diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx new file mode 100644 index 000000000..7b85f6658 --- /dev/null +++ b/website/docs/api/large-language-models.mdx @@ -0,0 +1,989 @@ +--- +title: Large Language Models +teaser: Integrating LLMs into structured NLP pipelines +--- + +[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large +Language Models (LLMs) into [spaCy](https://spacy.io), featuring a modular +system for **fast prototyping** and **prompting**, and turning unstructured +responses into **robust outputs** for various NLP tasks, **no training data** +required. + +## Config {id="config"} + +`spacy-llm` exposes a `llm` factory that accepts the following configuration +options: + +| Argument | Description | +| ---------------- | --------------------------------------------------------------------------------------------------------- | +| `task` | An LLMTask can generate prompts and parse LLM responses. See [docs](#tasks). ~~Optional[LLMTask]~~ | +| `backend` | Callable querying a specific LLM API. See [docs](#backends). ~~Callable[[Iterable[Any]], Iterable[Any]]~~ | +| `cache` | Cache to use for caching prompts and responses per doc (batch). See [docs](#cache). ~~Cache~~ | +| `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~bool~~ | +| `validate_types` | Whether to check if signatures of configured backend and task are consistent. ~~bool~~ | + +An `llm` component is defined by two main settings: + +- A [**task**](#tasks), defining the prompt to send to the LLM as well as the + functionality to parse the resulting response back into structured fields on + spaCy's [Doc](https://spacy.io/api/doc) objects. +- A [**backend**](#backends) defining the model to use and how to connect to it. + Note that `spacy-llm` supports both access to external APIs (such as OpenAI) + as well as access to self-hosted open-source LLMs (such as using Dolly through + Hugging Face). + +Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality +to avoid running the same document through an LLM service (be it local or +through a REST API) more than once. + +Finally, you can choose to save a stringified version of LLM prompts/responses +within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. +`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM +component within the spaCy pipeline. Each entry is itself a dictionary, with two +keys: `prompt` and `response`. + +A note on `validate_types`: by default, `spacy-llm` checks whether the +signatures of the `backend` and `task` callables are consistent with each other +and emits a warning if they don't. `validate_types` can be set to `False` if you +want to disable this behavior. + +### Tasks {id="tasks"} + +A _task_ defines an NLP problem or question, that will be sent to the LLM via a +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. + +#### function task.generate_prompts {id="task-generate-prompts"} + +Takes a collection of documents, and returns a collection of "prompts", which +can be of type `Any`. Often, prompts are of type `str` - but this is not +enforced to allow for maximum flexibility in the framework. + +| Argument | Description | +| ----------- | ---------------------------------------- | +| `docs` | The input documents. ~~Iterable[Doc]~~ | +| **RETURNS** | The generated prompts. ~~Iterable[Any]~~ | + +#### function task.parse_responses {id="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. The `parse_responses` function is free to set the annotations in any +way, including `Doc` fields like `ents`, `spans` or `cats`, or using custom +defined fields. + +The `responses` are of type `Iterable[Any]`, though they will often be `str` +objects. This depends on the return type of the [backend](#backends). + +| Argument | Description | +| ----------- | ------------------------------------------ | +| `docs` | The input documents. ~~Iterable[Doc]~~ | +| `responses` | The generated prompts. ~~Iterable[Any]~~ | +| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~ | + +#### spacy.NER.v2 {id="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. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NER.v2" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [ner.v2.jinja](https://github.com/spacy-llm/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +The NER task implementation doesn't currently ask the LLM for specific offsets, +but simply expects a list of strings that represent the enties in the document. +This means that a form of string matching is required. This can be configured by +the following parameters: + +- The `single_match` parameter is typically set to `False` to allow for multiple + matches. For instance, the response from the LLM might only mention the entity + "Paris" once, but you'd still want to mark it every time it occurs in the + document. +- The case-sensitive matching is typically set to `False` to be robust against + case variances in the LLM's output. +- The `alignment_mode` argument is used to match entities as returned by the LLM + to the tokens from the original `Doc` - specifically it's used as argument in + the call to [`doc.char_span()`](https://spacy.io/api/doc#char_span). The + `"strict"` mode will only keep spans that strictly adhere to the given token + boundaries. `"contract"` will only keep those tokens that are fully within the + given range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will + 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`. + +```yaml +- text: Jack and Jill went up the hill. + entities: + PERSON: + - Jack + - Jill + LOCATION: + - hill +- text: Jack fell down and broke his crown. + entities: + PERSON: + - Jack +``` + +```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" +``` + +> Label descriptions can also be used with explicit examples to give as much +> info to the LLM backend as possible. + +If you don't have specific examples to provide to the LLM, you can write +definitions for each label and provide them via the `label_definitions` +argument. This lets you tell the LLM exactly what you're looking for rather than +relying on the LLM to interpret its task given just the label name. Label +descriptions are freeform so you can write whatever you want here, but through +some experiments a brief description along with some examples and counter +examples seems to work quite well. + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = PERSON,SPORTS_TEAM +[components.llm.task.label_definitions] +PERSON = "Extract any named individual in the text." +SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid" +``` + +#### spacy.NER.v1 {id="ner-v1"} + +The original version of the built-in NER task supports both zero-shot and +few-shot prompting. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NER.v1" +> labels = PERSON,ORGANISATION,LOCATION +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +The NER task implementation doesn't currently ask the LLM for specific offsets, +but simply expects a list of strings that represent the enties in the document. +This means that a form of string matching is required. This can be configured by +the following parameters: + +- The `single_match` parameter is typically set to `False` to allow for multiple + matches. For instance, the response from the LLM might only mention the entity + "Paris" once, but you'd still want to mark it every time it occurs in the + document. +- The case-sensitive matching is typically set to `False` to be robust against + case variances in the LLM's output. +- The `alignment_mode` argument is used to match entities as returned by the LLM + to the tokens from the original `Doc` - specifically it's used as argument in + the call to [`doc.char_span()`](https://spacy.io/api/doc#char_span). The + `"strict"` mode will only keep spans that strictly adhere to the given token + boundaries. `"contract"` will only keep those tokens that are fully within the + given range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will + 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`. + +```yaml +- text: Jack and Jill went up the hill. + entities: + PERSON: + - Jack + - Jill + LOCATION: + - hill +- text: Jack fell down and broke his crown. + entities: + PERSON: + - Jack +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v1" +labels = PERSON,ORGANISATION,LOCATION +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "ner_examples.yml" +``` + +#### spacy.SpanCat.v2 {id="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`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.SpanCat.v2" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`spancat.v2.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +Except for the `spans_key` parameter, the SpanCat task reuses the configuration +from the NER task. Refer to [its documentation](#ner-v2) for more insight. + +#### spacy.SpanCat.v1 {id="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`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.SpanCat.v1" +> labels = PERSON,ORGANISATION,LOCATION +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +Except for the `spans_key` parameter, the SpanCat task reuses the configuration +from the NER task. Refer to [its documentation](#ner-v1) for more insight. + +#### spacy.TextCat.v3 {id="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. Those +definitions are included in the prompt. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v3" +> labels = ["COMPLIMENT", "INSULT"] +> label_definitions = { +> "COMPLIMENT": "a polite expression of praise or admiration.", +> "INSULT": "a disrespectful or scornfully abusive remark or act." +> } +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. Optional[Callable[[], Iterable[Any]]] | +| `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]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `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`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v3" +labels = ["COMPLIMENT", "INSULT"] +label_definitions = { + "COMPLIMENT": "a polite expression of praise or admiration.", + "INSULT": "a disrespectful or scornfully abusive remark or act." +} +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.TextCat.v2 {id="textcat-v2"} + +Version 2 of the built-in TextCat task supports both zero-shot and few-shot +prompting and includes an improved prompt template. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v2" +> labels = ["COMPLIMENT", "INSULT"] +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `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`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = ["COMPLIMENT", "INSULT"] +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.TextCat.v1 {id="textcat-v1"} + +Version 1 of the built-in TextCat task supports both zero-shot and few-shot +prompting. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v1" +> labels = COMPLIMENT,INSULT +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Deafults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Deafults to `False`. ~~bool~~ | +| `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`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = COMPLIMENT,INSULT +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.REL.v1 {id="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. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.REL.v1" +> labels = ["LivesIn", "Visits"] +> ``` + +| Argument | Description | +| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.jinja`](https://github.com/spacy-llm/spacy_llm/tasks/templates/rel.jinja). ~~str~~ | +| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `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`. + +```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"}]} +{"text": "Michael travelled through South America by bike.", "ents": [{"start_char": 0, "end_char": 7, "label": "PERSON"}, {"start_char": 26, "end_char": 39, "label": "LOC"}], "relations": [{"dep": 0, "dest": 1, "relation": "Visits"}]} +``` + +Note: the REL task relies on pre-extracted entities to make its prediction. +Hence, you'll need to add a component that populates `doc.ents` with recognized +spans to your spaCy pipeline and put it _before_ the REL component. + +```ini +[components.llm.task] +@llm_tasks = "spacy.REL.v1" +labels = ["LivesIn", "Visits"] +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "rel_examples.jsonl" +``` + +#### spacy.Lemma.v1 {id="lemma-v1"} + +The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` +attribute in the doc's tokens accordingly. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.Lemma.v1" +> examples = null +> ``` + +| Argument | Description | +| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM backend. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [lemma.jinja](https://github.com/spacy-llm/spacy_llm/tasks/templates/lemma.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | + +`Lemma.v1` prompts the LLM to lemmatize the passed text and return the +lemmatized version as a list of tokens and their corresponding lemma. E. g. the +text `I'm buying ice cream for my friends` should invoke the response + +``` +I: I +'m: be +buying: buy +ice: ice +cream: cream +for: for +my: my +friends: friend +.: . +``` + +If for any given text/doc instance the number of lemmas returned by the LLM +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`. + +```yaml +- text: I'm buying ice cream. + lemmas: + - 'I': 'I' + - "'m": 'be' + - 'buying': 'buy' + - 'ice': 'ice' + - 'cream': 'cream' + - '.': '.' + +- text: I've watered the plants. + lemmas: + - 'I': 'I' + - "'ve": 'have' + - 'watered': 'water' + - 'the': 'the' + - 'plants': 'plant' + - '.': '.' +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.Lemma.v1" +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "lemma_examples.yml" +``` + +#### spacy.NoOp.v1 {id="noop-v1"} + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.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`. + +### Backends {id="backends"} + +A _backend_ defines which LLM model to query, and how to query it. It can be a +simple function taking a collection of prompts (consistent with the output type +of `task.generate_prompts()`) and returning a collection of responses +(consistent with the expected input of `parse_responses`). Generally speaking, +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 backends are registered in `llm_backends`. If no backend is +specified, the repo currently connects to the [`OpenAI` API](#openai) by +default, using the built-in REST protocol, and accesses the `"gpt-3.5-turbo"` +model. + + +_Why are there backends for third-party libraries in addition to a +native REST backend and which should I choose?_ + +Third-party libraries like `langchain` or `minichain` 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 set of such third-party libraries and `spacy-llm` is not +identical - and users might want to take advantage of features not available in +`spacy-llm`. + +The advantage of offering our own REST backend is that we can ensure a larger +degree of stability of robustness, as we can guarantee backwards-compatibility +and more smoothly integrated error handling. + +Ultimately we recommend trying to implement your use case using the REST backend +first (which is configured as the default backend). If however there are +features or APIs not covered by `spacy-llm`, it's trivial to switch to the +backend of a third-party library - and easy to customize the prompting +mechanism, if so required. + + + +#### OpenAI {id="openai"} + +When the backend uses 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-..." +``` + +#### spacy.REST.v1 {id="rest-v1"} + +This default backend uses `requests` and a simple retry mechanism to access an +API. + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.REST.v1" +> api = "OpenAI" +> config = {"model": "gpt-3.5-turbo", "temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `api` | The name of a supported API. In v.0.1.0, only "OpenAI" is supported. ~~str~~ | +| `config` | Further configuration passed on to the backend. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +When `api` is set to `OpenAI`, the following settings can be defined in the +`config` dictionary: + +- `model`: one of the following list of supported models: + - `"gpt-4"` + - `"gpt-4-0314"` + - `"gpt-4-32k"` + - `"gpt-4-32k-0314"` + - `"gpt-3.5-turbo"` + - `"gpt-3.5-turbo-0301"` + - `"text-davinci-003"` + - `"text-davinci-002"` + - `"text-curie-001"` + - `"text-babbage-001"` + - `"text-ada-001"` + - `"davinci"` + - `"curie"` + - `"babbage"` + - `"ada"` +- `url`: By default, this is `https://api.openai.com/v1/completions`. For models + requiring the chat endpoint, use `https://api.openai.com/v1/chat/completions`. + +#### spacy.MiniChain.v1 {id="minichain-v1"} + +To use [MiniChain](https://github.com/srush/MiniChain) for the API retrieval +part, make sure you have installed it first: + +```shell +python -m pip install "minichain>=0.3,<0.4" +# Or install with spacy-llm directly +python -m pip install "spacy-llm[minichain]" +``` + +Note that MiniChain currently only supports Python 3.8, 3.9 and 3.10. + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.MiniChain.v1" +> api = "OpenAI" +> +> [components.llm.backend.query] +> @llm_queries = "spacy.RunMiniChain.v1" +> ``` + +| Argument | Description | +| -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `api` | The name of an API supported by MiniChain, e.g. "OpenAI". ~~str~~ | +| `config` | Further configuration passed on to the backend. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `query` | Function that executes the prompts. If `None`, defaults to `spacy.RunMiniChain.v1`. Defaults to `None`. ~~Optional[Callable[["minichain.backend.Backend", Iterable[str]], Iterable[str]]]~~ | + +The default `query` (`spacy.RunMiniChain.v1`) executes the prompts by running +`model(text).run()` for each given textual prompt. + +#### spacy.LangChain.v1 {id="langchain-v1"} + +To use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval +part, make sure you have installed it first: + +```shell +python -m pip install "langchain>=0.0.144,<0.1" +# Or install with spacy-llm directly +python -m pip install "spacy-llm[langchain]" +``` + +Note that LangChain currently only supports Python 3.9 and beyond. + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.LangChain.v1" +> api = "OpenAI" +> query = {"@llm_queries": "spacy.CallLangChain.v1"} +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `api` | The name of an API supported by LangChain, e.g. "OpenAI". ~~str~~ | +| `config` | Further configuration passed on to the backend. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `query` | Function that executes the prompts. If `None`, defaults to `spacy.CallLangChain.v1`. Defaults to `None`. ~~Optional[Callable[["langchain.llms.BaseLLM", Iterable[Any]], Iterable[Any]]]~~ | + +The default `query` (`spacy.CallLangChain.v1`) executes the prompts by running +`model(text)` for each given textual prompt. + +#### spacy.Dolly_HF.v1 {id="dollyhf-v1"} + +To use this backend, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. This allows you to +have the setting `device=cuda:0` in your config, which ensures that the model is +loaded entirely on the GPU (and fails otherwise). + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.Dolly_HF.v1" +> model = "databricks/dolly-v2-3b" +> ``` + +| Argument | Description | +| ------------- | ------------------------------------------------------------------------------------------------------------------------------------- | +| `model` | The name of a Dolly model that is supported. ~~str~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +Supported models (see the +[Databricks models page](https://huggingface.co/databricks) on Hugging Face for +details): + +- `"databricks/dolly-v2-3b"` +- `"databricks/dolly-v2-7b"` +- `"databricks/dolly-v2-12b"` + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.StableLM_HF.v1 {id="stablelmhf-v1"} + +To use this backend, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.StableLM_HF.v1" +> model = "stabilityai/stablelm-tuned-alpha-7b" +> ``` + +| Argument | Description | +| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `model` | The name of a StableLM model that is supported. ~~str~~ | +| `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]~~ | + +Supported models (see the +[Stability AI StableLM GitHub repo](https://github.com/Stability-AI/StableLM/#stablelm-alpha) +for details): + +- `"stabilityai/stablelm-base-alpha-3b"` +- `"stabilityai/stablelm-base-alpha-7b"` +- `"stabilityai/stablelm-tuned-alpha-3b"` +- `"stabilityai/stablelm-tuned-alpha-7b"` + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.OpenLLaMaHF.v1 {id="openllamahf-v1"} + +To use this backend, ideally you have a GPU enabled and have installed + +- `transformers[sentencepiece]` +- `torch` +- CUDA in your virtual environment. + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.backend] +> @llm_backends = "spacy.OpenLLaMaHF.v1" +> model = "openlm-research/open_llama_3b_350bt_preview" +> ``` + +| Argument | Description | +| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `model` | The name of a OpenLLaMa model that is supported. ~~str~~ | +| `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]~~ | + +Supported models (see the +[OpenLM Research OpenLLaMa GitHub repo](https://github.com/openlm-research/open_llama) +for details): + +- `"openlm-research/open_llama_3b_350bt_preview"` +- `"openlm-research/open_llama_3b_600bt_preview"` +- `"openlm-research/open_llama_7b_400bt_preview"` +- `"openlm-research/open_llama_7b_700bt_preview"` + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +### Cache {id="cache"} + +Interacting with LLMs, either through an external API or a local instance, is +costly. Since developing an NLP pipeline generally means a lot of exploration +and prototyping, `spacy-llm` implements a built-in cache to avoid reprocessing +the same documents at each run that keeps batches of documents stored on disk. + +> #### Example config +> +> ```ini +> [components.llm.cache] +> @llm_misc = "spacy.BatchCache.v1" +> path = "path/to/cache" +> batch_size = 64 +> max_batches_in_mem = 4 +> ``` + +| Argument | Description | +| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | +| `path` | Cache directory. If `None`, no caching is performed, and this component will act as a NoOp. Defaults to `None`. ~~Optional[Union[str, Path]]~~ | +| `batch_size` | Number of docs in one batch (file). Once a batch is full, it will be peristed to disk. Defaults to 64. ~~int~~ | +| `max_batches_in_mem` | Max. number of batches to hold in memory. Allows you to limit the effect on your memory if you're handling a lot of docs. Defaults to 4. ~~int~~ | + +When retrieving a document, the `BatchCache` will first figure out what batch +the document belongs to. If the batch isn't in memory it will try to load the +batch from disk and then move it into memory. + +Note that since the cache is generated by a registered function, you can also +provide your own registered function returning your own cache implementation. If +you wish to do so, ensure that your cache object adheres to the `Protocol` +defined in `spacy_llm.ty.Cache`. + +### Various functions {id="various-functions"} + +#### spacy.FewShotReader.v1 {id="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. + +> #### Example config +> +> ```ini +> [components.llm.task.examples] +> @misc = "spacy.FewShotReader.v1" +> path = "ner_examples.yml" +> ``` + +| Argument | Description | +| -------- | ----------------------------------------------------------------------------------------------- | +| `path` | Path to an examples file with suffix `.yml`, `.yaml`, `.json` or `.jsonl`. ~~Union[str, Path]~~ | + +#### spacy.FileReader.v1 {id="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. + +> #### Example config +> +> ```ini +> [components.llm.task.template] +> @misc = "spacy.FileReader.v1" +> path = "ner_template.jinja2" +> ``` + +| Argument | Description | +| -------- | ------------------------------------------------- | +| `path` | Path to the file to be read. ~~Union[str, Path]~~ | + +#### Normalizer functions {id="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. They are registered in spaCy's `misc` registry and have the signature +`Callable[[str], str]`. + +- `spacy.StripNormalizer.v1`: only apply `text.strip()` +- `spacy.LowercaseNormalizer.v1`: applies `text.strip().lower()` to compare + strings in a case-insensitive way. diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx new file mode 100644 index 000000000..ff99fff20 --- /dev/null +++ b/website/docs/usage/large-language-models.mdx @@ -0,0 +1,469 @@ +--- +title: Large Language Models +teaser: Integrating LLMs into structured NLP pipelines +menu: + - ['Motivation', 'motivation'] + - ['Install', 'install'] + - ['Usage', 'usage'] + - ['Logging', 'logging'] + - ['API', 'api'] + - ['Tasks', 'tasks'] + - ['Backends', 'backends'] + - ['Ongoing work', 'ongoing-work'] + - ['Issues', 'issues'] +--- + +[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large +Language Models (LLMs) into [spaCy](https://spacy.io), featuring a modular +system for **fast prototyping** and **prompting**, and turning unstructured +responses into **robust outputs** for various NLP tasks, **no training data** +required. + +- Serializable `llm` **component** to integrate prompts into your pipeline +- **Modular functions** to define the [**task**](#tasks) (prompting and parsing) + and [**backend**](#backends) (model to use) +- Support for **hosted APIs** and self-hosted **open-source models** +- Integration with [`MiniChain`](https://github.com/srush/MiniChain) and + [`LangChain`](https://github.com/hwchase17/langchain) +- Access to + **[OpenAI API](https://platform.openai.com/docs/api-reference/introduction)**, + including GPT-4 and various GPT-3 models +- Built-in support for **open-source + [Dolly](https://huggingface.co/databricks)** models hosted on Hugging Face +- Usage examples for **Named Entity Recognition** and **Text Classification** +- Easy implementation of **your own functions** via + [spaCy's registry](https://spacy.io/api/top-level#registry) for custom + prompting, parsing and model integrations + +## Motivation {id="motivation"} + +Large Language Models (LLMs) feature powerful natural language understanding +capabilities. With only a few (and sometimes no) examples, an LLM can be +prompted to perform custom NLP tasks such as text categorization, named entity +recognition, coreference resolution, information extraction and more. + +[spaCy](https://spacy.io) is a well-established library for building systems +that need to work with language in various ways. spaCy's built-in components are +generally powered by supervised learning or rule-based approaches. + +Supervised learning is much worse than LLM prompting for prototyping, but for +many tasks it's much better for production. A transformer model that runs +comfortably on a single GPU is extremely powerful, and it's likely to be a +better choice for any task for which you have a well-defined output. You train +the model with anything from a few hundred to a few thousand labelled examples, +and it will learn to do exactly that. Efficiency, reliability and control are +all better with supervised learning, and accuracy will generally be higher than +LLM prompting as well. + +`spacy-llm` lets you have **the best of both worlds**. You can quickly +initialize a pipeline with components powered by LLM prompts, and freely mix in +components powered by other approaches. As your project progresses, you can look +at replacing some or all of the LLM-powered components as you require. + +Of course, there can be components in your system for which the power of an LLM +is fully justified. If you want a system that can synthesize information from +multiple documents in subtle ways and generate a nuanced summary for you, bigger +is better. However, even if your production system needs an LLM for some of the +task, that doesn't mean you need an LLM for all of it. Maybe you want to use a +cheap text classification model to help you find the texts to summarize, or +maybe you want to add a rule-based system to sanity check the output of the +summary. These before-and-after tasks are much easier with a mature and +well-thought-out library, which is exactly what spaCy provides. + +## Install {id="install"} + +`spacy-llm` will be installed automatically in future spaCy versions. For now, +you can run the following in the same virtual environment where you already have +`spacy` [installed](https://spacy.io/usage). + +> ⚠️ This package is still experimental and it is possible that changes made to +> the interface will be breaking in minor version updates. + +```bash +python -m pip install spacy-llm +``` + +## Usage {id="usage"} + +The task and the backend have to be supplied to the `llm` pipeline component +using [spaCy's config system](https://spacy.io/api/data-formats#config). This +package provides various built-in functionality, as detailed in the [API](#-api) +documentation. + +### Example 1: Add a text classifier using a GPT-3 model from OpenAI {id="example-1"} + +Create a new API key from openai.com or fetch an existing one, and ensure the +keys are set as environmental variables. For more background information, see +the [OpenAI](#openai) section. + +Create a config file `config.cfg` containing at least the following (or see the +full example +[here](https://github.com/spacy-llm/usage_examples/textcat_openai)): + +```ini +[nlp] +lang = "en" +pipeline = ["llm"] + +[components] + +[components.llm] +factory = "llm" + +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = ["COMPLIMENT", "INSULT"] + +[components.llm.backend] +@llm_backends = "spacy.REST.v1" +api = "OpenAI" +config = {"model": "gpt-3.5-turbo", "temperature": 0.3} +``` + +Now run: + +```python +from spacy_llm.util import assemble + +nlp = assemble("config.cfg") +doc = nlp("You look gorgeous!") +print(doc.cats) +``` + +### Example 2: Add NER using an open-source model through Hugging Face {id="example-2"} + +To run this example, ensure that you have a GPU enabled, and `transformers`, +`torch` and CUDA installed. For more background information, see the +[DollyHF](#dollyhf-v1) section. + +Create a config file `config.cfg` containing at least the following (or see the +full example [here](https://github.com/spacy-llm/usage_examples/ner_dolly)): + +```ini +[nlp] +lang = "en" +pipeline = ["llm"] + +[components] + +[components.llm] +factory = "llm" + +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = ["PERSON", "ORGANISATION", "LOCATION"] + +[components.llm.backend] +@llm_backends = "spacy.Dolly_HF.v1" +# For better performance, use databricks/dolly-v2-12b instead +model = "databricks/dolly-v2-3b" +``` + +Now run: + +```python +from spacy_llm.util import assemble + +nlp = assemble("config.cfg") +doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") +print([(ent.text, ent.label_) for ent in doc.ents]) +``` + +Note that Hugging Face will download the `"databricks/dolly-v2-3b"` model the +first time you use it. You can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. Also, you can upgrade the model +to be `"databricks/dolly-v2-12b"` for better performance. + +### Example 3: Create the component directly in Python {id="example-3"} + +The `llm` component behaves as any other spaCy component does, so adding it to +an existing pipeline follows the same pattern: + +```python +import spacy + +nlp = spacy.blank("en") +nlp.add_pipe( + "llm", + config={ + "task": { + "@llm_tasks": "spacy.NER.v2", + "labels": ["PERSON", "ORGANISATION", "LOCATION"] + }, + "backend": { + "@llm_backends": "spacy.REST.v1", + "api": "OpenAI", + "config": {"model": "gpt-3.5-turbo"}, + }, + }, +) +nlp.initialize() +doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") +print([(ent.text, ent.label_) for ent in doc.ents]) +``` + +Note that for efficient usage of resources, typically you would use +[`nlp.pipe(docs)`](https://spacy.io/api/language#pipe) with a batch, instead of +calling `nlp(doc)` with a single document. + +### Example 4: Implement your own custom task {id="example-4"} + +To write a [`task`](#tasks), you need to implement two functions: +`generate_prompts` that takes a list of spaCy [`Doc`](https://spacy.io/api/doc) +objects and transforms them into a list of prompts, and `parse_responses` that +transforms the LLM outputs into annotations on the +[`Doc`](https://spacy.io/api/doc), e.g. entity spans, text categories and more. + +To register your custom task with spaCy, decorate a factory function using the +`spacy_llm.registry.llm_tasks` decorator with a custom name that you can refer +to in your config. + +> 📖 For more details, see the +> [**usage example on writing your own task**](https://github.com/spacy-llm/usage_examples/README.md#writing-your-own-task) + +```python +from typing import Iterable, List +from spacy.tokens import Doc +from spacy_llm.registry import registry +from spacy_llm.util import split_labels + + +@registry.llm_tasks("my_namespace.MyTask.v1") +def make_my_task(labels: str, my_other_config_val: float) -> "MyTask": + labels_list = split_labels(labels) + return MyTask(labels=labels_list, my_other_config_val=my_other_config_val) + + +class MyTask: + def __init__(self, labels: List[str], my_other_config_val: float): + ... + + def generate_prompts(self, docs: Iterable[Doc]) -> Iterable[str]: + ... + + def parse_responses( + self, docs: Iterable[Doc], responses: Iterable[str] + ) -> Iterable[Doc]: + ... +``` + +```ini +# config.cfg (excerpt) +[components.llm.task] +@llm_tasks = "my_namespace.MyTask.v1" +labels = LABEL1,LABEL2,LABEL3 +my_other_config_val = 0.3 +``` + +## Logging {id="logging"} + +spacy-llm has a built-in logger that can log the prompt sent to the LLM as well +as its raw response. This logger uses the debug level and by default has a +`logging.NullHandler()` configured. + +In order to use this logger, you can setup a simple handler like this: + +```python +import logging +import spacy_llm + + +spacy_llm.logger.addHandler(logging.StreamHandler()) +spacy_llm.logger.setLevel(logging.DEBUG) +``` + +> NOTE: Any `logging` handler will work here so you probably want to use some +> sort of rotating `FileHandler` as the generated prompts can be quite long, +> especially for tasks with few-shot examples. + +Then when using the pipeline you'll be able to view the prompt and response. + +E.g. with the config and code from [Example 1](#example-1) above: + +```python +from spacy_llm.util import assemble + + +nlp = assemble("config.cfg") +doc = nlp("You look gorgeous!") +print(doc.cats) +``` + +You will see `logging` output similar to: + +``` +Generated prompt for doc: You look gorgeous! + +You are an expert Text Classification system. Your task is to accept Text as input +and provide a category for the text based on the predefined labels. + +Classify the text below to any of the following labels: COMPLIMENT, INSULT +The task is non-exclusive, so you can provide more than one label as long as +they're comma-delimited. For example: Label1, Label2, Label3. +Do not put any other text in your answer, only one or more of the provided labels with nothing before or after. +If the text cannot be classified into any of the provided labels, answer `==NONE==`. + +Here is the text that needs classification + + +Text: +''' +You look gorgeous! +''' + +Backend response for doc: You look gorgeous! +COMPLIMENT +``` + +`print(doc.cats)` to standard output should look like: + +``` +{'COMPLIMENT': 1.0, 'INSULT': 0.0} +``` + +## API {id="api"} + +`spacy-llm` exposes a `llm` factory with [configurable settings](api/large-language-models#config). + + +An `llm` component is defined by two main settings: + +- A [**task**](#tasks), defining the prompt to send to the LLM as well as the + functionality to parse the resulting response back into structured fields on + spaCy's [Doc](https://spacy.io/api/doc) objects. +- A [**backend**](#backends) defining the model to use and how to connect to it. + Note that `spacy-llm` supports both access to external APIs (such as OpenAI) + as well as access to self-hosted open-source LLMs (such as using Dolly through + Hugging Face). + +Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality +to avoid running the same document through an LLM service (be it local or +through a REST API) more than once. + +Finally, you can choose to save a stringified version of LLM prompts/responses +within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. +`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM +component within the spaCy pipeline. Each entry is itself a dictionary, with two +keys: `prompt` and `response`. + +A note on `validate_types`: by default, `spacy-llm` checks whether the +signatures of the `backend` and `task` callables are consistent with each other +and emits a warning if they don't. `validate_types` can be set to `False` if you +want to disable this behavior. + +### Tasks {id="tasks"} + +A _task_ defines an NLP problem or question, that will be sent to the LLM via a +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. + +| 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.NER.v2`](/api/large-language-models#ner-v2) | 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.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.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`. | + +### Backends {id="backends"} + +A _backend_ defines which LLM model to query, and how to query it. It can be a +simple function taking a collection of prompts (consistent with the output type +of `task.generate_prompts()`) and returning a collection of responses +(consistent with the expected input of `parse_responses`). Generally speaking, +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 backends are registered in `llm_backends`. If no backend is +specified, the repo currently connects to the [`OpenAI` API](#openai) by +default, using the built-in REST protocol, and accesses the `"gpt-3.5-turbo"` +model. + + +_Why are there backends for third-party libraries in addition to a +native REST backend and which should I choose?_ + +Third-party libraries like `langchain` or `minichain` 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 set of such third-party libraries and `spacy-llm` is not +identical - and users might want to take advantage of features not available in +`spacy-llm`. + +The advantage of offering our own REST backend is that we can ensure a larger +degree of stability of robustness, as we can guarantee backwards-compatibility +and more smoothly integrated error handling. + +Ultimately we recommend trying to implement your use case using the REST backend +first (which is configured as the default backend). If however there are +features or APIs not covered by `spacy-llm`, it's trivial to switch to the +backend of a third-party library - and easy to customize the prompting +mechanism, if so required. + + + +| Component | Description | +| ------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | +| [`OpenAI`](/api/large-language-models#openai) | ?? | +| [`spacy.REST.v1`](/api/large-language-models#rest-v1) | This default backend uses `requests` and a simple retry mechanism to access an API. | +| [`spacy.MiniChain.v1`](/api/large-language-models#minichain-v1) | Use [MiniChain](https://github.com/srush/MiniChain) for the API retrieval. | +| [`spacy.LangChain.v1`](/api/large-language-models#langchain-v1) | Use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval. | +| [`spacy.Dolly_HF.v1`](/api/large-language-models#dollyhf-v1) | Use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval. | +| [`spacy.StableLM_HF.v1`](/api/large-language-models#stablelmhf-v1) | Use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval. | +| [`spacy.OpenLLaMaHF.v1`](/api/large-language-models#openllamahf-v1) | Use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval. | + +### Cache {id="cache"} + +Interacting with LLMs, either through an external API or a local instance, is +costly. Since developing an NLP pipeline generally means a lot of exploration +and prototyping, `spacy-llm` implements a built-in [cache](/api/large-language-models#cache) to avoid reprocessing +the same documents at each run that keeps batches of documents stored on disk. + +### Various functions {id="various-functions"} + +| Component | 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.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 12c3fce35..ebd302855 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -36,7 +36,8 @@ }, { "text": "spaCy Projects", "url": "/usage/projects", "tag": "new" }, { "text": "Saving & Loading", "url": "/usage/saving-loading" }, - { "text": "Visualizers", "url": "/usage/visualizers" } + { "text": "Visualizers", "url": "/usage/visualizers" }, + { "text": "Large Language Models", "url": "/usage/large-language-models", "tag": "new" } ] }, { @@ -133,6 +134,7 @@ { "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" }, diff --git a/website/pages/index.tsx b/website/pages/index.tsx index fc0dba378..089d75b52 100644 --- a/website/pages/index.tsx +++ b/website/pages/index.tsx @@ -106,50 +106,21 @@ const Landing = () => {

- - - + + The spacy-llm package + {' '} + integrates Large Language Models (LLMs) into spaCy, featuring a modular + system for fast prototyping and prompting, + and turning unstructured responses into robust outputs for + various NLP tasks, no training data required.

-

- - Get a custom spaCy pipeline, tailor-made for your NLP problem by - spaCy's core developers. - -

- {

- spaCy v3.0 features all new transformer-based pipelines{' '} - that bring spaCy's accuracy right up to the current{' '} - state-of-the-art. You can use any pretrained transformer to - train your own pipelines, and even share one transformer between multiple - components with multi-task learning. Training is now fully - configurable and extensible, and you can define your own custom models using{' '} - PyTorch, TensorFlow and other frameworks. + + +

+

+ + Get a custom spaCy pipeline, tailor-made for your NLP problem by + spaCy's core developers. + +

+
    +
  • + Streamlined. Nobody knows spaCy better than we do. Send + us your pipeline requirements and we'll be ready to start producing + your solution in no time at all. +
    • +
  • + Production ready. spaCy pipelines are robust and easy + to deploy. You'll get a complete spaCy project folder which is + ready to spacy project run. +
    • +
  • + Predictable. You'll know exactly what you're + going to get and what it's going to cost. We quote fees up-front, + let you try before you buy, and don't charge for over-runs at our + end — all the risk is on us. +
    • +
  • + Maintainable. spaCy is an industry standard, and + we'll deliver your pipeline with full code, data, tests and + documentation, so your team can retrain, update and extend the solution + as your requirements change. +
    • +
{ small >

- +