Release v0.2.23

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "superagent-py"
-version = "v0.2.22"
+version = "v0.2.23"
 description = ""
 readme = "README.md"
 authors = []

src/superagent/__init__.py

@@ -28,6 +28,7 @@
     DatasourceList,
     DatasourceStatus,
     DatasourceType,
+    EmbeddingsModelProvider,
     FunctionDefinition,
     HttpValidationError,
     LlmList,
@@ -96,6 +97,7 @@
     "DatasourceList",
     "DatasourceStatus",
     "DatasourceType",
+    "EmbeddingsModelProvider",
     "FunctionDefinition",
     "HttpValidationError",
     "LlmList",

src/superagent/core/client_wrapper.py

@@ -16,7 +16,7 @@ def get_headers(self) -> typing.Dict[str, str]:
         headers: typing.Dict[str, str] = {
             "X-Fern-Language": "Python",
             "X-Fern-SDK-Name": "superagent-py",
-            "X-Fern-SDK-Version": "v0.2.22",
+            "X-Fern-SDK-Version": "v0.2.23",
         }
         token = self._get_token()
         if token is not None:

src/superagent/types/__init__.py

@@ -27,6 +27,7 @@
 from .datasource_list import DatasourceList
 from .datasource_status import DatasourceStatus
 from .datasource_type import DatasourceType
+from .embeddings_model_provider import EmbeddingsModelProvider
 from .function_definition import FunctionDefinition
 from .http_validation_error import HttpValidationError
 from .llm_list import LlmList
@@ -93,6 +94,7 @@
     "DatasourceList",
     "DatasourceStatus",
     "DatasourceType",
+    "EmbeddingsModelProvider",
     "FunctionDefinition",
     "HttpValidationError",
     "LlmList",

src/superagent/types/app_models_request_datasource.py

@@ -4,6 +4,7 @@
 import typing
 
 from ..core.datetime_utils import serialize_datetime
+from .embeddings_model_provider import EmbeddingsModelProvider
 
 try:
     import pydantic.v1 as pydantic  # type: ignore
@@ -19,6 +20,9 @@ class AppModelsRequestDatasource(pydantic.BaseModel):
     url: typing.Optional[str] = None
     metadata: typing.Optional[typing.Dict[str, typing.Any]] = None
     vector_db_id: typing.Optional[str] = pydantic.Field(alias="vectorDbId", default=None)
+    embeddings_model_provider: typing.Optional[EmbeddingsModelProvider] = pydantic.Field(
+        alias="embeddingsModelProvider", default=None
+    )
 
     def json(self, **kwargs: typing.Any) -> str:
         kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}

src/superagent/types/embeddings_model_provider.py

@@ -0,0 +1,21 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class EmbeddingsModelProvider(str, enum.Enum):
+    """
+    An enumeration.
+    """
+
+    OPENAI = "OPENAI"
+    AZURE_OPENAI = "AZURE_OPENAI"
+
+    def visit(self, openai: typing.Callable[[], T_Result], azure_openai: typing.Callable[[], T_Result]) -> T_Result:
+        if self is EmbeddingsModelProvider.OPENAI:
+            return openai()
+        if self is EmbeddingsModelProvider.AZURE_OPENAI:
+            return azure_openai()

src/superagent/types/tool_type.py

@@ -31,6 +31,8 @@ class ToolType(str, enum.Enum):
     HTTP = "HTTP"
     SUPERRAG = "SUPERRAG"
     RESEARCH = "RESEARCH"
+    GITHUB = "GITHUB"
+    SCRAPER = "SCRAPER"
 
     def visit(
         self,
@@ -54,6 +56,8 @@ def visit(
         http: typing.Callable[[], T_Result],
         superrag: typing.Callable[[], T_Result],
         research: typing.Callable[[], T_Result],
+        github: typing.Callable[[], T_Result],
+        scraper: typing.Callable[[], T_Result],
     ) -> T_Result:
         if self is ToolType.ALGOLIA:
             return algolia()
@@ -95,3 +99,7 @@ def visit(
             return superrag()
         if self is ToolType.RESEARCH:
             return research()
+        if self is ToolType.GITHUB:
+            return github()
+        if self is ToolType.SCRAPER:
+            return scraper()