mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 06:12:14 +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
185 lines
6.2 KiB
Python
185 lines
6.2 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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"""
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Entry point script for the DeerFlow project.
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"""
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import argparse
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import asyncio
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from InquirerPy import inquirer
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from src.config.questions import BUILT_IN_QUESTIONS, BUILT_IN_QUESTIONS_ZH_CN
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from src.workflow import run_agent_workflow_async
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def ask(
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question,
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debug=False,
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max_plan_iterations=1,
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max_step_num=3,
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enable_background_investigation=True,
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enable_clarification=False,
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max_clarification_rounds=None,
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):
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"""Run the agent workflow with the given question.
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Args:
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question: The user's query or request
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debug: If True, enables debug level logging
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max_plan_iterations: Maximum number of plan iterations
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max_step_num: Maximum number of steps in a plan
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enable_background_investigation: If True, performs web search before planning to enhance context
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enable_clarification: If False (default), skip clarification; if True, enable multi-turn clarification
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max_clarification_rounds: Maximum number of clarification rounds (default: None, uses State default=3)
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"""
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asyncio.run(
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run_agent_workflow_async(
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user_input=question,
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debug=debug,
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max_plan_iterations=max_plan_iterations,
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max_step_num=max_step_num,
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enable_background_investigation=enable_background_investigation,
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enable_clarification=enable_clarification,
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max_clarification_rounds=max_clarification_rounds,
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)
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)
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def main(
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debug=False,
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max_plan_iterations=1,
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max_step_num=3,
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enable_background_investigation=True,
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enable_clarification=False,
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max_clarification_rounds=None,
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):
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"""Interactive mode with built-in questions.
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Args:
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enable_background_investigation: If True, performs web search before planning to enhance context
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debug: If True, enables debug level logging
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max_plan_iterations: Maximum number of plan iterations
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max_step_num: Maximum number of steps in a plan
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enable_clarification: If False (default), skip clarification; if True, enable multi-turn clarification
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max_clarification_rounds: Maximum number of clarification rounds (default: None, uses State default=3)
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"""
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# First select language
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language = inquirer.select(
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message="Select language / 选择语言:",
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choices=["English", "中文"],
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).execute()
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# Choose questions based on language
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questions = (
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BUILT_IN_QUESTIONS if language == "English" else BUILT_IN_QUESTIONS_ZH_CN
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)
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ask_own_option = (
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"[Ask my own question]" if language == "English" else "[自定义问题]"
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)
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# Select a question
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initial_question = inquirer.select(
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message=(
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"What do you want to know?" if language == "English" else "您想了解什么?"
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),
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choices=[ask_own_option] + questions,
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).execute()
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if initial_question == ask_own_option:
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initial_question = inquirer.text(
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message=(
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"What do you want to know?"
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if language == "English"
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else "您想了解什么?"
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),
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).execute()
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# Pass all parameters to ask function
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ask(
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question=initial_question,
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debug=debug,
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max_plan_iterations=max_plan_iterations,
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max_step_num=max_step_num,
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enable_background_investigation=enable_background_investigation,
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enable_clarification=enable_clarification,
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max_clarification_rounds=max_clarification_rounds,
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)
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if __name__ == "__main__":
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# Set up argument parser
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parser = argparse.ArgumentParser(description="Run the Deer")
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parser.add_argument("query", nargs="*", help="The query to process")
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parser.add_argument(
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"--interactive",
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action="store_true",
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help="Run in interactive mode with built-in questions",
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)
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parser.add_argument(
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"--max_plan_iterations",
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type=int,
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default=1,
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help="Maximum number of plan iterations (default: 1)",
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)
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parser.add_argument(
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"--max_step_num",
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type=int,
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default=3,
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help="Maximum number of steps in a plan (default: 3)",
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)
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parser.add_argument("--debug", action="store_true", help="Enable debug logging")
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parser.add_argument(
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"--no-background-investigation",
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action="store_false",
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dest="enable_background_investigation",
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help="Disable background investigation before planning",
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)
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parser.add_argument(
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"--enable-clarification",
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action="store_true",
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dest="enable_clarification",
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help="Enable multi-turn clarification for vague questions (default: disabled)",
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)
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parser.add_argument(
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"--max-clarification-rounds",
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type=int,
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dest="max_clarification_rounds",
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help="Maximum number of clarification rounds (default: 3)",
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)
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args = parser.parse_args()
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if args.interactive:
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# Pass command line arguments to main function
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main(
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debug=args.debug,
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max_plan_iterations=args.max_plan_iterations,
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max_step_num=args.max_step_num,
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enable_background_investigation=args.enable_background_investigation,
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enable_clarification=args.enable_clarification,
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max_clarification_rounds=args.max_clarification_rounds,
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)
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else:
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# Parse user input from command line arguments or user input
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if args.query:
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user_query = " ".join(args.query)
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else:
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# Loop until user provides non-empty input
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while True:
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user_query = input("Enter your query: ")
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if user_query is not None and user_query != "":
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break
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# Run the agent workflow with the provided parameters
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ask(
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question=user_query,
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debug=args.debug,
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max_plan_iterations=args.max_plan_iterations,
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max_step_num=args.max_step_num,
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enable_background_investigation=args.enable_background_investigation,
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enable_clarification=args.enable_clarification,
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max_clarification_rounds=args.max_clarification_rounds,
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)
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