chore: cleanup ruff flake8-simplify linter rules (#8286)

Co-authored-by: -LAN- <laipz8200@outlook.com>

api/core/app/task_pipeline/based_generate_task_pipeline.py

@@ -65,7 +65,7 @@ class BasedGenerateTaskPipeline:
 
         if isinstance(e, InvokeAuthorizationError):
             err = InvokeAuthorizationError("Incorrect API key provided")
-        elif isinstance(e, InvokeError) or isinstance(e, ValueError):
+        elif isinstance(e, InvokeError | ValueError):
             err = e
         else:
             err = Exception(e.description if getattr(e, "description", None) is not None else str(e))

api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py

@@ -45,7 +45,7 @@ class BaichuanModel:
         parameters: dict[str, Any],
         tools: Optional[list[PromptMessageTool]] = None,
     ) -> dict[str, Any]:
-        if model in self._model_mapping.keys():
+        if model in self._model_mapping:
             # the LargeLanguageModel._code_block_mode_wrapper() method will remove the response_format of parameters.
             # we need to rename it to res_format to get its value
             if parameters.get("res_format") == "json_object":
@@ -94,7 +94,7 @@ class BaichuanModel:
         timeout: int,
         tools: Optional[list[PromptMessageTool]] = None,
     ) -> Union[Iterator, dict]:
-        if model in self._model_mapping.keys():
+        if model in self._model_mapping:
             api_base = "https://api.baichuan-ai.com/v1/chat/completions"
         else:
             raise BadRequestError(f"Unknown model: {model}")

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -337,9 +337,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = f"{human_prompt} {content}"
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = f"{human_prompt} {content}"
         else:
             raise ValueError(f"Got unknown type {message}")

api/core/model_runtime/model_providers/oci/llm/llm.py

@@ -442,9 +442,7 @@ class OCILargeLanguageModel(LargeLanguageModel):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = f"{human_prompt} {content}"
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = f"{human_prompt} {content}"
         else:
             raise ValueError(f"Got unknown type {message}")

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -350,9 +350,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
                         break
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = content
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = content
         else:
             raise ValueError(f"Got unknown type {message}")

api/core/model_runtime/model_providers/vertex_ai/llm/llm.py

@@ -633,9 +633,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = f"{human_prompt} {content}"
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = f"{human_prompt} {content}"
         else:
             raise ValueError(f"Got unknown type {message}")

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -272,11 +272,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         """
         text = ""
         for item in message:
-            if isinstance(item, UserPromptMessage):
-                text += item.content
-            elif isinstance(item, SystemPromptMessage):
-                text += item.content
-            elif isinstance(item, AssistantPromptMessage):
+            if isinstance(item, UserPromptMessage | SystemPromptMessage | AssistantPromptMessage):
                 text += item.content
             else:
                 raise NotImplementedError(f"PromptMessage type {type(item)} is not supported")

api/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -209,9 +209,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                 ):
                     new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
                 else:
-                    if copy_prompt_message.role == PromptMessageRole.USER:
-                        new_prompt_messages.append(copy_prompt_message)
-                    elif copy_prompt_message.role == PromptMessageRole.TOOL:
+                    if (
+                        copy_prompt_message.role == PromptMessageRole.USER
+                        or copy_prompt_message.role == PromptMessageRole.TOOL
+                    ):
                         new_prompt_messages.append(copy_prompt_message)
                     elif copy_prompt_message.role == PromptMessageRole.SYSTEM:
                         new_prompt_message = SystemPromptMessage(content=copy_prompt_message.content)
@@ -461,9 +462,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = content
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = content
         else:
             raise ValueError(f"Got unknown type {message}")

api/core/moderation/keywords/keywords.py

@@ -56,14 +56,7 @@ class KeywordsModeration(Moderation):
         )
 
     def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
-        for value in inputs.values():
-            if self._check_keywords_in_value(keywords_list, value):
-                return True
+        return any(self._check_keywords_in_value(keywords_list, value) for value in inputs.values())
 
-        return False
-
-    def _check_keywords_in_value(self, keywords_list, value):
-        for keyword in keywords_list:
-            if keyword.lower() in value.lower():
-                return True
-        return False
+    def _check_keywords_in_value(self, keywords_list, value) -> bool:
+        return any(keyword.lower() in value.lower() for keyword in keywords_list)

api/core/ops/ops_trace_manager.py

@@ -223,7 +223,7 @@ class OpsTraceManager:
         :return:
         """
         # auth check
-        if tracing_provider not in provider_config_map.keys() and tracing_provider is not None:
+        if tracing_provider not in provider_config_map and tracing_provider is not None:
             raise ValueError(f"Invalid tracing provider: {tracing_provider}")
 
         app_config: App = db.session.query(App).filter(App.id == app_id).first()

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -127,27 +127,26 @@ class RelytVector(BaseVector):
         )
 
         chunks_table_data = []
-        with self.client.connect() as conn:
-            with conn.begin():
-                for document, metadata, chunk_id, embedding in zip(texts, metadatas, ids, embeddings):
-                    chunks_table_data.append(
-                        {
-                            "id": chunk_id,
-                            "embedding": embedding,
-                            "document": document,
-                            "metadata": metadata,
-                        }
-                    )
+        with self.client.connect() as conn, conn.begin():
+            for document, metadata, chunk_id, embedding in zip(texts, metadatas, ids, embeddings):
+                chunks_table_data.append(
+                    {
+                        "id": chunk_id,
+                        "embedding": embedding,
+                        "document": document,
+                        "metadata": metadata,
+                    }
+                )
 
-                    # Execute the batch insert when the batch size is reached
-                    if len(chunks_table_data) == 500:
-                        conn.execute(insert(chunks_table).values(chunks_table_data))
-                        # Clear the chunks_table_data list for the next batch
-                        chunks_table_data.clear()
-
-                # Insert any remaining records that didn't make up a full batch
-                if chunks_table_data:
+                # Execute the batch insert when the batch size is reached
+                if len(chunks_table_data) == 500:
                     conn.execute(insert(chunks_table).values(chunks_table_data))
+                    # Clear the chunks_table_data list for the next batch
+                    chunks_table_data.clear()
+
+            # Insert any remaining records that didn't make up a full batch
+            if chunks_table_data:
+                conn.execute(insert(chunks_table).values(chunks_table_data))
 
         return ids
 
@@ -186,11 +185,10 @@ class RelytVector(BaseVector):
         )
 
         try:
-            with self.client.connect() as conn:
-                with conn.begin():
-                    delete_condition = chunks_table.c.id.in_(ids)
-                    conn.execute(chunks_table.delete().where(delete_condition))
-                    return True
+            with self.client.connect() as conn, conn.begin():
+                delete_condition = chunks_table.c.id.in_(ids)
+                conn.execute(chunks_table.delete().where(delete_condition))
+                return True
         except Exception as e:
             print("Delete operation failed:", str(e))
             return False

api/core/rag/datasource/vdb/tencent/tencent_vector.py

@@ -63,10 +63,7 @@ class TencentVector(BaseVector):
 
     def _has_collection(self) -> bool:
         collections = self._db.list_collections()
-        for collection in collections:
-            if collection.collection_name == self._collection_name:
-                return True
-        return False
+        return any(collection.collection_name == self._collection_name for collection in collections)
 
     def _create_collection(self, dimension: int) -> None:
         lock_name = "vector_indexing_lock_{}".format(self._collection_name)

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -124,20 +124,19 @@ class TiDBVector(BaseVector):
         texts = [d.page_content for d in documents]
 
         chunks_table_data = []
-        with self._engine.connect() as conn:
-            with conn.begin():
-                for id, text, meta, embedding in zip(ids, texts, metas, embeddings):
-                    chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
+        with self._engine.connect() as conn, conn.begin():
+            for id, text, meta, embedding in zip(ids, texts, metas, embeddings):
+                chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
 
-                    # Execute the batch insert when the batch size is reached
-                    if len(chunks_table_data) == 500:
-                        conn.execute(insert(table).values(chunks_table_data))
-                        # Clear the chunks_table_data list for the next batch
-                        chunks_table_data.clear()
-
-                # Insert any remaining records that didn't make up a full batch
-                if chunks_table_data:
+                # Execute the batch insert when the batch size is reached
+                if len(chunks_table_data) == 500:
                     conn.execute(insert(table).values(chunks_table_data))
+                    # Clear the chunks_table_data list for the next batch
+                    chunks_table_data.clear()
+
+            # Insert any remaining records that didn't make up a full batch
+            if chunks_table_data:
+                conn.execute(insert(table).values(chunks_table_data))
         return ids
 
     def text_exists(self, id: str) -> bool:
@@ -160,11 +159,10 @@ class TiDBVector(BaseVector):
             raise ValueError("No ids provided to delete.")
         table = self._table(self._dimension)
         try:
-            with self._engine.connect() as conn:
-                with conn.begin():
-                    delete_condition = table.c.id.in_(ids)
-                    conn.execute(table.delete().where(delete_condition))
-                    return True
+            with self._engine.connect() as conn, conn.begin():
+                delete_condition = table.c.id.in_(ids)
+                conn.execute(table.delete().where(delete_condition))
+                return True
         except Exception as e:
             print("Delete operation failed:", str(e))
             return False

api/core/rag/extractor/word_extractor.py

@@ -48,7 +48,8 @@ class WordExtractor(BaseExtractor):
                 raise ValueError(f"Check the url of your file; returned status code {r.status_code}")
 
             self.web_path = self.file_path
-            self.temp_file = tempfile.NamedTemporaryFile()
+            # TODO: use a better way to handle the file
+            self.temp_file = tempfile.NamedTemporaryFile()  # noqa: SIM115
             self.temp_file.write(r.content)
             self.file_path = self.temp_file.name
         elif not os.path.isfile(self.file_path):

api/core/rag/rerank/weight_rerank.py

@@ -120,8 +120,8 @@ class WeightRerankRunner:
             intersection = set(vec1.keys()) & set(vec2.keys())
             numerator = sum(vec1[x] * vec2[x] for x in intersection)
 
-            sum1 = sum(vec1[x] ** 2 for x in vec1.keys())
-            sum2 = sum(vec2[x] ** 2 for x in vec2.keys())
+            sum1 = sum(vec1[x] ** 2 for x in vec1)
+            sum2 = sum(vec2[x] ** 2 for x in vec2)
             denominator = math.sqrt(sum1) * math.sqrt(sum2)
 
             if not denominator:

api/core/rag/retrieval/dataset_retrieval.py

@@ -581,8 +581,8 @@ class DatasetRetrieval:
             intersection = set(vec1.keys()) & set(vec2.keys())
             numerator = sum(vec1[x] * vec2[x] for x in intersection)
 
-            sum1 = sum(vec1[x] ** 2 for x in vec1.keys())
-            sum2 = sum(vec2[x] ** 2 for x in vec2.keys())
+            sum1 = sum(vec1[x] ** 2 for x in vec1)
+            sum2 = sum(vec2[x] ** 2 for x in vec2)
             denominator = math.sqrt(sum1) * math.sqrt(sum2)
 
             if not denominator:

api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py

@@ -201,9 +201,7 @@ class ListWorksheetRecordsTool(BuiltinTool):
             elif value.startswith('[{"organizeId"'):
                 value = json.loads(value)
                 value = "、".join([item["organizeName"] for item in value])
-            elif value.startswith('[{"file_id"'):
-                value = ""
-            elif value == "[]":
+            elif value.startswith('[{"file_id"') or value == "[]":
                 value = ""
         elif hasattr(value, "accountId"):
             value = value["fullname"]

api/core/tools/provider/builtin/novitaai/tools/novitaai_modelquery.py

@@ -35,7 +35,7 @@ class NovitaAiModelQueryTool(BuiltinTool):
             models_data=[],
             headers=headers,
             params=params,
-            recursive=False if result_type == "first sd_name" or result_type == "first name sd_name pair" else True,
+            recursive=not (result_type == "first sd_name" or result_type == "first name sd_name pair"),
         )
 
         result_str = ""

api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py

@@ -39,7 +39,7 @@ class QRCodeGeneratorTool(BuiltinTool):
 
         # get error_correction
         error_correction = tool_parameters.get("error_correction", "")
-        if error_correction not in self.error_correction_levels.keys():
+        if error_correction not in self.error_correction_levels:
             return self.create_text_message("Invalid parameter error_correction")
 
         try:

api/core/tools/provider/builtin/searchapi/tools/google.py

@@ -44,36 +44,36 @@ class SearchAPI:
     @staticmethod
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
-        if "error" in res.keys():
+        if "error" in res:
             raise ValueError(f"Got error from SearchApi: {res['error']}")
 
         toret = ""
         if type == "text":
-            if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
+            if "answer_box" in res and "answer" in res["answer_box"]:
                 toret += res["answer_box"]["answer"] + "\n"
-            if "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
+            if "answer_box" in res and "snippet" in res["answer_box"]:
                 toret += res["answer_box"]["snippet"] + "\n"
-            if "knowledge_graph" in res.keys() and "description" in res["knowledge_graph"].keys():
+            if "knowledge_graph" in res and "description" in res["knowledge_graph"]:
                 toret += res["knowledge_graph"]["description"] + "\n"
-            if "organic_results" in res.keys() and "snippet" in res["organic_results"][0].keys():
+            if "organic_results" in res and "snippet" in res["organic_results"][0]:
                 for item in res["organic_results"]:
                     toret += "content: " + item["snippet"] + "\n" + "link: " + item["link"] + "\n"
             if toret == "":
                 toret = "No good search result found"
 
         elif type == "link":
-            if "answer_box" in res.keys() and "organic_result" in res["answer_box"].keys():
-                if "title" in res["answer_box"]["organic_result"].keys():
+            if "answer_box" in res and "organic_result" in res["answer_box"]:
+                if "title" in res["answer_box"]["organic_result"]:
                     toret = f"[{res['answer_box']['organic_result']['title']}]({res['answer_box']['organic_result']['link']})\n"
-            elif "organic_results" in res.keys() and "link" in res["organic_results"][0].keys():
+            elif "organic_results" in res and "link" in res["organic_results"][0]:
                 toret = ""
                 for item in res["organic_results"]:
                     toret += f"[{item['title']}]({item['link']})\n"
-            elif "related_questions" in res.keys() and "link" in res["related_questions"][0].keys():
+            elif "related_questions" in res and "link" in res["related_questions"][0]:
                 toret = ""
                 for item in res["related_questions"]:
                     toret += f"[{item['title']}]({item['link']})\n"
-            elif "related_searches" in res.keys() and "link" in res["related_searches"][0].keys():
+            elif "related_searches" in res and "link" in res["related_searches"][0]:
                 toret = ""
                 for item in res["related_searches"]:
                     toret += f"[{item['title']}]({item['link']})\n"