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517 lines
26 KiB
Plaintext
---
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title: Large Language Models
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teaser: Integrating LLMs into structured NLP pipelines
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menu:
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- ['Motivation', 'motivation']
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- ['Install', 'install']
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- ['Usage', 'usage']
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- ['Logging', 'logging']
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- ['API', 'api']
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- ['Tasks', 'tasks']
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- ['Models', 'models']
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---
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[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large
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Language Models (LLMs) into spaCy pipelines, featuring a modular system for
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**fast prototyping** and **prompting**, and turning unstructured responses into
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**robust outputs** for various NLP tasks, **no training data** required.
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- Serializable `llm` **component** to integrate prompts into your pipeline
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- **Modular functions** to define the [**task**](#tasks) (prompting and parsing)
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and [**model**](#models) (model to use)
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- Support for **hosted APIs** and self-hosted **open-source models**
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- Integration with [`LangChain`](https://github.com/hwchase17/langchain)
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- Access to
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**[OpenAI API](https://platform.openai.com/docs/api-reference/introduction)**,
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including GPT-4 and various GPT-3 models
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- Built-in support for various **open-source** models hosted on
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[Hugging Face](https://huggingface.co/)
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- Usage examples for standard NLP tasks such as **Named Entity Recognition** and
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**Text Classification**
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- Easy implementation of **your own functions** via the
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[registry](/api/top-level#registry) for custom prompting, parsing and model
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integrations
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## Motivation {id="motivation"}
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Large Language Models (LLMs) feature powerful natural language understanding
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capabilities. With only a few (and sometimes no) examples, an LLM can be
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prompted to perform custom NLP tasks such as text categorization, named entity
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recognition, coreference resolution, information extraction and more.
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Supervised learning is much worse than LLM prompting for prototyping, but for
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many tasks it's much better for production. A transformer model that runs
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comfortably on a single GPU is extremely powerful, and it's likely to be a
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better choice for any task for which you have a well-defined output. You train
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the model with anything from a few hundred to a few thousand labelled examples,
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and it will learn to do exactly that. Efficiency, reliability and control are
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all better with supervised learning, and accuracy will generally be higher than
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LLM prompting as well.
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`spacy-llm` lets you have **the best of both worlds**. You can quickly
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initialize a pipeline with components powered by LLM prompts, and freely mix in
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components powered by other approaches. As your project progresses, you can look
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at replacing some or all of the LLM-powered components as you require.
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Of course, there can be components in your system for which the power of an LLM
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is fully justified. If you want a system that can synthesize information from
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multiple documents in subtle ways and generate a nuanced summary for you, bigger
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is better. However, even if your production system needs an LLM for some of the
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task, that doesn't mean you need an LLM for all of it. Maybe you want to use a
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cheap text classification model to help you find the texts to summarize, or
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maybe you want to add a rule-based system to sanity check the output of the
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summary. These before-and-after tasks are much easier with a mature and
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well-thought-out library, which is exactly what spaCy provides.
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## Install {id="install"}
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`spacy-llm` will be installed automatically in future spaCy versions. For now,
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you can run the following in the same virtual environment where you already have
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`spacy` [installed](/usage).
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> ⚠️ This package is still experimental and it is possible that changes made to
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> the interface will be breaking in minor version updates.
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```bash
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python -m pip install spacy-llm
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```
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## Usage {id="usage"}
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The task and the model have to be supplied to the `llm` pipeline component using
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the [config system](/api/data-formats#config). This package provides various
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built-in functionality, as detailed in the [API](#-api) documentation.
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### Example 1: Add a text classifier using a GPT-3 model from OpenAI {id="example-1"}
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Create a new API key from openai.com or fetch an existing one, and ensure the
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keys are set as environmental variables. For more background information, see
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the [OpenAI](/api/large-language-models#gpt-3-5) section.
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Create a config file `config.cfg` containing at least the following (or see the
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full example
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[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/textcat_openai)):
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```ini
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[nlp]
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lang = "en"
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pipeline = ["llm"]
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[components]
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[components.llm]
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factory = "llm"
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[components.llm.task]
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@llm_tasks = "spacy.TextCat.v2"
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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": 0.0}
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```
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Now run:
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```python
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from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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doc = nlp("You look gorgeous!")
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print(doc.cats)
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```
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### Example 2: Add NER using an open-source model through Hugging Face {id="example-2"}
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To run this example, ensure that you have a GPU enabled, and `transformers`,
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`torch` and CUDA installed. For more background information, see the
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[DollyHF](/api/large-language-models#dolly) section.
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Create a config file `config.cfg` containing at least the following (or see the
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full example
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[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly)):
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```ini
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[nlp]
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lang = "en"
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pipeline = ["llm"]
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[components]
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[components.llm]
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factory = "llm"
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[components.llm.task]
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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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@llm_models = "spacy.Dolly.v1"
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# For better performance, use dolly-v2-12b instead
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name = "dolly-v2-3b"
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```
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Now run:
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```python
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from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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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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```
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Note that Hugging Face will download the `"databricks/dolly-v2-3b"` model the
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first time you use it. You can
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[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
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by setting the environmental variable `HF_HOME`. Also, you can upgrade the model
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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, and there are
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[task-specific components](/api/large-language-models#config) defined to help
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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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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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```
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Note that for efficient usage of resources, typically you would use
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[`nlp.pipe(docs)`](/api/language#pipe) with a batch, instead of calling
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`nlp(doc)` with a single document.
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### Example 4: Implement your own custom task {id="example-4"}
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To write a [`task`](#tasks), you need to implement two functions:
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`generate_prompts` that takes a list of [`Doc`](/api/doc) objects and transforms
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them into a list of prompts, and `parse_responses` that transforms the LLM
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outputs into annotations on the [`Doc`](/api/doc), e.g. entity spans, text
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categories and more.
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To register your custom task, decorate a factory function using the
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`spacy_llm.registry.llm_tasks` decorator with a custom name that you can refer
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to in your config.
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> 📖 For more details, see the
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> [**usage example on writing your own task**](https://github.com/explosion/spacy-llm/tree/main/usage_examples#writing-your-own-task)
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```python
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from typing import Iterable, List
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from spacy.tokens import Doc
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from spacy_llm.registry import registry
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from spacy_llm.util import split_labels
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@registry.llm_tasks("my_namespace.MyTask.v1")
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def make_my_task(labels: str, my_other_config_val: float) -> "MyTask":
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labels_list = split_labels(labels)
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return MyTask(labels=labels_list, my_other_config_val=my_other_config_val)
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class MyTask:
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def __init__(self, labels: List[str], my_other_config_val: float):
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...
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def generate_prompts(self, docs: Iterable[Doc]) -> Iterable[str]:
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...
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def parse_responses(
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self, docs: Iterable[Doc], responses: Iterable[str]
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) -> Iterable[Doc]:
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...
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```
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```ini
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# config.cfg (excerpt)
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[components.llm.task]
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@llm_tasks = "my_namespace.MyTask.v1"
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labels = LABEL1,LABEL2,LABEL3
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my_other_config_val = 0.3
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```
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## Logging {id="logging"}
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spacy-llm has a built-in logger that can log the prompt sent to the LLM as well
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as its raw response. This logger uses the debug level and by default has a
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`logging.NullHandler()` configured.
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In order to use this logger, you can setup a simple handler like this:
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```python
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import logging
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import spacy_llm
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spacy_llm.logger.addHandler(logging.StreamHandler())
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spacy_llm.logger.setLevel(logging.DEBUG)
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```
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> NOTE: Any `logging` handler will work here so you probably want to use some
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> sort of rotating `FileHandler` as the generated prompts can be quite long,
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> especially for tasks with few-shot examples.
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Then when using the pipeline you'll be able to view the prompt and response.
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E.g. with the config and code from [Example 1](#example-1) above:
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```python
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from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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doc = nlp("You look gorgeous!")
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print(doc.cats)
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```
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You will see `logging` output similar to:
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```
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Generated prompt for doc: You look gorgeous!
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You are an expert Text Classification system. Your task is to accept Text as input
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and provide a category for the text based on the predefined labels.
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Classify the text below to any of the following labels: COMPLIMENT, INSULT
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The task is non-exclusive, so you can provide more than one label as long as
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they're comma-delimited. For example: Label1, Label2, Label3.
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Do not put any other text in your answer, only one or more of the provided labels with nothing before or after.
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If the text cannot be classified into any of the provided labels, answer `==NONE==`.
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Here is the text that needs classification
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Text:
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'''
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You look gorgeous!
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'''
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Model response for doc: You look gorgeous!
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COMPLIMENT
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```
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`print(doc.cats)` to standard output should look like:
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```
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{'COMPLIMENT': 1.0, 'INSULT': 0.0}
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```
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## API {id="api"}
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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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- A [**task**](#tasks), defining the prompt to send to the LLM as well as the
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functionality to parse the resulting response back into structured fields on
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the [Doc](/api/doc) objects.
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- A [**model**](#models) defining the model to use and how to connect to it.
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Note that `spacy-llm` supports both access to external APIs (such as OpenAI)
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as well as access to self-hosted open-source LLMs (such as using Dolly through
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Hugging Face).
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Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality
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to avoid running the same document through an LLM service (be it local or
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through a REST API) more than once.
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Finally, you can choose to save a stringified version of LLM prompts/responses
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within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`.
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`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM
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component within the `nlp` pipeline. Each entry is itself a dictionary, with two
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keys: `prompt` and `response`.
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A note on `validate_types`: by default, `spacy-llm` checks whether the
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signatures of the `model` and `task` callables are consistent with each other
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and emits a warning if they don't. `validate_types` can be set to `False` if you
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want to disable this behavior.
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### Tasks {id="tasks"}
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A _task_ defines an NLP problem or question, that will be sent to the LLM via a
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prompt. Further, the task defines how to parse the LLM's responses back into
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structured information. All tasks are registered in the `llm_tasks` registry.
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Practically speaking, a task should adhere to the `Protocol` `LLMTask` defined
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in [`ty.py`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/ty.py).
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It needs to define a `generate_prompts` function and a `parse_responses`
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function.
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| Task | Description |
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| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [`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`. |
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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` 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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| [`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.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.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.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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#### Providing examples for few-shot prompts {id="few-shot-prompts"}
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All built-in tasks support few-shot prompts, i. e. including examples in a
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prompt. Examples can be supplied in two ways: (1) as a separate file containing
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only examples or (2) by initializing `llm` with a `get_examples()` callback
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(like any other pipeline component).
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##### (1) Few-shot example file
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A file containing examples for few-shot prompting can be configured like this:
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```ini
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[components.llm.task]
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@llm_tasks = "spacy.NER.v2"
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labels = PERSON,ORGANISATION,LOCATION
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[components.llm.task.examples]
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@misc = "spacy.FewShotReader.v1"
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path = "ner_examples.yml"
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```
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The supplied file has to conform to the format expected by the required task
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(see the task documentation further down).
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##### (2) Initializing the `llm` component with a `get_examples()` callback
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Alternatively, you can initialize your `nlp` pipeline by providing a
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`get_examples` callback for [`nlp.initialize`](/api/language#initialize) and
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setting `n_prompt_examples` to a positive number to automatically fetch a few
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examples for few-shot learning. Set `n_prompt_examples` to `-1` to use all
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examples as part of the few-shot learning prompt.
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```ini
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[initialize.components.llm]
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n_prompt_examples = 3
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```
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### Model {id="models"}
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A _model_ defines which LLM model to query, and how to query it. It can be a
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simple function taking a collection of prompts (consistent with the output type
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of `task.generate_prompts()`) and returning a collection of responses
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(consistent with the expected input of `parse_responses`). Generally speaking,
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it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific
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implementations can have other signatures, like
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`Callable[[Iterable[str]], Iterable[str]]`.
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All built-in models are registered in `llm_models`. If no model is specified,
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the repo currently connects to the `OpenAI` API by default using REST, and
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accesses the `"gpt-3.5-turbo"` model.
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Currently three different approaches to use LLMs are supported:
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1. `spacy-llm`s native REST interface. This is the default for all hosted models
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(e. g. OpenAI, Cohere, Anthropic, ...).
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2. A HuggingFace integration that allows to run a limited set of HF models
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locally.
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3. A LangChain integration that allows to run any model supported by LangChain
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(hosted or locally).
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Approaches 1. and 2 are the default for hosted model and local models,
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respectively. Alternatively you can use LangChain to access hosted or local
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models by specifying one of the models registered with the `langchain.` prefix.
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<Infobox>
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_Why LangChain if there are also are native REST and HuggingFace interfaces? When should I use what?_
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Third-party libraries like `langchain` focus on prompt management, integration
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of many different LLM APIs, and other related features such as conversational
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memory or agents. `spacy-llm` on the other hand emphasizes features we consider
|
||
useful in the context of NLP pipelines utilizing LLMs to process documents
|
||
(mostly) independent from each other. It makes sense that the feature sets of
|
||
such third-party libraries and `spacy-llm` aren't identical - and users might
|
||
want to take advantage of features not available in `spacy-llm`.
|
||
|
||
The advantage of implementing our own REST and HuggingFace integrations is that
|
||
we can ensure a larger degree of stability and robustness, as we can guarantee
|
||
backwards-compatibility and more smoothly integrated error handling.
|
||
|
||
If however there are features or APIs not natively covered by `spacy-llm`, it's
|
||
trivial to utilize LangChain to cover this - and easy to customize the prompting
|
||
mechanism, if so required.
|
||
|
||
</Infobox>
|
||
|
||
<Infobox variant="warning">
|
||
Note that when using hosted services, you have to ensure that the [proper API
|
||
keys](/api/large-language-models#api-keys) are set as environment variables as described by the corresponding
|
||
provider's documentation.
|
||
|
||
</Infobox>
|
||
|
||
| Model | Description |
|
||
| ----------------------------------------------------------------------- | ---------------------------------------------- |
|
||
| [`spacy.GPT-4.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-4` model family. |
|
||
| [`spacy.GPT-3-5.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-3-5` model family. |
|
||
| [`spacy.Text-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-davinci` model family. |
|
||
| [`spacy.Code-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `code-davinci` model family. |
|
||
| [`spacy.Text-Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-curie` model family. |
|
||
| [`spacy.Text-Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-babbage` model family. |
|
||
| [`spacy.Text-Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-ada` model family. |
|
||
| [`spacy.Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `davinci` model family. |
|
||
| [`spacy.Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `curie` model family. |
|
||
| [`spacy.Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `babbage` model family. |
|
||
| [`spacy.Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `ada` model family. |
|
||
| [`spacy.Azure.v1`](/api/large-language-models#models-rest) | Azure's OpenAI models. |
|
||
| [`spacy.Command.v1`](/api/large-language-models#models-rest) | Cohere’s `command` model family. |
|
||
| [`spacy.Claude-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-2` model family. |
|
||
| [`spacy.Claude-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1` model family. |
|
||
| [`spacy.Claude-instant-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1` model family. |
|
||
| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1.1` model family. |
|
||
| [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.0` model family. |
|
||
| [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.2` model family. |
|
||
| [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.3` model family. |
|
||
| [`spacy.PaLM.v1`](/api/large-language-models#models-rest) | Google’s `PaLM` model family. |
|
||
| [`spacy.Dolly.v1`](/api/large-language-models#models-hf) | Dolly models through HuggingFace. |
|
||
| [`spacy.Falcon.v1`](/api/large-language-models#models-hf) | Falcon models through HuggingFace. |
|
||
| [`spacy.Mistral.v1`](/api/large-language-models#models-hf) | Mistral models through HuggingFace. |
|
||
| [`spacy.Llama2.v1`](/api/large-language-models#models-hf) | Llama2 models through HuggingFace. |
|
||
| [`spacy.StableLM.v1`](/api/large-language-models#models-hf) | StableLM models through HuggingFace. |
|
||
| [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf) | OpenLLaMA models through HuggingFace. |
|
||
| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. |
|
||
|
||
Note that the chat models variants of Llama 2 are currently not supported. This
|
||
is because they need a particular prompting setup and don't add any discernible
|
||
benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared to
|
||
the completion model variants.
|
||
|
||
### Cache {id="cache"}
|
||
|
||
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"}
|
||
|
||
| Function | Description |
|
||
| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||
| [`spacy.FewShotReader.v1`](/api/large-language-models#fewshotreader-v1) | This function is registered in spaCy's `misc` registry, and reads in examples from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses [`srsly`](https://github.com/explosion/srsly) to read in these files and parses them depending on the file extension. |
|
||
| [`spacy.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. |
|