refactor: rm some dict api/controllers/console/app/generator.py api/core/llm_generator/llm_generator.py (#31709)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

api/controllers/console/app/generator.py

@@ -1,5 +1,4 @@
 from collections.abc import Sequence
-from typing import Any
 
 from flask_restx import Resource
 from pydantic import BaseModel, Field
@@ -12,10 +11,12 @@ from controllers.console.app.error import (
     ProviderQuotaExceededError,
 )
 from controllers.console.wraps import account_initialization_required, setup_required
+from core.app.app_config.entities import ModelConfig
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
 from core.helper.code_executor.javascript.javascript_code_provider import JavascriptCodeProvider
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
+from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
@@ -26,28 +27,13 @@ from services.workflow_service import WorkflowService
 DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
-class RuleGeneratePayload(BaseModel):
-    instruction: str = Field(..., description="Rule generation instruction")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-    no_variable: bool = Field(default=False, description="Whether to exclude variables")
-
-
-class RuleCodeGeneratePayload(RuleGeneratePayload):
-    code_language: str = Field(default="javascript", description="Programming language for code generation")
-
-
-class RuleStructuredOutputPayload(BaseModel):
-    instruction: str = Field(..., description="Structured output generation instruction")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-
-
 class InstructionGeneratePayload(BaseModel):
     flow_id: str = Field(..., description="Workflow/Flow ID")
     node_id: str = Field(default="", description="Node ID for workflow context")
     current: str = Field(default="", description="Current instruction text")
     language: str = Field(default="javascript", description="Programming language (javascript/python)")
     instruction: str = Field(..., description="Instruction for generation")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
     ideal_output: str = Field(default="", description="Expected ideal output")
 
 
@@ -64,6 +50,7 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
+reg(ModelConfig)
 
 
 @console_ns.route("/rule-generate")
@@ -82,12 +69,7 @@ class RuleGenerateApi(Resource):
         _, current_tenant_id = current_account_with_tenant()
 
         try:
-            rules = LLMGenerator.generate_rule_config(
+            rules = LLMGenerator.generate_rule_config(tenant_id=current_tenant_id, args=args)
-                tenant_id=current_tenant_id,
-                instruction=args.instruction,
-                model_config=args.model_config_data,
-                no_variable=args.no_variable,
-            )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
@@ -118,9 +100,7 @@ class RuleCodeGenerateApi(Resource):
         try:
             code_result = LLMGenerator.generate_code(
                 tenant_id=current_tenant_id,
-                instruction=args.instruction,
+                args=args,
-                model_config=args.model_config_data,
-                code_language=args.code_language,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -152,8 +132,7 @@ class RuleStructuredOutputGenerateApi(Resource):
         try:
             structured_output = LLMGenerator.generate_structured_output(
                 tenant_id=current_tenant_id,
-                instruction=args.instruction,
+                args=args,
-                model_config=args.model_config_data,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -204,23 +183,29 @@ class InstructionGenerateApi(Resource):
                     case "llm":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            instruction=args.instruction,
+                            args=RuleGeneratePayload(
-                            model_config=args.model_config_data,
+                                instruction=args.instruction,
-                            no_variable=True,
+                                model_config=args.model_config_data,
+                                no_variable=True,
+                            ),
                         )
                     case "agent":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            instruction=args.instruction,
+                            args=RuleGeneratePayload(
-                            model_config=args.model_config_data,
+                                instruction=args.instruction,
-                            no_variable=True,
+                                model_config=args.model_config_data,
+                                no_variable=True,
+                            ),
                         )
                     case "code":
                         return LLMGenerator.generate_code(
                             tenant_id=current_tenant_id,
-                            instruction=args.instruction,
+                            args=RuleCodeGeneratePayload(
-                            model_config=args.model_config_data,
+                                instruction=args.instruction,
-                            code_language=args.language,
+                                model_config=args.model_config_data,
+                                code_language=args.language,
+                            ),
                         )
                     case _:
                         return {"error": f"invalid node type: {node_type}"}

api/core/llm_generator/entities.py

@@ -0,0 +1,20 @@
+"""Shared payload models for LLM generator helpers and controllers."""
+
+from pydantic import BaseModel, Field
+
+from core.app.app_config.entities import ModelConfig
+
+
+class RuleGeneratePayload(BaseModel):
+    instruction: str = Field(..., description="Rule generation instruction")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
+    no_variable: bool = Field(default=False, description="Whether to exclude variables")
+
+
+class RuleCodeGeneratePayload(RuleGeneratePayload):
+    code_language: str = Field(default="javascript", description="Programming language for code generation")
+
+
+class RuleStructuredOutputPayload(BaseModel):
+    instruction: str = Field(..., description="Structured output generation instruction")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")

api/core/llm_generator/llm_generator.py

@@ -6,6 +6,8 @@ from typing import Protocol, cast
 
 import json_repair
 
+from core.app.app_config.entities import ModelConfig
+from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import (
@@ -151,19 +153,19 @@ class LLMGenerator:
         return questions
 
     @classmethod
-    def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool):
+    def generate_rule_config(cls, tenant_id: str, args: RuleGeneratePayload):
         output_parser = RuleConfigGeneratorOutputParser()
 
         error = ""
         error_step = ""
         rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
-        model_parameters = model_config.get("completion_params", {})
+        model_parameters = args.model_config_data.completion_params
-        if no_variable:
+        if args.no_variable:
             prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
 
             prompt_generate = prompt_template.format(
                 inputs={
-                    "TASK_DESCRIPTION": instruction,
+                    "TASK_DESCRIPTION": args.instruction,
                 },
                 remove_template_variables=False,
             )
@@ -175,8 +177,8 @@ class LLMGenerator:
             model_instance = model_manager.get_model_instance(
                 tenant_id=tenant_id,
                 model_type=ModelType.LLM,
-                provider=model_config.get("provider", ""),
+                provider=args.model_config_data.provider,
-                model=model_config.get("name", ""),
+                model=args.model_config_data.name,
             )
 
             try:
@@ -190,7 +192,7 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+                logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -209,7 +211,7 @@ class LLMGenerator:
         # format the prompt_generate_prompt
         prompt_generate_prompt = prompt_template.format(
             inputs={
-                "TASK_DESCRIPTION": instruction,
+                "TASK_DESCRIPTION": args.instruction,
             },
             remove_template_variables=False,
         )
@@ -220,8 +222,8 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
+            provider=args.model_config_data.provider,
-            model=model_config.get("name", ""),
+            model=args.model_config_data.name,
         )
 
         try:
@@ -250,7 +252,7 @@ class LLMGenerator:
             # the second step to generate the task_parameter and task_statement
             statement_generate_prompt = statement_template.format(
                 inputs={
-                    "TASK_DESCRIPTION": instruction,
+                    "TASK_DESCRIPTION": args.instruction,
                     "INPUT_TEXT": prompt_content.message.get_text_content(),
                 },
                 remove_template_variables=False,
@@ -276,7 +278,7 @@ class LLMGenerator:
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+            logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -284,16 +286,20 @@ class LLMGenerator:
         return rule_config
 
     @classmethod
-    def generate_code(cls, tenant_id: str, instruction: str, model_config: dict, code_language: str = "javascript"):
+    def generate_code(
-        if code_language == "python":
+        cls,
+        tenant_id: str,
+        args: RuleCodeGeneratePayload,
+    ):
+        if args.code_language == "python":
             prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
         else:
             prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
 
         prompt = prompt_template.format(
             inputs={
-                "INSTRUCTION": instruction,
+                "INSTRUCTION": args.instruction,
-                "CODE_LANGUAGE": code_language,
+                "CODE_LANGUAGE": args.code_language,
             },
             remove_template_variables=False,
         )
@@ -302,28 +308,28 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
+            provider=args.model_config_data.provider,
-            model=model_config.get("name", ""),
+            model=args.model_config_data.name,
         )
 
         prompt_messages = [UserPromptMessage(content=prompt)]
-        model_parameters = model_config.get("completion_params", {})
+        model_parameters = args.model_config_data.completion_params
         try:
             response: LLMResult = model_instance.invoke_llm(
                 prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
             )
 
             generated_code = response.message.get_text_content()
-            return {"code": generated_code, "language": code_language, "error": ""}
+            return {"code": generated_code, "language": args.code_language, "error": ""}
 
         except InvokeError as e:
             error = str(e)
-            return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
+            return {"code": "", "language": args.code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
             logger.exception(
-                "Failed to invoke LLM model, model: %s, language: %s", model_config.get("name"), code_language
+                "Failed to invoke LLM model, model: %s, language: %s", args.model_config_data.name, args.code_language
             )
-            return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
+            return {"code": "", "language": args.code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
     @classmethod
     def generate_qa_document(cls, tenant_id: str, query, document_language: str):
@@ -353,20 +359,20 @@ class LLMGenerator:
         return answer.strip()
 
     @classmethod
-    def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
+    def generate_structured_output(cls, tenant_id: str, args: RuleStructuredOutputPayload):
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
+            provider=args.model_config_data.provider,
-            model=model_config.get("name", ""),
+            model=args.model_config_data.name,
         )
 
         prompt_messages = [
             SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
-            UserPromptMessage(content=instruction),
+            UserPromptMessage(content=args.instruction),
         ]
-        model_parameters = model_config.get("model_parameters", {})
+        model_parameters = args.model_config_data.completion_params
 
         try:
             response: LLMResult = model_instance.invoke_llm(
@@ -390,12 +396,17 @@ class LLMGenerator:
             error = str(e)
             return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
         except Exception as e:
-            logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
+            logger.exception("Failed to invoke LLM model, model: %s", args.model_config_data.name)
             return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
 
     @staticmethod
     def instruction_modify_legacy(
-        tenant_id: str, flow_id: str, current: str, instruction: str, model_config: dict, ideal_output: str | None
+        tenant_id: str,
+        flow_id: str,
+        current: str,
+        instruction: str,
+        model_config: ModelConfig,
+        ideal_output: str | None,
     ):
         last_run: Message | None = (
             db.session.query(Message).where(Message.app_id == flow_id).order_by(Message.created_at.desc()).first()
@@ -434,7 +445,7 @@ class LLMGenerator:
         node_id: str,
         current: str,
         instruction: str,
-        model_config: dict,
+        model_config: ModelConfig,
         ideal_output: str | None,
         workflow_service: WorkflowServiceInterface,
     ):
@@ -505,7 +516,7 @@ class LLMGenerator:
     @staticmethod
     def __instruction_modify_common(
         tenant_id: str,
-        model_config: dict,
+        model_config: ModelConfig,
         last_run: dict | None,
         current: str | None,
         error_message: str | None,
@@ -526,8 +537,8 @@ class LLMGenerator:
         model_instance = ModelManager().get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
+            provider=model_config.provider,
-            model=model_config.get("name", ""),
+            model=model_config.name,
         )
         match node_type:
             case "llm" | "agent":
@@ -570,7 +581,5 @@ class LLMGenerator:
             error = str(e)
             return {"error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
-            logger.exception(
+            logger.exception("Failed to invoke LLM model, model: %s", json.dumps(model_config.name), exc_info=True)
-                "Failed to invoke LLM model, model: %s", json.dumps(model_config.get("name")), exc_info=True
-            )
             return {"error": f"An unexpected error occurred: {str(e)}"}