mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-05 15:10:20 +08:00
* fix: support local models by making thought field optional in Plan model - Make thought field optional in Plan model to fix Pydantic validation errors with local models - Add Ollama configuration example to conf.yaml.example - Update documentation to include local model support - Improve planner prompt with better JSON format requirements Fixes local model integration issues where models like qwen3:14b would fail due to missing thought field in JSON output. * feat: Add intelligent clarification feature for research queries - Add multi-turn clarification process to refine vague research questions - Implement three-dimension clarification standard (Tech/App, Focus, Scope) - Add clarification state management in coordinator node - Update coordinator prompt with detailed clarification guidelines - Add UI settings to enable/disable clarification feature (disabled by default) - Update workflow to handle clarification rounds recursively - Add comprehensive test coverage for clarification functionality - Update documentation with clarification feature usage guide Key components: - src/graph/nodes.py: Core clarification logic and state management - src/prompts/coordinator.md: Detailed clarification guidelines - src/workflow.py: Recursive clarification handling - web/: UI settings integration - tests/: Comprehensive test coverage - docs/: Updated configuration guide * fix: Improve clarification conversation continuity - Add comprehensive conversation history to clarification context - Include previous exchanges summary in system messages - Add explicit guidelines for continuing rounds in coordinator prompt - Prevent LLM from starting new topics during clarification - Ensure topic continuity across clarification rounds Fixes issue where LLM would restart clarification instead of building upon previous exchanges. * fix: Add conversation history to clarification context * fix: resolve clarification feature message to planer, prompt, test issues - Optimize coordinator.md prompt template for better clarification flow - Simplify final message sent to planner after clarification - Fix API key assertion issues in test_search.py * fix: Add configurable max_clarification_rounds and comprehensive tests - Add max_clarification_rounds parameter for external configuration - Add comprehensive test cases for clarification feature in test_app.py - Fixes issues found during interactive mode testing where: - Recursive call failed due to missing initial_state parameter - Clarification exited prematurely at max rounds - Incorrect logging of max rounds reached * Move clarification tests to test_nodes.py and add max_clarification_rounds to zh.json * fix: add max_clarification_rounds parameter passing from frontend to backend - Add max_clarification_rounds parameter in store.ts sendMessage function - Add max_clarification_rounds type definition in chat.ts - Ensure frontend settings page clarification rounds are correctly passed to backend * fix: refine clarification workflow state handling and coverage - Add clarification history reconstruction - Fix clarified topic accumulation - Add clarified_research_topic state field - Preserve clarification state in recursive calls - Add comprehensive test coverage * refactor: optimize coordinator logic and type annotations - Simplify handoff topic logic in coordinator_node - Update type annotations from Tuple to tuple - Improve code readability and maintainability --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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from typing import Any
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ASSISTANT_SPEAKER_NAMES = {
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"coordinator",
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"planner",
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"researcher",
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"coder",
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"reporter",
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"background_investigator",
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}
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def get_message_content(message: Any) -> str:
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"""Extract message content from dict or LangChain message."""
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if isinstance(message, dict):
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return message.get("content", "")
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return getattr(message, "content", "")
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def is_user_message(message: Any) -> bool:
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"""Return True if the message originated from the end user."""
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if isinstance(message, dict):
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role = (message.get("role") or "").lower()
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if role in {"user", "human"}:
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return True
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if role in {"assistant", "system"}:
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return False
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name = (message.get("name") or "").lower()
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if name and name in ASSISTANT_SPEAKER_NAMES:
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return False
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return role == "" and name not in ASSISTANT_SPEAKER_NAMES
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message_type = (getattr(message, "type", "") or "").lower()
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name = (getattr(message, "name", "") or "").lower()
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if message_type == "human":
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return not (name and name in ASSISTANT_SPEAKER_NAMES)
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role_attr = getattr(message, "role", None)
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if isinstance(role_attr, str) and role_attr.lower() in {"user", "human"}:
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return True
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additional_role = getattr(message, "additional_kwargs", {}).get("role")
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if isinstance(additional_role, str) and additional_role.lower() in {
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"user",
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"human",
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}:
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return True
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return False
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def get_latest_user_message(messages: list[Any]) -> tuple[Any, str]:
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"""Return the latest user-authored message and its content."""
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for message in reversed(messages or []):
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if is_user_message(message):
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content = get_message_content(message)
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if content:
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return message, content
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return None, ""
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def build_clarified_topic_from_history(
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clarification_history: list[str],
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) -> tuple[str, list[str]]:
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"""Construct clarified topic string from an ordered clarification history."""
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sequence = [item for item in clarification_history if item]
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if not sequence:
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return "", []
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if len(sequence) == 1:
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return sequence[0], sequence
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head, *tail = sequence
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clarified_string = f"{head} - {', '.join(tail)}"
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return clarified_string, sequence
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def reconstruct_clarification_history(
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messages: list[Any],
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fallback_history: list[str] | None = None,
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base_topic: str = "",
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) -> list[str]:
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"""Rebuild clarification history from user-authored messages, with fallback.
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Args:
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messages: Conversation messages in chronological order.
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fallback_history: Optional existing history to use if no user messages found.
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base_topic: Optional topic to use when no user messages are available.
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Returns:
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A cleaned clarification history containing unique consecutive user contents.
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"""
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sequence: list[str] = []
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for message in messages or []:
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if not is_user_message(message):
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continue
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content = get_message_content(message)
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if not content:
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continue
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if sequence and sequence[-1] == content:
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continue
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sequence.append(content)
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if sequence:
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return sequence
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fallback = [item for item in (fallback_history or []) if item]
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if fallback:
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return fallback
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base_topic = (base_topic or "").strip()
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return [base_topic] if base_topic else []
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