langgenius/dify · error · ValueError

Invalid type: {req_data.type}

Error message

Invalid type: {req_data.type}

What it means

ValueError raised by InstructionGenerationTemplateApi.post when req_data.type does not match 'prompt' or 'code' in the match/case. Only those two template types are defined. Flask maps the uncaught ValueError to a 400 response with the message including the offending value.

Source

Thrown at api/controllers/console/app/generator.py:446

    @console_ns.expect(console_ns.models[InstructionTemplatePayload.__name__])
    @console_ns.response(200, "Template retrieved successfully", console_ns.models[SimpleDataResponse.__name__])
    @console_ns.response(400, "Invalid request parameters")
    @setup_required
    @login_required
    @account_initialization_required
    @model_validate(InstructionTemplatePayload)
    def post(self, req_data: InstructionTemplatePayload):
        match req_data.type:
            case "prompt":
                from core.llm_generator.prompts import INSTRUCTION_GENERATE_TEMPLATE_PROMPT

                return {"data": INSTRUCTION_GENERATE_TEMPLATE_PROMPT}
            case "code":
                from core.llm_generator.prompts import INSTRUCTION_GENERATE_TEMPLATE_CODE

                return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
            case _:
                raise ValueError(f"Invalid type: {req_data.type}")


def _workflow_instruction_guard(args: WorkflowGeneratePayload) -> tuple[dict, int] | None:
    """Shared boundary guard for the workflow-generate endpoints.

    Returns a ``(body, 400)`` tuple when the instruction is empty / whitespace
    or either free-text field exceeds the cap, else ``None``. Pydantic only
    validates the field is a str; a whitespace-only or pasted-document input
    would otherwise waste a slow planner+builder roundtrip on a response the
    validator rejects anyway. Both the blocking and streaming endpoints call
    this so they reject identical inputs.
    """
    if not args.instruction.strip():
        return {
            "error": "Instruction is required",
            "errors": [{"code": WorkflowGenerateErrorCode.EMPTY_INSTRUCTION, "detail": "Instruction is required"}],
        }, 400
    if len(args.instruction) > _MAX_INSTRUCTION_LENGTH or len(args.ideal_output) > _MAX_INSTRUCTION_LENGTH:

View on GitHub (pinned to ef8544b173)

Solutions

1. Send type='prompt' or type='code' in the request body.
2. If you need a new template type, extend the match/case in generator.py and add the template constant in core/llm_generator/prompts.py.
3. Add a client-side enum/dropdown limited to the two valid values to prevent invalid submissions.

Example fix

// before
POST /instruction-generate/template  { "type": "text" }
// after
POST /instruction-generate/template  { "type": "prompt" }

const VALID_TEMPLATE_TYPES = new Set(['prompt', 'code']);
function isValidTemplateType(t: string): boolean {
  return VALID_TEMPLATE_TYPES.has(t);
}
// guard before send
if (!isValidTemplateType(payload.type)) throw new Error(`type must be prompt or code, got ${payload.type}`);

type InstructionTemplateType = 'prompt' | 'code';
function isInstructionTemplateType(t: string): t is InstructionTemplateType {
  return t === 'prompt' || t === 'code';
}

When it happens

Trigger: POST /console/api/apps/<app_id>/instruction-generate/template with a body whose 'type' field is anything other than 'prompt' or 'code' (e.g. 'text', 'json', empty, or a typo like 'promt').

Common situations: Client sending a wrong type string; frontend regression that stopped constraining the dropdown; API consumer guessing the field vocabulary.

Related errors

• unsupported argument value
• metadata must be one of all, only, without
• rating must be either 'like' or 'dislike'
• Invalid status
• streaming response body missing

AI-assisted analysis of langgenius/dify@ef8544b173 (2026-08-12). Data as JSON: /api/errors/85dfd7b1bdbee696. Report an issue: GitHub.