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Docs for spacy-llm 0.5.0 (#12968)
* Update incorrect example config. (#12893) * spacy-llm docs cleanup (#12945) * Shorten NER section * fix template references * simplify sections * set temperature to 0.0 in examples * condense model information * fix parameters for REST models * set temperature to 0.0 * spelling fix * trigger preview * fix quotes * add small note on noop.v1 * move up example noop config * set appropriate model example configs * explain config * fix Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> * Docs for ner.v3 and spancat.v3 spacy-llm tasks (#12949) * formatting * update usage table with NER.v3 * fix typo in links * v3 overview of parameters * add spancat.v3 * add further v3 explanations * remove TODO comment * few more small fixes * Add doc section on LLM + task factories (#12905) * Add section on LLM + task factories. * Apply suggestions from code review --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * add default config to openai models (#12961) * Docs for spacy-llm 0.5.0 (#12967) * simplify Python example * simplify Python example * Refer only to latest OpenAI model versions from usage doc * Typo fix Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> * clarify accuracy claim --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
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@ -108,7 +108,7 @@ labels = ["COMPLIMENT", "INSULT"]
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[components.llm.model]
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@llm_models = "spacy.GPT-3-5.v1"
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config = {"temperature": 0.3}
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config = {"temperature": 0.0}
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```
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Now run:
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@ -142,7 +142,7 @@ pipeline = ["llm"]
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factory = "llm"
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[components.llm.task]
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@llm_tasks = "spacy.NER.v2"
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@llm_tasks = "spacy.NER.v3"
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labels = ["PERSON", "ORGANISATION", "LOCATION"]
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[components.llm.model]
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@ -169,25 +169,17 @@ 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 an
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existing pipeline follows the same pattern:
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The `llm` component behaves as any other component does, and there are
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[task-specific components](/api/large-language-models#config) defined to
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help you hit the ground running with a reasonable built-in task implementation.
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```python
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import spacy
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nlp = spacy.blank("en")
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nlp.add_pipe(
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"llm",
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config={
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"task": {
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"@llm_tasks": "spacy.NER.v2",
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"labels": ["PERSON", "ORGANISATION", "LOCATION"]
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},
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"model": {
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"@llm_models": "spacy.GPT-3-5.v1",
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},
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},
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)
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llm_ner = nlp.add_pipe("llm_ner")
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llm_ner.add_label("PERSON")
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llm_ner.add_label("LOCATION")
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nlp.initialize()
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doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes")
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print([(ent.text, ent.label_) for ent in doc.ents])
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@ -314,7 +306,7 @@ COMPLIMENT
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## API {id="api"}
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`spacy-llm` exposes a `llm` factory with
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`spacy-llm` exposes an `llm` factory with
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[configurable settings](/api/large-language-models#config).
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An `llm` component is defined by two main settings:
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@ -359,22 +351,24 @@ function.
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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 [`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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It should accept an iterable of `Example` objects 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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| ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
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| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. |
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| [`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. |
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| [`spacy.NER.v3`](/api/large-language-models#ner-v3) | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2. |
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| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | Builds on v1 and additionally supports defining the provided labels with explicit descriptions. |
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| [`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. |
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| [`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`. |
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| [`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`. |
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| [`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. |
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| [`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. |
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| [`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`. |
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| [`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`. |
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| [`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`. |
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| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | Relation Extraction task supporting both zero-shot and few-shot prompting. |
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| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 builds on v2 and allows setting definitions of labels. |
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| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 builds on v1 and includes an improved prompt template. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | Lemmatizes the provided text and updates the `lemma_` attribute of the tokens accordingly. |
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| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. |
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| [`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`. |
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@ -469,32 +463,39 @@ provider's documentation.
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</Infobox>
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| Component | Description |
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| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ |
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| [`spacy.GPT-4.v1`](/api/large-language-models#gpt-4) | OpenAI’s `gpt-4` model family. |
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| [`spacy.GPT-3-5.v1`](/api/large-language-models#gpt-3-5) | OpenAI’s `gpt-3-5` model family. |
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| [`spacy.Text-Davinci.v1`](/api/large-language-models#text-davinci) | OpenAI’s `text-davinci` model family. |
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| [`spacy.Code-Davinci.v1`](/api/large-language-models#code-davinci) | OpenAI’s `code-davinci` model family. |
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| [`spacy.Text-Curie.v1`](/api/large-language-models#text-curie) | OpenAI’s `text-curie` model family. |
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| [`spacy.Text-Babbage.v1`](/api/large-language-models#text-babbage) | OpenAI’s `text-babbage` model family. |
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| [`spacy.Text-Ada.v1`](/api/large-language-models#text-ada) | OpenAI’s `text-ada` model family. |
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| [`spacy.Davinci.v1`](/api/large-language-models#davinci) | OpenAI’s `davinci` model family. |
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| [`spacy.Curie.v1`](/api/large-language-models#curie) | OpenAI’s `curie` model family. |
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| [`spacy.Babbage.v1`](/api/large-language-models#babbage) | OpenAI’s `babbage` model family. |
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| [`spacy.Ada.v1`](/api/large-language-models#ada) | OpenAI’s `ada` model family. |
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| [`spacy.Command.v1`](/api/large-language-models#command) | Cohere’s `command` model family. |
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| [`spacy.Claude-1.v1`](/api/large-language-models#claude-1) | Anthropic’s `claude-1` model family. |
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| [`spacy.Claude-instant-1.v1`](/api/large-language-models#claude-instant-1) | Anthropic’s `claude-instant-1` model family. |
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| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#claude-instant-1-1) | Anthropic’s `claude-instant-1.1` model family. |
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| [`spacy.Claude-1-0.v1`](/api/large-language-models#claude-1-0) | Anthropic’s `claude-1.0` model family. |
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| [`spacy.Claude-1-2.v1`](/api/large-language-models#claude-1-2) | Anthropic’s `claude-1.2` model family. |
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| [`spacy.Claude-1-3.v1`](/api/large-language-models#claude-1-3) | Anthropic’s `claude-1.3` model family. |
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| [`spacy.Dolly.v1`](/api/large-language-models#dolly) | Dolly models through [Databricks](https://huggingface.co/databricks) on HuggingFace. |
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| [`spacy.Falcon.v1`](/api/large-language-models#falcon) | Falcon model through HuggingFace. |
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| [`spacy.StableLM.v1`](/api/large-language-models#stablelm) | StableLM model through HuggingFace. |
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| [`spacy.OpenLLaMA.v1`](/api/large-language-models#openllama) | OpenLLaMA model through HuggingFace. |
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| Model | Description |
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| ----------------------------------------------------------------------- | ---------------------------------------------- |
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| [`spacy.GPT-4.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-4` model family. |
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| [`spacy.GPT-3-5.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-3-5` model family. |
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| [`spacy.Text-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-davinci` model family. |
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| [`spacy.Code-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `code-davinci` model family. |
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| [`spacy.Text-Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-curie` model family. |
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| [`spacy.Text-Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-babbage` model family. |
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| [`spacy.Text-Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-ada` model family. |
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| [`spacy.Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `davinci` model family. |
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| [`spacy.Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `curie` model family. |
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| [`spacy.Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `babbage` model family. |
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| [`spacy.Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `ada` model family. |
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| [`spacy.Command.v1`](/api/large-language-models#models-rest) | Cohere’s `command` model family. |
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| [`spacy.Claude-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-2` model family. |
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| [`spacy.Claude-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1` model family. |
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| [`spacy.Claude-instant-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1` model family. |
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| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1.1` model family. |
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| [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.0` model family. |
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| [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.2` model family. |
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| [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.3` model family. |
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| [`spacy.Dolly.v1`](/api/large-language-models#models-hf) | Dolly models through HuggingFace. |
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| [`spacy.Falcon.v1`](/api/large-language-models#models-hf) | Falcon models through HuggingFace. |
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| [`spacy.Llama2.v1`](/api/large-language-models#models-hf) | Llama2 models through HuggingFace. |
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| [`spacy.StableLM.v1`](/api/large-language-models#models-hf) | StableLM models through HuggingFace. |
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| [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf) | OpenLLaMA models through HuggingFace. |
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| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. |
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Note that the chat models variants of Llama 2 are currently not supported. This
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is because they need a particular prompting setup and don't add any discernible
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benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared to
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the completion model variants.
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### Cache {id="cache"}
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Interacting with LLMs, either through an external API or a local instance, is
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@ -505,7 +506,7 @@ documents at each run that keeps batches of documents stored on disk.
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### Various functions {id="various-functions"}
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| Component | Description |
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| Function | Description |
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| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [`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. |
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| [`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. |
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