jimmyuconn1982
003f081a7b
* 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>
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import logging
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import os
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from dataclasses import dataclass, field, fields
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from typing import Any, Optional
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from langchain_core.runnables import RunnableConfig
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from src.config.loader import get_bool_env, get_int_env, get_str_env
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from src.config.report_style import ReportStyle
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from src.rag.retriever import Resource
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logger = logging.getLogger(__name__)
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def get_recursion_limit(default: int = 25) -> int:
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"""Get the recursion limit from environment variable or use default.
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Args:
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default: Default recursion limit if environment variable is not set or invalid
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Returns:
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int: The recursion limit to use
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"""
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env_value_str = get_str_env("AGENT_RECURSION_LIMIT", str(default))
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parsed_limit = get_int_env("AGENT_RECURSION_LIMIT", default)
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if parsed_limit > 0:
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logger.info(f"Recursion limit set to: {parsed_limit}")
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return parsed_limit
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else:
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logger.warning(
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f"AGENT_RECURSION_LIMIT value '{env_value_str}' (parsed as {parsed_limit}) is not positive. "
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f"Using default value {default}."
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)
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return default
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@dataclass(kw_only=True)
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class Configuration:
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"""The configurable fields."""
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resources: list[Resource] = field(
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default_factory=list
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) # Resources to be used for the research
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max_plan_iterations: int = 1 # Maximum number of plan iterations
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max_step_num: int = 3 # Maximum number of steps in a plan
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max_search_results: int = 3 # Maximum number of search results
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mcp_settings: dict = None # MCP settings, including dynamic loaded tools
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report_style: str = ReportStyle.ACADEMIC.value # Report style
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enable_deep_thinking: bool = False # Whether to enable deep thinking
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enforce_web_search: bool = (
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False # Enforce at least one web search step in every plan
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)
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@classmethod
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def from_runnable_config(
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cls, config: Optional[RunnableConfig] = None
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) -> "Configuration":
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"""Create a Configuration instance from a RunnableConfig."""
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configurable = (
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config["configurable"] if config and "configurable" in config else {}
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)
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values: dict[str, Any] = {
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f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
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for f in fields(cls)
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if f.init
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}
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return cls(**{k: v for k, v in values.items() if v})
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