base

Abstract base class for model reference backend providers.

ModelReferenceBackend

Bases: ABC

Abstract interface for model reference data providers.

This interface defines the contract that all backend implementations must fulfill. Backends are responsible for fetching raw model reference data from their source (GitHub, database, API, etc.) and providing it in a standardized dictionary format.

The ModelReferenceManager uses backends as pluggable data sources and handles caching, TTL management, and conversion to pydantic models.

Source code in src/horde_model_reference/backends/base.py

class ModelReferenceBackend(ABC):
    """Abstract interface for model reference data providers.

    This interface defines the contract that all backend implementations must fulfill.
    Backends are responsible for fetching raw model reference data from their source
    (GitHub, database, API, etc.) and providing it in a standardized dictionary format.

    The ModelReferenceManager uses backends as pluggable data sources and handles
    caching, TTL management, and conversion to pydantic models.
    """

    _replicate_mode = ReplicateMode.REPLICA
    _invalidation_callbacks: list[Callable[[MODEL_REFERENCE_CATEGORY], None]]

    def __init__(
        self,
        mode: ReplicateMode = ReplicateMode.REPLICA,
    ) -> None:
        """Initialize the backend."""
        super().__init__()

        self._replicate_mode = mode
        self._invalidation_callbacks = []

    @property
    def replicate_mode(self) -> ReplicateMode:
        """Get the replicate mode of this backend."""
        return self._replicate_mode

    @abstractmethod
    def fetch_category(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        *,
        force_refresh: bool = False,
    ) -> dict[str, Any] | None:
        """Fetch model reference data for a specific category.

        Args:
            category: The category to fetch.
            force_refresh: If True, bypass any backend-level caching
                and fetch fresh data. Defaults to False.

        Returns:
            dict[str, Any] | None: The model reference data as a dictionary mapping
                model names to their attributes, or None if the category cannot be fetched.

        Implementation Requirements:
            - Return data as a dictionary: `{model_name: {attribute: value, ...}, ...}`
            - Return `None` if category cannot be fetched
            - Honor `force_refresh` to bypass internal caches
            - Handle errors gracefully (log and return `None`)

        Example Implementation:
            ```python
            def fetch_category(self, category, *, force_refresh=False):
                def fetch():
                    # Your fetch logic here
                    response = httpx.get(f"{self.base_url}/{category}")
                    return response.json() if response.status_code == 200 else None

                return self._fetch_with_cache(category, fetch, force_refresh=force_refresh)
            ```

        See Also:
            - [fetch_all_categories()][(c).fetch_all_categories]: Batch fetching of all categories
            - [fetch_category_async()][(c).fetch_category_async]: Async variant
            - [ReplicaBackendBase._fetch_with_cache()]
              [^^^.replica_backend_base.ReplicaBackendBase._fetch_with_cache]:
              Helper for cache management

        """

    @abstractmethod
    def fetch_all_categories(
        self,
        *,
        force_refresh: bool = False,
    ) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]:
        """Fetch model reference data for all categories.

        Args:
            force_refresh: If True, bypass any backend-level caching
                and fetch fresh data. Defaults to False.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories
                to their model reference data. Categories that cannot be fetched have None values.

        Implementation Requirements:
            - Return a dictionary mapping each category to its data
            - Use `None` values for categories that cannot be fetched
            - Typically implemented as a loop over [fetch_category()][(c).fetch_category]

        Example Implementation:
            ```python
            def fetch_all_categories(self, *, force_refresh=False):
                return {
                    category: self.fetch_category(category, force_refresh=force_refresh)
                    for category in MODEL_REFERENCE_CATEGORY
                }
            ```

        See Also:
            - [fetch_category()][(c).fetch_category]: Single category fetching
            - [fetch_all_categories_async()][(c).fetch_all_categories_async]: Async variant

        """

    @abstractmethod
    async def fetch_category_async(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        *,
        httpx_client: httpx.AsyncClient | None = None,
        force_refresh: bool = False,
    ) -> dict[str, Any] | None:
        """Asynchronously fetch model reference data for a specific category.

        Args:
            category: The category to fetch.
            httpx_client: An optional httpx async client for connection pooling.
            force_refresh: If True, bypass any backend-level caching. Defaults to False.

        Returns:
            dict[str, Any] | None: The model reference data, or None if fetch failed.

        Implementation Requirements:
            - Use async I/O where possible (network requests, file operations with aiofiles)
            - Accept optional `httpx_client` for connection pooling
            - Create temporary client if not provided
            - Same return format as synchronous version
            - Share cache with synchronous methods when using
              [ReplicaBackendBase][^^^.replica_backend_base.ReplicaBackendBase]

        Example Implementation:
            ```python
            async def fetch_category_async(self, category, *, httpx_client=None, force_refresh=False):
                close_client = httpx_client is None
                if httpx_client is None:
                    httpx_client = httpx.AsyncClient()

                try:
                    response = await httpx_client.get(f"{self.base_url}/{category}")
                    return response.json() if response.status_code == 200 else None
                finally:
                    if close_client:
                        await httpx_client.aclose()
            ```

        See Also:
            - [fetch_category()][(c).fetch_category]: Synchronous variant
            - [fetch_all_categories_async()][(c).fetch_all_categories_async]: Async batch fetching

        """

    @abstractmethod
    async def fetch_all_categories_async(
        self,
        *,
        httpx_client: httpx.AsyncClient | None = None,
        force_refresh: bool = False,
    ) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]:
        """Asynchronously fetch model reference data for all categories.

        Args:
            httpx_client: An optional httpx async client for connection pooling.
            force_refresh: If True, bypass any backend-level caching. Defaults to False.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories to their data.

        Implementation Requirements:
            - Use `asyncio.gather()` for concurrent fetching when possible
            - Share `httpx_client` across fetches for connection pooling
            - Same return format as synchronous version

        Example Implementation:
            ```python
            async def fetch_all_categories_async(self, *, httpx_client=None, force_refresh=False):
                close_client = httpx_client is None
                if httpx_client is None:
                    httpx_client = httpx.AsyncClient()

                try:
                    tasks = [
                        self.fetch_category_async(cat, httpx_client=httpx_client, force_refresh=force_refresh)
                        for cat in MODEL_REFERENCE_CATEGORY
                    ]
                    results = await asyncio.gather(*tasks)
                    return dict(zip(MODEL_REFERENCE_CATEGORY, results, strict=False))
                finally:
                    if close_client:
                        await httpx_client.aclose()
            ```

        See Also:
            - [fetch_all_categories()][(c).fetch_all_categories]: Synchronous variant
            - [fetch_category_async()][(c).fetch_category_async]: Async single category fetch

        """

    @abstractmethod
    def needs_refresh(self, category: MODEL_REFERENCE_CATEGORY) -> bool:
        """Check if existing cached data for a category needs to be refreshed.

        This method indicates whether cached data has become stale and should be
        re-fetched from the source. It does NOT indicate whether an initial fetch
        is needed for data that has never been loaded.

        Semantic distinction:
        - Returns False when no cached data exists (no initial fetch needed via this method)
        - Returns True when cached data exists but has become stale

        Backends can implement staleness detection based on file modification times,
        database timestamps, ETags, TTL expiration, explicit invalidation, etc.

        Args:
            category: The category to check.

        Returns:
            bool: True if cached data needs refresh due to staleness. False if no
                  cached data exists or if cached data is still fresh.

        Implementation Requirements:
            - Return `False` if no data has been cached yet (initial fetch is different concern)
            - Return `True` if cached data exists but is stale
            - Staleness can be based on: TTL expiration, file mtime changes, ETags, explicit invalidation

        Note:
            [ReplicaBackendBase][^^^.replica_backend_base.ReplicaBackendBase]
            provides a concrete implementation that checks explicit staleness marking,
            TTL expiration, file mtime changes, and custom validation hooks.

        See Also:
            - [mark_stale()][(c).mark_stale]: Explicitly mark a category as stale
            - [ReplicaBackendBase.should_fetch_data()][^^^.replica_backend_base.ReplicaBackendBase.should_fetch_data]:
              Combined initial fetch + refresh check

        """

    def register_invalidation_callback(
        self,
        callback: Callable[[MODEL_REFERENCE_CATEGORY], None],
    ) -> None:
        """Register a callback to be called when a category is invalidated.

        This allows external components (like ModelReferenceManager) to be notified
        when cached data becomes stale and needs to be refreshed.

        Args:
            callback: Function to call with the invalidated category.

        """
        self._invalidation_callbacks.append(callback)
        logger.debug(f"Registered invalidation callback: {getattr(callback, '__name__', repr(callback))}")

    def _notify_invalidation(self, category: MODEL_REFERENCE_CATEGORY) -> None:
        """Notify all registered callbacks that a category has been invalidated.

        Args:
            category: The category that was invalidated.

        """
        for callback in self._invalidation_callbacks:
            try:
                callback(category)
            except Exception as e:
                cb_name = getattr(callback, "__name__", repr(callback))
                logger.error(f"Invalidation callback {cb_name} failed for {category}: {e}")

    @abstractmethod
    def _mark_stale_impl(self, category: MODEL_REFERENCE_CATEGORY) -> None:
        """Backend-specific implementation of marking a category as stale.

        Subclasses must implement this to handle their specific staleness tracking.

        Args:
            category: The category to mark as stale.

        Implementation Requirements:
            - Update backend-specific staleness tracking (e.g., add to `_stale_categories` set)
            - Called by public [mark_stale()][(c).mark_stale] method before notifying callbacks
            - Implementations should override this method, not `mark_stale()`

        Note:
            The public `mark_stale()` method calls this implementation and then automatically
            notifies all registered invalidation callbacks.

        """

    def mark_stale(self, category: MODEL_REFERENCE_CATEGORY) -> None:
        """Mark a category's data as stale, requiring refresh on next access.

        This method calls the backend-specific implementation and then notifies
        all registered callbacks.

        Args:
            category: The category to mark as stale.

        Implementation Note:
            The base class provides this public implementation. Subclasses should override
            [_mark_stale_impl()][(c)._mark_stale_impl]
            instead of this method.

        See Also:
            - [_mark_stale_impl()][(c)._mark_stale_impl]: Backend-specific staleness tracking
            - [register_invalidation_callback()][(c).register_invalidation_callback]:
              Register callbacks for invalidation events

        """
        self._mark_stale_impl(category)
        self._notify_invalidation(category)

    @abstractmethod
    def get_category_file_path(self, category: MODEL_REFERENCE_CATEGORY) -> Path | None:
        """Get the file path for a category's data, if applicable.

        Some backends (like file-based ones) have a physical file path associated
        with each category. Others (like database backends) may return None.

        Args:
            category: The category to get the path for.

        Returns:
            Path | None: The file path, or None if not applicable for this backend.

        Implementation Requirements:
            - Return `Path` object for file-based backends
            - Return `None` for backends without file storage (HTTP-only, database, etc.)

        Example Implementations:
            ```python
            # File-based backend
            def get_category_file_path(self, category):
                return self.base_path / f"{category.value}.json"

            # HTTP-only backend
            def get_category_file_path(self, category):
                return None
            ```

        See Also:
            - [get_all_category_file_paths()][(c).get_all_category_file_paths]: Get all file paths

        """

    @abstractmethod
    def get_all_category_file_paths(self) -> dict[MODEL_REFERENCE_CATEGORY, Path | None]:
        """Get file paths for all categories, if applicable.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, Path | None]: Mapping of categories to their file paths.
                Returns None values for categories without file paths.

        Implementation Requirements:
            - Return dictionary with all categories
            - Use `None` values for categories without file paths
            - Typically implemented by iterating over categories and calling `get_category_file_path()`

        Example Implementation:
            ```python
            def get_all_category_file_paths(self):
                return {
                    category: self.get_category_file_path(category)
                    for category in MODEL_REFERENCE_CATEGORY
                }
            ```

        See Also:
            - [get_category_file_path()][(c).get_category_file_path]: Get single category file path

        """

    @abstractmethod
    def get_legacy_json(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        redownload: bool = False,
    ) -> dict[str, Any] | None:
        """Get raw legacy JSON for a specific category without pydantic validation.

        This method returns cached legacy format JSON data, downloading if needed.
        The cache is populated during initialization, downloads, and on-demand loads.

        Args:
            category: Category to retrieve.
            redownload: If True, redownload before returning and refresh cache.

        Returns:
            dict[str, Any] | None: The raw legacy JSON dict, or None if not found.

        Implementation Requirements:
            - Return legacy format data as dictionary
            - Support caching with optional redownload
            - Return `None` if not available
            - The `redownload` parameter is analogous to `force_refresh` in fetch methods

        See Also:
            - [get_legacy_json_string()][(c).get_legacy_json_string]: Get as string instead of dict

        """

    @abstractmethod
    def get_legacy_json_string(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        redownload: bool = False,
    ) -> str | None:
        """Get raw legacy JSON string for a specific category without pydantic validation.

        This method returns cached legacy format JSON data as a string, downloading if needed.
        The cache is populated during initialization, downloads, and on-demand loads.

        Args:
            category: Category to retrieve.
            redownload: If True, redownload before returning and refresh cache.

        Returns:
            str | None: The raw legacy JSON string, or None if not found.

        Implementation Requirements:
            - Return legacy format data as JSON string
            - Same caching semantics as [get_legacy_json()][(c).get_legacy_json]
            - Return `None` if not available

        See Also:
            - [get_legacy_json()][(c).get_legacy_json]: Get as dict instead of string

        """

    def support_any_writes(self) -> bool:
        """Check if this backend supports any write operations (v2 or legacy).

        Returns:
            bool: True if any write operations are supported, False otherwise.

        """
        return self.supports_writes() or self.supports_legacy_writes()

    def supports_writes(self) -> bool:
        """Check if this backend supports write operations (v2 format).

        Write operations include update_model() and delete_model().
        Typically only PRIMARY mode backends support writes.

        Returns:
            bool: True if write operations are supported, False otherwise.

        """
        return False

    def supports_legacy_writes(self) -> bool:
        """Check if this backend supports write operations in legacy format.

        Legacy write operations include update_model_legacy() and delete_model_legacy().
        Only available when canonical_format='LEGACY' in PRIMARY mode.

        Returns:
            bool: True if legacy write operations are supported, False otherwise.

        """
        return False

    def supports_cache_warming(self) -> bool:
        """Check if this backend supports cache warming operations.

        Cache warming pre-populates the cache with data to improve initial request performance.
        Typically only backends with distributed caching (like Redis) support this.

        Returns:
            bool: True if cache warming is supported, False otherwise.

        """
        return False

    def supports_health_checks(self) -> bool:
        """Check if this backend supports health check operations.

        Health checks verify that the backend's external dependencies (Redis, databases, etc.)
        are accessible and functioning correctly.

        Returns:
            bool: True if health checks are supported, False otherwise.

        """
        return False

    def supports_statistics(self) -> bool:
        """Check if this backend supports statistics retrieval.

        Statistics provide insights into backend performance, cache hits/misses, etc.

        Returns:
            bool: True if statistics are supported, False otherwise.

        """
        return False

    def update_model(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        record_dict: dict[str, Any],
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Update or create a model reference.

        This is an optional method that write-capable backends can implement.
        Read-only backends should leave the default implementation which raises NotImplementedError.

        Args:
            category: The category to update.
            model_name: The name of the model to update or create.
            record_dict: The model record data as a dictionary.

        Args:
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support write operations.

        Implementation Requirements:
            - Create model if it doesn't exist
            - Update model if it exists
            - Ensure atomic writes (use temp files with rename for file-based backends)
            - Call [mark_stale()][(c).mark_stale] after successful write to invalidate cache
            - Override [supports_writes()][(c).supports_writes] to return `True`

        See Also:
            - [update_model_from_base_model()][(c).update_model_from_base_model]:
              Update from pydantic model (automatically provided)
            - [delete_model()][(c).delete_model]: Delete a model
            - [supports_writes()][(c).supports_writes]: Feature detection method

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

    def update_model_from_base_model(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        record_model: BaseModel,
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Update or create a model reference from a pydantic BaseModel.

        This is an optional method that write-capable backends can implement.
        Read-only backends should leave the default implementation which raises NotImplementedError.

        Args:
            category: The category to update.
            model_name: The name of the model to update or create.
            record_model: The model record data as a pydantic BaseModel.
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support write operations.

        Implementation Note:
            The base class provides this implementation automatically. It:
            1. Checks [supports_writes()][(c).supports_writes] returns `True`
            2. Converts the pydantic model to dict using `model_dump(exclude_unset=True)`
            3. Calls [update_model()][(c).update_model] with the dictionary

            Backends that support writes typically don't need to override this method.

        See Also:
            - [update_model()][(c).update_model]: Update from dictionary (implement this)
            - [supports_writes()][(c).supports_writes]: Feature detection method

        """
        if not self.supports_writes():
            raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

        record_dict = record_model.model_dump(exclude_unset=True)
        self.update_model(
            category,
            model_name,
            record_dict,
            logical_user_id=logical_user_id,
            request_id=request_id,
        )

    def delete_model(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Delete a model reference.

        This is an optional method that write-capable backends can implement.
        Read-only backends should leave the default implementation which raises NotImplementedError.

        Args:
            category: The category containing the model.
            model_name: The name of the model to delete.

        Args:
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support write operations.
            KeyError: If the model doesn't exist.

        Implementation Requirements:
            - Raise `KeyError` if model doesn't exist
            - Ensure atomic writes
            - Call [mark_stale()][(c).mark_stale] after successful delete to invalidate cache
            - Override [supports_writes()][(c).supports_writes] to return `True`

        See Also:
            - [update_model()][(c).update_model]: Update or create a model
            - [supports_writes()][(c).supports_writes]: Feature detection method

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

    def update_model_legacy(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        record_dict: dict[str, Any],
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Update or create a model reference in legacy format.

        This is an optional method that legacy-write-capable backends can implement.
        Only available when canonical_format='LEGACY' in PRIMARY mode.

        Args:
            category: The category to update.
            model_name: The name of the model to update or create.
            record_dict: The model record data in legacy format as a dictionary.
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support legacy write operations.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

    def update_model_legacy_from_base_model(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        record_model: BaseModel,
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Update or create a model reference in legacy format from a pydantic BaseModel.

        This is an optional method that legacy-write-capable backends can implement.
        Only available when canonical_format='LEGACY' in PRIMARY mode.

        Args:
            category: The category to update.
            model_name: The name of the model to update or create.
            record_model: The model record data as a pydantic BaseModel.
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support legacy write operations.

        """
        if not self.supports_legacy_writes():
            raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

        record_dict = record_model.model_dump(exclude_unset=True)
        self.update_model_legacy(
            category,
            model_name,
            record_dict,
            logical_user_id=logical_user_id,
            request_id=request_id,
        )

    def delete_model_legacy(
        self,
        category: MODEL_REFERENCE_CATEGORY,
        model_name: str,
        *,
        logical_user_id: str | None = None,
        request_id: str | None = None,
    ) -> None:
        """Delete a model reference from legacy format files.

        This is an optional method that legacy-write-capable backends can implement.
        Only available when canonical_format='LEGACY' in PRIMARY mode.

        Args:
            category: The category containing the model.
            model_name: The name of the model to delete.
            logical_user_id: Immutable Horde user id for auditing contexts (optional).
            request_id: Optional tracing/idempotency identifier for audit correlation.

        Raises:
            NotImplementedError: If the backend does not support legacy write operations.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

    def warm_cache(self) -> None:
        """Pre-populate cache with all categories for faster initial requests.

        This is an optional method that backends with cache warming support can implement.
        Backends without cache warming should leave the default implementation.

        Raises:
            NotImplementedError: If the backend does not support cache warming.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support cache warming")

    async def warm_cache_async(self) -> None:
        """Asynchronously pre-populate cache with all categories for faster initial requests.

        This is an optional method that backends with cache warming support can implement.
        Backends without cache warming should leave the default implementation.

        Raises:
            NotImplementedError: If the backend does not support async cache warming.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support async cache warming")

    def health_check(self) -> bool:
        """Check the health of the backend's external dependencies.

        This is an optional method that backends with health check support can implement.
        Backends without external dependencies should leave the default implementation.

        Returns:
            bool: True if healthy, False otherwise.

        Raises:
            NotImplementedError: If the backend does not support health checks.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support health checks")

    def get_statistics(self) -> dict[str, Any]:
        """Get backend performance and usage statistics.

        This is an optional method that backends with statistics support can implement.
        The structure of returned statistics is backend-specific.

        Returns:
            dict[str, Any]: Backend-specific statistics.

        Raises:
            NotImplementedError: If the backend does not support statistics.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support statistics")

    def get_replicate_mode(self) -> ReplicateMode:
        """Get the replication mode of this backend.

        Returns:
            ReplicateMode: The replicate mode (PRIMARY or REPLICA).

        """
        return self._replicate_mode

    def supports_metadata(self) -> bool:
        """Check if this backend supports metadata tracking.

        Metadata tracking records operation counts, timestamps, and health metrics
        for both legacy (v1) and v2 format operations. Typically only PRIMARY mode
        backends support metadata tracking.

        Returns:
            bool: True if metadata tracking is supported, False otherwise.

        """
        return False

    def get_legacy_metadata(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
        """Get legacy format metadata for a specific category.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Args:
            category: The category to get metadata for.

        Returns:
            CategoryMetadata: The legacy metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

    async def get_legacy_metadata_async(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
        """Asynchronously get legacy format metadata for a specific category.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Args:
            category: The category to get metadata for.

        Returns:
            CategoryMetadata: The legacy metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

    def get_metadata(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
        """Get v2 format metadata for a specific category.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Args:
            category: The category to get metadata for.

        Returns:
            CategoryMetadata: The v2 metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

    async def get_metadata_async(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
        """Asynchronously get v2 format metadata for a specific category.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Args:
            category: The category to get metadata for.

        Returns:
            CategoryMetadata: The v2 metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

    def get_all_legacy_metadata(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
        """Get legacy format metadata for all categories.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

    async def get_all_legacy_metadata_async(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
        """Asynchronously get legacy format metadata for all categories.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

    def get_all_metadata(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
        """Get v2 format metadata for all categories.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

    async def get_all_metadata_async(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
        """Asynchronously get v2 format metadata for all categories.

        This is an optional method that metadata-capable backends can implement.
        Backends without metadata support should leave the default implementation.

        Returns:
            dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

        Raises:
            NotImplementedError: If the backend does not support metadata.

        """
        raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

_replicate_mode `class-attribute` `instance-attribute`

_replicate_mode = mode

_invalidation_callbacks `instance-attribute`

_invalidation_callbacks: list[
    Callable[[MODEL_REFERENCE_CATEGORY], None]
] = []

replicate_mode `property`

replicate_mode: ReplicateMode

Get the replicate mode of this backend.

init

__init__(
    mode: ReplicateMode = ReplicateMode.REPLICA,
) -> None

Initialize the backend.

Source code in src/horde_model_reference/backends/base.py

def __init__(
    self,
    mode: ReplicateMode = ReplicateMode.REPLICA,
) -> None:
    """Initialize the backend."""
    super().__init__()

    self._replicate_mode = mode
    self._invalidation_callbacks = []

fetch_category `abstractmethod`

fetch_category(
    category: MODEL_REFERENCE_CATEGORY,
    *,
    force_refresh: bool = False,
) -> dict[str, Any] | None

Fetch model reference data for a specific category.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to fetch.
force_refresh (bool, default: False ) –

If True, bypass any backend-level caching and fetch fresh data. Defaults to False.

Returns:

dict[str, Any] | None –

dict[str, Any] | None: The model reference data as a dictionary mapping model names to their attributes, or None if the category cannot be fetched.

Implementation Requirements

Return data as a dictionary: {model_name: {attribute: value, ...}, ...}
Return None if category cannot be fetched
Honor force_refresh to bypass internal caches
Handle errors gracefully (log and return None)

Example Implementation

def fetch_category(self, category, *, force_refresh=False):
    def fetch():
        # Your fetch logic here
        response = httpx.get(f"{self.base_url}/{category}")
        return response.json() if response.status_code == 200 else None

    return self._fetch_with_cache(category, fetch, force_refresh=force_refresh)

See Also

fetch_all_categories(): Batch fetching of all categories
fetch_category_async(): Async variant
[ReplicaBackendBase._fetch_with_cache()] [^^^.replica_backend_base.ReplicaBackendBase._fetch_with_cache]: Helper for cache management

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def fetch_category(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    *,
    force_refresh: bool = False,
) -> dict[str, Any] | None:
    """Fetch model reference data for a specific category.

    Args:
        category: The category to fetch.
        force_refresh: If True, bypass any backend-level caching
            and fetch fresh data. Defaults to False.

    Returns:
        dict[str, Any] | None: The model reference data as a dictionary mapping
            model names to their attributes, or None if the category cannot be fetched.

    Implementation Requirements:
        - Return data as a dictionary: `{model_name: {attribute: value, ...}, ...}`
        - Return `None` if category cannot be fetched
        - Honor `force_refresh` to bypass internal caches
        - Handle errors gracefully (log and return `None`)

    Example Implementation:
        ```python
        def fetch_category(self, category, *, force_refresh=False):
            def fetch():
                # Your fetch logic here
                response = httpx.get(f"{self.base_url}/{category}")
                return response.json() if response.status_code == 200 else None

            return self._fetch_with_cache(category, fetch, force_refresh=force_refresh)
        ```

    See Also:
        - [fetch_all_categories()][(c).fetch_all_categories]: Batch fetching of all categories
        - [fetch_category_async()][(c).fetch_category_async]: Async variant
        - [ReplicaBackendBase._fetch_with_cache()]
          [^^^.replica_backend_base.ReplicaBackendBase._fetch_with_cache]:
          Helper for cache management

    """

fetch_all_categories `abstractmethod`

fetch_all_categories(
    *, force_refresh: bool = False
) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]

Fetch model reference data for all categories.

Parameters:

force_refresh (bool, default: False ) –

If True, bypass any backend-level caching and fetch fresh data. Defaults to False.

Returns:

dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None] –

dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories to their model reference data. Categories that cannot be fetched have None values.

Implementation Requirements

Return a dictionary mapping each category to its data
Use None values for categories that cannot be fetched
Typically implemented as a loop over fetch_category()

Example Implementation

def fetch_all_categories(self, *, force_refresh=False):
    return {
        category: self.fetch_category(category, force_refresh=force_refresh)
        for category in MODEL_REFERENCE_CATEGORY
    }

See Also

fetch_category(): Single category fetching
fetch_all_categories_async(): Async variant

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def fetch_all_categories(
    self,
    *,
    force_refresh: bool = False,
) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]:
    """Fetch model reference data for all categories.

    Args:
        force_refresh: If True, bypass any backend-level caching
            and fetch fresh data. Defaults to False.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories
            to their model reference data. Categories that cannot be fetched have None values.

    Implementation Requirements:
        - Return a dictionary mapping each category to its data
        - Use `None` values for categories that cannot be fetched
        - Typically implemented as a loop over [fetch_category()][(c).fetch_category]

    Example Implementation:
        ```python
        def fetch_all_categories(self, *, force_refresh=False):
            return {
                category: self.fetch_category(category, force_refresh=force_refresh)
                for category in MODEL_REFERENCE_CATEGORY
            }
        ```

    See Also:
        - [fetch_category()][(c).fetch_category]: Single category fetching
        - [fetch_all_categories_async()][(c).fetch_all_categories_async]: Async variant

    """

fetch_category_async `abstractmethod` `async`

fetch_category_async(
    category: MODEL_REFERENCE_CATEGORY,
    *,
    httpx_client: AsyncClient | None = None,
    force_refresh: bool = False,
) -> dict[str, Any] | None

Asynchronously fetch model reference data for a specific category.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to fetch.
httpx_client (AsyncClient | None, default: None ) –

An optional httpx async client for connection pooling.
force_refresh (bool, default: False ) –

If True, bypass any backend-level caching. Defaults to False.

Returns:

dict[str, Any] | None –

dict[str, Any] | None: The model reference data, or None if fetch failed.

Implementation Requirements

Use async I/O where possible (network requests, file operations with aiofiles)
Accept optional httpx_client for connection pooling
Create temporary client if not provided
Same return format as synchronous version
Share cache with synchronous methods when using ReplicaBackendBase

Example Implementation

async def fetch_category_async(self, category, *, httpx_client=None, force_refresh=False):
    close_client = httpx_client is None
    if httpx_client is None:
        httpx_client = httpx.AsyncClient()

    try:
        response = await httpx_client.get(f"{self.base_url}/{category}")
        return response.json() if response.status_code == 200 else None
    finally:
        if close_client:
            await httpx_client.aclose()

See Also

fetch_category(): Synchronous variant
fetch_all_categories_async(): Async batch fetching

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
async def fetch_category_async(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    *,
    httpx_client: httpx.AsyncClient | None = None,
    force_refresh: bool = False,
) -> dict[str, Any] | None:
    """Asynchronously fetch model reference data for a specific category.

    Args:
        category: The category to fetch.
        httpx_client: An optional httpx async client for connection pooling.
        force_refresh: If True, bypass any backend-level caching. Defaults to False.

    Returns:
        dict[str, Any] | None: The model reference data, or None if fetch failed.

    Implementation Requirements:
        - Use async I/O where possible (network requests, file operations with aiofiles)
        - Accept optional `httpx_client` for connection pooling
        - Create temporary client if not provided
        - Same return format as synchronous version
        - Share cache with synchronous methods when using
          [ReplicaBackendBase][^^^.replica_backend_base.ReplicaBackendBase]

    Example Implementation:
        ```python
        async def fetch_category_async(self, category, *, httpx_client=None, force_refresh=False):
            close_client = httpx_client is None
            if httpx_client is None:
                httpx_client = httpx.AsyncClient()

            try:
                response = await httpx_client.get(f"{self.base_url}/{category}")
                return response.json() if response.status_code == 200 else None
            finally:
                if close_client:
                    await httpx_client.aclose()
        ```

    See Also:
        - [fetch_category()][(c).fetch_category]: Synchronous variant
        - [fetch_all_categories_async()][(c).fetch_all_categories_async]: Async batch fetching

    """

fetch_all_categories_async `abstractmethod` `async`

fetch_all_categories_async(
    *,
    httpx_client: AsyncClient | None = None,
    force_refresh: bool = False,
) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]

Asynchronously fetch model reference data for all categories.

Parameters:

httpx_client (AsyncClient | None, default: None ) –

An optional httpx async client for connection pooling.
force_refresh (bool, default: False ) –

If True, bypass any backend-level caching. Defaults to False.

Returns:

dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None] –

dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories to their data.

Implementation Requirements

Use asyncio.gather() for concurrent fetching when possible
Share httpx_client across fetches for connection pooling
Same return format as synchronous version

Example Implementation

async def fetch_all_categories_async(self, *, httpx_client=None, force_refresh=False):
    close_client = httpx_client is None
    if httpx_client is None:
        httpx_client = httpx.AsyncClient()

    try:
        tasks = [
            self.fetch_category_async(cat, httpx_client=httpx_client, force_refresh=force_refresh)
            for cat in MODEL_REFERENCE_CATEGORY
        ]
        results = await asyncio.gather(*tasks)
        return dict(zip(MODEL_REFERENCE_CATEGORY, results, strict=False))
    finally:
        if close_client:
            await httpx_client.aclose()

See Also

fetch_all_categories(): Synchronous variant
fetch_category_async(): Async single category fetch

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
async def fetch_all_categories_async(
    self,
    *,
    httpx_client: httpx.AsyncClient | None = None,
    force_refresh: bool = False,
) -> dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]:
    """Asynchronously fetch model reference data for all categories.

    Args:
        httpx_client: An optional httpx async client for connection pooling.
        force_refresh: If True, bypass any backend-level caching. Defaults to False.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, dict[str, Any] | None]: A mapping of categories to their data.

    Implementation Requirements:
        - Use `asyncio.gather()` for concurrent fetching when possible
        - Share `httpx_client` across fetches for connection pooling
        - Same return format as synchronous version

    Example Implementation:
        ```python
        async def fetch_all_categories_async(self, *, httpx_client=None, force_refresh=False):
            close_client = httpx_client is None
            if httpx_client is None:
                httpx_client = httpx.AsyncClient()

            try:
                tasks = [
                    self.fetch_category_async(cat, httpx_client=httpx_client, force_refresh=force_refresh)
                    for cat in MODEL_REFERENCE_CATEGORY
                ]
                results = await asyncio.gather(*tasks)
                return dict(zip(MODEL_REFERENCE_CATEGORY, results, strict=False))
            finally:
                if close_client:
                    await httpx_client.aclose()
        ```

    See Also:
        - [fetch_all_categories()][(c).fetch_all_categories]: Synchronous variant
        - [fetch_category_async()][(c).fetch_category_async]: Async single category fetch

    """

needs_refresh `abstractmethod`

needs_refresh(category: MODEL_REFERENCE_CATEGORY) -> bool

Check if existing cached data for a category needs to be refreshed.

This method indicates whether cached data has become stale and should be re-fetched from the source. It does NOT indicate whether an initial fetch is needed for data that has never been loaded.

Semantic distinction: - Returns False when no cached data exists (no initial fetch needed via this method) - Returns True when cached data exists but has become stale

Backends can implement staleness detection based on file modification times, database timestamps, ETags, TTL expiration, explicit invalidation, etc.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to check.

Returns:

bool ( bool ) –

True if cached data needs refresh due to staleness. False if no cached data exists or if cached data is still fresh.

Implementation Requirements

Return False if no data has been cached yet (initial fetch is different concern)
Return True if cached data exists but is stale
Staleness can be based on: TTL expiration, file mtime changes, ETags, explicit invalidation

Note

ReplicaBackendBase provides a concrete implementation that checks explicit staleness marking, TTL expiration, file mtime changes, and custom validation hooks.

See Also

mark_stale(): Explicitly mark a category as stale
ReplicaBackendBase.should_fetch_data(): Combined initial fetch + refresh check

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def needs_refresh(self, category: MODEL_REFERENCE_CATEGORY) -> bool:
    """Check if existing cached data for a category needs to be refreshed.

    This method indicates whether cached data has become stale and should be
    re-fetched from the source. It does NOT indicate whether an initial fetch
    is needed for data that has never been loaded.

    Semantic distinction:
    - Returns False when no cached data exists (no initial fetch needed via this method)
    - Returns True when cached data exists but has become stale

    Backends can implement staleness detection based on file modification times,
    database timestamps, ETags, TTL expiration, explicit invalidation, etc.

    Args:
        category: The category to check.

    Returns:
        bool: True if cached data needs refresh due to staleness. False if no
              cached data exists or if cached data is still fresh.

    Implementation Requirements:
        - Return `False` if no data has been cached yet (initial fetch is different concern)
        - Return `True` if cached data exists but is stale
        - Staleness can be based on: TTL expiration, file mtime changes, ETags, explicit invalidation

    Note:
        [ReplicaBackendBase][^^^.replica_backend_base.ReplicaBackendBase]
        provides a concrete implementation that checks explicit staleness marking,
        TTL expiration, file mtime changes, and custom validation hooks.

    See Also:
        - [mark_stale()][(c).mark_stale]: Explicitly mark a category as stale
        - [ReplicaBackendBase.should_fetch_data()][^^^.replica_backend_base.ReplicaBackendBase.should_fetch_data]:
          Combined initial fetch + refresh check

    """

register_invalidation_callback

register_invalidation_callback(
    callback: Callable[[MODEL_REFERENCE_CATEGORY], None],
) -> None

Register a callback to be called when a category is invalidated.

This allows external components (like ModelReferenceManager) to be notified when cached data becomes stale and needs to be refreshed.

Parameters:

callback (Callable[[MODEL_REFERENCE_CATEGORY], None]) –

Function to call with the invalidated category.

Source code in src/horde_model_reference/backends/base.py

def register_invalidation_callback(
    self,
    callback: Callable[[MODEL_REFERENCE_CATEGORY], None],
) -> None:
    """Register a callback to be called when a category is invalidated.

    This allows external components (like ModelReferenceManager) to be notified
    when cached data becomes stale and needs to be refreshed.

    Args:
        callback: Function to call with the invalidated category.

    """
    self._invalidation_callbacks.append(callback)
    logger.debug(f"Registered invalidation callback: {getattr(callback, '__name__', repr(callback))}")

_notify_invalidation

_notify_invalidation(
    category: MODEL_REFERENCE_CATEGORY,
) -> None

Notify all registered callbacks that a category has been invalidated.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category that was invalidated.

Source code in src/horde_model_reference/backends/base.py

def _notify_invalidation(self, category: MODEL_REFERENCE_CATEGORY) -> None:
    """Notify all registered callbacks that a category has been invalidated.

    Args:
        category: The category that was invalidated.

    """
    for callback in self._invalidation_callbacks:
        try:
            callback(category)
        except Exception as e:
            cb_name = getattr(callback, "__name__", repr(callback))
            logger.error(f"Invalidation callback {cb_name} failed for {category}: {e}")

_mark_stale_impl `abstractmethod`

_mark_stale_impl(
    category: MODEL_REFERENCE_CATEGORY,
) -> None

Backend-specific implementation of marking a category as stale.

Subclasses must implement this to handle their specific staleness tracking.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to mark as stale.

Implementation Requirements

Update backend-specific staleness tracking (e.g., add to _stale_categories set)
Called by public mark_stale() method before notifying callbacks
Implementations should override this method, not mark_stale()

Note

The public mark_stale() method calls this implementation and then automatically notifies all registered invalidation callbacks.

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def _mark_stale_impl(self, category: MODEL_REFERENCE_CATEGORY) -> None:
    """Backend-specific implementation of marking a category as stale.

    Subclasses must implement this to handle their specific staleness tracking.

    Args:
        category: The category to mark as stale.

    Implementation Requirements:
        - Update backend-specific staleness tracking (e.g., add to `_stale_categories` set)
        - Called by public [mark_stale()][(c).mark_stale] method before notifying callbacks
        - Implementations should override this method, not `mark_stale()`

    Note:
        The public `mark_stale()` method calls this implementation and then automatically
        notifies all registered invalidation callbacks.

    """

mark_stale

mark_stale(category: MODEL_REFERENCE_CATEGORY) -> None

Mark a category's data as stale, requiring refresh on next access.

This method calls the backend-specific implementation and then notifies all registered callbacks.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to mark as stale.

Implementation Note

The base class provides this public implementation. Subclasses should override _mark_stale_impl() instead of this method.

See Also

_mark_stale_impl(): Backend-specific staleness tracking
register_invalidation_callback(): Register callbacks for invalidation events

Source code in src/horde_model_reference/backends/base.py

def mark_stale(self, category: MODEL_REFERENCE_CATEGORY) -> None:
    """Mark a category's data as stale, requiring refresh on next access.

    This method calls the backend-specific implementation and then notifies
    all registered callbacks.

    Args:
        category: The category to mark as stale.

    Implementation Note:
        The base class provides this public implementation. Subclasses should override
        [_mark_stale_impl()][(c)._mark_stale_impl]
        instead of this method.

    See Also:
        - [_mark_stale_impl()][(c)._mark_stale_impl]: Backend-specific staleness tracking
        - [register_invalidation_callback()][(c).register_invalidation_callback]:
          Register callbacks for invalidation events

    """
    self._mark_stale_impl(category)
    self._notify_invalidation(category)

get_category_file_path `abstractmethod`

get_category_file_path(
    category: MODEL_REFERENCE_CATEGORY,
) -> Path | None

Get the file path for a category's data, if applicable.

Some backends (like file-based ones) have a physical file path associated with each category. Others (like database backends) may return None.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to get the path for.

Returns:

Path | None –

Path | None: The file path, or None if not applicable for this backend.

Implementation Requirements

Return Path object for file-based backends
Return None for backends without file storage (HTTP-only, database, etc.)

Example Implementations

# File-based backend
def get_category_file_path(self, category):
    return self.base_path / f"{category.value}.json"

# HTTP-only backend
def get_category_file_path(self, category):
    return None

See Also

get_all_category_file_paths(): Get all file paths

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def get_category_file_path(self, category: MODEL_REFERENCE_CATEGORY) -> Path | None:
    """Get the file path for a category's data, if applicable.

    Some backends (like file-based ones) have a physical file path associated
    with each category. Others (like database backends) may return None.

    Args:
        category: The category to get the path for.

    Returns:
        Path | None: The file path, or None if not applicable for this backend.

    Implementation Requirements:
        - Return `Path` object for file-based backends
        - Return `None` for backends without file storage (HTTP-only, database, etc.)

    Example Implementations:
        ```python
        # File-based backend
        def get_category_file_path(self, category):
            return self.base_path / f"{category.value}.json"

        # HTTP-only backend
        def get_category_file_path(self, category):
            return None
        ```

    See Also:
        - [get_all_category_file_paths()][(c).get_all_category_file_paths]: Get all file paths

    """

get_all_category_file_paths `abstractmethod`

get_all_category_file_paths() -> dict[
    MODEL_REFERENCE_CATEGORY, Path | None
]

Get file paths for all categories, if applicable.

Returns:

dict[MODEL_REFERENCE_CATEGORY, Path | None] –

dict[MODEL_REFERENCE_CATEGORY, Path | None]: Mapping of categories to their file paths. Returns None values for categories without file paths.

Implementation Requirements

Return dictionary with all categories
Use None values for categories without file paths
Typically implemented by iterating over categories and calling get_category_file_path()

Example Implementation

def get_all_category_file_paths(self):
    return {
        category: self.get_category_file_path(category)
        for category in MODEL_REFERENCE_CATEGORY
    }

See Also

get_category_file_path(): Get single category file path

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def get_all_category_file_paths(self) -> dict[MODEL_REFERENCE_CATEGORY, Path | None]:
    """Get file paths for all categories, if applicable.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, Path | None]: Mapping of categories to their file paths.
            Returns None values for categories without file paths.

    Implementation Requirements:
        - Return dictionary with all categories
        - Use `None` values for categories without file paths
        - Typically implemented by iterating over categories and calling `get_category_file_path()`

    Example Implementation:
        ```python
        def get_all_category_file_paths(self):
            return {
                category: self.get_category_file_path(category)
                for category in MODEL_REFERENCE_CATEGORY
            }
        ```

    See Also:
        - [get_category_file_path()][(c).get_category_file_path]: Get single category file path

    """

get_legacy_json `abstractmethod`

get_legacy_json(
    category: MODEL_REFERENCE_CATEGORY,
    redownload: bool = False,
) -> dict[str, Any] | None

Get raw legacy JSON for a specific category without pydantic validation.

This method returns cached legacy format JSON data, downloading if needed. The cache is populated during initialization, downloads, and on-demand loads.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

Category to retrieve.
redownload (bool, default: False ) –

If True, redownload before returning and refresh cache.

Returns:

dict[str, Any] | None –

dict[str, Any] | None: The raw legacy JSON dict, or None if not found.

Implementation Requirements

Return legacy format data as dictionary
Support caching with optional redownload
Return None if not available
The redownload parameter is analogous to force_refresh in fetch methods

See Also

get_legacy_json_string(): Get as string instead of dict

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def get_legacy_json(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    redownload: bool = False,
) -> dict[str, Any] | None:
    """Get raw legacy JSON for a specific category without pydantic validation.

    This method returns cached legacy format JSON data, downloading if needed.
    The cache is populated during initialization, downloads, and on-demand loads.

    Args:
        category: Category to retrieve.
        redownload: If True, redownload before returning and refresh cache.

    Returns:
        dict[str, Any] | None: The raw legacy JSON dict, or None if not found.

    Implementation Requirements:
        - Return legacy format data as dictionary
        - Support caching with optional redownload
        - Return `None` if not available
        - The `redownload` parameter is analogous to `force_refresh` in fetch methods

    See Also:
        - [get_legacy_json_string()][(c).get_legacy_json_string]: Get as string instead of dict

    """

get_legacy_json_string `abstractmethod`

get_legacy_json_string(
    category: MODEL_REFERENCE_CATEGORY,
    redownload: bool = False,
) -> str | None

Get raw legacy JSON string for a specific category without pydantic validation.

This method returns cached legacy format JSON data as a string, downloading if needed. The cache is populated during initialization, downloads, and on-demand loads.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

Category to retrieve.
redownload (bool, default: False ) –

If True, redownload before returning and refresh cache.

Returns:

str | None –

str | None: The raw legacy JSON string, or None if not found.

Implementation Requirements

Return legacy format data as JSON string
Same caching semantics as get_legacy_json()
Return None if not available

See Also

get_legacy_json(): Get as dict instead of string

Source code in src/horde_model_reference/backends/base.py

@abstractmethod
def get_legacy_json_string(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    redownload: bool = False,
) -> str | None:
    """Get raw legacy JSON string for a specific category without pydantic validation.

    This method returns cached legacy format JSON data as a string, downloading if needed.
    The cache is populated during initialization, downloads, and on-demand loads.

    Args:
        category: Category to retrieve.
        redownload: If True, redownload before returning and refresh cache.

    Returns:
        str | None: The raw legacy JSON string, or None if not found.

    Implementation Requirements:
        - Return legacy format data as JSON string
        - Same caching semantics as [get_legacy_json()][(c).get_legacy_json]
        - Return `None` if not available

    See Also:
        - [get_legacy_json()][(c).get_legacy_json]: Get as dict instead of string

    """

support_any_writes

support_any_writes() -> bool

Check if this backend supports any write operations (v2 or legacy).

Returns:

bool ( bool ) –

True if any write operations are supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def support_any_writes(self) -> bool:
    """Check if this backend supports any write operations (v2 or legacy).

    Returns:
        bool: True if any write operations are supported, False otherwise.

    """
    return self.supports_writes() or self.supports_legacy_writes()

supports_writes

supports_writes() -> bool

Check if this backend supports write operations (v2 format).

Write operations include update_model() and delete_model(). Typically only PRIMARY mode backends support writes.

Returns:

bool ( bool ) –

True if write operations are supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_writes(self) -> bool:
    """Check if this backend supports write operations (v2 format).

    Write operations include update_model() and delete_model().
    Typically only PRIMARY mode backends support writes.

    Returns:
        bool: True if write operations are supported, False otherwise.

    """
    return False

supports_legacy_writes

supports_legacy_writes() -> bool

Check if this backend supports write operations in legacy format.

Legacy write operations include update_model_legacy() and delete_model_legacy(). Only available when canonical_format='LEGACY' in PRIMARY mode.

Returns:

bool ( bool ) –

True if legacy write operations are supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_legacy_writes(self) -> bool:
    """Check if this backend supports write operations in legacy format.

    Legacy write operations include update_model_legacy() and delete_model_legacy().
    Only available when canonical_format='LEGACY' in PRIMARY mode.

    Returns:
        bool: True if legacy write operations are supported, False otherwise.

    """
    return False

supports_cache_warming

supports_cache_warming() -> bool

Check if this backend supports cache warming operations.

Cache warming pre-populates the cache with data to improve initial request performance. Typically only backends with distributed caching (like Redis) support this.

Returns:

bool ( bool ) –

True if cache warming is supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_cache_warming(self) -> bool:
    """Check if this backend supports cache warming operations.

    Cache warming pre-populates the cache with data to improve initial request performance.
    Typically only backends with distributed caching (like Redis) support this.

    Returns:
        bool: True if cache warming is supported, False otherwise.

    """
    return False

supports_health_checks

supports_health_checks() -> bool

Check if this backend supports health check operations.

Health checks verify that the backend's external dependencies (Redis, databases, etc.) are accessible and functioning correctly.

Returns:

bool ( bool ) –

True if health checks are supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_health_checks(self) -> bool:
    """Check if this backend supports health check operations.

    Health checks verify that the backend's external dependencies (Redis, databases, etc.)
    are accessible and functioning correctly.

    Returns:
        bool: True if health checks are supported, False otherwise.

    """
    return False

supports_statistics

supports_statistics() -> bool

Check if this backend supports statistics retrieval.

Statistics provide insights into backend performance, cache hits/misses, etc.

Returns:

bool ( bool ) –

True if statistics are supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_statistics(self) -> bool:
    """Check if this backend supports statistics retrieval.

    Statistics provide insights into backend performance, cache hits/misses, etc.

    Returns:
        bool: True if statistics are supported, False otherwise.

    """
    return False

update_model

update_model(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_dict: dict[str, Any],
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Update or create a model reference.

This is an optional method that write-capable backends can implement. Read-only backends should leave the default implementation which raises NotImplementedError.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to update.
model_name (str) –

The name of the model to update or create.
record_dict (dict[str, Any]) –

The model record data as a dictionary.

Parameters:

logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support write operations.

Implementation Requirements

Create model if it doesn't exist
Update model if it exists
Ensure atomic writes (use temp files with rename for file-based backends)
Call mark_stale() after successful write to invalidate cache
Override supports_writes() to return True

See Also

update_model_from_base_model(): Update from pydantic model (automatically provided)
delete_model(): Delete a model
supports_writes(): Feature detection method

Source code in src/horde_model_reference/backends/base.py

def update_model(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_dict: dict[str, Any],
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Update or create a model reference.

    This is an optional method that write-capable backends can implement.
    Read-only backends should leave the default implementation which raises NotImplementedError.

    Args:
        category: The category to update.
        model_name: The name of the model to update or create.
        record_dict: The model record data as a dictionary.

    Args:
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support write operations.

    Implementation Requirements:
        - Create model if it doesn't exist
        - Update model if it exists
        - Ensure atomic writes (use temp files with rename for file-based backends)
        - Call [mark_stale()][(c).mark_stale] after successful write to invalidate cache
        - Override [supports_writes()][(c).supports_writes] to return `True`

    See Also:
        - [update_model_from_base_model()][(c).update_model_from_base_model]:
          Update from pydantic model (automatically provided)
        - [delete_model()][(c).delete_model]: Delete a model
        - [supports_writes()][(c).supports_writes]: Feature detection method

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

update_model_from_base_model

update_model_from_base_model(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_model: BaseModel,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Update or create a model reference from a pydantic BaseModel.

This is an optional method that write-capable backends can implement. Read-only backends should leave the default implementation which raises NotImplementedError.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to update.
model_name (str) –

The name of the model to update or create.
record_model (BaseModel) –

The model record data as a pydantic BaseModel.
logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support write operations.

Implementation Note

The base class provides this implementation automatically. It: 1. Checks supports_writes() returns True 2. Converts the pydantic model to dict using model_dump(exclude_unset=True) 3. Calls update_model() with the dictionary

Backends that support writes typically don't need to override this method.

See Also

update_model(): Update from dictionary (implement this)
supports_writes(): Feature detection method

Source code in src/horde_model_reference/backends/base.py

def update_model_from_base_model(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_model: BaseModel,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Update or create a model reference from a pydantic BaseModel.

    This is an optional method that write-capable backends can implement.
    Read-only backends should leave the default implementation which raises NotImplementedError.

    Args:
        category: The category to update.
        model_name: The name of the model to update or create.
        record_model: The model record data as a pydantic BaseModel.
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support write operations.

    Implementation Note:
        The base class provides this implementation automatically. It:
        1. Checks [supports_writes()][(c).supports_writes] returns `True`
        2. Converts the pydantic model to dict using `model_dump(exclude_unset=True)`
        3. Calls [update_model()][(c).update_model] with the dictionary

        Backends that support writes typically don't need to override this method.

    See Also:
        - [update_model()][(c).update_model]: Update from dictionary (implement this)
        - [supports_writes()][(c).supports_writes]: Feature detection method

    """
    if not self.supports_writes():
        raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

    record_dict = record_model.model_dump(exclude_unset=True)
    self.update_model(
        category,
        model_name,
        record_dict,
        logical_user_id=logical_user_id,
        request_id=request_id,
    )

delete_model

delete_model(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Delete a model reference.

This is an optional method that write-capable backends can implement. Read-only backends should leave the default implementation which raises NotImplementedError.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category containing the model.
model_name (str) –

The name of the model to delete.

Parameters:

logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support write operations.
KeyError –

If the model doesn't exist.

Implementation Requirements

Raise KeyError if model doesn't exist
Ensure atomic writes
Call mark_stale() after successful delete to invalidate cache
Override supports_writes() to return True

See Also

update_model(): Update or create a model
supports_writes(): Feature detection method

Source code in src/horde_model_reference/backends/base.py

def delete_model(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Delete a model reference.

    This is an optional method that write-capable backends can implement.
    Read-only backends should leave the default implementation which raises NotImplementedError.

    Args:
        category: The category containing the model.
        model_name: The name of the model to delete.

    Args:
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support write operations.
        KeyError: If the model doesn't exist.

    Implementation Requirements:
        - Raise `KeyError` if model doesn't exist
        - Ensure atomic writes
        - Call [mark_stale()][(c).mark_stale] after successful delete to invalidate cache
        - Override [supports_writes()][(c).supports_writes] to return `True`

    See Also:
        - [update_model()][(c).update_model]: Update or create a model
        - [supports_writes()][(c).supports_writes]: Feature detection method

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support write operations")

update_model_legacy

update_model_legacy(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_dict: dict[str, Any],
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Update or create a model reference in legacy format.

This is an optional method that legacy-write-capable backends can implement. Only available when canonical_format='LEGACY' in PRIMARY mode.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to update.
model_name (str) –

The name of the model to update or create.
record_dict (dict[str, Any]) –

The model record data in legacy format as a dictionary.
logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support legacy write operations.

Source code in src/horde_model_reference/backends/base.py

def update_model_legacy(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_dict: dict[str, Any],
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Update or create a model reference in legacy format.

    This is an optional method that legacy-write-capable backends can implement.
    Only available when canonical_format='LEGACY' in PRIMARY mode.

    Args:
        category: The category to update.
        model_name: The name of the model to update or create.
        record_dict: The model record data in legacy format as a dictionary.
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support legacy write operations.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

update_model_legacy_from_base_model

update_model_legacy_from_base_model(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_model: BaseModel,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Update or create a model reference in legacy format from a pydantic BaseModel.

This is an optional method that legacy-write-capable backends can implement. Only available when canonical_format='LEGACY' in PRIMARY mode.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to update.
model_name (str) –

The name of the model to update or create.
record_model (BaseModel) –

The model record data as a pydantic BaseModel.
logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support legacy write operations.

Source code in src/horde_model_reference/backends/base.py

def update_model_legacy_from_base_model(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    record_model: BaseModel,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Update or create a model reference in legacy format from a pydantic BaseModel.

    This is an optional method that legacy-write-capable backends can implement.
    Only available when canonical_format='LEGACY' in PRIMARY mode.

    Args:
        category: The category to update.
        model_name: The name of the model to update or create.
        record_model: The model record data as a pydantic BaseModel.
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support legacy write operations.

    """
    if not self.supports_legacy_writes():
        raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

    record_dict = record_model.model_dump(exclude_unset=True)
    self.update_model_legacy(
        category,
        model_name,
        record_dict,
        logical_user_id=logical_user_id,
        request_id=request_id,
    )

delete_model_legacy

delete_model_legacy(
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None

Delete a model reference from legacy format files.

This is an optional method that legacy-write-capable backends can implement. Only available when canonical_format='LEGACY' in PRIMARY mode.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category containing the model.
model_name (str) –

The name of the model to delete.
logical_user_id (str | None, default: None ) –

Immutable Horde user id for auditing contexts (optional).
request_id (str | None, default: None ) –

Optional tracing/idempotency identifier for audit correlation.

Raises:

NotImplementedError –

If the backend does not support legacy write operations.

Source code in src/horde_model_reference/backends/base.py

def delete_model_legacy(
    self,
    category: MODEL_REFERENCE_CATEGORY,
    model_name: str,
    *,
    logical_user_id: str | None = None,
    request_id: str | None = None,
) -> None:
    """Delete a model reference from legacy format files.

    This is an optional method that legacy-write-capable backends can implement.
    Only available when canonical_format='LEGACY' in PRIMARY mode.

    Args:
        category: The category containing the model.
        model_name: The name of the model to delete.
        logical_user_id: Immutable Horde user id for auditing contexts (optional).
        request_id: Optional tracing/idempotency identifier for audit correlation.

    Raises:
        NotImplementedError: If the backend does not support legacy write operations.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support legacy write operations")

warm_cache

warm_cache() -> None

Pre-populate cache with all categories for faster initial requests.

This is an optional method that backends with cache warming support can implement. Backends without cache warming should leave the default implementation.

Raises:

NotImplementedError –

If the backend does not support cache warming.

Source code in src/horde_model_reference/backends/base.py

def warm_cache(self) -> None:
    """Pre-populate cache with all categories for faster initial requests.

    This is an optional method that backends with cache warming support can implement.
    Backends without cache warming should leave the default implementation.

    Raises:
        NotImplementedError: If the backend does not support cache warming.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support cache warming")

warm_cache_async `async`

warm_cache_async() -> None

Asynchronously pre-populate cache with all categories for faster initial requests.

This is an optional method that backends with cache warming support can implement. Backends without cache warming should leave the default implementation.

Raises:

NotImplementedError –

If the backend does not support async cache warming.

Source code in src/horde_model_reference/backends/base.py

async def warm_cache_async(self) -> None:
    """Asynchronously pre-populate cache with all categories for faster initial requests.

    This is an optional method that backends with cache warming support can implement.
    Backends without cache warming should leave the default implementation.

    Raises:
        NotImplementedError: If the backend does not support async cache warming.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support async cache warming")

health_check

health_check() -> bool

Check the health of the backend's external dependencies.

This is an optional method that backends with health check support can implement. Backends without external dependencies should leave the default implementation.

Returns:

bool ( bool ) –

True if healthy, False otherwise.

Raises:

NotImplementedError –

If the backend does not support health checks.

Source code in src/horde_model_reference/backends/base.py

def health_check(self) -> bool:
    """Check the health of the backend's external dependencies.

    This is an optional method that backends with health check support can implement.
    Backends without external dependencies should leave the default implementation.

    Returns:
        bool: True if healthy, False otherwise.

    Raises:
        NotImplementedError: If the backend does not support health checks.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support health checks")

get_statistics

get_statistics() -> dict[str, Any]

Get backend performance and usage statistics.

This is an optional method that backends with statistics support can implement. The structure of returned statistics is backend-specific.

Returns:

dict[str, Any] –

dict[str, Any]: Backend-specific statistics.

Raises:

NotImplementedError –

If the backend does not support statistics.

Source code in src/horde_model_reference/backends/base.py

def get_statistics(self) -> dict[str, Any]:
    """Get backend performance and usage statistics.

    This is an optional method that backends with statistics support can implement.
    The structure of returned statistics is backend-specific.

    Returns:
        dict[str, Any]: Backend-specific statistics.

    Raises:
        NotImplementedError: If the backend does not support statistics.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support statistics")

get_replicate_mode

get_replicate_mode() -> ReplicateMode

Get the replication mode of this backend.

Returns:

ReplicateMode ( ReplicateMode ) –

The replicate mode (PRIMARY or REPLICA).

Source code in src/horde_model_reference/backends/base.py

def get_replicate_mode(self) -> ReplicateMode:
    """Get the replication mode of this backend.

    Returns:
        ReplicateMode: The replicate mode (PRIMARY or REPLICA).

    """
    return self._replicate_mode

supports_metadata

supports_metadata() -> bool

Check if this backend supports metadata tracking.

Metadata tracking records operation counts, timestamps, and health metrics for both legacy (v1) and v2 format operations. Typically only PRIMARY mode backends support metadata tracking.

Returns:

bool ( bool ) –

True if metadata tracking is supported, False otherwise.

Source code in src/horde_model_reference/backends/base.py

def supports_metadata(self) -> bool:
    """Check if this backend supports metadata tracking.

    Metadata tracking records operation counts, timestamps, and health metrics
    for both legacy (v1) and v2 format operations. Typically only PRIMARY mode
    backends support metadata tracking.

    Returns:
        bool: True if metadata tracking is supported, False otherwise.

    """
    return False

get_legacy_metadata

get_legacy_metadata(
    category: MODEL_REFERENCE_CATEGORY,
) -> CategoryMetadata

Get legacy format metadata for a specific category.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to get metadata for.

Returns:

CategoryMetadata ( CategoryMetadata ) –

The legacy metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

def get_legacy_metadata(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
    """Get legacy format metadata for a specific category.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Args:
        category: The category to get metadata for.

    Returns:
        CategoryMetadata: The legacy metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

get_legacy_metadata_async `async`

get_legacy_metadata_async(
    category: MODEL_REFERENCE_CATEGORY,
) -> CategoryMetadata

Asynchronously get legacy format metadata for a specific category.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to get metadata for.

Returns:

CategoryMetadata ( CategoryMetadata ) –

The legacy metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

async def get_legacy_metadata_async(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
    """Asynchronously get legacy format metadata for a specific category.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Args:
        category: The category to get metadata for.

    Returns:
        CategoryMetadata: The legacy metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

get_metadata

get_metadata(
    category: MODEL_REFERENCE_CATEGORY,
) -> CategoryMetadata

Get v2 format metadata for a specific category.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to get metadata for.

Returns:

CategoryMetadata ( CategoryMetadata ) –

The v2 metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

def get_metadata(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
    """Get v2 format metadata for a specific category.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Args:
        category: The category to get metadata for.

    Returns:
        CategoryMetadata: The v2 metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

get_metadata_async `async`

get_metadata_async(
    category: MODEL_REFERENCE_CATEGORY,
) -> CategoryMetadata

Asynchronously get v2 format metadata for a specific category.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Parameters:

category (MODEL_REFERENCE_CATEGORY) –

The category to get metadata for.

Returns:

CategoryMetadata ( CategoryMetadata ) –

The v2 metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

async def get_metadata_async(self, category: MODEL_REFERENCE_CATEGORY) -> CategoryMetadata:
    """Asynchronously get v2 format metadata for a specific category.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Args:
        category: The category to get metadata for.

    Returns:
        CategoryMetadata: The v2 metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

get_all_legacy_metadata

get_all_legacy_metadata() -> dict[
    MODEL_REFERENCE_CATEGORY, CategoryMetadata
]

Get legacy format metadata for all categories.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Returns:

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata] –

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

def get_all_legacy_metadata(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
    """Get legacy format metadata for all categories.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

get_all_legacy_metadata_async `async`

get_all_legacy_metadata_async() -> dict[
    MODEL_REFERENCE_CATEGORY, CategoryMetadata
]

Asynchronously get legacy format metadata for all categories.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Returns:

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata] –

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

async def get_all_legacy_metadata_async(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
    """Asynchronously get legacy format metadata for all categories.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their legacy metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

get_all_metadata

get_all_metadata() -> dict[
    MODEL_REFERENCE_CATEGORY, CategoryMetadata
]

Get v2 format metadata for all categories.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Returns:

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata] –

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

def get_all_metadata(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
    """Get v2 format metadata for all categories.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support metadata tracking")

get_all_metadata_async `async`

get_all_metadata_async() -> dict[
    MODEL_REFERENCE_CATEGORY, CategoryMetadata
]

Asynchronously get v2 format metadata for all categories.

This is an optional method that metadata-capable backends can implement. Backends without metadata support should leave the default implementation.

Returns:

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata] –

dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

Raises:

NotImplementedError –

If the backend does not support metadata.

Source code in src/horde_model_reference/backends/base.py

async def get_all_metadata_async(self) -> dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]:
    """Asynchronously get v2 format metadata for all categories.

    This is an optional method that metadata-capable backends can implement.
    Backends without metadata support should leave the default implementation.

    Returns:
        dict[MODEL_REFERENCE_CATEGORY, CategoryMetadata]: Mapping of categories to their v2 metadata.

    Raises:
        NotImplementedError: If the backend does not support metadata.

    """
    raise NotImplementedError(f"{self.__class__.__name__} does not support async metadata tracking")

base

ModelReferenceBackend

_replicate_mode class-attribute instance-attribute

_invalidation_callbacks instance-attribute

replicate_mode property

__init__

fetch_category abstractmethod

fetch_all_categories abstractmethod

fetch_category_async abstractmethod async

fetch_all_categories_async abstractmethod async

needs_refresh abstractmethod

register_invalidation_callback

_notify_invalidation

_mark_stale_impl abstractmethod

mark_stale

get_category_file_path abstractmethod

get_all_category_file_paths abstractmethod

get_legacy_json abstractmethod

get_legacy_json_string abstractmethod

support_any_writes

supports_writes

supports_legacy_writes

supports_cache_warming

supports_health_checks

supports_statistics

update_model

update_model_from_base_model

delete_model

update_model_legacy

update_model_legacy_from_base_model

delete_model_legacy

warm_cache

warm_cache_async async

health_check

get_statistics

get_replicate_mode

supports_metadata

get_legacy_metadata

get_legacy_metadata_async async

get_metadata

get_metadata_async async

get_all_legacy_metadata

get_all_legacy_metadata_async async

get_all_metadata

get_all_metadata_async async

_replicate_mode `class-attribute` `instance-attribute`

_invalidation_callbacks `instance-attribute`

replicate_mode `property`

init

fetch_category `abstractmethod`

fetch_all_categories `abstractmethod`

fetch_category_async `abstractmethod` `async`

fetch_all_categories_async `abstractmethod` `async`

needs_refresh `abstractmethod`

_mark_stale_impl `abstractmethod`

get_category_file_path `abstractmethod`

get_all_category_file_paths `abstractmethod`

get_legacy_json `abstractmethod`

get_legacy_json_string `abstractmethod`

warm_cache_async `async`

get_legacy_metadata_async `async`

get_metadata_async `async`

get_all_legacy_metadata_async `async`

get_all_metadata_async `async`