1`](/api/large-language-models#claude-1-2) | Anthropic’s `claude-1.2` model family. | | [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.0` model family. |
| [`spacy.Claude-1-3.v1`](/api/large-language-models#claude-1-3) | Anthropic’s `claude-1.3` model family. | | [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.2` model family. |
| [`spacy.Dolly.v1`](/api/large-language-models#dolly) | Dolly models through [Databricks](https://huggingface.co/databricks) on HuggingFace. | | [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.3` model family. |
| [`spacy.Falcon.v1`](/api/large-language-models#falcon) | Falcon model through HuggingFace. | | [`spacy.Dolly.v1`](/api/large-language-models#models-hf) | Dolly models through HuggingFace. |
| [`spacy.StableLM.v1`](/api/large-language-models#stablelm) | StableLM model through HuggingFace. | | [`spacy.Falcon.v1`](/api/large-language-models#models-hf) | Falcon models through HuggingFace. |
| [`spacy.OpenLLaMA.v1`](/api/large-language-models#openllama) | OpenLLaMA model through HuggingFace. | | [`spacy.Llama2.v1`](/api/large-language-models#models-hf) | Llama2 models through HuggingFace. |
| [`spacy.StableLM.v1`](/api/large-language-models#models-hf) | StableLM models through HuggingFace. |
| [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf) | OpenLLaMA models through HuggingFace. |
| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. | | [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. |
Note that the chat models variants of Llama 2 are currently not supported. This
is because they need a particular prompting setup and don't add any discernible
benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared to
the completion model variants.
### Cache {id="cache"} ### Cache {id="cache"}
Interacting with LLMs, either through an external API or a local instance, is Interacting with LLMs, either through an external API or a local instance, is
@ -505,7 +514,7 @@ documents at each run that keeps batches of documents stored on disk.
### Various functions {id="various-functions"} ### Various functions {id="various-functions"}
| Component | Description | | Function | Description |
| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [`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.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. | | [`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. |