diff --git a/.fern/metadata.json b/.fern/metadata.json index 6a70e76..b864a4c 100644 --- a/.fern/metadata.json +++ b/.fern/metadata.json @@ -1,7 +1,7 @@ { - "cliVersion": "5.28.2", + "cliVersion": "5.45.3", "generatorName": "fernapi/fern-python-sdk", - "generatorVersion": "5.12.12", + "generatorVersion": "5.14.12", "generatorConfig": { "client": { "class_name": "BaseNewscatcherApi", @@ -10,10 +10,10 @@ "exported_filename": "client.py" } }, - "originGitCommit": "f44434d1b1da5712ce6098d90232ec46cf645a0d", + "originGitCommit": "56ea60da11c1b8d90274a137f5fbc3bb89b9fac4", "originGitCommitIsDirty": true, "invokedBy": "ci", "requestedVersion": "AUTO", "ciProvider": "unknown", - "sdkVersion": "3.0.0" + "sdkVersion": "3.0.1" } \ No newline at end of file diff --git a/.fern/replay.lock b/.fern/replay.lock new file mode 100644 index 0000000..df6bd04 --- /dev/null +++ b/.fern/replay.lock @@ -0,0 +1,10 @@ +# DO NOT EDIT MANUALLY - Managed by Fern Replay +version: "1.0" +generations: + - commit_sha: 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b/pyproject.toml @@ -4,7 +4,7 @@ dynamic = ["version"] [tool.poetry] name = "newscatcher-sdk" -version = "3.0.0" +version = "3.0.1" description = "" readme = "README.md" authors = [] @@ -38,9 +38,9 @@ Repository = 'https://github.com/Newscatcher/newscatcher-python' [tool.poetry.dependencies] python = "^3.10" -aiohttp = { version = ">=3.13.4,<4", optional = true, python = ">=3.9"} +aiohttp = { version = ">=3.14.0,<4", optional = true, python = ">=3.10"} httpx = ">=0.21.2" -httpx-aiohttp = { version = "0.1.8", optional = true, python = ">=3.9"} +httpx-aiohttp = { version = "0.1.8", optional = true, python = ">=3.10"} pydantic = ">= 1.9.2" pydantic-core = ">=2.18.2,<3.0.0" typing_extensions = ">= 4.0.0" diff --git a/src/newscatcher/core/client_wrapper.py b/src/newscatcher/core/client_wrapper.py index 8080e17..5bd124c 100644 --- a/src/newscatcher/core/client_wrapper.py +++ b/src/newscatcher/core/client_wrapper.py @@ -29,12 +29,12 @@ def get_headers(self) -> typing.Dict[str, str]: import platform headers: typing.Dict[str, str] = { - "User-Agent": "newscatcher-sdk/3.0.0", + "User-Agent": "newscatcher-sdk/3.0.1", "X-Fern-Language": "Python", "X-Fern-Runtime": f"python/{platform.python_version()}", "X-Fern-Platform": f"{platform.system().lower()}/{platform.release()}", "X-Fern-SDK-Name": "newscatcher-sdk", - "X-Fern-SDK-Version": "3.0.0", + "X-Fern-SDK-Version": "3.0.1", **(self.get_custom_headers() or {}), } headers["x-api-token"] = self.api_key diff --git a/src/newscatcher/core/pydantic_utilities.py b/src/newscatcher/core/pydantic_utilities.py index fea3a08..6587f5e 100644 --- a/src/newscatcher/core/pydantic_utilities.py +++ b/src/newscatcher/core/pydantic_utilities.py @@ -135,111 +135,21 @@ def _decimal_encoder(dec_value: Any) -> Any: Model = TypeVar("Model", bound=pydantic.BaseModel) -def _get_discriminator_and_variants(type_: Type[Any]) -> Tuple[Optional[str], Optional[List[Type[Any]]]]: - """ - Extract the discriminator field name and union variants from a discriminated union type. - Supports Annotated[Union[...], Field(discriminator=...)] patterns. - Returns (discriminator, variants) or (None, None) if not a discriminated union. - """ - origin = typing_extensions.get_origin(type_) - - if origin is typing_extensions.Annotated: - args = typing_extensions.get_args(type_) - if len(args) >= 2: - inner_type = args[0] - # Check annotations for discriminator - discriminator = None - for annotation in args[1:]: - if hasattr(annotation, "discriminator"): - discriminator = getattr(annotation, "discriminator", None) - break - - if discriminator: - inner_origin = typing_extensions.get_origin(inner_type) - if inner_origin is Union: - variants = list(typing_extensions.get_args(inner_type)) - return discriminator, variants - return None, None - - -def _get_field_annotation(model: Type[Any], field_name: str) -> Optional[Type[Any]]: - """Get the type annotation of a field from a Pydantic model.""" - if IS_PYDANTIC_V2: - fields = getattr(model, "model_fields", {}) - field_info = fields.get(field_name) - if field_info: - return cast(Optional[Type[Any]], field_info.annotation) - else: - fields = getattr(model, "__fields__", {}) - field_info = fields.get(field_name) - if field_info: - return cast(Optional[Type[Any]], field_info.outer_type_) - return None - - -def _find_variant_by_discriminator( - variants: List[Type[Any]], - discriminator: str, - discriminator_value: Any, -) -> Optional[Type[Any]]: - """Find the union variant that matches the discriminator value.""" - for variant in variants: - if not (inspect.isclass(variant) and issubclass(variant, pydantic.BaseModel)): - continue - - disc_annotation = _get_field_annotation(variant, discriminator) - if disc_annotation and is_literal_type(disc_annotation): - literal_args = get_args(disc_annotation) - if literal_args and literal_args[0] == discriminator_value: - return variant - return None - - -def _is_string_type(type_: Type[Any]) -> bool: - """Check if a type is str or Optional[str].""" - if type_ is str: - return True - - origin = typing_extensions.get_origin(type_) - if origin is Union: - args = typing_extensions.get_args(type_) - # Optional[str] = Union[str, None] - non_none_args = [a for a in args if a is not type(None)] - if len(non_none_args) == 1 and non_none_args[0] is str: - return True - - return False - - def parse_sse_obj(sse: "ServerSentEvent", type_: Type[T]) -> T: """ Parse a ServerSentEvent into the appropriate type. - Handles two scenarios based on where the discriminator field is located: - - 1. Data-level discrimination: The discriminator (e.g., 'type') is inside the 'data' payload. - The union describes the data content, not the SSE envelope. - -> Returns: json.loads(data) parsed into the type + This function handles data-level discrimination where the discriminator + (e.g., 'type') is inside the 'data' payload. It parses the SSE data field + as JSON and deserializes it into the target type. - Example: ChatStreamResponse with discriminator='type' - Input: ServerSentEvent(event="message", data='{"type": "content-delta", ...}', id="") - Output: ContentDeltaEvent (parsed from data, SSE envelope stripped) - - 2. Event-level discrimination: The discriminator (e.g., 'event') is at the SSE event level. - The union describes the full SSE event structure. - -> Returns: SSE envelope with 'data' field JSON-parsed only if the variant expects non-string - - Example: JobStreamResponse with discriminator='event' - Input: ServerSentEvent(event="ERROR", data='{"code": "FAILED", ...}', id="123") - Output: JobStreamResponse_Error with data as ErrorData object - - But for variants where data is str (like STATUS_UPDATE): - Input: ServerSentEvent(event="STATUS_UPDATE", data='{"status": "processing"}', id="1") - Output: JobStreamResponse_StatusUpdate with data as string (not parsed) + Note: Protocol-level discrimination (where the discriminator comes from + the SSE event: field) is handled at code-generation time and does not + use this function. Args: sse: The ServerSentEvent object to parse - type_: The target discriminated union type + type_: The target type to deserialize into Returns: The parsed object of type T @@ -248,66 +158,30 @@ def parse_sse_obj(sse: "ServerSentEvent", type_: Type[T]) -> T: This function is only available in SDK contexts where http_sse module exists. """ sse_event = asdict(sse) - discriminator, variants = _get_discriminator_and_variants(type_) - - if discriminator is None or variants is None: - # Not a discriminated union - parse the data field as JSON - data_value = sse_event.get("data") - if isinstance(data_value, str) and data_value: - try: - parsed_data = json.loads(data_value) - return parse_obj_as(type_, parsed_data) - except json.JSONDecodeError as e: - _logger.warning( - "Failed to parse SSE data field as JSON: %s, data: %s", - e, - data_value[:100] if len(data_value) > 100 else data_value, - ) - return parse_obj_as(type_, sse_event) - data_value = sse_event.get("data") + if isinstance(data_value, str) and data_value: + try: + parsed_data = json.loads(data_value) + return parse_obj_as(type_, parsed_data) + except json.JSONDecodeError as e: + _logger.warning( + "Failed to parse SSE data field as JSON: %s, data: %s", + e, + data_value[:100] if len(data_value) > 100 else data_value, + ) + return parse_obj_as(type_, sse_event) - # Check if discriminator is at the top level (event-level discrimination) - if discriminator in sse_event: - # Case 2: Event-level discrimination - # Find the matching variant to check if 'data' field needs JSON parsing - disc_value = sse_event.get(discriminator) - matching_variant = _find_variant_by_discriminator(variants, discriminator, disc_value) - - if matching_variant is not None: - # Check what type the variant expects for 'data' - data_type = _get_field_annotation(matching_variant, "data") - if data_type is not None and not _is_string_type(data_type): - # Variant expects non-string data - parse JSON - if isinstance(data_value, str) and data_value: - try: - parsed_data = json.loads(data_value) - new_object = dict(sse_event) - new_object["data"] = parsed_data - return parse_obj_as(type_, new_object) - except json.JSONDecodeError as e: - _logger.warning( - "Failed to parse SSE data field as JSON for event-level discrimination: %s, data: %s", - e, - data_value[:100] if len(data_value) > 100 else data_value, - ) - # Either no matching variant, data is string type, or JSON parse failed - return parse_obj_as(type_, sse_event) - else: - # Case 1: Data-level discrimination - # The discriminator is inside the data payload - extract and parse data only - if isinstance(data_value, str) and data_value: - try: - parsed_data = json.loads(data_value) - return parse_obj_as(type_, parsed_data) - except json.JSONDecodeError as e: - _logger.warning( - "Failed to parse SSE data field as JSON for data-level discrimination: %s, data: %s", - e, - data_value[:100] if len(data_value) > 100 else data_value, - ) - return parse_obj_as(type_, sse_event) +_type_adapter_cache: Dict[int, Any] = {} + + +def _get_type_adapter(type_: Type[Any]) -> Any: + key = id(type_) + adapter = _type_adapter_cache.get(key) + if adapter is None: + adapter = pydantic.TypeAdapter(type_) # type: ignore[attr-defined] + _type_adapter_cache[key] = adapter + return adapter def parse_obj_as(type_: Type[T], object_: Any) -> T: @@ -342,8 +216,8 @@ def parse_obj_as(type_: Type[T], object_: Any) -> T: else: dealiased_object = convert_and_respect_annotation_metadata(object_=object_, annotation=type_, direction="read") if IS_PYDANTIC_V2: - adapter = pydantic.TypeAdapter(type_) # type: ignore[attr-defined] - return adapter.validate_python(dealiased_object) + adapter = _get_type_adapter(type_) + return adapter.validate_python(dealiased_object) # type: ignore[no-any-return] return pydantic.parse_obj_as(type_, dealiased_object) diff --git a/src/newscatcher/core/serialization.py b/src/newscatcher/core/serialization.py index c36e865..1d753e2 100644 --- a/src/newscatcher/core/serialization.py +++ b/src/newscatcher/core/serialization.py @@ -26,6 +26,75 @@ def __init__(self, *, alias: str) -> None: self.alias = alias +# Resolving type hints (typing.get_type_hints) is expensive because it eval/compiles +# forward-reference annotations. The result is constant for a given type, so we cache it. +# This is critical for hot paths like SSE event parsing, where the same (often large +# discriminated-union) type is converted on every single event. +_type_hints_cache: typing.Dict[typing.Any, typing.Dict[str, typing.Any]] = {} + + +def _get_cached_type_hints(expected_type: typing.Any) -> typing.Dict[str, typing.Any]: + try: + cached = _type_hints_cache.get(expected_type) + except TypeError: + # Unhashable type; resolve without caching. + return _resolve_type_hints(expected_type) + if cached is None: + cached = _resolve_type_hints(expected_type) + _type_hints_cache[expected_type] = cached + return cached + + +def _resolve_type_hints(expected_type: typing.Any) -> typing.Dict[str, typing.Any]: + try: + return typing_extensions.get_type_hints(expected_type, include_extras=True) + except NameError: + # The type contains a circular reference, so we use the __annotations__ attribute directly. + return getattr(expected_type, "__annotations__", {}) + + +# Whether convert_and_respect_annotation_metadata can possibly rewrite anything for a given +# annotation, i.e. whether any reachable model/TypedDict field carries a FieldMetadata alias. +# This is constant per type, so we cache it and use it to short-circuit the recursive walk. +_requires_conversion_cache: typing.Dict[typing.Any, bool] = {} + + +def _requires_conversion(type_: typing.Any) -> bool: + try: + cached = _requires_conversion_cache.get(type_) + except TypeError: + # Unhashable annotation; compute without caching. + return _compute_requires_conversion(type_, set()) + if cached is None: + cached = _compute_requires_conversion(type_, set()) + _requires_conversion_cache[type_] = cached + return cached + + +def _compute_requires_conversion(type_: typing.Any, seen: typing.Set[typing.Any]) -> bool: + clean_type = _remove_annotations(type_) + + try: + if clean_type in seen: + return False + seen = seen | {clean_type} + except TypeError: + # Unhashable type; skip cycle tracking (the type graph is finite in practice). + pass + + # Models / TypedDicts: a field alias here means we must dealias; otherwise recurse into fields. + if (inspect.isclass(clean_type) and issubclass(clean_type, pydantic.BaseModel)) or typing_extensions.is_typeddict( + clean_type + ): + annotations = _get_cached_type_hints(clean_type) + if _get_alias_to_field_name(annotations): + return True + return any(_compute_requires_conversion(hint, seen) for hint in annotations.values()) + + # Containers / unions: recurse into the type arguments (List/Set/Sequence/Dict/Union/etc.). + return any(_compute_requires_conversion(arg, seen) for arg in typing_extensions.get_args(clean_type)) + + def convert_and_respect_annotation_metadata( *, object_: typing.Any, @@ -57,6 +126,13 @@ def convert_and_respect_annotation_metadata( return None if inner_type is None: inner_type = annotation + # The only thing this function ever rewrites is keys that carry a FieldMetadata + # alias. If nothing in the (cached) type graph has such an alias, the conversion is + # a content-identity transform, so we can skip the entire recursive walk. This is + # the hot path for SSE streaming, where a large discriminated union would otherwise + # be traversed on every single event. + if not _requires_conversion(annotation): + return object_ clean_type = _remove_annotations(inner_type) # Pydantic models @@ -160,12 +236,7 @@ def _convert_mapping( direction: typing.Literal["read", "write"], ) -> typing.Mapping[str, object]: converted_object: typing.Dict[str, object] = {} - try: - annotations = typing_extensions.get_type_hints(expected_type, include_extras=True) - except NameError: - # The TypedDict contains a circular reference, so - # we use the __annotations__ attribute directly. - annotations = getattr(expected_type, "__annotations__", {}) + annotations = _get_cached_type_hints(expected_type) aliases_to_field_names = _get_alias_to_field_name(annotations) for key, value in object_.items(): if direction == "read" and key in aliases_to_field_names: @@ -221,12 +292,12 @@ def _remove_annotations(type_: typing.Any) -> typing.Any: def get_alias_to_field_mapping(type_: typing.Any) -> typing.Dict[str, str]: - annotations = typing_extensions.get_type_hints(type_, include_extras=True) + annotations = _get_cached_type_hints(type_) return _get_alias_to_field_name(annotations) def get_field_to_alias_mapping(type_: typing.Any) -> typing.Dict[str, str]: - annotations = typing_extensions.get_type_hints(type_, include_extras=True) + annotations = _get_cached_type_hints(type_) return _get_field_to_alias_name(annotations)