From 0bd1fd230c950b14de5e2be60d9a29a2b5c98972 Mon Sep 17 00:00:00 2001 From: Adam Kells Date: Mon, 4 Nov 2024 18:58:33 +0000 Subject: [PATCH] Spacy and Huggingface integrations (#84) * first pass at adding spacy and hf integrations * fix pyproject * adding langchain and modifying document * added testing * adding docs * finish docs * fix test * fix test2 * skip transformers test * fix tests * adding magicmock for iterable * respond to feedback * finalise document attributes * update lock * constrain numpy version --- docs/reference/pipeline/integrations.md | 220 +++++ docs/reference/pipeline/pipeline.md | 6 + healthchain/io/containers.py | 153 ++-- .../pipeline/components/integrations.py | 45 + .../pipeline/components/postprocessors.py | 4 +- mkdocs.yml | 2 + poetry.lock | 847 +++++++++--------- pyproject.toml | 1 + tests/pipeline/test_containers.py | 71 ++ tests/pipeline/test_postprocessor.py | 40 +- tests/test_pipeline_integrations.py | 77 ++ 11 files changed, 952 insertions(+), 514 deletions(-) create mode 100644 docs/reference/pipeline/integrations.md create mode 100644 healthchain/pipeline/components/integrations.py create mode 100644 tests/pipeline/test_containers.py create mode 100644 tests/test_pipeline_integrations.py diff --git a/docs/reference/pipeline/integrations.md b/docs/reference/pipeline/integrations.md new file mode 100644 index 0000000..43a0885 --- /dev/null +++ b/docs/reference/pipeline/integrations.md @@ -0,0 +1,220 @@ +# HealthChain Integrations + +This document provides an overview of the integration components available in the HealthChain package. These components allow you to easily incorporate popular NLP libraries into your HealthChain pipelines. + +## Table of Contents + +1. [SpacyComponent](#spacycomponent) +2. [HuggingFaceComponent](#huggingfacecomponent) +3. [LangChainComponent](#langchaincomponent) + +## Installation Requirements +Before utilizing the integration components, it is important to note that the required third-party libraries are not included in HealthChain's default installation. This design decision was made to: + +- Maintain a lean and flexible core package +- Allow users to selectively install only the necessary dependencies +- Avoid potential version conflicts with other packages in your environment + +To use these integrations, you will need to manually install the corresponding libraries using pip. + +```python +pip install spacy +python -m spacy download en_core_web_sm # or another desired model +pip install transformers +pip install langchain +``` + + +## SpacyComponent + +The `SpacyComponent` allows you to integrate spaCy models into your HealthChain pipeline. There are several ways to initialize this component with different types of spaCy models: + +1. Using standard spaCy models: + ```python + # Using a standard spaCy model (requires: python -m spacy download en_core_web_sm) + spacy_component = SpacyComponent("en_core_web_sm") + ``` + +2. Loading custom trained pipelines from a directory: + ```python + # Using a custom pipeline saved to disk + spacy_component = SpacyComponent("/path/to/your/custom/model") + ``` + +3. Using specialized domain models like [scispaCy](https://allenai.github.io/scispacy/) which can be used for classifying clinical or biomedical text: + ```python + # Using scispaCy models for biomedical text (requires: pip install scispacy) + spacy_component = SpacyComponent("en_core_sci_sm") + ``` + +Choose the appropriate model based on your specific needs - standard models for general text, custom-trained models for domain-specific tasks, or specialized models like scispaCy for biomedical text analysis. + +```python +from healthchain.pipeline.components.integrations import SpacyComponent + + +spacy_component = SpacyComponent(path_to_pipeline="en_core_web_sm") +``` + +When called on a document, this component processes the input document using the specified spaCy model and adds the resulting spaCy Doc object to the HealthChain Document. + +### Example + +```python +from healthchain.io.containers import Document +from healthchain.pipeline.base import Pipeline +from healthchain.pipeline.components.integrations import SpacyComponent + +pipeline = Pipeline() +pipeline.add_node(SpacyComponent(path_to_pipeline="en_core_web_sm")) + +doc = Document("This is a test sentence.") +processed_doc = pipeline(doc) + +# Access spaCy annotations +spacy_doc = processed_doc.get_spacy_doc() +for token in spacy_doc:https://github.com/dotimplement/HealthChain + print(f"Token: {token.text}, POS: {token.pos_}, Lemma: {token.lemma_}") +``` + +## HuggingFaceComponent + +The `HuggingFaceComponent` integrates HuggingFace Transformers models into your HealthChain pipeline. Models can be browsed on the [HuggingFace website](https://huggingface.co/models). HuggingFace offers models for a wide range of different tasks, and while not all of these have been throughly tested for HealthChain compatability, we expect that all NLP models and tasks should be compatible. If you have an issues integrating any models please raise an issue on our [Github homepage](https://github.com/dotimplement/HealthChain)! + +```python +from healthchain.pipeline.components.integrations import HuggingFaceComponent + +huggingface_component = HuggingFaceComponent(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") +``` + + +- `task` (str): The NLP task to perform (e.g., "sentiment-analysis", "named-entity-recognition"). +- `model` (str): The name or path of the Hugging Face model to use. + +This component applies the specified Hugging Face model to the input document and stores the output in the HealthChain Document's `huggingface_outputs` dictionary. + +### Example + +```python +from healthchain.io.containers import Document +from healthchain.pipeline.base import Pipeline +from healthchain.pipeline.components.integrations import HuggingFaceComponent + +pipeline = Pipeline() +pipeline.add_node(HuggingFaceComponent(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")) + +doc = Document("I love using HealthChain for my NLP projects!") +processed_doc = pipeline(doc) + +# Access Hugging Face output +sentiment_result = processed_doc.get_huggingface_output("sentiment-analysis") +print(f"Sentiment: {sentiment_result}") +``` + +## LangChainComponent + +The `LangChainComponent` allows you to integrate LangChain chains into your HealthChain pipeline. + +```python +from langchain import PromptTemplate, LLMChain +from langchain_core +from langchain.llms import FakeListLLM +from langchain_core.output_parsers import StrOutputParser +from healthchain.pipeline.components.integrations import LangChainComponent + +# Let's create a simple FakeListLLM for demonstration +fake_llm = FakeListLLM(responses=["This is a great summary!"]) + +# Define the prompt template +prompt = PromptTemplate.from_template("Summarize the following text: {text}") + +# Create the LCEL chain +chain = prompt | fake_llm | StrOutputParser() + +langchain_component = LangChainComponent(chain=llm_chain) +``` + +- `chain`: A LangChain chain object to be executed within the pipeline. + +This component runs the specified LangChain chain on the input document's text and stores the output in the HealthChain Document's `langchain_outputs` dictionary. + +### Example + +```python +from healthchain.io.containers import Document +from healthchain.pipeline.base import Pipeline +from healthchain.pipeline.components.integrations import LangChainComponent +from langchain import PromptTemplate, LLMChain +from langchain_core.output_parsers import StrOutputParser +from langchain.llms import FakeListLLM + +# Set up LangChain with a FakeListLLM +fake_llm = FakeListLLM(responses=["HealthChain integrates NLP libraries for easy pipeline creation."]) +# Define the prompt template +prompt = PromptTemplate.from_template("Summarize the following text: {text}") + +# Create the LCEL chain +chain = prompt | fake_llm | StrOutputParser() + +# Set up your HealthChain pipeline +pipeline = Pipeline() +pipeline.add_node(LangChainComponent(chain=llm_chain)) + +# Let's summarize something +doc = Document("HealthChain is a powerful package for building NLP pipelines. It integrates seamlessly with popular libraries like spaCy, Hugging Face Transformers, and LangChain, allowing users to create complex NLP workflows with ease.") +processed_doc = pipeline(doc) + +# What summary did we get? +summary = processed_doc.get_langchain_output("chain_output") +print(f"Summary: {summary}") +``` + +## Combining Components + +You can easily combine multiple integration components in a single HealthChain pipeline: + +```python +from healthchain.io.containers import Document +from healthchain.pipeline.base import Pipeline +from healthchain.pipeline.components.integrations import SpacyComponent, HuggingFaceComponent, LangChainComponent +from langchain import PromptTemplate, LLMChain +from langchain.llms import FakeListLLM + +# Set up our components +spacy_component = SpacyComponent(path_to_pipeline="en_core_web_sm") +huggingface_component = HuggingFaceComponent(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") + +# Set up LangChain with a FakeListLLM +fake_llm = FakeListLLM(responses=["HealthChain: Powerful NLP pipeline builder."]) +# Define the prompt template +prompt = PromptTemplate.from_template("Summarize the following text: {text}") +# Create the LCEL chain +chain = prompt | fake_llm | StrOutputParser() +langchain_component = LangChainComponent(chain=llm_chain) + +# Build our pipeline +pipeline = Pipeline() +pipeline.add_node(spacy_component) +pipeline.add_node(huggingface_component) +pipeline.add_node(langchain_component) +pipeline.build() + +# Process a document +doc = Document("HealthChain makes it easy to build powerful NLP pipelines!") +processed_doc = pipeline(doc) + +# Let's see what we got! +spacy_doc = processed_doc.get_spacy_doc() +sentiment = processed_doc.get_huggingface_output("sentiment-analysis") +summary = processed_doc.get_langchain_output("chain_output") + +print(f"Tokens: {[token.text for token in spacy_doc]}") +print(f"Sentiment: {sentiment}") +print(f"Summary: {summary}") +``` + +This documentation provides an overview of the integration components available in HealthChain. For more detailed information on each library, please refer to their respective documentation: + +- [spaCy Documentation](https://spacy.io/api) +- [Hugging Face Transformers Documentation](https://huggingface.co/transformers/) +- [LangChain Documentation](https://python.langchain.com/docs/introduction/) diff --git a/docs/reference/pipeline/pipeline.md b/docs/reference/pipeline/pipeline.md index b302c98..4ca7904 100644 --- a/docs/reference/pipeline/pipeline.md +++ b/docs/reference/pipeline/pipeline.md @@ -131,6 +131,12 @@ pipeline.add_input(cda_connector) pipeline.add_output(cda_connector) ``` +### Integrations + +HealthChain offers powerful integrations with popular NLP libraries, enhancing its capabilities and allowing you to build more sophisticated pipelines. These integrations include components for spaCy, Hugging Face Transformers, and LangChain, enabling you to leverage state-of-the-art NLP models and techniques within your HealthChain workflows. + +Integrations are covered in detail on the [Integration](./integrations.md) homepage. + ## Pipeline Management 🔨 #### Adding diff --git a/healthchain/io/containers.py b/healthchain/io/containers.py index ec8fa47..6d3fbeb 100644 --- a/healthchain/io/containers.py +++ b/healthchain/io/containers.py @@ -62,94 +62,121 @@ def from_json(cls, json_str: str) -> "DataContainer": @dataclass class Document(DataContainer[str]): """ - A container for document data, optionally wrapping a spaCy Doc object. + A container for document data with support for various NLP processing outputs. This class extends DataContainer to specifically handle textual document data. - It provides functionality to work with raw text, tokenized text, spaCy Doc objects, - and structured clinical data. + It provides functionality to work with raw text, tokenized text, and outputs from + various NLP libraries like spaCy, Hugging Face, and LangChain. Attributes: data (str): The raw text content of the document. preprocessed_text (str): The preprocessed version of the text. - tokens (List[str]): A list of individual tokens extracted from the text. - pos_tags (List[str]): A list of part-of-speech tags corresponding to the tokens. - entities (List[str]): A list of named entities identified in the text. ccd_data (Optional[CcdData]): An optional CcdData object containing structured clinical data. fhir_resources (Optional[CdsFhirData]): Optional FHIR resources data. cds_cards (Optional[List[Card]]): Optional list of CDS cards. cds_actions (Optional[List[Action]]): Optional list of CDS actions. - text (str): The current text content, which may be updated when setting a spaCy Doc. - _doc (SpacyDoc): An internal reference to the spaCy Doc object, if set. Methods: - __post_init__(): Initializes the text attribute and _doc reference. - _update_attributes(): Updates tokens, pos_tags, and entities from the spaCy Doc. - doc (property): Returns the spaCy Doc object if set, or raises an error. - set_spacy_doc(doc: SpacyDoc): Sets the spaCy Doc and updates related attributes. + add_spacy_doc(doc: SpacyDoc): Sets the spaCy Doc and updates related attributes. + add_huggingface_output(task: str, output: Any): Adds output from a Hugging Face model. + add_langchain_output(task: str, output: Any): Adds output from a LangChain process. + get_tokens() -> List[str]: Returns the document's tokens. + get_entities() -> List[Dict[str, Any]]: Returns the named entities with their details. + get_embeddings() -> Optional[List[float]]: Returns the document embeddings. + set_embeddings(embeddings: List[float]): Sets the document embeddings. + get_spacy_doc() -> Optional[SpacyDoc]: Returns the spaCy Doc if available. + get_huggingface_output(task: str) -> Any: Retrieves output for a specific Hugging Face task. + get_langchain_output(task: str) -> Any: Retrieves output for a specific LangChain task. word_count() -> int: Returns the number of tokens in the document. - char_count() -> int: Returns the number of characters in the text. - get_entities() -> List[Dict[str, Any]]: Returns a list of entities with their details. - update_ccd(new_problems: List[ProblemConcept], new_medications: List[MedicationConcept], new_allergies: List[AllergyConcept], overwrite: bool): Updates the existing CcdData object. + char_count() -> int: Returns the number of characters across all tokens. + update_ccd(...): Updates the CCD data with new clinical information. __iter__() -> Iterator[str]: Allows iteration over the document's tokens. __len__() -> int: Returns the word count of the document. - Raises: - ValueError: When attempting to access the spaCy Doc before it's set. - - Note: - The spaCy Doc object needs to be set using a preprocessor before accessing - certain attributes and methods that depend on it. + Notes: + - The class supports multiple NLP processing pipelines and maintains their outputs separately. + - Basic tokenization is performed if tokens are not provided during initialization. + - Clinical data can be updated incrementally or overwritten completely. """ preprocessed_text: str = field(default="") - tokens: List[str] = field(default_factory=list) - pos_tags: List[str] = field(default_factory=list) - entities: List[str] = field(default_factory=list) ccd_data: Optional[CcdData] = field(default=None) fhir_resources: Optional[CdsFhirData] = field(default=None) cds_cards: Optional[List[Card]] = field(default=None) cds_actions: Optional[List[Action]] = field(default=None) + # Internal attributes + _tokens: List[str] = field(default_factory=list) + _pos_tags: List[str] = field(default_factory=list) + _entities: List[Dict[str, Any]] = field(default_factory=list) + _embeddings: Optional[List[float]] = field(default=None) + _spacy_doc: Optional[SpacyDoc] = field(default=None) + _huggingface_results: Dict[str, Any] = field(default_factory=dict) + _langchain_results: Dict[str, Any] = field(default_factory=dict) + def __post_init__(self): + """Initialize the document with basic tokenization if needed.""" self.text = self.data - self._doc = None + if not self._tokens: + self._tokens = self.text.split() # Basic tokenization if not provided + + def add_spacy_doc(self, doc: SpacyDoc) -> None: + """Add a spaCy Doc and update related attributes.""" + self._spacy_doc = doc + self.text = doc.text + self._update_from_spacy() + + def _update_from_spacy(self) -> None: + """Update internal attributes from spaCy Doc.""" + if self._spacy_doc: + self._tokens = [token.text for token in self._spacy_doc] + self._pos_tags = [token.pos_ for token in self._spacy_doc] + self._entities = [ + { + "text": ent.text, + "label": ent.label_, + "start": ent.start_char, + "end": ent.end_char, + } + for ent in self._spacy_doc.ents + ] + + def add_huggingface_output(self, task: str, output: Any) -> None: + self._huggingface_results[task] = output + + def add_langchain_output(self, task: str, output: Any) -> None: + self._langchain_results[task] = output + + def get_tokens(self) -> List[str]: + return self._tokens + + def set_entities(self, entities: List[Dict[str, Any]]) -> None: + self._entities = entities - def _update_attributes(self): - self.tokens = [token.text for token in self._doc] - self.pos_tags = [token.pos_ for token in self._doc] - self.entities = [ent.text for ent in self._doc.ents] + def get_entities(self) -> List[Dict[str, Any]]: + return self._entities - @property - def doc(self) -> SpacyDoc: - if self._doc is None: - raise ValueError( - "spaCy Doc is not set. Use a preprocessor to set the spaCy Doc." - ) - return self._doc - - def set_spacy_doc(self, doc: SpacyDoc) -> None: - self._doc = doc - self.text = self._doc.text - self._update_attributes() + def get_embeddings(self) -> Optional[List[float]]: + return self._embeddings + + def set_embeddings(self, embeddings: List[float]) -> None: + self._embeddings = embeddings + + def get_spacy_doc(self) -> Optional[SpacyDoc]: + return self._spacy_doc + + def get_huggingface_output(self, task: str) -> Any: + return self._huggingface_results.get(task) + + def get_langchain_output(self, task: str) -> Any: + return self._langchain_results.get(task) def word_count(self) -> int: - return len(self.tokens) + return len(self._tokens) def char_count(self) -> int: - return len(self.text) - - def get_entities(self) -> List[Dict[str, Any]]: - if self._doc is None: - return self.entities - return [ - { - "text": ent.text, - "label": ent.label_, - "start": ent.start_char, - "end": ent.end_char, - } - for ent in self._doc.ents - ] + """Get the total number of characters across all tokens.""" + return sum(len(token) for token in self._tokens) def update_ccd( self, @@ -159,13 +186,13 @@ def update_ccd( overwrite: bool = False, ) -> None: """ - Updates the existing CcdData object with new data. + Update the CCD data with new clinical information. Args: - new_problems (List[ProblemConcept]): List of new problem concepts to add or update. - new_medications (List[MedicationConcept]): List of new medication concepts to add or update. - new_allergies (List[AllergyConcept]): List of new allergy concepts to add or update. - overwrite (bool, optional): If True, replaces existing data; if False, appends new data. Defaults to False. + new_problems: List of new problem concepts to add or update. + new_medications: List of new medication concepts to add or update. + new_allergies: List of new allergy concepts to add or update. + overwrite: If True, replaces existing data; if False, appends new data. Raises: ValueError: If there is no existing CcdData object to update. @@ -183,9 +210,11 @@ def update_ccd( self.ccd_data.allergies.extend(new_allergies) def __iter__(self) -> Iterator[str]: - return iter(self.tokens) + """Allow iteration over the document's tokens.""" + return iter(self._tokens) def __len__(self) -> int: + """Return the word count of the document.""" return self.word_count() diff --git a/healthchain/pipeline/components/integrations.py b/healthchain/pipeline/components/integrations.py new file mode 100644 index 0000000..29bb5fd --- /dev/null +++ b/healthchain/pipeline/components/integrations.py @@ -0,0 +1,45 @@ +from healthchain.io.containers import Document +from healthchain.pipeline.components.base import BaseComponent + + +class SpacyComponent(BaseComponent[str]): + def __init__(self, path_to_pipeline: str): + import spacy + + nlp = spacy.load(path_to_pipeline) + self.nlp = nlp + + def __call__(self, doc: Document) -> Document: + spacy_doc = self.nlp(doc.data) + doc.add_spacy_doc(spacy_doc) + return doc + + +class HuggingFaceComponent(BaseComponent[str]): + def __init__(self, task, model): + try: + from transformers import pipeline + except ImportError: + raise ImportError( + "Could not import transformers. Please install it with: " + "`pip install transformers`" + ) + + nlp = pipeline(task=task, model=model) + self.nlp = nlp + self.task = task + + def __call__(self, doc: Document) -> Document: + output = self.nlp(doc.data) + doc.add_huggingface_output(self.task, output) + return doc + + +class LangChainComponent(BaseComponent[str]): + def __init__(self, chain): + self.chain = chain + + def __call__(self, doc: Document) -> Document: + output = self.chain.invoke(doc.data) + doc.add_langchain_output("chain_output", output) + return doc diff --git a/healthchain/pipeline/components/postprocessors.py b/healthchain/pipeline/components/postprocessors.py index 3d01206..5ce87ea 100644 --- a/healthchain/pipeline/components/postprocessors.py +++ b/healthchain/pipeline/components/postprocessors.py @@ -44,7 +44,7 @@ def __call__(self, doc: Document) -> Document: If the entity_lookup is empty or the document has no 'entities' attribute, the document is returned unchanged. """ - if not self.entity_lookup or not hasattr(doc, "entities"): + if not self.entity_lookup or not hasattr(doc, "_entities"): return doc refined_entities = [] @@ -54,6 +54,6 @@ def __call__(self, doc: Document) -> Document: entity["text"] = self.entity_lookup[entity_text] refined_entities.append(entity) - doc.entities = refined_entities + doc.set_entities(refined_entities) return doc diff --git a/mkdocs.yml b/mkdocs.yml index bb918ff..cb77de4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -22,12 +22,14 @@ nav: - Overview: reference/pipeline/pipeline.md - Data Container: reference/pipeline/data_container.md - Component: reference/pipeline/component.md + - Integrations: reference/pipeline/integrations.md - Connectors: - Overview: reference/pipeline/connectors/connectors.md - CDA Connector: reference/pipeline/connectors/cdaconnector.md - CDS FHIR Connector: reference/pipeline/connectors/cdsfhirconnector.md - Prebuilt Pipelines: - Medical Coding: reference/pipeline/prebuilt_pipelines/medicalcoding.md + - Models: - Overview: reference/pipeline/models/models.md - Sandbox: diff --git a/poetry.lock b/poetry.lock index f653cf9..430c954 100644 --- a/poetry.lock +++ b/poetry.lock @@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.5.0" +version = "4.5.2" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.5.0-py3-none-any.whl", hash = "sha256:fdeb095b7cc5a5563175eedd926ec4ae55413bb4be5770c424af0ba46ccb4a78"}, - {file = "anyio-4.5.0.tar.gz", hash = "sha256:c5a275fe5ca0afd788001f58fca1e69e29ce706d746e317d660e21f70c530ef9"}, + {file = "anyio-4.5.2-py3-none-any.whl", hash = "sha256:c011ee36bc1e8ba40e5a81cb9df91925c218fe9b778554e0b56a21e1b5d4716f"}, + {file = "anyio-4.5.2.tar.gz", hash = "sha256:23009af4ed04ce05991845451e11ef02fc7c5ed29179ac9a420e5ad0ac7ddc5b"}, ] [package.dependencies] @@ -33,7 +33,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"] trio = ["trio (>=0.26.1)"] [[package]] @@ -290,101 +290,116 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.3.2" +version = "3.4.0" description = "The Real First Universal Charset Detector. 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+import pytest +from healthchain.io.containers import Document + + +@pytest.fixture +def sample_document(): + return Document(data="This is a sample text for testing.") + + +def test_document_initialization(sample_document): + assert sample_document.data == "This is a sample text for testing." + assert sample_document.text == "This is a sample text for testing." + assert sample_document.get_tokens() == [ + "This", + "is", + "a", + "sample", + "text", + "for", + "testing.", + ] + assert sample_document.get_entities() == [] + assert sample_document.get_embeddings() is None + + +def test_document_word_count(sample_document): + assert sample_document.word_count() == 7 + + +def test_document_char_count(sample_document): + assert sample_document.char_count() == 28 + + +def test_document_add_huggingface_output(sample_document): + mock_output = {"label": "POSITIVE", "score": 0.9} + + sample_document.add_huggingface_output("sentiment", mock_output) + + assert sample_document.get_huggingface_output("sentiment") == mock_output + + +def test_document_add_langchain_output(sample_document): + mock_output = "Summarized text" + + sample_document.add_langchain_output("summarization", mock_output) + + assert sample_document.get_langchain_output("summarization") == mock_output + + +def test_document_embeddings(sample_document): + embeddings = [0.1, 0.2, 0.3] + + sample_document.set_embeddings(embeddings) + + assert sample_document.get_embeddings() == embeddings + + +def test_document_iteration(sample_document): + assert list(sample_document) == [ + "This", + "is", + "a", + "sample", + "text", + "for", + "testing.", + ] + + +def test_document_length(sample_document): + assert len(sample_document) == 7 diff --git a/tests/pipeline/test_postprocessor.py b/tests/pipeline/test_postprocessor.py index d474dee..20fb805 100644 --- a/tests/pipeline/test_postprocessor.py +++ b/tests/pipeline/test_postprocessor.py @@ -13,11 +13,13 @@ def test_text_postprocessor_initialization_and_processing(): # Test processing with empty lookup doc = Document(data="") - doc.entities = [ - {"text": "high blood pressure"}, - {"text": "fever"}, - {"text": "heart attack"}, - ] + doc.set_entities( + [ + {"text": "high blood pressure"}, + {"text": "fever"}, + {"text": "heart attack"}, + ] + ) processed_doc = processor(doc) assert [entity["text"] for entity in processed_doc.get_entities()] == [ "high blood pressure", @@ -31,11 +33,13 @@ def test_text_postprocessor_with_entities(sample_lookup): # Test with matching entities doc = Document(data="") - doc.entities = [ - {"text": "high blood pressure"}, - {"text": "fever"}, - {"text": "heart attack"}, - ] + doc.set_entities( + [ + {"text": "high blood pressure"}, + {"text": "fever"}, + {"text": "heart attack"}, + ] + ) processed_doc = processor(doc) assert [entity["text"] for entity in processed_doc.get_entities()] == [ "hypertension", @@ -45,12 +49,14 @@ def test_text_postprocessor_with_entities(sample_lookup): # Test with mixed entities doc = Document(data="") - doc.entities = [ - {"text": "high blood pressure"}, - {"text": "cough"}, - {"text": "heart attack"}, - {"text": "fever"}, - ] + doc.set_entities( + [ + {"text": "high blood pressure"}, + {"text": "cough"}, + {"text": "heart attack"}, + {"text": "fever"}, + ] + ) processed_doc = processor(doc) assert [entity["text"] for entity in processed_doc.get_entities()] == [ "hypertension", @@ -70,7 +76,7 @@ def test_text_postprocessor_edge_cases(sample_lookup): # Test with empty entities list doc = Document(data="") - doc.entities = [] + doc.set_entities([]) processed_doc = processor(doc) assert processed_doc.get_entities() == [] diff --git a/tests/test_pipeline_integrations.py b/tests/test_pipeline_integrations.py new file mode 100644 index 0000000..608710d --- /dev/null +++ b/tests/test_pipeline_integrations.py @@ -0,0 +1,77 @@ +import pytest +import importlib.util +from unittest.mock import Mock, patch, MagicMock +from healthchain.io.containers import Document +from healthchain.pipeline.components.integrations import ( + SpacyComponent, + HuggingFaceComponent, + LangChainComponent, +) + +transformers_installed = importlib.util.find_spec("transformers") is not None + + +@pytest.fixture +def sample_document(): + return Document(data="This is a sample text for testing.") + + +@pytest.mark.parametrize( + "component_class,mock_module", + [ + (SpacyComponent, "spacy.load"), + pytest.param( + HuggingFaceComponent, + "transformers.pipeline", + marks=pytest.mark.skipif( + not transformers_installed, reason="transformers package not installed" + ), + ), + ], +) +def test_component_initialization(component_class, mock_module): + with patch(mock_module) as mock: + mock_instance = Mock() + mock.return_value = mock_instance + if component_class == SpacyComponent: + component = component_class("dummy_path") + else: + component = component_class("dummy_task", "dummy_model") + mock.assert_called_once() + assert hasattr(component, "nlp") + assert component.nlp == mock_instance + + +def test_spacy_component(sample_document): + with patch("spacy.load") as mock_load: + mock_instance = MagicMock(items=[]) + mock_instance.__iter__.return_value = [] + mock_load.return_value = mock_instance + component = SpacyComponent("en_core_web_sm") + result = component(sample_document) + assert result.get_spacy_doc() + + +@pytest.mark.skipif( + not transformers_installed, reason="transformers package not installed" +) +def test_huggingface_component(sample_document): + with patch("transformers.pipeline") as mock_pipeline: + mock_instance = Mock() + mock_pipeline.return_value = mock_instance + component = HuggingFaceComponent( + "sentiment-analysis", "distilbert-base-uncased-finetuned-sst-2-english" + ) + result = component(sample_document) + assert result.get_huggingface_output("sentiment-analysis") + + +def test_langchain_component(sample_document): + mock_chain = Mock() + mock_chain.invoke.return_value = "mocked chain output" + + component = LangChainComponent(mock_chain) + result = component(sample_document) + + mock_chain.invoke.assert_called_once_with(sample_document.data) + assert result.get_langchain_output("chain_output") == "mocked chain output"