🤖 Update SDK to version v1.89.4

.sdk-version

@@ -1 +1 @@
-v1.89.2
+v1.89.4

README.md

@@ -401,6 +401,7 @@ Class | Method | HTTP request | Description
  - [MetaModel](docs/MetaModel.md)
  - [ModelName](docs/ModelName.md)
  - [ModelsResponse](docs/ModelsResponse.md)
+ - [NameConfidence](docs/NameConfidence.md)
  - [NearestNeighbor](docs/NearestNeighbor.md)
  - [NetworkOverviewDns](docs/NetworkOverviewDns.md)
  - [NetworkOverviewDnsAnswer](docs/NetworkOverviewDnsAnswer.md)

docs/AnalysisFunctionMatchingRequest.md

@@ -5,8 +5,11 @@
 
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
-**min_similarity** | **float** | Minimum similarity expected for a match, default is 0.9 | [optional] [default to 0.9]
+**min_similarity** | **float** | Minimum similarity expected for a match as a percentage, default is 90 | [optional] [default to 90.0]
 **filters** | [**FunctionMatchingFilters**](FunctionMatchingFilters.md) |  | [optional] 
+**results_per_function** | **int** | Maximum number of matches to return per function, default is 1, max is 10 | [optional] [default to 1]
+**page** | **int** | Page number for paginated results, default is 1 (first page) | [optional] [default to 1]
+**page_size** | **int** | Number of functions to return per page, default is 0 (all functions), max is 1000 | [optional] [default to 0]
 
 ## Example
 

docs/AutoUnstripRequest.md

@@ -5,9 +5,9 @@
 
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
-**min_similarity** | **float** | Minimum similarity expected for a match, default is 0.9 | [optional] [default to 0.9]
+**min_similarity** | **float** | Minimum similarity expected for a match as a percentage, default is 90 | [optional] [default to 90.0]
 **apply** | **bool** | Whether to apply the matched function names to the target binary, default is False | [optional] [default to False]
-**confidence_threshold** | **float** | Confidence threshold for applying function names, default is 0.9 | [optional] [default to 0.9]
+**confidence_threshold** | **float** | Confidence threshold for applying function names as a percentage, default is 90 | [optional] [default to 90.0]
 **min_group_size** | **int** | Minimum number of matching functions required to consider for a match, default is 10 | [optional] [default to 10]
 
 ## Example

docs/FunctionMatchingBatchResponse.md

@@ -9,6 +9,8 @@ Name | Type | Description | Notes
 **status** | **str** |  | [optional] 
 **total_time** | **int** |  | [optional] 
 **error_message** | **str** |  | [optional] 
+**current_page** | **int** |  | [optional] 
+**total_pages** | **int** |  | [optional] 
 **matches** | [**List[FunctionMatchingResultWithBestMatch]**](FunctionMatchingResultWithBestMatch.md) |  | 
 
 ## Example

docs/FunctionMatchingRequest.md

@@ -7,8 +7,11 @@ Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **model_id** | **int** | ID of the model used for function matching, used to determine the embedding model | 
 **function_ids** | **List[int]** | ID's of functions to find matches for, must be at least one function ID | 
-**min_similarity** | **float** | Minimum similarity expected for a match, default is 0.9 | [optional] [default to 0.9]
+**min_similarity** | **float** | Minimum similarity expected for a match as a percentage, default is 90 | [optional] [default to 90.0]
 **filters** | [**FunctionMatchingFilters**](FunctionMatchingFilters.md) |  | [optional] 
+**results_per_function** | **int** | Maximum number of matches to return per function, default is 1, max is 10 | [optional] [default to 1]
+**page** | **int** | Page number for paginated results, default is 1 (first page) | [optional] [default to 1]
+**page_size** | **int** | Number of functions to return per page, default is 0 (all functions), max is 1000 | [optional] [default to 0]
 
 ## Example
 

docs/FunctionMatchingResultWithBestMatch.md

@@ -6,9 +6,8 @@
 Name | Type | Description | Notes
 ------------ | ------------- | ------------- | -------------
 **function_id** | **int** |  | 
-**matched_function** | [**MatchedFunction**](MatchedFunction.md) |  | 
-**suggested_name** | **str** |  | [optional] 
-**suggested_name_confidence** | **float** |  | [optional] 
+**matched_functions** | [**List[MatchedFunction]**](MatchedFunction.md) |  | 
+**confidences** | [**List[NameConfidence]**](NameConfidence.md) |  | [optional] 
 
 ## Example
 

docs/MatchedFunction.md

@@ -15,6 +15,7 @@ Name | Type | Description | Notes
 **sha_256_hash** | **str** |  | 
 **analysis_id** | **int** |  | 
 **similarity** | **float** |  | [optional] 
+**confidence** | **float** |  | [optional] 
 
 ## Example
 

docs/NameConfidence.md

@@ -0,0 +1,30 @@
+# NameConfidence
+
+
+## Properties
+
+Name | Type | Description | Notes
+------------ | ------------- | ------------- | -------------
+**name** | **str** | The suggested function name | 
+**confidence** | **float** | Confidence score as a percentage | 
+
+## Example
+
+```python
+from revengai.models.name_confidence import NameConfidence
+
+# TODO update the JSON string below
+json = "{}"
+# create an instance of NameConfidence from a JSON string
+name_confidence_instance = NameConfidence.from_json(json)
+# print the JSON string representation of the object
+print(NameConfidence.to_json())
+
+# convert the object into a dict
+name_confidence_dict = name_confidence_instance.to_dict()
+# create an instance of NameConfidence from a dict
+name_confidence_from_dict = NameConfidence.from_dict(name_confidence_dict)
+```
+[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
+
+

revengai/__init__.py

@@ -13,7 +13,7 @@
 """  # noqa: E501
 
 
-__version__ = "v1.89.2"
+__version__ = "v1.89.4"
 
 # Define package exports
 __all__ = [
@@ -285,6 +285,7 @@
     "MetaModel",
     "ModelName",
     "ModelsResponse",
+    "NameConfidence",
     "NearestNeighbor",
     "NetworkOverviewDns",
     "NetworkOverviewDnsAnswer",
@@ -633,6 +634,7 @@
 from revengai.models.meta_model import MetaModel as MetaModel
 from revengai.models.model_name import ModelName as ModelName
 from revengai.models.models_response import ModelsResponse as ModelsResponse
+from revengai.models.name_confidence import NameConfidence as NameConfidence
 from revengai.models.nearest_neighbor import NearestNeighbor as NearestNeighbor
 from revengai.models.network_overview_dns import NetworkOverviewDns as NetworkOverviewDns
 from revengai.models.network_overview_dns_answer import NetworkOverviewDnsAnswer as NetworkOverviewDnsAnswer

revengai/api_client.py

@@ -90,7 +90,7 @@ def __init__(
             self.default_headers[header_name] = header_value
         self.cookie = cookie
         # Set default User-Agent.
-        self.user_agent = 'OpenAPI-Generator/v1.89.2/python'
+        self.user_agent = 'OpenAPI-Generator/v1.89.4/python'
         self.client_side_validation = configuration.client_side_validation
 
     def __enter__(self):

revengai/configuration.py

@@ -529,8 +529,8 @@ def to_debug_report(self) -> str:
         return "Python SDK Debug Report:\n"\
                "OS: {env}\n"\
                "Python Version: {pyversion}\n"\
-               "Version of the API: v1.89.2\n"\
-               "SDK Package Version: v1.89.2".\
+               "Version of the API: v1.89.4\n"\
+               "SDK Package Version: v1.89.4".\
                format(env=sys.platform, pyversion=sys.version)
 
     def get_host_settings(self) -> List[HostSetting]:

revengai/models/__init__.py

@@ -251,6 +251,7 @@
 from revengai.models.meta_model import MetaModel
 from revengai.models.model_name import ModelName
 from revengai.models.models_response import ModelsResponse
+from revengai.models.name_confidence import NameConfidence
 from revengai.models.nearest_neighbor import NearestNeighbor
 from revengai.models.network_overview_dns import NetworkOverviewDns
 from revengai.models.network_overview_dns_answer import NetworkOverviewDnsAnswer

revengai/models/analysis_function_matching_request.py

@@ -27,9 +27,12 @@ class AnalysisFunctionMatchingRequest(BaseModel):
     """
     AnalysisFunctionMatchingRequest
     """ # noqa: E501
-    min_similarity: Optional[Union[Annotated[float, Field(le=1.0, strict=True, ge=0.0)], Annotated[int, Field(le=1, strict=True, ge=0)]]] = Field(default=0.9, description="Minimum similarity expected for a match, default is 0.9")
+    min_similarity: Optional[Union[Annotated[float, Field(le=100.0, strict=True, ge=0.0)], Annotated[int, Field(le=100, strict=True, ge=0)]]] = Field(default=90.0, description="Minimum similarity expected for a match as a percentage, default is 90")
     filters: Optional[FunctionMatchingFilters] = None
-    __properties: ClassVar[List[str]] = ["min_similarity", "filters"]
+    results_per_function: Optional[Annotated[int, Field(le=10, strict=True, ge=1)]] = Field(default=1, description="Maximum number of matches to return per function, default is 1, max is 10")
+    page: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=1, description="Page number for paginated results, default is 1 (first page)")
+    page_size: Optional[Annotated[int, Field(le=1000, strict=True, ge=0)]] = Field(default=0, description="Number of functions to return per page, default is 0 (all functions), max is 1000")
+    __properties: ClassVar[List[str]] = ["min_similarity", "filters", "results_per_function", "page", "page_size"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -90,8 +93,11 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
             return cls.model_validate(obj)
 
         _obj = cls.model_validate({
-            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 0.9,
-            "filters": FunctionMatchingFilters.from_dict(obj["filters"]) if obj.get("filters") is not None else None
+            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 90.0,
+            "filters": FunctionMatchingFilters.from_dict(obj["filters"]) if obj.get("filters") is not None else None,
+            "results_per_function": obj.get("results_per_function") if obj.get("results_per_function") is not None else 1,
+            "page": obj.get("page") if obj.get("page") is not None else 1,
+            "page_size": obj.get("page_size") if obj.get("page_size") is not None else 0
         })
         return _obj
 

revengai/models/auto_unstrip_request.py

@@ -26,9 +26,9 @@ class AutoUnstripRequest(BaseModel):
     """
     AutoUnstripRequest
     """ # noqa: E501
-    min_similarity: Optional[Union[Annotated[float, Field(le=1.0, strict=True, ge=0.0)], Annotated[int, Field(le=1, strict=True, ge=0)]]] = Field(default=0.9, description="Minimum similarity expected for a match, default is 0.9")
+    min_similarity: Optional[Union[Annotated[float, Field(le=100.0, strict=True, ge=0.0)], Annotated[int, Field(le=100, strict=True, ge=0)]]] = Field(default=90.0, description="Minimum similarity expected for a match as a percentage, default is 90")
     apply: Optional[StrictBool] = Field(default=False, description="Whether to apply the matched function names to the target binary, default is False")
-    confidence_threshold: Optional[Union[Annotated[float, Field(le=1.0, strict=True, ge=0.0)], Annotated[int, Field(le=1, strict=True, ge=0)]]] = Field(default=0.9, description="Confidence threshold for applying function names, default is 0.9")
+    confidence_threshold: Optional[Union[Annotated[float, Field(le=100.0, strict=True, ge=0.0)], Annotated[int, Field(le=100, strict=True, ge=0)]]] = Field(default=90.0, description="Confidence threshold for applying function names as a percentage, default is 90")
     min_group_size: Optional[Annotated[int, Field(le=20, strict=True, ge=1)]] = Field(default=10, description="Minimum number of matching functions required to consider for a match, default is 10")
     __properties: ClassVar[List[str]] = ["min_similarity", "apply", "confidence_threshold", "min_group_size"]
 
@@ -83,9 +83,9 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
             return cls.model_validate(obj)
 
         _obj = cls.model_validate({
-            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 0.9,
+            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 90.0,
             "apply": obj.get("apply") if obj.get("apply") is not None else False,
-            "confidence_threshold": obj.get("confidence_threshold") if obj.get("confidence_threshold") is not None else 0.9,
+            "confidence_threshold": obj.get("confidence_threshold") if obj.get("confidence_threshold") is not None else 90.0,
             "min_group_size": obj.get("min_group_size") if obj.get("min_group_size") is not None else 10
         })
         return _obj

revengai/models/function_matching_batch_response.py

@@ -30,8 +30,10 @@ class FunctionMatchingBatchResponse(BaseModel):
     status: Optional[StrictStr] = None
     total_time: Optional[StrictInt] = None
     error_message: Optional[StrictStr] = None
+    current_page: Optional[StrictInt] = None
+    total_pages: Optional[StrictInt] = None
     matches: List[FunctionMatchingResultWithBestMatch]
-    __properties: ClassVar[List[str]] = ["progress", "status", "total_time", "error_message", "matches"]
+    __properties: ClassVar[List[str]] = ["progress", "status", "total_time", "error_message", "current_page", "total_pages", "matches"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -94,6 +96,16 @@ def to_dict(self) -> Dict[str, Any]:
         if self.error_message is None and "error_message" in self.model_fields_set:
             _dict['error_message'] = None
 
+        # set to None if current_page (nullable) is None
+        # and model_fields_set contains the field
+        if self.current_page is None and "current_page" in self.model_fields_set:
+            _dict['current_page'] = None
+
+        # set to None if total_pages (nullable) is None
+        # and model_fields_set contains the field
+        if self.total_pages is None and "total_pages" in self.model_fields_set:
+            _dict['total_pages'] = None
+
         return _dict
 
     @classmethod
@@ -110,6 +122,8 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
             "status": obj.get("status"),
             "total_time": obj.get("total_time"),
             "error_message": obj.get("error_message"),
+            "current_page": obj.get("current_page"),
+            "total_pages": obj.get("total_pages"),
             "matches": [FunctionMatchingResultWithBestMatch.from_dict(_item) for _item in obj["matches"]] if obj.get("matches") is not None else None
         })
         return _obj

revengai/models/function_matching_request.py

@@ -29,9 +29,12 @@ class FunctionMatchingRequest(BaseModel):
     """ # noqa: E501
     model_id: StrictInt = Field(description="ID of the model used for function matching, used to determine the embedding model")
     function_ids: List[StrictInt] = Field(description="ID's of functions to find matches for, must be at least one function ID")
-    min_similarity: Optional[Union[Annotated[float, Field(le=1.0, strict=True, ge=0.0)], Annotated[int, Field(le=1, strict=True, ge=0)]]] = Field(default=0.9, description="Minimum similarity expected for a match, default is 0.9")
+    min_similarity: Optional[Union[Annotated[float, Field(le=100.0, strict=True, ge=0.0)], Annotated[int, Field(le=100, strict=True, ge=0)]]] = Field(default=90.0, description="Minimum similarity expected for a match as a percentage, default is 90")
     filters: Optional[FunctionMatchingFilters] = None
-    __properties: ClassVar[List[str]] = ["model_id", "function_ids", "min_similarity", "filters"]
+    results_per_function: Optional[Annotated[int, Field(le=10, strict=True, ge=1)]] = Field(default=1, description="Maximum number of matches to return per function, default is 1, max is 10")
+    page: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=1, description="Page number for paginated results, default is 1 (first page)")
+    page_size: Optional[Annotated[int, Field(le=1000, strict=True, ge=0)]] = Field(default=0, description="Number of functions to return per page, default is 0 (all functions), max is 1000")
+    __properties: ClassVar[List[str]] = ["model_id", "function_ids", "min_similarity", "filters", "results_per_function", "page", "page_size"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -94,8 +97,11 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
         _obj = cls.model_validate({
             "model_id": obj.get("model_id"),
             "function_ids": obj.get("function_ids"),
-            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 0.9,
-            "filters": FunctionMatchingFilters.from_dict(obj["filters"]) if obj.get("filters") is not None else None
+            "min_similarity": obj.get("min_similarity") if obj.get("min_similarity") is not None else 90.0,
+            "filters": FunctionMatchingFilters.from_dict(obj["filters"]) if obj.get("filters") is not None else None,
+            "results_per_function": obj.get("results_per_function") if obj.get("results_per_function") is not None else 1,
+            "page": obj.get("page") if obj.get("page") is not None else 1,
+            "page_size": obj.get("page_size") if obj.get("page_size") is not None else 0
         })
         return _obj