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* feat(frontend): add structured human input cards for ask_clarification Implement a reusable Human Input Card flow for ask_clarification while keeping the existing text fallback for older clients and IM channels. Backend: - Add structured ToolMessage.artifact.human_input payloads for clarification requests. - Preserve ToolMessage.content as the readable Markdown/text fallback. - Normalize clarification options from native lists, JSON strings, plain strings, mixed scalar values, None, and missing options. - Derive input_mode as choice_with_other when options exist, otherwise free_text. - Keep disable_clarification non-interactive behavior as a plain ToolMessage with no human_input artifact. - Cover artifact persistence and Gateway message metadata preservation in tests. Frontend: - Add human input protocol types, runtime guards, extractors, response builders, and thread-state helpers. - Add reusable HumanInputCard with option buttons, free-text input, pending, read-only, disabled, and answered states. - Render structured clarification cards from artifact.human_input, with Markdown fallback for malformed or legacy tool messages. - Preserve line breaks in structured question/context/option text. - Hide submitted clarification bridge messages from the chat UI via additional_kwargs.hide_from_ui. - Send structured human_input_response metadata through the fourth sendMessage options argument, preserving run context in the third argument. - Wire submissions for normal chats, custom agent chats, agent bootstrap chats, and sidecar chats. - Derive answered state from raw thread.messages so hidden replies still update the original card. - Clear pending state when the hidden reply arrives, dispatch is dropped, or a later async stream failure appears on thread.error. * perf(frontend): optimize HumanInputCard UI interactions - Support Enter key to submit text input (Shift+Enter for newline) - Render question and context fields as Markdown instead of plain text - Replace deprecated FormEventHandler type with structural typing * test(frontend): add unit test cover optimize HumanInputCard UI interactions * feat(frontend): disabled chatbox when has new human-input-card * fix(style): lint error fix * fix: sanitize hidden human input replies - Preserve IME composition safety for human input card Enter submits - Treat hidden human input responses as genuine user messages for sanitization - Keep hidden card replies in memory filtering while excluding malformed/internal hidden messages - Add regression coverage for card IME handling and hidden reply sanitization * fix: tighten human input response validation - Reject empty hidden human input response values - Remove invalid list ARIA role from human input card options - Add backend coverage for empty response payloads --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
24 lines
814 B
Python
24 lines
814 B
Python
from deerflow.agents.human_input import read_human_input_response
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def _text_response(value: str):
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return {
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"version": 1,
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"kind": "human_input_response",
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"source": "ask_clarification",
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"request_id": "clarification:call-abc",
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"response_kind": "text",
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"value": value,
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}
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def test_read_human_input_response_requires_non_empty_value():
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assert read_human_input_response({"human_input_response": _text_response("")}) is None
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assert read_human_input_response({"human_input_response": _text_response(" ")}) is None
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def test_read_human_input_response_preserves_non_empty_value():
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response = read_human_input_response({"human_input_response": _text_response(" staging ")})
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assert response is not None
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assert response["value"] == " staging "
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