Langchain

zenml.integrations.langchain

Initialization of the langchain integration.

Attributes

LANGCHAIN = 'langchain' module-attribute

logger = get_logger(__name__) module-attribute

Classes

Integration

Base class for integration in ZenML.

Functions

activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py

@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""

check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type	Description
bool	True if all required packages are installed, False otherwise.

Source code in src/zenml/integrations/integration.py

@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for r in cls.get_requirements():
        try:
            # First check if the base package is installed
            dist = pkg_resources.get_distribution(r)

            # Next, check if the dependencies (including extras) are
            # installed
            deps: List[Requirement] = []

            _, extras = parse_requirement(r)
            if extras:
                extra_list = extras[1:-1].split(",")
                for extra in extra_list:
                    try:
                        requirements = dist.requires(extras=[extra])  # type: ignore[arg-type]
                    except pkg_resources.UnknownExtra as e:
                        logger.debug(f"Unknown extra: {str(e)}")
                        return False
                    deps.extend(requirements)
            else:
                deps = dist.requires()

            for ri in deps:
                try:
                    # Remove the "extra == ..." part from the requirement string
                    cleaned_req = re.sub(
                        r"; extra == \"\w+\"", "", str(ri)
                    )
                    pkg_resources.get_distribution(cleaned_req)
                except pkg_resources.DistributionNotFound as e:
                    logger.debug(
                        f"Unable to find required dependency "
                        f"'{e.req}' for requirement '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False
                except pkg_resources.VersionConflict as e:
                    logger.debug(
                        f"Package version '{e.dist}' does not match "
                        f"version '{e.req}' required by '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False

        except pkg_resources.DistributionNotFound as e:
            logger.debug(
                f"Unable to find required package '{e.req}' for "
                f"integration {cls.NAME}."
            )
            return False
        except pkg_resources.VersionConflict as e:
            logger.debug(
                f"Package version '{e.dist}' does not match version "
                f"'{e.req}' necessary for integration {cls.NAME}."
            )
            return False

    logger.debug(
        f"Integration {cls.NAME} is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True

flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type	Description
List[Type[Flavor]]	A list of new stack component flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []

get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None
python_version	Optional[str]	The Python version to use for the requirements.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS

get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type	Description
List[Type[BasePluginFlavor]]	A list of new plugin flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

LangchainIntegration

Bases: Integration

Definition of langchain integration for ZenML.

Functions

activate() -> None classmethod

Activates the integration.

Source code in src/zenml/integrations/langchain/__init__.py

@classmethod
def activate(cls) -> None:
    """Activates the integration."""
    from zenml.integrations.langchain import materializers  # noqa

Functions

get_logger(logger_name: str) -> logging.Logger

Main function to get logger name,.

Parameters:

Name	Type	Description	Default
logger_name	str	Name of logger to initialize.	required

Returns:

Type	Description
Logger	A logger object.

Source code in src/zenml/logger.py

def get_logger(logger_name: str) -> logging.Logger:
    """Main function to get logger name,.

    Args:
        logger_name: Name of logger to initialize.

    Returns:
        A logger object.
    """
    logger = logging.getLogger(logger_name)
    logger.setLevel(get_logging_level().value)
    logger.addHandler(get_console_handler())

    logger.propagate = False
    return logger

Modules

materializers

Initialization of the langchain materializer.

Classes

Modules

document_materializer

Implementation of ZenML's Langchain Document materializer.

Classes

LangchainDocumentMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BaseMaterializer

Handle Langchain Document objects.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

extract_metadata(data: Document) -> Dict[str, MetadataType]

Extract metadata from the given BaseModel object.

Parameters:

Name	Type	Description	Default
data	Document	The BaseModel object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/integrations/langchain/materializers/document_materializer.py

def extract_metadata(self, data: Document) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given BaseModel object.

    Args:
        data: The BaseModel object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {"schema": data.schema()}

load(data_type: Type[Document]) -> Any

Reads BaseModel from JSON.

Parameters:

Name	Type	Description	Default
data_type	Type[Document]	The type of the data to read.	required

Returns:

Type	Description
Any	The data read.

Source code in src/zenml/integrations/langchain/materializers/document_materializer.py

def load(self, data_type: Type["Document"]) -> Any:
    """Reads BaseModel from JSON.

    Args:
        data_type: The type of the data to read.

    Returns:
        The data read.
    """
    data_path = os.path.join(self.uri, DEFAULT_FILENAME)
    contents = yaml_utils.read_json(data_path)
    return data_type.parse_raw(contents)

save(data: Document) -> None

Serialize a BaseModel to JSON.

Parameters:

Name	Type	Description	Default
data	Document	The data to store.	required

Source code in src/zenml/integrations/langchain/materializers/document_materializer.py

def save(self, data: "Document") -> None:
    """Serialize a BaseModel to JSON.

    Args:
        data: The data to store.
    """
    data_path = os.path.join(self.uri, DEFAULT_FILENAME)
    yaml_utils.write_json(data_path, data.json())

Modules

openai_embedding_materializer

Implementation of the Langchain OpenAI embedding materializer.

Classes

LangchainOpenaiEmbeddingMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: CloudpickleMaterializer

Materializer for Langchain OpenAI Embeddings.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

load(data_type: Type[Any]) -> Any

Loads the embeddings model and lets it recreate clients when needed.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the data to load.	required

Returns:

Type	Description
Any	The loaded embeddings model.

Source code in src/zenml/integrations/langchain/materializers/openai_embedding_materializer.py

def load(self, data_type: Type[Any]) -> Any:
    """Loads the embeddings model and lets it recreate clients when needed.

    Args:
        data_type: The type of the data to load.

    Returns:
        The loaded embeddings model.
    """
    return super().load(data_type)

save(embeddings: Any) -> None

Saves the embeddings model after clearing non-picklable clients.

Parameters:

Name	Type	Description	Default
embeddings	Any	The embeddings model to save.	required

Source code in src/zenml/integrations/langchain/materializers/openai_embedding_materializer.py

def save(self, embeddings: Any) -> None:
    """Saves the embeddings model after clearing non-picklable clients.

    Args:
        embeddings: The embeddings model to save.
    """
    # Clear the clients which will be recreated on load
    embeddings.client = None
    embeddings.async_client = None

    # Use the parent class's save implementation which uses cloudpickle
    super().save(embeddings)

vector_store_materializer

Implementation of the langchain vector store materializer.

Classes

LangchainVectorStoreMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: CloudpickleMaterializer

Handle langchain vector store objects.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store