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refactor: port reqparse to Pydantic model (#28913)

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Asuka Minato
2025-11-30 16:09:42 +09:00
committed by GitHub
parent bb096f4ae3
commit 247069c7e9
19 changed files with 1013 additions and 1369 deletions
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219
api/controllers/console/app/generator.py
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@@ -1,6 +1,8 @@
from collections.abc import Sequence
from typing import Any
from flask_restx import Resource, fields, reqparse
from flask_restx import Resource
from pydantic import BaseModel, Field
from controllers.console import console_ns
from controllers.console.app.error import (
@@ -21,21 +23,54 @@ from libs.login import current_account_with_tenant, login_required
from models import App
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")
ideal_output: str = Field(default="", description="Expected ideal output")
class InstructionTemplatePayload(BaseModel):
type: str = Field(..., description="Instruction template type")
def reg(cls: type[BaseModel]):
console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
reg(RuleGeneratePayload)
reg(RuleCodeGeneratePayload)
reg(RuleStructuredOutputPayload)
reg(InstructionGeneratePayload)
reg(InstructionTemplatePayload)
@console_ns.route("/rule-generate")
class RuleGenerateApi(Resource):
@console_ns.doc("generate_rule_config")
@console_ns.doc(description="Generate rule configuration using LLM")
@console_ns.expect(
console_ns.model(
"RuleGenerateRequest",
{
"instruction": fields.String(required=True, description="Rule generation instruction"),
"model_config": fields.Raw(required=True, description="Model configuration"),
"no_variable": fields.Boolean(required=True, default=False, description="Whether to exclude variables"),
},
)
)
@console_ns.expect(console_ns.models[RuleGeneratePayload.__name__])
@console_ns.response(200, "Rule configuration generated successfully")
@console_ns.response(400, "Invalid request parameters")
@console_ns.response(402, "Provider quota exceeded")
@@ -43,21 +78,15 @@ class RuleGenerateApi(Resource):
@login_required
@account_initialization_required
def post(self):
parser = (
reqparse.RequestParser()
.add_argument("instruction", type=str, required=True, nullable=False, location="json")
.add_argument("model_config", type=dict, required=True, nullable=False, location="json")
.add_argument("no_variable", type=bool, required=True, default=False, location="json")
)
args = parser.parse_args()
args = RuleGeneratePayload.model_validate(console_ns.payload)
_, current_tenant_id = current_account_with_tenant()
try:
rules = LLMGenerator.generate_rule_config(
tenant_id=current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
no_variable=args["no_variable"],
instruction=args.instruction,
model_config=args.model_config_data,
no_variable=args.no_variable,
)
except ProviderTokenNotInitError as ex:
raise ProviderNotInitializeError(ex.description)
@@ -75,19 +104,7 @@ class RuleGenerateApi(Resource):
class RuleCodeGenerateApi(Resource):
@console_ns.doc("generate_rule_code")
@console_ns.doc(description="Generate code rules using LLM")
@console_ns.expect(
console_ns.model(
"RuleCodeGenerateRequest",
{
"instruction": fields.String(required=True, description="Code generation instruction"),
"model_config": fields.Raw(required=True, description="Model configuration"),
"no_variable": fields.Boolean(required=True, default=False, description="Whether to exclude variables"),
"code_language": fields.String(
default="javascript", description="Programming language for code generation"
),
},
)
)
@console_ns.expect(console_ns.models[RuleCodeGeneratePayload.__name__])
@console_ns.response(200, "Code rules generated successfully")
@console_ns.response(400, "Invalid request parameters")
@console_ns.response(402, "Provider quota exceeded")
@@ -95,22 +112,15 @@ class RuleCodeGenerateApi(Resource):
@login_required
@account_initialization_required
def post(self):
parser = (
reqparse.RequestParser()
.add_argument("instruction", type=str, required=True, nullable=False, location="json")
.add_argument("model_config", type=dict, required=True, nullable=False, location="json")
.add_argument("no_variable", type=bool, required=True, default=False, location="json")
.add_argument("code_language", type=str, required=False, default="javascript", location="json")
)
args = parser.parse_args()
args = RuleCodeGeneratePayload.model_validate(console_ns.payload)
_, current_tenant_id = current_account_with_tenant()
try:
code_result = LLMGenerator.generate_code(
tenant_id=current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
code_language=args["code_language"],
instruction=args.instruction,
model_config=args.model_config_data,
code_language=args.code_language,
)
except ProviderTokenNotInitError as ex:
raise ProviderNotInitializeError(ex.description)
@@ -128,15 +138,7 @@ class RuleCodeGenerateApi(Resource):
class RuleStructuredOutputGenerateApi(Resource):
@console_ns.doc("generate_structured_output")
@console_ns.doc(description="Generate structured output rules using LLM")
@console_ns.expect(
console_ns.model(
"StructuredOutputGenerateRequest",
{
"instruction": fields.String(required=True, description="Structured output generation instruction"),
"model_config": fields.Raw(required=True, description="Model configuration"),
},
)
)
@console_ns.expect(console_ns.models[RuleStructuredOutputPayload.__name__])
@console_ns.response(200, "Structured output generated successfully")
@console_ns.response(400, "Invalid request parameters")
@console_ns.response(402, "Provider quota exceeded")
@@ -144,19 +146,14 @@ class RuleStructuredOutputGenerateApi(Resource):
@login_required
@account_initialization_required
def post(self):
parser = (
reqparse.RequestParser()
.add_argument("instruction", type=str, required=True, nullable=False, location="json")
.add_argument("model_config", type=dict, required=True, nullable=False, location="json")
)
args = parser.parse_args()
args = RuleStructuredOutputPayload.model_validate(console_ns.payload)
_, current_tenant_id = current_account_with_tenant()
try:
structured_output = LLMGenerator.generate_structured_output(
tenant_id=current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
instruction=args.instruction,
model_config=args.model_config_data,
)
except ProviderTokenNotInitError as ex:
raise ProviderNotInitializeError(ex.description)
@@ -174,20 +171,7 @@ class RuleStructuredOutputGenerateApi(Resource):
class InstructionGenerateApi(Resource):
@console_ns.doc("generate_instruction")
@console_ns.doc(description="Generate instruction for workflow nodes or general use")
@console_ns.expect(
console_ns.model(
"InstructionGenerateRequest",
{
"flow_id": fields.String(required=True, description="Workflow/Flow ID"),
"node_id": fields.String(description="Node ID for workflow context"),
"current": fields.String(description="Current instruction text"),
"language": fields.String(default="javascript", description="Programming language (javascript/python)"),
"instruction": fields.String(required=True, description="Instruction for generation"),
"model_config": fields.Raw(required=True, description="Model configuration"),
"ideal_output": fields.String(description="Expected ideal output"),
},
)
)
@console_ns.expect(console_ns.models[InstructionGeneratePayload.__name__])
@console_ns.response(200, "Instruction generated successfully")
@console_ns.response(400, "Invalid request parameters or flow/workflow not found")
@console_ns.response(402, "Provider quota exceeded")
@@ -195,79 +179,69 @@ class InstructionGenerateApi(Resource):
@login_required
@account_initialization_required
def post(self):
parser = (
reqparse.RequestParser()
.add_argument("flow_id", type=str, required=True, default="", location="json")
.add_argument("node_id", type=str, required=False, default="", location="json")
.add_argument("current", type=str, required=False, default="", location="json")
.add_argument("language", type=str, required=False, default="javascript", location="json")
.add_argument("instruction", type=str, required=True, nullable=False, location="json")
.add_argument("model_config", type=dict, required=True, nullable=False, location="json")
.add_argument("ideal_output", type=str, required=False, default="", location="json")
)
args = parser.parse_args()
args = InstructionGeneratePayload.model_validate(console_ns.payload)
_, current_tenant_id = current_account_with_tenant()
providers: list[type[CodeNodeProvider]] = [Python3CodeProvider, JavascriptCodeProvider]
code_provider: type[CodeNodeProvider] | None = next(
(p for p in providers if p.is_accept_language(args["language"])), None
(p for p in providers if p.is_accept_language(args.language)), None
)
code_template = code_provider.get_default_code() if code_provider else ""
try:
# Generate from nothing for a workflow node
if (args["current"] == code_template or args["current"] == "") and args["node_id"] != "":
app = db.session.query(App).where(App.id == args["flow_id"]).first()
if (args.current in (code_template, "")) and args.node_id != "":
app = db.session.query(App).where(App.id == args.flow_id).first()
if not app:
return {"error": f"app {args['flow_id']} not found"}, 400
return {"error": f"app {args.flow_id} not found"}, 400
workflow = WorkflowService().get_draft_workflow(app_model=app)
if not workflow:
return {"error": f"workflow {args['flow_id']} not found"}, 400
return {"error": f"workflow {args.flow_id} not found"}, 400
nodes: Sequence = workflow.graph_dict["nodes"]
node = [node for node in nodes if node["id"] == args["node_id"]]
node = [node for node in nodes if node["id"] == args.node_id]
if len(node) == 0:
return {"error": f"node {args['node_id']} not found"}, 400
return {"error": f"node {args.node_id} not found"}, 400
node_type = node[0]["data"]["type"]
match node_type:
case "llm":
return LLMGenerator.generate_rule_config(
current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
instruction=args.instruction,
model_config=args.model_config_data,
no_variable=True,
)
case "agent":
return LLMGenerator.generate_rule_config(
current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
instruction=args.instruction,
model_config=args.model_config_data,
no_variable=True,
)
case "code":
return LLMGenerator.generate_code(
tenant_id=current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
code_language=args["language"],
instruction=args.instruction,
model_config=args.model_config_data,
code_language=args.language,
)
case _:
return {"error": f"invalid node type: {node_type}"}
if args["node_id"] == "" and args["current"] != "": # For legacy app without a workflow
if args.node_id == "" and args.current != "": # For legacy app without a workflow
return LLMGenerator.instruction_modify_legacy(
tenant_id=current_tenant_id,
flow_id=args["flow_id"],
current=args["current"],
instruction=args["instruction"],
model_config=args["model_config"],
ideal_output=args["ideal_output"],
flow_id=args.flow_id,
current=args.current,
instruction=args.instruction,
model_config=args.model_config_data,
ideal_output=args.ideal_output,
)
if args["node_id"] != "" and args["current"] != "": # For workflow node
if args.node_id != "" and args.current != "": # For workflow node
return LLMGenerator.instruction_modify_workflow(
tenant_id=current_tenant_id,
flow_id=args["flow_id"],
node_id=args["node_id"],
current=args["current"],
instruction=args["instruction"],
model_config=args["model_config"],
ideal_output=args["ideal_output"],
flow_id=args.flow_id,
node_id=args.node_id,
current=args.current,
instruction=args.instruction,
model_config=args.model_config_data,
ideal_output=args.ideal_output,
workflow_service=WorkflowService(),
)
return {"error": "incompatible parameters"}, 400
@@ -285,24 +259,15 @@ class InstructionGenerateApi(Resource):
class InstructionGenerationTemplateApi(Resource):
@console_ns.doc("get_instruction_template")
@console_ns.doc(description="Get instruction generation template")
@console_ns.expect(
console_ns.model(
"InstructionTemplateRequest",
{
"instruction": fields.String(required=True, description="Template instruction"),
"ideal_output": fields.String(description="Expected ideal output"),
},
)
)
@console_ns.expect(console_ns.models[InstructionTemplatePayload.__name__])
@console_ns.response(200, "Template retrieved successfully")
@console_ns.response(400, "Invalid request parameters")
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser().add_argument("type", type=str, required=True, default=False, location="json")
args = parser.parse_args()
match args["type"]:
args = InstructionTemplatePayload.model_validate(console_ns.payload)
match args.type:
case "prompt":
from core.llm_generator.prompts import INSTRUCTION_GENERATE_TEMPLATE_PROMPT
@@ -312,4 +277,4 @@ class InstructionGenerationTemplateApi(Resource):
return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
case _:
raise ValueError(f"Invalid type: {args['type']}")
raise ValueError(f"Invalid type: {args.type}")