diff --git a/.env.example b/.env.example index 2ee635ef..824c6d6f 100644 --- a/.env.example +++ b/.env.example @@ -1,14 +1,42 @@ -AZURE_API_VERSION=xxxx -AZURE_API_BASE=xxxx -AZURE_API_KEY=xxx -# LITELLM_PROXY_API_BASE=xxx -# LITELLM_PROXY_API_KEY=xxxx -# DEEPSEEK_API_KEY=xxxx +# Azure +AZURE_API_VERSION=2024-12-01-preview +AZURE_API_BASE=https://your-azure-endpoint.openai.azure.com/ +AZURE_API_KEY=your-azure-api-key +# Optional model provider keys +DEEPSEEK_API_KEY= +LITELLM_PROXY_API_BASE=http://litellm.example.com:4000 +LITELLM_PROXY_API_KEY=your-litellm-proxy-key +GPT_KEY=your-gpt-key +OPENAI_API_KEY=your-openai-api-key + +# Bohrium / Materials +BOHRIUM_USER_ID=your-bohrium-user-id +BOHRIUM_BASE_URL=https://openapi.test.dp.tech +BOHRIUM_ACCESS_KEY=your-bohrium-access-key +BOHRIUM_PROJECT_ID=your-bohrium-project-id +MATERIALS_ACCESS_KEY=your-materials-access-key +MATERIALS_PROJECT_ID=your-materials-project-id +MATERIALS_SKU_ID=your-materials-sku-id + +# OPIK +OPIK_URL_OVERRIDE=https://your-opik-url/api +OPIK_WORKSPACE=default OPIK_PROJECT_NAME=test -MATERIALS_ACCESS_KEY=xxxx -MATERIALS_PROJECT_ID=xxxx -OPENAI_API_KEY=sk-XX -GPT_CHAT_MODEL=gpt-5-chat -GPT_BASE_URL=xxx +# Default model +DEFAULT_MODEL=azure/gpt-5-chat +# DEFAULT_MODEL=deepseek/deepseek-chat +BOHRIUM_USE_SANDBOX=1 + +# RAG plan-make (used by rag/settings.yaml and rag/plan_llm.py) +PLAN_MAKE_MODEL=qwen3-vl-plus +PLAN_MAKE_API_KEY=your-plan-make-api-key +PLAN_MAKE_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1 + +# OSS +OSS_ENABLED=true +OSS_BUCKET_NAME=your-oss-bucket +OSS_ACCESS_KEY_ID=your-oss-access-key-id +OSS_ACCESS_KEY_SECRET=your-oss-access-key-secret +OSS_ENDPOINT=https://oss-cn-zhangjiakou.aliyuncs.com diff --git a/agents/matmaster_agent/core_agents/comp_agents/recommend_summary_agent/agent.py b/agents/matmaster_agent/core_agents/comp_agents/recommend_summary_agent/agent.py index 8099f740..f6841d29 100644 --- a/agents/matmaster_agent/core_agents/comp_agents/recommend_summary_agent/agent.py +++ b/agents/matmaster_agent/core_agents/comp_agents/recommend_summary_agent/agent.py @@ -54,6 +54,9 @@ ToolConnectAgent, ) from agents.matmaster_agent.flow_agents.model import PlanStepStatusEnum +from agents.matmaster_agent.flow_agents.tool_name_utils import ( + normalize_tool_name_to_canonical, +) from agents.matmaster_agent.llm_config import MatMasterLlmConfig from agents.matmaster_agent.locales import i18n from agents.matmaster_agent.logger import PrefixFilter @@ -209,7 +212,11 @@ async def _run_events(self, ctx: InvocationContext) -> AsyncGenerator[Event, Non if isinstance(tool_schema, str): tool_schema = tool_schema.replace('{', '[').replace('}', ']') - tool_args_recommend_prompt = ALL_TOOLS[current_step_tool_name].get( + canonical_tool = ( + normalize_tool_name_to_canonical(current_step_tool_name) + or current_step_tool_name + ) + tool_args_recommend_prompt = ALL_TOOLS.get(canonical_tool, {}).get( 'args_setting', '' ) @@ -338,8 +345,12 @@ async def _run_events(self, ctx: InvocationContext) -> AsyncGenerator[Event, Non yield update_state_event(ctx, state_delta={'matmaster_flow_active': None}) # TODO: needs a better way to handle customized summary prompt - if ALL_TOOLS[current_step_tool_name].get('summary_prompt') is not None: - custom_prompt = ALL_TOOLS[current_step_tool_name].get('summary_prompt') + canonical_tool = ( + normalize_tool_name_to_canonical(current_step_tool_name) + or current_step_tool_name + ) + if ALL_TOOLS.get(canonical_tool, {}).get('summary_prompt') is not None: + custom_prompt = ALL_TOOLS[canonical_tool].get('summary_prompt') self.summary_agent.instruction = ( f"{custom_prompt}\n\n{get_vocabulary_enforce_prompt()}" ) diff --git a/agents/matmaster_agent/flow_agents/agent.py b/agents/matmaster_agent/flow_agents/agent.py index 06369b6e..dc1691a5 100644 --- a/agents/matmaster_agent/flow_agents/agent.py +++ b/agents/matmaster_agent/flow_agents/agent.py @@ -1,7 +1,10 @@ import copy import json import logging -from asyncio import CancelledError +import os +import sys +from asyncio import CancelledError, to_thread +from pathlib import Path from typing import AsyncGenerator from google.adk.agents import InvocationContext, LlmAgent @@ -98,6 +101,9 @@ filter_thinking_llm_contents, ) from agents.matmaster_agent.flow_agents.thinking_agent.constant import THINKING_AGENT +from agents.matmaster_agent.flow_agents.tool_name_utils import ( + normalize_tool_name_to_canonical, +) from agents.matmaster_agent.flow_agents.utils import ( check_plan, get_tools_list, @@ -159,6 +165,95 @@ logger.addFilter(PrefixFilter(MATMASTER_AGENT_NAME)) logger.setLevel(logging.INFO) +DEFAULT_RAG_ROOT_DIR = Path( + os.getenv( + "MATMASTER_RAG_ROOT_DIR", + str(Path(__file__).resolve().parents[3] / "rag"), + ) +).resolve() +DEFAULT_RAG_SETTINGS_PATH = Path( + os.getenv( + "MATMASTER_RAG_SETTINGS_PATH", + str(DEFAULT_RAG_ROOT_DIR / "settings.yaml"), + ) +).resolve() + + +def rag_result_to_multi_plans(parsed_data: dict) -> dict: + """把 rag parsed_data 适配为 flow 所需 multi_plans 结构。""" + intro = parsed_data.get('intro', '') if isinstance(parsed_data, dict) else '' + overall = parsed_data.get('overall', '') if isinstance(parsed_data, dict) else '' + raw_plans = parsed_data.get('plans', []) if isinstance(parsed_data, dict) else [] + plans: list[dict] = [] + + for index, raw_plan in enumerate(raw_plans, start=1): + if not isinstance(raw_plan, dict): + continue + + raw_steps = raw_plan.get('steps', []) + mapped_steps = [] + total_steps = 0 + real_tool_steps = 0 + for raw_step in raw_steps: + if not isinstance(raw_step, dict): + continue + total_steps += 1 + raw_tool_name = raw_step.get('tool_name') + if raw_tool_name: + real_tool_steps += 1 + + step_description = ( + raw_step.get('step_description') + or raw_step.get('description') + or '' + ) + canonical_tool = ( + (normalize_tool_name_to_canonical(raw_tool_name) or raw_tool_name) + if raw_tool_name + else None + ) + mapped_steps.append( + { + 'tool_name': canonical_tool or 'llm_tool', + 'step_description': step_description, + 'feasibility': raw_step.get('feasibility', ''), + 'status': raw_step.get('status') or 'plan', + } + ) + + if total_steps == 0: + feasibility = 'null' + elif real_tool_steps == 0: + feasibility = 'null' + elif real_tool_steps < total_steps: + feasibility = 'part' + else: + feasibility = 'full' + + raw_plan_description = ( + raw_plan.get('plan_description') + or raw_plan.get('strategy') + or f'方案 {index}' + ) + plan_description = str(raw_plan_description) + if not plan_description.startswith(f'方案 {index}：'): + plan_description = f'方案 {index}：{plan_description}' + + plans.append( + { + 'plan_id': raw_plan.get('plan_id', f'plan_{index}'), + 'plan_description': plan_description, + 'steps': mapped_steps, + 'feasibility': feasibility, + } + ) + + return { + 'intro': intro, + 'plans': plans, + 'overall': overall, + } + class MatMasterFlowAgent(LlmAgent): @model_validator(mode='after') @@ -338,7 +433,8 @@ def _build_execution_agent_for_plan( tool_name = step.get('tool_name') if not tool_name: continue - belonging_agent = ALL_TOOLS.get(tool_name, {}).get('belonging_agent') + canonical = normalize_tool_name_to_canonical(tool_name) or tool_name + belonging_agent = ALL_TOOLS.get(canonical, {}).get('belonging_agent') if belonging_agent and belonging_agent not in agent_names: agent_names.append(belonging_agent) @@ -488,10 +584,6 @@ async def _run_scene_agent( async def _run_plan_make_agent( self, ctx: InvocationContext, - UPDATE_USER_CONTENT, - TOOLCHAIN_EXAMPLES_PROMPT, - *, - skip_thinking: bool = False, ) -> AsyncGenerator[Event, None]: # 制定计划 if check_plan(ctx) == FlowStatusEnum.FAILED: @@ -499,30 +591,12 @@ async def _run_plan_make_agent( else: plan_title = i18n.t('PlanMake') - scenes = ctx.session.state['scenes'] - available_tools = get_tools_list(ctx, scenes) - if not available_tools: - available_tools = ALL_AGENT_TOOLS_LIST - available_tools_with_info = { - item: { - 'scene': ALL_TOOLS[item]['scene'], - 'description': ALL_TOOLS[item]['description'], - } - for item in available_tools - } - available_tools_with_info_str = '\n'.join( - [ - f"{key}\n scene: {', '.join(value['scene'])}\n description: {value['description']}" - for key, value in available_tools_with_info.items() - ] - ) - query_for_memory = ctx.session.state.get('expand', {}).get( - 'update_user_content', '' - ) or ( + query_for_memory = ( ctx.user_content.parts[0].text if ctx.user_content and ctx.user_content.parts else '' ) + query_for_memory = query_for_memory.strip() short_term_memory_block = await format_short_term_memory( query_text=query_for_memory, session_id=ctx.session.id, @@ -547,105 +621,43 @@ async def _run_plan_make_agent( else: session_file_summary = 'Session has uploaded file(s): no.' - expand_state = ctx.session.state.get('expand', {}) - original_query = expand_state.get('origin_user_content') or ( - ctx.user_content.parts[0].text - if ctx.user_content and ctx.user_content.parts - else '' - ) - expanded_query = expand_state.get('update_user_content', '') + rag_root_dir = DEFAULT_RAG_ROOT_DIR + rag_settings_path = DEFAULT_RAG_SETTINGS_PATH + if str(rag_root_dir) not in sys.path: + sys.path.insert(0, str(rag_root_dir)) + + from make_plan_rag import run_make_plan_rag + + rag_result = None + rag_errors = [] + rag_memory_context_parts = [] + if short_term_memory_block: + rag_memory_context_parts.append(short_term_memory_block) + if session_file_summary: + rag_memory_context_parts.append(session_file_summary) + rag_memory_context = '\n\n'.join(rag_memory_context_parts) + for _ in range(2): + rag_result = await to_thread( + run_make_plan_rag, + mode='default', + query=query_for_memory, + root_dir=rag_root_dir, + memory_context=rag_memory_context, + settings_path=rag_settings_path, + ) + if rag_result.is_valid and isinstance(rag_result.parsed_data, dict): + break + rag_errors = rag_result.errors - # Thinking: skip for "query job status only" (e.g. 查看任务状态); run otherwise - thinking_text = '' - if not skip_thinking: - try: - for _thinking_ui_event in context_function_event( - ctx, - self.name, - MATMASTER_THINKING_UI, - None, - ModelRole, - { - 'matmaster_thinking_ui_args': json.dumps( - { - 'title': '正在思考', - 'status': 'start', - } - ) - }, - ): - yield _thinking_ui_event - - self._thinking_agent.set_thinking_params( - available_tools_with_info_str, - session_file_summary, - original_query, - expanded_query, - short_term_memory=short_term_memory_block, - ) - last_full_text = '' - async for thinking_event in self._thinking_agent.run_async(ctx): - yield thinking_event - if ( - not getattr(thinking_event, 'partial', True) - and getattr(thinking_event, 'content', None) - and getattr(thinking_event.content, 'parts', None) - ): - parts_text = ''.join( - p.text or '' - for p in thinking_event.content.parts - if getattr(p, 'text', None) - ) - if parts_text.strip(): - last_full_text = parts_text.strip() - thinking_text = (last_full_text or '').strip() - if ( - getattr(self._thinking_agent, '_last_thinking_text', None) - is not None - ): - thinking_text = self._thinking_agent._last_thinking_text - logger.info( - f'{ctx.session.id} reasoning_agent result length={len(thinking_text)}, ' - f'preview={repr(thinking_text[:300]) if thinking_text else "empty"}' - ) - for _thinking_ui_event in context_function_event( - ctx, - self.name, - MATMASTER_THINKING_UI, - None, - ModelRole, - { - 'matmaster_thinking_ui_args': json.dumps( - { - 'title': '已思考', - 'status': 'end', - } - ) - }, - ): - yield _thinking_ui_event - except Exception as e: - logger.warning( - f'{ctx.session.id} reasoning_agent failed: {e}, proceed without thinking' - ) - else: - logger.info( - f'{ctx.session.id} skip reasoning_agent (query_job_status_only)' + if rag_result is None or not rag_result.is_valid: + raise RuntimeError( + f'{ctx.session.id} rag make plan failed: {rag_errors}' ) - self.plan_make_agent.instruction = get_plan_make_instruction( - available_tools_with_info_str - + UPDATE_USER_CONTENT - + TOOLCHAIN_EXAMPLES_PROMPT, - short_term_memory=short_term_memory_block, - thinking_context=thinking_text, - session_file_summary=session_file_summary, - ) - self.plan_make_agent.output_schema = create_dynamic_multi_plans_schema( - available_tools - ) - async for plan_event in self.plan_make_agent.run_async(ctx): - yield plan_event + update_multi_plans = rag_result_to_multi_plans(rag_result.parsed_data) + if not update_multi_plans.get('plans'): + raise RuntimeError(f'{ctx.session.id} rag make plan empty plans') + yield update_state_event(ctx, state_delta={'multi_plans': update_multi_plans}) # 记忆写入：用 memory_writer_agent 从当前请求和计划提炼 insights，写入 kernel（不向用户展示） plan_info = ctx.session.state.get(MULTI_PLANS) or {} @@ -662,9 +674,9 @@ async def _run_plan_make_agent( ) parts.append(f"方案{i + 1}摘要: {desc}\n步骤: {steps_brief}") plan_intro = '\n\n'.join(parts) - is_long_context = len(UPDATE_USER_CONTENT) >= LONG_CONTEXT_THRESHOLD + is_long_context = len(query_for_memory) >= LONG_CONTEXT_THRESHOLD self.memory_writer_agent.instruction = get_memory_writer_instruction( - UPDATE_USER_CONTENT, plan_intro, is_long_context=is_long_context + query_for_memory, plan_intro, is_long_context=is_long_context ) async for _ in self.memory_writer_agent.run_async(ctx): pass @@ -819,9 +831,11 @@ async def _run_plan_execute_and_summary_agent( plan_steps = ctx.session.state['plan'].get('steps', []) tool_count = sum(1 for step in plan_steps if step.get('tool_name')) + first_tool = plan_steps[0].get('tool_name') + canonical_first = normalize_tool_name_to_canonical(first_tool) or first_tool is_async_agent = issubclass( AGENT_CLASS_MAPPING[ - ALL_TOOLS[plan_steps[0]['tool_name']]['belonging_agent'] + ALL_TOOLS[canonical_first]['belonging_agent'] ], BaseAsyncJobAgent, ) @@ -967,50 +981,17 @@ async def _run_plan_execute_and_summary_agent( async def _run_research_flow( self, ctx: InvocationContext ) -> AsyncGenerator[Event, None]: - # 先取短期记忆和会话已有文件，再扩写，避免第二步仍从头 expand（如“第一步的Fe，扩胞到20A”只做扩胞） - raw_user_text = ctx.user_content.parts[0].text if ctx.user_content.parts else '' - short_term_memory_block = await format_short_term_memory( - raw_user_text, ctx.session.id - ) - session_files = await get_session_files(ctx.session.id) - session_file_summary = '\n'.join(session_files) if session_files else '' - # 扩写用户问题（带记忆 + 会话文件，延续上一步时只 expand 新步骤） - async for _expand_event in self._run_expand_agent( - ctx, - short_term_memory_block=short_term_memory_block, - session_file_summary=session_file_summary, - ): - yield _expand_event - - # 构造 UPDATE_USER_CONTENT, SCENE_EXAMPLES_PROMPT, TOOLCHAIN_EXAMPLES_PROMPT - UPDATE_USER_CONTENT, SCENE_EXAMPLES_PROMPT, TOOLCHAIN_EXAMPLES_PROMPT = ( - await self._build_icl_prompt(ctx) - ) - - # 划分问题场景 - async for _scene_event in self._run_scene_agent( - ctx, UPDATE_USER_CONTENT, SCENE_EXAMPLES_PROMPT - ): - yield _scene_event - # 清空 Plan 和 MULTI_PLANS if check_plan(ctx) == FlowStatusEnum.COMPLETE: yield update_state_event(ctx, state_delta={PLAN: {}, MULTI_PLANS: {}}) # 制定计划（1. 无计划；2. 计划已完成；3. 计划失败；4. 用户未确认计划） - # 仅查询任务状态时跳过 thinking（查任务状态不 thinking） - skip_thinking = scenes_contain_query_job_status(ctx) if check_plan(ctx) in [ FlowStatusEnum.NO_PLAN, FlowStatusEnum.COMPLETE, FlowStatusEnum.FAILED, ] or not is_plan_confirmed(ctx): - async for _plan_make_event in self._run_plan_make_agent( - ctx, - UPDATE_USER_CONTENT, - TOOLCHAIN_EXAMPLES_PROMPT, - skip_thinking=skip_thinking, - ): + async for _plan_make_event in self._run_plan_make_agent(ctx): yield _plan_make_event # 从 MultiPlans 中选择某个计划 diff --git a/agents/matmaster_agent/flow_agents/tool_name_utils.py b/agents/matmaster_agent/flow_agents/tool_name_utils.py new file mode 100644 index 00000000..f0684971 --- /dev/null +++ b/agents/matmaster_agent/flow_agents/tool_name_utils.py @@ -0,0 +1,28 @@ +""" +工具名规范化：将 RAG/计划中的工具名（如 BUILD_BULK_STRUCTURE_BY_TEMPLATE） +规范为 ALL_TOOLS 的键（如 build_bulk_structure_by_template）。 +独立模块避免 flow_agents.utils 与 recommend_summary_agent 等循环导入。 +""" +from __future__ import annotations + +from agents.matmaster_agent.sub_agents.tools import ALL_TOOLS + + +def normalize_tool_name_to_canonical(tool_name: str) -> str | None: + """ + 将 RAG/计划中的工具名规范为 ALL_TOOLS 的键。 + 大小写不敏感，- 与 _ 视为等价匹配。 + """ + if not tool_name or not str(tool_name).strip(): + return None + name = str(tool_name).strip() + if name in ALL_TOOLS: + return name + low = name.lower() + if low in ALL_TOOLS: + return low + norm = low.replace("-", "_") + for k in ALL_TOOLS: + if k.lower().replace("-", "_") == norm: + return k + return name diff --git a/agents/matmaster_agent/flow_agents/utils.py b/agents/matmaster_agent/flow_agents/utils.py index dd97c500..5031c0b2 100644 --- a/agents/matmaster_agent/flow_agents/utils.py +++ b/agents/matmaster_agent/flow_agents/utils.py @@ -22,6 +22,10 @@ ) from agents.matmaster_agent.sub_agents.tools import ALL_TOOLS +from agents.matmaster_agent.flow_agents.tool_name_utils import ( + normalize_tool_name_to_canonical, +) + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -48,8 +52,11 @@ def get_tools_list(ctx: InvocationContext, scenes: list): def get_agent_name(tool_name, sub_agents): + canonical = normalize_tool_name_to_canonical(tool_name) + if canonical is None or canonical not in ALL_TOOLS: + raise RuntimeError(f"ToolName Error: {tool_name}") try: - target_agent_name = ALL_TOOLS[tool_name]['belonging_agent'] + target_agent_name = ALL_TOOLS[canonical]['belonging_agent'] except BaseException: raise RuntimeError(f"ToolName Error: {tool_name}") @@ -62,12 +69,16 @@ def get_agent_for_tool(tool_name, sub_agents): """ 根据 tool_name 获取对应 Agent。若当前在 sub_agents 中则直接返回， 否则动态构建（用于更换工具时，新工具所属 Agent 可能不在初始 sub_agents 中）。 + 支持 RAG/计划中的大写工具名（如 BUILD_BULK_STRUCTURE_BY_TEMPLATE）自动规范为 ALL_TOOLS 键。 """ - agent = get_agent_name(tool_name, sub_agents) + canonical = normalize_tool_name_to_canonical(tool_name) + if canonical is None or canonical not in ALL_TOOLS: + raise RuntimeError(f"ToolName Error: {tool_name}") + agent = get_agent_name(canonical, sub_agents) if agent is not None: return agent try: - target_agent_name = ALL_TOOLS[tool_name]['belonging_agent'] + target_agent_name = ALL_TOOLS[canonical]['belonging_agent'] except BaseException: raise RuntimeError(f"ToolName Error: {tool_name}") if target_agent_name not in AGENT_CLASS_MAPPING: @@ -126,8 +137,10 @@ def should_bypass_confirmation(ctx: InvocationContext) -> bool: # Check if there is exactly one tool in the plan if tool_count == 1: - # Find the first (and only) tool name - first_tool_name = plan_steps[0].get('tool_name', '') + # Find the first (and only) tool name (normalize for ALL_TOOLS lookup) + first_tool_name = normalize_tool_name_to_canonical( + plan_steps[0].get('tool_name', '') + ) or plan_steps[0].get('tool_name', '') # Check if this tool has bypass_confirmation set to True if ALL_TOOLS.get(first_tool_name, {}).get('bypass_confirmation') is True: @@ -135,8 +148,14 @@ def should_bypass_confirmation(ctx: InvocationContext) -> bool: # TODO: Add more logic here for handling multiple tools in the plan elif tool_count == 2: - first_tool_name = plan_steps[0].get('tool_name', '') - second_tool_name = plan_steps[1].get('tool_name', '') + first_tool_name = ( + normalize_tool_name_to_canonical(plan_steps[0].get('tool_name', '')) + or plan_steps[0].get('tool_name', '') + ) + second_tool_name = ( + normalize_tool_name_to_canonical(plan_steps[1].get('tool_name', '')) + or plan_steps[1].get('tool_name', '') + ) if ( first_tool_name == 'web-search' @@ -149,7 +168,8 @@ def should_bypass_confirmation(ctx: InvocationContext) -> bool: def find_alternative_tool(current_tool_name: str) -> List[str]: """Return alternative tool names for the current tool (maybe empty).""" - tool = ALL_TOOLS.get(current_tool_name) + canonical = normalize_tool_name_to_canonical(current_tool_name) + tool = ALL_TOOLS.get(canonical or current_tool_name) if not tool: return [] return tool.get('alternative', []) @@ -157,7 +177,8 @@ def find_alternative_tool(current_tool_name: str) -> List[str]: def has_self_check(current_tool_name: str) -> bool: """Return self check info for the current tool.""" - tool = ALL_TOOLS.get(current_tool_name) + canonical = normalize_tool_name_to_canonical(current_tool_name) + tool = ALL_TOOLS.get(canonical or current_tool_name) if not tool: return False return tool.get('self_check', False) diff --git a/pyproject.toml b/pyproject.toml index 16fb8e02..5d7d228c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,8 +21,17 @@ dependencies = [ "deepdiff>=8.6.1", "fastmcp>=2.13.0.2", "mcp==1.22.0", + # RAG / graphrag (rag 目录 run_make_plan 等) + "graphrag==2.7.0", + "pyyaml>=6.0", + "openai>=1.0.0", + "pydantic>=2.0", ] +# 首次或清锁后需执行: uv sync --prerelease=allow（graphrag 与 fastmcp 传递依赖冲突，需预发布版 graspologic 解析） +[tool.uv] +prerelease = "allow" + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/rag/README.md b/rag/README.md new file mode 100644 index 00000000..9e5a0714 --- /dev/null +++ b/rag/README.md @@ -0,0 +1,86 @@ +# RAG make_plan 独立运行包 + +本文件夹包含运行 **RAG make_plan** 所需的全部代码、配置与索引，可单独拷贝到任意位置，仅依赖本目录即可执行任务。 + +## 运行环境 + +### 要求 + +- **Python 3.10+** +- 以下包需已安装（见 `requirements-rag.txt`）： + - **pyyaml**：读 `settings.yaml` + - **litellm** 或 **openai**：调大模型（优先 litellm，无则退化为 openai） + - **graphrag**：本地检索、实体/边、embedding（[GraphRAG](https://github.com/microsoft/graphrag)） + - **pydantic**：DashScope 补丁用 + +### 安装示例 + +```bash +# 建议用 conda 建独立环境 +conda create -n rag python=3.12 +conda activate rag + +# 按顺序安装 +pip install pyyaml litellm openai pydantic +# graphrag 需单独安装（见官方文档，或 pip install graphrag） +pip install graphrag +``` + +或使用本目录提供的依赖列表（不含 graphrag 安装方式）： + +```bash +pip install -r requirements-rag.txt +``` + +- 无需环境变量即可运行；若使用代理可在系统或 litellm 中配置。 + +## 目录结构 + +``` +rag/ +├── README.md +├── requirements-rag.txt # 运行依赖列表 +├── settings.yaml # LLM 与 GraphRAG 配置 +├── run_make_plan.py # 主入口脚本 +├── make_plan_rag.py # 统一入口逻辑 +├── plan_llm.py # LLM 调用与 plan 校验 +├── context_builder.py # RAG 上下文构建 +├── patch_local_context_entity_cap.py +├── run_index_with_dashscope_fix.py +├── prompts/ +│ └── plan_make_rag.txt +├── output/ # GraphRAG 索引（lancedb、parquet 等） +├── run_plan_16_to_30_append_bianpai.py # 批量跑题（可选） +└── 1.json # 批量用题集（可选） +``` + +## 用法 + +在 **本目录** 下执行（`--root .` 表示以当前目录为项目根）： + +```bash +cd /path/to/rag +python run_make_plan.py --query "你的问题" --root . +``` + +指定输出文件： + +```bash +python run_make_plan.py --query "你的问题" --root . \ + --out output/plan.txt --out-context output/context.txt +``` + +批量对 1.json 第 16～30 题跑计划并追加到 bianpai.md： + +```bash +python run_plan_16_to_30_append_bianpai.py +``` + +## 配置 + +- **LLM**：在 `settings.yaml` 的 `models.default_chat_model` 中配置 `api_key`、`api_base`、`model`（如 DashScope、OpenAI 等兼容接口）。 +- **索引**：`output/` 内为已有 GraphRAG 索引；若需重建，在本目录执行 `python run_index_with_dashscope_fix.py index --root .`（需先准备 `input/` 下的文档）。 + +## 仅依赖本目录 + +所有路径均相对本目录（`--root .`）：配置读 `./settings.yaml`，prompt 读 `./prompts/`，索引读 `./output/`。将整个 `rag` 文件夹拷贝到任意位置后，在该目录下按上述命令执行即可。 diff --git a/rag/context_builder.py b/rag/context_builder.py new file mode 100644 index 00000000..cfe361d1 --- /dev/null +++ b/rag/context_builder.py @@ -0,0 +1,587 @@ +""" +GraphRAG 上下文构建：用户查询 → 实体映射 → 上下文构建，返回 context_chunks 供 MatMaster 计划构建使用。 + +可被 MatMaster evaluate 或 agent 通过 sys.path 或 RAG_ROOT 导入调用。 +""" +import json +import re +import time +from pathlib import Path +from typing import Optional + +# GraphRAG 上下文的五部分（section 标题，不区分大小写） +CONTEXT_PART_NAMES = ("reports", "entities", "relationships", "claims", "sources") + +# 预编译正则，避免热路径重复编译 +_CONTEXT_PART_SPLIT = re.compile(r"\n-----") +_JSON_FUNCTION_PATTERN = re.compile(r'"function"\s*:\s*"((?:[^"\\]|\\.)*)"') +# 固定注入实体名（边补召时始终加入），用 frozenset 便于快速差集 +_FIXED_ENTITY_NAMES = frozenset({ + "WEB-SEARCH", "web-search", + "LLM_TOOL", "llm_tool", + "EXTRACT_INFO_FROM_WEBPAGE", "extract_info_from_webpage", + "SEARCH-PAPERS-ENHANCED", "search-papers-enhanced", + "FILE_PARSE", "file_parse", +}) + + +def _section_name_from_part(part: str) -> str: + """从块的首行解析 section 名，如 \"-----Reports-----\" 或 \"Entities-----\" -> \"reports\" / \"entities\"。""" + first_line = part.split("\n")[0].strip() + name = first_line.strip("-").strip() + return name.lower() if name else "" + + +def keep_only_context_parts(context_chunks: str, include_parts: list[str] | None) -> str: + """ + 只保留指定的部分。include_parts 为部分名列表（不区分大小写），如 + ["reports", "entities", "relationships"]；为 None 或空列表时不截断。 + 可选部分名：reports, entities, relationships, claims, sources。 + """ + if not context_chunks or not context_chunks.strip(): + return context_chunks + if not include_parts: + return context_chunks + want = {p.strip().lower() for p in include_parts if p and p.strip()} + if not want: + return context_chunks + parts = _CONTEXT_PART_SPLIT.split(context_chunks) + kept = [] + for part in parts: + if not part.strip(): + continue + name = _section_name_from_part(part) + if name and name in want: + kept.append(part) + if not kept: + return context_chunks + return "\n-----".join(kept) + + +def keep_only_first_three_context_parts(context_chunks: str) -> str: + """兼容旧接口：只保留前三部分（reports, entities, relationships）。""" + return keep_only_context_parts(context_chunks, ["reports", "entities", "relationships"]) + + +def parse_entity_names_from_context(context: str) -> list[str]: + """ + 从 RAG 上下文的 Entities 块中解析实体名（第二列 entity）。 + 返回去重且保持顺序的列表。供 make_plan 校验 tool_name 等使用。 + """ + seen: set[str] = set() + names: list[str] = [] + parts = _CONTEXT_PART_SPLIT.split(context) + for part in parts: + if not part.strip(): + continue + name = _section_name_from_part(part) + if name != "entities": + continue + lines = part.strip().split("\n") + header_idx = None + for i, line in enumerate(lines): + if "|" in line and "entity" in line.lower(): + header_idx = i + break + if header_idx is None: + break + for i in range(header_idx + 1, len(lines)): + line = lines[i] + if not line.strip(): + continue + cols = line.split("|", 2) + if len(cols) >= 2: + entity_name = cols[1].strip() + if entity_name and entity_name not in seen: + seen.add(entity_name) + names.append(entity_name) + break + return names + + +def entities_only_cap( + context_chunks: str, + max_entities: int = 20, + sort_by_entity_name: bool = True, +) -> str: + """ + 只保留 Entities 部分，最多 max_entities 条实体数据行；不包含 Relationships（边仅用于上游补召实体）。 + sort_by_entity_name=True 时对实体行按实体名（第二列）排序后再截断，便于多次运行结果一致。 + """ + if not context_chunks or not context_chunks.strip(): + return context_chunks + parts = _CONTEXT_PART_SPLIT.split(context_chunks) + entities_part = None + for p in parts: + if not p.strip(): + continue + name = _section_name_from_part(p) + if name == "entities": + entities_part = p + break + if not entities_part: + return "" + lines = entities_part.strip().split("\n") + # 定位表头行（id|entity|description|...），其前为 section 标题如 "Entities-----" + header_idx = None + for i, line in enumerate(lines): + if "|" in line and "entity" in line.lower(): + header_idx = i + break + if header_idx is None: + return entities_part.strip() + section_header = "\n".join(lines[:header_idx]) if header_idx > 0 else "" + column_header = lines[header_idx] + data_lines = [ln for ln in lines[header_idx + 1 :] if ln.strip()] + if not data_lines: + return (section_header + "\n" + column_header).strip() if section_header else column_header + if sort_by_entity_name: + def _entity_key(ln: str) -> str: + cols = ln.split("|", 2) + return (cols[1].strip() if len(cols) >= 2 else "") + data_lines = sorted(data_lines, key=_entity_key) + capped = data_lines[:max_entities] + block = [section_header, column_header] if section_header else [column_header] + block.extend(capped) + return "\n".join(block) + + +def _parse_entities_block(part: str) -> tuple[str, list[str], set[str]]: + """解析 Entities 块，返回 (首行含标题, 数据行列表, 已出现的实体名集合)。""" + lines = part.strip().split("\n") + if not lines: + return "", [], set() + header = lines[0] + data_lines = [] + entity_names = set() + for line in lines[1:]: + if not line.strip(): + continue + # 格式: id|entity|description|number of relationships，description 可能不含 | + parts = line.split("|", 3) + if len(parts) >= 2: + entity_names.add(parts[1].strip()) + data_lines.append(line) + return header, data_lines, entity_names + + +def _parse_relationships_block(part: str) -> tuple[str, list[str], set[str]]: + """解析 Relationships 块，返回 (首行含标题, 数据行列表, 边上的 source/target 实体名集合)。""" + lines = part.strip().split("\n") + if not lines: + return "", [], set() + header = lines[0] + edge_names = set() + data_lines = [] + for line in lines[1:]: + if not line.strip(): + continue + parts = line.split("|", 3) + if len(parts) >= 3: + edge_names.add(parts[1].strip()) + edge_names.add(parts[2].strip()) + data_lines.append(line) + return header, data_lines, edge_names + + +def _extract_first_json_object(s: str): + """从字符串中提取第一个完整 JSON 对象（按花括号匹配），解析失败返回 None。""" + start = s.find("{") + if start < 0: + return None + depth = 0 + for i in range(start, len(s)): + if s[i] == "{": + depth += 1 + elif s[i] == "}": + depth -= 1 + if depth == 0: + try: + return json.loads(s[start : i + 1]) + except (json.JSONDecodeError, TypeError): + return None + return None + + +def _extract_function_from_json_like_string(s: str) -> str: + """ + 从类 JSON 字符串中尽量抽出 function 字段的纯文本。 + 优先用预编译正则提取（快），失败再尝试 JSON 解析，避免整段 JSON 泄露。 + """ + if not s or not s.strip(): + return "" + s = s.strip() + # 快路径：正则直接提取 "function":"..."，避免重复编译与完整 JSON 解析 + m = _JSON_FUNCTION_PATTERN.search(s) + if m: + raw = m.group(1) + if raw: + return raw.replace('\\"', '"').replace("\\n", "\n").replace("\\\\", "\\")[:500] + # 回退：完整 JSON 解析（处理嵌套等） + obj = _extract_first_json_object(s) + if isinstance(obj, dict) and "function" in obj: + fn = obj.get("function") + if isinstance(fn, str) and fn.strip(): + return fn.strip() + if fn is not None: + return str(fn).strip() + if s.startswith("{"): + return "" + return s[:300].strip() + + +def reduce_context_to_entities_function_only(context_chunks: str) -> str: + """ + 将「Entities + Relationships」上下文缩减为仅「Entities」，且每行描述只保留 JSON 中的 function 字段； + 不包含 Relationships（边）。传给 LLM 的计划上下文更精简。 + description 可能带后缀（如 )<|COMPLETE|>|0），先提取首尾匹配的 JSON 再取 function； + 解析失败时用正则抽取 function，绝不输出整段 JSON 或 applicable_tasks 等。 + """ + if not context_chunks or not context_chunks.strip(): + return context_chunks + parts = _CONTEXT_PART_SPLIT.split(context_chunks) + entities_part = None + for p in parts: + if not p.strip(): + continue + name = _section_name_from_part(p) + if name == "entities": + entities_part = p + break + if not entities_part: + return "" + lines = entities_part.strip().split("\n") + if not lines: + return "" + out_lines = ["Entities-----", "id|entity|function"] + header_idx = None + for i, line in enumerate(lines): + if "|" in line and "entity" in line.lower(): + header_idx = i + break + if header_idx is None: + return entities_part.strip() + for line in lines[header_idx + 1 :]: + if not line.strip(): + continue + cols = line.split("|", 3) + if len(cols) < 3: + continue + id_part = cols[0].strip() + entity_part = cols[1].strip() + rest = cols[2].strip() + sub = rest.rsplit("|", 1) + desc_str = sub[0].strip() if sub else "" + function_text = _extract_function_from_json_like_string(desc_str) + if not function_text: + function_text = "(无功能描述)" + out_lines.append(f"{id_part}|{entity_part}|{function_text}") + return "\n".join(out_lines) + + +def _expand_entities_by_edges_and_drop_sources( + context_chunks: str, + entities_list: list, +) -> str: + """ + 根据边补召实体：从 Relationships 中出现的 source/target 里，若某实体未出现在 Entities 中， + 则从 entities_list 中补入其 description（一行 id|entity|description|0）。 + 返回仅包含 Entities + Relationships 的上下文，不含 Sources（也不含 Reports/Claims，若原本有则去掉）。 + entities_list: graphrag Entity 列表，每项需有 .short_id, .title, .description。 + """ + if not context_chunks or not context_chunks.strip(): + return context_chunks + parts = _CONTEXT_PART_SPLIT.split(context_chunks) + entities_part = None + relationships_part = None + for p in parts: + if not p.strip(): + continue + name = _section_name_from_part(p) + if name == "entities": + entities_part = p + elif name == "relationships": + relationships_part = p + if not entities_part or not relationships_part: + return keep_only_context_parts(context_chunks, ["entities", "relationships"]) + + entity_header, entity_data_lines, entity_names = _parse_entities_block(entities_part) + rel_header, rel_data_lines, edge_names = _parse_relationships_block(relationships_part) + supplemental_names = (edge_names | _FIXED_ENTITY_NAMES) - entity_names + if not supplemental_names: + return keep_only_context_parts(context_chunks, ["entities", "relationships"]) + + by_title = {} + for e in entities_list: + t = getattr(e, "title", None) + if t: + by_title[t] = e + supplemental_rows = [] + for name in sorted(supplemental_names): + e = by_title.get(name) + if e is None: + continue + sid = getattr(e, "short_id", e.id) + desc = getattr(e, "description", "") or "" + if isinstance(desc, dict): + desc = json.dumps(desc, ensure_ascii=False) + supplemental_rows.append(f"{sid}|{name}|{desc}|0") + + new_entity_lines = [entity_header] + entity_data_lines + supplemental_rows + new_entities_block = "\n".join(new_entity_lines) + new_relationships_block = "\n".join([rel_header] + rel_data_lines) + return new_entities_block + "\n\n-----" + new_relationships_block + + +def warmup_graphrag_imports() -> None: + """ + 预执行 build_local_context_only 所需的全部延迟 import，使首次检索时 retrieve_imports≈0。 + 在调用 build_local_context_* 前调用一次即可（如 run_make_plan 开头）。 + """ + import asyncio # noqa: F401 + import run_index_with_dashscope_fix + run_index_with_dashscope_fix.apply_dashscope_patches() + from graphrag.config.load_config import load_config # noqa: F401 + from graphrag.config.embeddings import entity_description_embedding # noqa: F401 + from graphrag.config.models.graph_rag_config import GraphRagConfig # noqa: F401 + from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey # noqa: F401 + from graphrag.query.structured_search.local_search.mixed_context import LocalSearchMixedContext # noqa: F401 + from graphrag.query.indexer_adapters import ( + read_indexer_entities, + read_indexer_reports, + read_indexer_text_units, + read_indexer_relationships, + read_indexer_covariates, + ) # noqa: F401 + from graphrag.utils.api import get_embedding_store, create_storage_from_config # noqa: F401 + from graphrag.utils.storage import load_table_from_storage, storage_has_table # noqa: F401 + from graphrag.language_model.manager import ModelManager # noqa: F401 + from graphrag.tokenizer.get_tokenizer import get_tokenizer # noqa: F401 + from types import MethodType # noqa: F401 + from patch_local_context_entity_cap import ( + apply_entity_token_cap_patch, + wrap_build_local_context, + ) # noqa: F401 + return None + + +# 延迟导入，避免未装 graphrag 时 MatMaster 无法启动 +def build_local_context_only( + root_dir: Path, + query: str, + community_level: int | None = None, + include_community: bool = False, + include_parts: list[str] | None = None, + return_entities: bool = False, + top_k_entities: int | None = None, + top_k_relationships: int | None = None, + timings: dict | None = None, +): + """ + 只执行：用户查询 → 实体映射 → 上下文构建；返回 (context_chunks, context_records) 或当 return_entities=True 时为 (context_chunks, context_records, entities_list)。 + include_parts 为要保留的部分名列表；top_k_entities / top_k_relationships 若传入则覆盖 config，用于加速（取更少实体/边）。 + timings 为可选的 mutable dict，若传入则写入各子步骤用时（秒）。 + """ + t0_entry = time.perf_counter() + import asyncio + _t = time.perf_counter + # 与 run_index_with_dashscope_fix 一致：在任意 graphrag 导入前打补丁 + import run_index_with_dashscope_fix + run_index_with_dashscope_fix.apply_dashscope_patches() + + from graphrag.config.load_config import load_config + from graphrag.config.embeddings import entity_description_embedding + from graphrag.config.models.graph_rag_config import GraphRagConfig + from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey + from graphrag.query.structured_search.local_search.mixed_context import ( + LocalSearchMixedContext, + ) + from graphrag.query.indexer_adapters import ( + read_indexer_entities, + read_indexer_reports, + read_indexer_text_units, + read_indexer_relationships, + read_indexer_covariates, + ) + from graphrag.utils.api import get_embedding_store, create_storage_from_config + from graphrag.utils.storage import load_table_from_storage, storage_has_table + from graphrag.language_model.manager import ModelManager + from graphrag.tokenizer.get_tokenizer import get_tokenizer + from types import MethodType + from patch_local_context_entity_cap import ( + apply_entity_token_cap_patch, + wrap_build_local_context, + ) + if timings is not None: + timings["retrieve_imports"] = time.perf_counter() - t0_entry + + async def _load_tables_async(storage, names): + return await asyncio.gather(*[load_table_from_storage(name=n, storage=storage) for n in names]) + + def _load_dataframes(config: GraphRagConfig): + storage = create_storage_from_config(config.output) + names = ["entities", "communities", "community_reports", "text_units", "relationships"] + # 并行加载前 5 张表，加速构建上下文；asyncio.run 需传入 coroutine 不能传 Future + tables = asyncio.run(_load_tables_async(storage, names)) + result = dict(zip(names, tables)) + result["covariates"] = None + if asyncio.run(storage_has_table("covariates", storage)): + result["covariates"] = asyncio.run(load_table_from_storage(name="covariates", storage=storage)) + return result + + root = Path(root_dir).resolve() + t0 = _t() + config = load_config(root, None, {}) + if timings is not None: + timings["retrieve_load_config"] = _t() - t0 + + t0 = _t() + dfs = _load_dataframes(config) + if timings is not None: + timings["retrieve_load_dataframes"] = _t() - t0 + entities_df = dfs["entities"] + communities_df = dfs["communities"] + community_reports_df = dfs["community_reports"] + text_units_df = dfs["text_units"] + relationships_df = dfs["relationships"] + covariates_df = dfs["covariates"] + + t0 = _t() + vector_store_args = {k: v.model_dump() for k, v in config.vector_store.items()} + description_embedding_store = get_embedding_store( + config_args=vector_store_args, + embedding_name=entity_description_embedding, + ) + embedding_config = config.get_language_model_config(config.local_search.embedding_model_id) + embedding_model = ModelManager().get_or_create_embedding_model( + name="local_search_embedding", + model_type=embedding_config.type, + config=embedding_config, + ) + tokenizer = get_tokenizer(model_config=config.get_language_model_config(config.local_search.chat_model_id)) + if timings is not None: + timings["retrieve_embedding_store_and_model"] = _t() - t0 + + t0 = _t() + entities_ = read_indexer_entities(entities_df, communities_df, community_level=community_level) + reports = read_indexer_reports(community_reports_df, communities_df, community_level=community_level) + text_units = read_indexer_text_units(text_units_df) + relationships = read_indexer_relationships(relationships_df) + covariates_ = read_indexer_covariates(covariates_df) if covariates_df is not None else [] + if timings is not None: + timings["retrieve_read_indexers"] = _t() - t0 + + t0 = _t() + ls_config = config.local_search + context_builder = LocalSearchMixedContext( + community_reports=reports, + text_units=text_units, + entities=entities_, + relationships=relationships, + covariates={"claims": covariates_}, + entity_text_embeddings=description_embedding_store, + text_embedder=embedding_model, + tokenizer=tokenizer, + embedding_vectorstore_key=EntityVectorStoreKey.ID, + ) + # 实体表最多占 local_tokens 的 55%，剩余给 Relationships + apply_entity_token_cap_patch(entity_prop=0.55) + wrapped = wrap_build_local_context(LocalSearchMixedContext._build_local_context, entity_prop=0.55) + context_builder._build_local_context = MethodType(wrapped, context_builder) + + community_prop = 0.0 if not include_community else ls_config.community_prop + n_ent = top_k_entities if top_k_entities is not None else ls_config.top_k_entities + n_rel = top_k_relationships if top_k_relationships is not None else ls_config.top_k_relationships + context_builder_params = { + "text_unit_prop": ls_config.text_unit_prop, + "community_prop": community_prop, + "conversation_history_max_turns": ls_config.conversation_history_max_turns, + "conversation_history_user_turns_only": True, + "top_k_mapped_entities": n_ent, + "top_k_relationships": n_rel, + "include_entity_rank": True, + "include_relationship_weight": True, + "include_community_rank": False, + "return_candidate_context": False, + "max_context_tokens": ls_config.max_context_tokens, + } + if timings is not None: + timings["retrieve_init_mixed_context"] = _t() - t0 + + t0 = _t() + result = context_builder.build_context(query=query, **context_builder_params) + chunks = result.context_chunks + if timings is not None: + timings["retrieve_build_context"] = _t() - t0 + t0 = _t() + if include_parts: + chunks = keep_only_context_parts(chunks, include_parts) + if timings is not None: + timings["retrieve_keep_parts"] = _t() - t0 + if return_entities: + return chunks, result.context_records, entities_ + return chunks, result.context_records + + +def build_local_context_entities_relations_only( + root_dir: Path, + query: str, + community_level: int | None = None, + include_community: bool = False, + top_k_entities: int | None = None, + top_k_relationships: int | None = None, + timings: dict | None = None, +): + """ + 仅检索实体与边，根据边补召实体，不包含 Source（也不含 Reports/Claims）。 + 返回 (context_chunks, context_records)。可选 top_k_entities/top_k_relationships 减小以加速。 + timings 为可选的 mutable dict，若传入则写入各子步骤用时（含 retrieve_* 与 expand_edges）。 + """ + chunks, records, entities_list = build_local_context_only( + root_dir, + query, + community_level=community_level, + include_community=include_community, + include_parts=["entities", "relationships"], + return_entities=True, + top_k_entities=top_k_entities, + top_k_relationships=top_k_relationships, + timings=timings, + ) + t0 = time.perf_counter() + chunks = _expand_entities_by_edges_and_drop_sources(chunks, entities_list) + if timings is not None: + timings["retrieve_expand_edges"] = time.perf_counter() - t0 + return chunks, records + + +def get_graphrag_context_for_query( + query: str, + root_dir: Optional[Path] = None, + community_level: int | None = None, + include_parts: list[str] | None = None, + entities_relations_only: bool = False, +) -> Optional[str]: + """ + 对给定 query 构建 GraphRAG 上下文，返回 context_chunks 字符串；失败返回 None。 + include_parts 为要保留的部分名列表（如 ["reports","entities","relationships"]）， + 可选：reports, entities, relationships, claims, sources；None 表示不截断（全部）。 + entities_relations_only=True 时：仅实体+边、按边补召实体、不含 Source，忽略 include_parts。 + """ + if not query or not query.strip(): + return None + root = root_dir or Path(__file__).resolve().parent + try: + if entities_relations_only: + chunks, _ = build_local_context_entities_relations_only( + root, query, community_level=community_level, + ) + else: + chunks, _ = build_local_context_only( + root, query, + community_level=community_level, + include_parts=include_parts, + ) + return chunks if chunks else None + except Exception: + return None diff --git a/rag/make_plan_rag.py b/rag/make_plan_rag.py new file mode 100644 index 00000000..5df8a6d8 --- /dev/null +++ b/rag/make_plan_rag.py @@ -0,0 +1,160 @@ +""" +RAG make_plan 统一入口：构建上下文、加载 prompt、调 LLM、校验。 +Prompt 外置，LLM/校验走 plan_llm。 +""" +from __future__ import annotations + +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from context_builder import ( + build_local_context_entities_relations_only, + parse_entity_names_from_context, + reduce_context_to_entities_function_only, + warmup_graphrag_imports, +) +from plan_llm import call_llm, load_llm_config, validate_plan_json + +# mode -> 相对项目根的 prompt 文件名 +PROMPT_FILES = { + "default": "prompts/plan_make_rag.txt", +} + + +def load_plan_prompt(mode: str, root_dir: Path) -> str: + """按 mode 加载 plan-make 的 system prompt。root_dir 为项目根（含 prompts/）。""" + name = PROMPT_FILES.get(mode) + if not name: + raise ValueError(f"未知 mode: {mode}，支持: {list(PROMPT_FILES)}") + path = Path(root_dir).resolve() / name + if not path.exists(): + raise FileNotFoundError(f"Prompt 文件不存在: {path}") + return path.read_text(encoding="utf-8").strip() + + +def build_graphrag_context(root_dir: Path, query: str, timings: dict | None = None) -> str: + """仅实体+边、按边补召、不含 Source；返回完整 context_chunks（Entities+Relationships）。""" + context_chunks, _ = build_local_context_entities_relations_only( + root_dir, + query, + community_level=None, + include_community=False, + timings=timings, + ) + return context_chunks or "" + + +def _build_context_reduced(root_dir: Path, query: str, timings: dict | None) -> tuple[str, str]: + """先检索实体+边，再缩减为仅实体 function（无边）。返回 (context_full, context_reduced)。""" + context_full = build_graphrag_context(root_dir, query, timings) + t0 = time.perf_counter() + context_reduced = reduce_context_to_entities_function_only(context_full) + if timings is not None: + timings["2b_reduce_to_function_only"] = time.perf_counter() - t0 + return context_full, context_reduced + + +def build_user_message(query: str, context: str, memory_context: str = "") -> str: + """用户消息：问题 + 可选会话记忆 + 仅实体 function 的上下文。""" + memory_block = "" + if memory_context and memory_context.strip(): + memory_block = f""" +<会话补充上下文> +{memory_context.strip()} + +""" + return f"""用户原始问题: {query}{memory_block} + +以下是从知识库检索到的工具/实体列表，每行仅包含实体名与其功能描述（function）。请仅根据此处出现的实体名称与功能推断可用工具，并制定执行计划。不要使用上下文中未出现的工具名。 + +<知识库上下文（仅实体与功能）> +{context} + + +请根据用户问题和上述上下文，制定执行计划。工具名必须来自上文 Entities 中的实体名；若无合适工具则使用 tool_name = null。""" + + +@dataclass +class RunResult: + """run_make_plan_rag 的返回结果。""" + raw_output: str + is_valid: bool + errors: list[str] + parsed_data: Any + context_entity_names: list[str] + context: str + timings: dict + + +def run_make_plan_rag( + mode: str, + query: str, + root_dir: Path, + *, + memory_context: str = "", + settings_path: Path | None = None, + timings: dict | None = None, + out_context: Path | None = None, + out_context_full: Path | None = None, +) -> RunResult: + """ + 执行 RAG make_plan 全流程：加载配置、warmup、构建上下文、调 LLM、校验。 + mode 目前仅支持 "default"。 + 若传入 timings，会写入各步耗时；若传入 out_context/out_context_full，会写入对应文件。 + """ + root_dir = Path(root_dir).resolve() + settings_path = settings_path or root_dir / "settings.yaml" + if timings is None: + timings = {} + + t0 = time.perf_counter() + llm_config = load_llm_config(settings_path) + timings["1_load_llm_config"] = time.perf_counter() - t0 + + t0 = time.perf_counter() + warmup_graphrag_imports() + timings["1b_warmup_imports"] = time.perf_counter() - t0 + + if mode == "default": + t0 = time.perf_counter() + context_full, context = _build_context_reduced(root_dir, query, timings) + timings["2a_build_context"] = time.perf_counter() - t0 + if out_context_full: + out_context_full.parent.mkdir(parents=True, exist_ok=True) + out_context_full.write_text(context_full, encoding="utf-8") + if out_context: + out_context.parent.mkdir(parents=True, exist_ok=True) + out_context.write_text(context, encoding="utf-8") + else: + raise ValueError(f"未知 mode: {mode}") + + if not context or not context.strip(): + raise RuntimeError("RAG 上下文为空，请检查 --root 与知识库。") + + t0 = time.perf_counter() + context_entity_names = parse_entity_names_from_context(context) + timings["3_parse_entities"] = time.perf_counter() - t0 + + system_prompt = load_plan_prompt(mode, root_dir) + user_message = build_user_message(query, context, memory_context=memory_context) + timings["4_build_message"] = 0.0 # 可忽略 + + t0 = time.perf_counter() + raw_output = call_llm(system_prompt, user_message, llm_config) + timings["5_call_llm"] = time.perf_counter() - t0 + + t0 = time.perf_counter() + is_valid, errors, parsed_data = validate_plan_json(raw_output, context_entity_names) + timings["6_validate_json"] = time.perf_counter() - t0 + + return RunResult( + raw_output=raw_output, + is_valid=is_valid, + errors=errors, + parsed_data=parsed_data, + context_entity_names=context_entity_names, + context=context, + timings=timings, + ) diff --git a/rag/output/communities.parquet b/rag/output/communities.parquet new file mode 100644 index 00000000..f3da56fb Binary files /dev/null and b/rag/output/communities.parquet differ diff --git a/rag/output/community_reports.parquet b/rag/output/community_reports.parquet new file mode 100644 index 00000000..bef8b5a5 Binary files /dev/null and b/rag/output/community_reports.parquet differ diff --git 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b/rag/output/lancedb/default-text_unit-text.lance/data/110101010110011100011100cca5a34e17935485ab2b0822d5.lance differ diff --git a/rag/output/relationships.parquet b/rag/output/relationships.parquet new file mode 100644 index 00000000..53a65573 Binary files /dev/null and b/rag/output/relationships.parquet differ diff --git a/rag/output/stats.json b/rag/output/stats.json new file mode 100644 index 00000000..ae66dd90 --- /dev/null +++ b/rag/output/stats.json @@ -0,0 +1,38 @@ +{ + "total_runtime": 163.99825644493103, + "num_documents": 107, + "update_documents": 0, + "input_load_time": 0, + "workflows": { + "load_input_documents": { + "overall": 0.14981484413146973 + }, + "create_base_text_units": { + "overall": 0.2954246997833252 + }, + "create_final_documents": { + "overall": 0.029008150100708008 + }, + "extract_graph": { + "overall": 140.95852780342102 + }, + "finalize_graph": { + "overall": 0.03591465950012207 + }, + "extract_covariates": { + "overall": 0.00022029876708984375 + }, + "create_communities": { + "overall": 0.030314922332763672 + }, + "create_final_text_units": { + "overall": 0.03886866569519043 + }, + "create_community_reports": { + "overall": 18.343321323394775 + }, + "generate_text_embeddings": { + "overall": 4.114162445068359 + } + } +} \ No newline at end of file diff --git a/rag/output/text_units.parquet b/rag/output/text_units.parquet new file mode 100644 index 00000000..fe571e23 Binary files /dev/null and b/rag/output/text_units.parquet differ diff --git a/rag/patch_local_context_entity_cap.py b/rag/patch_local_context_entity_cap.py new file mode 100644 index 00000000..5239adec --- /dev/null +++ b/rag/patch_local_context_entity_cap.py @@ -0,0 +1,96 @@ +""" +为 local 上下文中的「实体表」和「关系表」设置 token 上限，保证裁剪后仍保留 Entities + Relationships + Sources。 + +GraphRAG 的 mixed_context 在 import 时绑定了 build_entity_context / build_relationship_context， +所以必须同时 patch local_context 与 mixed_context 两个模块，调用方才会用到 capped 版本。 +""" +import graphrag.query.context_builder.local_context as _local_context +import graphrag.query.structured_search.local_search.mixed_context as _mixed_context + +_ENTITY_TOKEN_CAP_ATTR = "_entity_token_cap_for_relationships" +_RELATIONSHIP_TOKEN_CAP_ATTR = "_relationship_token_cap" + +# 原始函数 +_orig_build_entity_context = _local_context.build_entity_context +_orig_build_relationship_context = _local_context.build_relationship_context + + +# 默认实体占比（当 per-call cap 未设置时仍为 Relationships 预留空间） +_DEFAULT_ENTITY_PROP = 0.50 +_DEFAULT_RELATIONSHIP_PROP = 0.50 + + +def _build_entity_context_capped(*args, max_context_tokens=8000, **kwargs): + cap = getattr(_local_context, _ENTITY_TOKEN_CAP_ATTR, None) + if cap is not None: + max_context_tokens = min(max_context_tokens, cap) + else: + # 未设置 cap 时也强制上限，避免实体占满导致 Relationships 被 revert 掉 + max_context_tokens = max(1, int(max_context_tokens * _DEFAULT_ENTITY_PROP)) + return _orig_build_entity_context(*args, max_context_tokens=max_context_tokens, **kwargs) + + +def _build_relationship_context_capped(*args, max_context_tokens=8000, **kwargs): + cap = getattr(_local_context, _RELATIONSHIP_TOKEN_CAP_ATTR, None) + if cap is not None: + max_context_tokens = min(max_context_tokens, cap) + else: + max_context_tokens = max(1, int(max_context_tokens * _DEFAULT_RELATIONSHIP_PROP)) + return _orig_build_relationship_context(*args, max_context_tokens=max_context_tokens, **kwargs) + + +def apply_entity_token_cap_patch(entity_prop: float = 0.55): + """应用补丁：实体表与关系表分别限制 token；同时 patch 调用方 mixed_context。""" + _local_context.build_entity_context = _build_entity_context_capped + _local_context.build_relationship_context = _build_relationship_context_capped + _mixed_context.build_entity_context = _build_entity_context_capped + _mixed_context.build_relationship_context = _build_relationship_context_capped + + +def revert_entity_token_cap_patch(): + """恢复原始函数。""" + _local_context.build_entity_context = _orig_build_entity_context + _local_context.build_relationship_context = _orig_build_relationship_context + _mixed_context.build_entity_context = _orig_build_entity_context + _mixed_context.build_relationship_context = _orig_build_relationship_context + + +def set_entity_token_cap_for_this_call(cap: int | None): + """在本次 _build_local_context 调用中，限制实体表 token 上限。""" + setattr(_local_context, _ENTITY_TOKEN_CAP_ATTR, cap) + + +def set_relationship_token_cap_for_this_call(cap: int | None): + """在本次 _build_local_context 调用中，限制关系表 token 上限。""" + setattr(_local_context, _RELATIONSHIP_TOKEN_CAP_ATTR, cap) + + +def clear_entity_token_cap(): + setattr(_local_context, _ENTITY_TOKEN_CAP_ATTR, None) + + +def clear_relationship_token_cap(): + setattr(_local_context, _RELATIONSHIP_TOKEN_CAP_ATTR, None) + + +def wrap_build_local_context(original_build_local_context, entity_prop: float = 0.55): + """返回包装后的 _build_local_context：在调用原方法前设置实体/关系 token 上限，调用后清除。""" + + def wrapped(self, selected_entities, max_context_tokens=8000, **kwargs): + entity_cap = max(1, int(max_context_tokens * entity_prop)) + relationship_cap = max(1, int(max_context_tokens * (1.0 - entity_prop))) # 剩余给关系 + set_entity_token_cap_for_this_call(entity_cap) + set_relationship_token_cap_for_this_call(relationship_cap) + try: + return original_build_local_context( + self, selected_entities=selected_entities, max_context_tokens=max_context_tokens, **kwargs + ) + finally: + clear_entity_token_cap() + clear_relationship_token_cap() + + return wrapped + + +# 模块加载时即应用补丁，确保任何使用 mixed_context 的代码都能用到 capped 版本 +apply_entity_token_cap_patch(0.55) diff --git a/rag/plan_llm.py b/rag/plan_llm.py new file mode 100644 index 00000000..2d4bc31a --- /dev/null +++ b/rag/plan_llm.py @@ -0,0 +1,152 @@ +""" +Plan 相关 LLM 与校验：供 make_plan 统一复用。 +- load_llm_config: 从 settings.yaml 读 default_chat_model +- call_llm: 调用大模型（litellm 或 openai） +- validate_plan_json: 校验 plan JSON 并规范 tool_name +""" +import json +import os +from pathlib import Path +from typing import Any + +import yaml +from dotenv import find_dotenv, load_dotenv + + +def _resolve_env_value(raw_value: Any) -> str: + """ + Resolve config values that may reference environment variables. + Supports: + - ${VAR_NAME} + - plain string (returned as-is) + """ + value = "" if raw_value is None else str(raw_value).strip() + if value.startswith("${") and value.endswith("}") and len(value) > 3: + env_name = value[2:-1].strip() + return os.getenv(env_name, "").strip() + return value + + +def load_llm_config(settings_path: Path) -> dict: + """从 settings.yaml 读取 default_chat_model 配置。""" + # Ensure .env is available for local runs (prefer project/root .env if exists) + load_dotenv(find_dotenv(), override=False) + + with open(settings_path, "r", encoding="utf-8") as f: + settings = yaml.safe_load(f) + chat_cfg = settings["models"]["default_chat_model"] + api_key = _resolve_env_value(chat_cfg.get("api_key")) + api_base = _resolve_env_value(chat_cfg.get("api_base")) + model = _resolve_env_value(chat_cfg.get("model")) + if not api_key: + raise ValueError( + "default_chat_model.api_key is empty. " + "Please set PLAN_MAKE_API_KEY in .env or provide api_key in settings.yaml." + ) + return { + "api_key": api_key, + "api_base": api_base, + "model": model, + } + + +def call_llm( + prompt: str, + user_query: str, + llm_config: dict, +) -> str: + """使用 litellm 或 openai 调用大模型（OpenAI 兼容接口）。""" + try: + from litellm import completion + except ImportError: + import openai + + client = openai.OpenAI( + api_key=llm_config["api_key"], + base_url=llm_config["api_base"], + ) + response = client.chat.completions.create( + model=llm_config["model"], + messages=[ + {"role": "system", "content": prompt}, + {"role": "user", "content": user_query}, + ], + temperature=0, + ) + return response.choices[0].message.content + + model = llm_config["model"] + if not model.startswith(("openai/", "azure/", "litellm_proxy/")): + model = f"openai/{model}" + response = completion( + model=model, + api_key=llm_config["api_key"], + api_base=llm_config["api_base"], + messages=[ + {"role": "system", "content": prompt}, + {"role": "user", "content": user_query}, + ], + temperature=0, + ) + return response.choices[0].message.content + + +def validate_plan_json(raw_output: str, valid_tool_names: list[str]) -> tuple[bool, list[str], Any]: + """ + 校验 LLM 输出的 plan JSON： + - 存在 plans 字段，每个 plan 有 steps + - 每个 step 的 tool_name 在 valid_tool_names 中或为 null，status == "plan" + 返回 (is_valid, errors, parsed_json)；校验时会把 step["tool_name"] 规范为 canonical 名。 + """ + errors = [] + text = raw_output.strip() + if text.startswith("```"): + lines = text.split("\n") + start_idx = 1 if lines[0].startswith("```") else 0 + end_idx = len(lines) - 1 if lines[-1].strip() == "```" else len(lines) + text = "\n".join(lines[start_idx:end_idx]) + + try: + data = json.loads(text) + except json.JSONDecodeError as e: + return False, [f"JSON 解析失败: {e}"], None + + if "plans" not in data: + errors.append("缺少 'plans' 字段") + return False, errors, data + + plans = data["plans"] + if not isinstance(plans, list): + errors.append("'plans' 不是列表") + return False, errors, data + + if len(plans) == 0: + errors.append("'plans' 列表为空") + + lower_to_canonical = {t.lower(): t for t in valid_tool_names} + + for i, plan in enumerate(plans): + plan_id = plan.get("plan_id", f"plan_{i}") + if "steps" not in plan: + errors.append(f"Plan '{plan_id}' 缺少 'steps' 字段") + continue + steps = plan["steps"] + if not isinstance(steps, list): + errors.append(f"Plan '{plan_id}' 的 'steps' 不是列表") + continue + for j, step in enumerate(steps): + step_desc = f"Plan '{plan_id}' Step {j + 1}" + tool_name = step.get("tool_name") + if tool_name is not None: + tool_lower = tool_name.lower() + if tool_lower not in lower_to_canonical: + errors.append(f"{step_desc}: tool_name '{tool_name}' 不在可用工具列表中") + else: + step["tool_name"] = lower_to_canonical[tool_lower] + status = step.get("status") + if status != "plan": + errors.append(f"{step_desc}: status 应为 'plan'，实际为 '{status}'") + if "description" not in step: + errors.append(f"{step_desc}: 缺少 'description' 字段") + + return len(errors) == 0, errors, data diff --git a/rag/run_index_with_dashscope_fix.py b/rag/run_index_with_dashscope_fix.py new file mode 100644 index 00000000..a5062097 --- /dev/null +++ b/rag/run_index_with_dashscope_fix.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +""" +补丁后执行 graphrag（index / query 等），解决 DashScope 兼容问题： +1) DashScope embedding 的 encoding_format 必须为 "float"/"base64" +2) create_final_text_units 写 text_units.parquet 时，entity_ids 等列为 numpy array， + PyArrow 无法推断类型导致 ArrowInvalid，需先转为 list。 +3) create_community_reports 调用 chat 时，DashScope 对 json_schema 会报 400/500，改为 json_object。 +4) DashScope embedding 单次 input 条数不能超过 10，需把大批次拆成每批 ≤10 再合并结果。 + +index 与 query 都须通过本脚本调用，否则 query 会报 encoding_format 400。 + +用法（在已安装 graphrag 的环境中执行）： + python run_index_with_dashscope_fix.py index --root . + python run_index_with_dashscope_fix.py query --root . "你的问题" + 或：python run_index_with_dashscope_fix.py index -r . +""" +import sys + + +def _apply_dashscope_response_format_patch(): + """DashScope 用 json_schema 易出 400/500，对 DashScope 且 Pydantic 时改用 json_object。""" + import inspect + import litellm + from pydantic import BaseModel + + _orig_acompletion = litellm.acompletion + + async def _patched_acompletion(**kwargs): + api_base = kwargs.get("api_base") or "" + rf = kwargs.get("response_format") + if ( + "dashscope" in str(api_base).lower() + and rf is not None + and inspect.isclass(rf) + and issubclass(rf, BaseModel) + ): + # 仅要求返回 JSON 对象，由 graphrag 侧用 Pydantic 解析，避免 DashScope 的 Postprocessor 500 + kwargs = {**kwargs, "response_format": {"type": "json_object"}} + return await _orig_acompletion(**kwargs) + + litellm.acompletion = _patched_acompletion + + +def _apply_dashscope_embedding_patch(): + from graphrag.language_model.providers.litellm import embedding_model + + _original_get_kwargs = embedding_model.LitellmEmbeddingModel._get_kwargs + + def _patched_get_kwargs(self, **kwargs): + result = _original_get_kwargs(self, **kwargs) + api_base = getattr(self.config, "api_base", None) or "" + if "dashscope" in api_base: + result["encoding_format"] = "float" + return result + + embedding_model.LitellmEmbeddingModel._get_kwargs = _patched_get_kwargs + + # DashScope embedding 单次 input 不能超过 10 条，对 aembed_batch / embed_batch 做分批 + _orig_aembed_batch = embedding_model.LitellmEmbeddingModel.aembed_batch + _orig_embed_batch = embedding_model.LitellmEmbeddingModel.embed_batch + _DASHSCOPE_EMBED_BATCH_SIZE = 10 + + async def _patched_aembed_batch(self, text_list, **kwargs): + api_base = getattr(self.config, "api_base", None) or "" + if "dashscope" not in str(api_base).lower() or len(text_list) <= _DASHSCOPE_EMBED_BATCH_SIZE: + return await _orig_aembed_batch(self, text_list, **kwargs) + out = [] + for i in range(0, len(text_list), _DASHSCOPE_EMBED_BATCH_SIZE): + chunk = text_list[i : i + _DASHSCOPE_EMBED_BATCH_SIZE] + out.extend(await _orig_aembed_batch(self, chunk, **kwargs)) + return out + + def _patched_embed_batch(self, text_list, **kwargs): + api_base = getattr(self.config, "api_base", None) or "" + if "dashscope" not in str(api_base).lower() or len(text_list) <= _DASHSCOPE_EMBED_BATCH_SIZE: + return _orig_embed_batch(self, text_list, **kwargs) + out = [] + for i in range(0, len(text_list), _DASHSCOPE_EMBED_BATCH_SIZE): + chunk = text_list[i : i + _DASHSCOPE_EMBED_BATCH_SIZE] + out.extend(_orig_embed_batch(self, chunk, **kwargs)) + return out + + embedding_model.LitellmEmbeddingModel.aembed_batch = _patched_aembed_batch + embedding_model.LitellmEmbeddingModel.embed_batch = _patched_embed_batch + + +def _apply_text_units_parquet_patch(): + """写 text_units.parquet 前把 entity_ids/relationship_ids/covariate_ids 转为 list，避免 ArrowInvalid。""" + import graphrag.utils.storage as storage_mod + + _original = storage_mod.write_table_to_storage + + async def _patched_write_table_to_storage(table, name, storage): + if name == "text_units" and hasattr(table, "columns"): + df = table.copy() + for col in ("entity_ids", "relationship_ids", "covariate_ids"): + if col in df.columns: + df[col] = df[col].apply( + lambda x: list(x) if hasattr(x, "__iter__") and not isinstance(x, (str, bytes)) else x + ) + table = df + return await _original(table, name, storage) + + storage_mod.write_table_to_storage = _patched_write_table_to_storage + + +def apply_dashscope_patches(): + """ + 应用 DashScope 兼容补丁（与 main 中一致）。 + 供 context_builder 等在被 MatMaster 调用前调用，确保 query 路径与「python run_index_with_dashscope_fix.py query」一致。 + """ + _apply_dashscope_response_format_patch() + _apply_dashscope_embedding_patch() + _apply_text_units_parquet_patch() + + +def main(): + # 必须在任何 from graphrag 之前打 response_format 补丁，否则 graphrag 已缓存 litellm.acompletion + apply_dashscope_patches() + # 保持与 graphrag 一致：argv[0]=程序名，后面为 index --root . 等 + sys.argv = ["graphrag"] + (sys.argv[1:] or ["index", "--root", "."]) + from graphrag.cli.main import app + app() + + +if __name__ == "__main__": + main() diff --git a/rag/run_make_plan.py b/rag/run_make_plan.py new file mode 100644 index 00000000..9179e960 --- /dev/null +++ b/rag/run_make_plan.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +""" +RAG make_plan：根据用户问题与知识库上下文制定执行计划。 + - 检索实体+边、按边补召，再缩减为仅实体 function 传给 LLM + - 校验时 tool_name 须来自上下文 Entities + +用法： + python run_make_plan.py --query "对 SiO₂ 液态熔体在 1200 K 下运行短时分子动力学" --root . + python run_make_plan.py --query "..." --root . --out output/plan.txt --out-context output/context.txt + python run_make_plan.py --query "..." --root . --out-context-full output/context_full.txt + --query2：同进程再跑一条查询仅检索，验证 retrieve_imports≈0 +""" +import argparse +import sys +import time +from pathlib import Path + +from make_plan_rag import build_graphrag_context, run_make_plan_rag + + +def main(): + parser = argparse.ArgumentParser( + description="RAG make_plan：仅实体 + function 传给 LLM，无边" + ) + parser.add_argument("--query", type=str, required=True, help="用户查询") + parser.add_argument("--settings", type=Path, default=Path("settings.yaml"), help="settings.yaml 路径") + parser.add_argument("--root", type=Path, default=Path("."), help="GraphRAG 项目根目录") + parser.add_argument("--out", type=Path, default=None, help="输出文件（默认 stdout）") + parser.add_argument("--out-context", type=Path, default=None, dest="out_context", help="将传给 LLM 的 RAG 上下文（仅实体 function）写入该文件") + parser.add_argument("--out-context-full", type=Path, default=None, dest="out_context_full", help="将缩减前的完整上下文（Entities+Relationships，含边）写入该文件") + parser.add_argument("--query2", type=str, default=None, help="同进程再跑一条查询的检索（仅检索不调 LLM），用于验证第二次 retrieve_imports≈0") + args = parser.parse_args() + + t0_total = time.perf_counter() + timings = {} + + print("[1/5] 加载 LLM 配置...", file=sys.stderr) + print("[1b/5] 预加载 graphrag 模块（首次约 10s，之后 retrieve_imports≈0）...", file=sys.stderr) + print("[2/5] 构建 GraphRAG 上下文（实体+边 → 缩减为仅实体 function）...", file=sys.stderr) + print("[3/5] 从上下文解析实体...", file=sys.stderr) + print("[4/5] 构建用户消息（问题 + 仅实体 function 上下文）...", file=sys.stderr) + print("[5/5] 调用 LLM 生成计划...", file=sys.stderr) + + try: + result = run_make_plan_rag( + "default", + args.query, + args.root, + settings_path=args.settings, + timings=timings, + out_context=args.out_context, + out_context_full=args.out_context_full, + ) + except Exception as e: + print(f"[ERROR] {e}", file=sys.stderr) + sys.exit(1) + + raw_output = result.raw_output + is_valid = result.is_valid + errors = result.errors + parsed_data = result.parsed_data + context_entity_names = result.context_entity_names + + print(f" 模型: (见 settings) 用时: {timings.get('1_load_llm_config', 0):.2f}s", file=sys.stderr) + print(f" 用时: {timings.get('1b_warmup_imports', 0):.2f}s", file=sys.stderr) + print(f" 检索+边补召 总用时: {timings.get('2a_build_context', 0):.2f}s", file=sys.stderr) + for key in sorted(timings.keys()): + if key.startswith("retrieve_"): + print(f" {key}: {timings[key]:.2f}s", file=sys.stderr) + if args.out_context_full: + print(f" 完整上下文（含边）已写入: {args.out_context_full}", file=sys.stderr) + print(f" 缩减为仅 entity+function 用时: {timings.get('2b_reduce_to_function_only', 0):.2f}s", file=sys.stderr) + print(f" 传给 LLM 的上下文长度: {len(result.context)} 字符", file=sys.stderr) + if args.out_context: + print(f" RAG 上下文已写入: {args.out_context}", file=sys.stderr) + print(f"[3/5] 从上下文解析到 {len(context_entity_names)} 个实体 用时: {timings.get('3_parse_entities', 0):.2f}s", file=sys.stderr) + print(f" 用时: {timings.get('4_build_message', 0):.2f}s", file=sys.stderr) + print(f" 用时: {timings.get('5_call_llm', 0):.2f}s", file=sys.stderr) + print("校验输出 JSON...", file=sys.stderr) + print(f" 用时: {timings.get('6_validate_json', 0):.2f}s", file=sys.stderr) + + total_elapsed = time.perf_counter() - t0_total + print("", file=sys.stderr) + print("--- 各步用时 ---", file=sys.stderr) + main_keys = ["1_load_llm_config", "1b_warmup_imports", "2a_build_context", "2b_reduce_to_function_only", + "3_parse_entities", "4_build_message", "5_call_llm", "6_validate_json"] + for name in main_keys: + if name in timings: + print(f" {name}: {timings[name]:.2f}s", file=sys.stderr) + for name in sorted(timings.keys()): + if name.startswith("retrieve_"): + print(f" {name}: {timings[name]:.2f}s", file=sys.stderr) + for name in sorted(timings.keys()): + if name not in main_keys and not name.startswith("retrieve_"): + print(f" {name}: {timings[name]:.2f}s", file=sys.stderr) + print(f" 总用时: {total_elapsed:.2f}s", file=sys.stderr) + print("", file=sys.stderr) + sys.stderr.flush() + + output_lines = [ + "=" * 60, + "MatMaster make_plan（RAG：仅实体 function，无边）", + "=" * 60, + "", + f"用户查询: {args.query}", + f"上下文中实体数: {len(context_entity_names)}", + "", + "--- LLM 原始输出 ---", + raw_output, + "", + "--- JSON 校验结果 ---", + f"校验通过: {'✓ 是' if is_valid else '✗ 否'}", + ] + if errors: + output_lines.append("") + output_lines.append("校验错误:") + for err in errors: + output_lines.append(f" - {err}") + if parsed_data and is_valid: + output_lines.append("") + output_lines.append("--- 计划摘要 ---") + for i, plan in enumerate(parsed_data.get("plans", [])): + plan_id = plan.get("plan_id", f"plan_{i}") + strategy = plan.get("strategy", "N/A") + steps = plan.get("steps", []) + output_lines.append("") + output_lines.append(f"方案 {i + 1} [{plan_id}]: {strategy}") + output_lines.append(f" 步骤数: {len(steps)}") + for j, step in enumerate(steps): + tool = step.get("tool_name") or "(无工具)" + desc = (step.get("description") or "")[:60] + output_lines.append(f" {j + 1}. [{tool}] {desc}...") + output_lines.append("") + output_lines.append("=" * 60) + + text = "\n".join(output_lines) + if args.out: + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(text, encoding="utf-8") + print(f"结果已写入: {args.out}", file=sys.stderr) + else: + print(text) + + if args.query2: + print("", file=sys.stderr) + print("[同进程 第二问] 仅检索（不调 LLM），验证 retrieve_imports 是否≈0 ...", file=sys.stderr) + timings2 = {} + t0_2 = time.perf_counter() + context_full2 = build_graphrag_context(args.root, args.query2, timings=timings2) + elapsed2 = time.perf_counter() - t0_2 + print(f" 检索+边补召 总用时: {elapsed2:.2f}s", file=sys.stderr) + for key in sorted(timings2.keys()): + if key.startswith("retrieve_"): + print(f" {key}: {timings2[key]:.2f}s", file=sys.stderr) + if "retrieve_imports" in timings2: + v = timings2["retrieve_imports"] + print(f" => retrieve_imports={v:.2f}s （同进程第二次应为≈0）", file=sys.stderr) + sys.stderr.flush() + + sys.exit(0 if is_valid else 1) + + +if __name__ == "__main__": + main() diff --git a/rag/scripts/merge_fetch_entities.py b/rag/scripts/merge_fetch_entities.py new file mode 100644 index 00000000..be2d0604 --- /dev/null +++ b/rag/scripts/merge_fetch_entities.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +""" +将 RAG output 中 6 个 FETCH_* 实体合并为 FETCH_STRUCTURES_FROM_DB， +并合并 relationships 中涉及这 6 个的边为统一指向/来自 FETCH_STRUCTURES_FROM_DB 的边。 + +用法（在 MatMaster-test 或 rag 目录下）： + python rag/scripts/merge_fetch_entities.py + 或 cd rag && python scripts/merge_fetch_entities.py +""" +from __future__ import annotations + +import uuid +from pathlib import Path + +import pandas as pd + +# rag/scripts/merge_fetch_entities.py -> rag/output +OUTPUT_DIR = Path(__file__).resolve().parents[1] / "output" +ENTITIES_PATH = OUTPUT_DIR / "entities.parquet" +RELATIONSHIPS_PATH = OUTPUT_DIR / "relationships.parquet" + +FETCH_OLD = [ + "FETCH_BOHRIUM_CRYSTALS", + "FETCH_MOFS_SQL", + "FETCH_OPENLAM_STRUCTURES", + "FETCH_STRUCTURES_WITH_BANDGAP", + "FETCH_STRUCTURES_WITH_FILTER", + "FETCH_STRUCTURES_WITH_SPG", +] +FETCH_NEW = "FETCH_STRUCTURES_FROM_DB" + +# 与 tools.py / structure_search_agent.prompt 中 StructureSearchAgentToolDescription 一致，保持简短 +FETCH_STRUCTURES_FROM_DB_DESCRIPTION = ( + "What it does: Retrieve structures across multiple sources (BohriumPublic / OpenLAM / OPTIMADE providers) and run advanced SQL queries on MOFdb. " + "When to use: Any \"find crystal structures\" request, including formula/elements/space group/band gap filters, time/energy filters (OpenLAM), cross-provider searches (OPTIMADE), or MOF-specific analytics (MOFdb SQL). " + "Prerequisites / Inputs: Provide either structured filters (formula/elements/space group ranges), OpenLAM filters (energy/time), OPTIMADE filter strings, or MOFdb SQL. " + "Outputs: Structures or metadata in CIF/JSON; MOFdb returns SQL rows and optional structure file links. " + "Cannot do / Limits: OPTIMADE filters must follow the standard grammar; MOFdb is MOF-specific; OpenLAM does not provide space group/band gap. " + "Cost / Notes: Default to BohriumPublic for speed; use OPTIMADE for flexible/cross-provider retrieval; use MOFdb for complex MOF analytics." +) + + +def main() -> None: + if not ENTITIES_PATH.exists(): + raise FileNotFoundError(f"entities not found: {ENTITIES_PATH}") + if not RELATIONSHIPS_PATH.exists(): + raise FileNotFoundError(f"relationships not found: {RELATIONSHIPS_PATH}") + + # ---- entities ---- + e = pd.read_parquet(ENTITIES_PATH) + fetch_mask = e["title"].isin(FETCH_OLD) + fetch_rows = e[fetch_mask] + if len(fetch_rows) > 0: + other_entities = e[~fetch_mask] + new_id = str(uuid.uuid4()) + new_hr_id = int(e["human_readable_id"].max()) + 1 if len(e) else 0 + tu_ids = fetch_rows["text_unit_ids"].dropna() + if len(tu_ids) and hasattr(tu_ids.iloc[0], "__iter__") and not isinstance(tu_ids.iloc[0], str): + all_tu = [] + for x in tu_ids: + all_tu.extend(x if x is not None else []) + merged_tu_ids = list(dict.fromkeys(all_tu)) + else: + merged_tu_ids = tu_ids.astype(str).str.cat(sep=",").split(",") if len(tu_ids) else [] + merged_tu_ids = [x.strip() for x in merged_tu_ids if x.strip()] + merged_row = pd.DataFrame( + [ + { + "id": new_id, + "human_readable_id": new_hr_id, + "title": FETCH_NEW, + "type": fetch_rows["type"].iloc[0], + "description": FETCH_STRUCTURES_FROM_DB_DESCRIPTION, + "text_unit_ids": merged_tu_ids if merged_tu_ids else None, + "frequency": int(fetch_rows["frequency"].sum()), + "degree": int(fetch_rows["degree"].sum()), + "x": float(fetch_rows["x"].mean()), + "y": float(fetch_rows["y"].mean()), + } + ] + ) + entities_new = pd.concat([other_entities, merged_row], ignore_index=True) + entities_new.to_parquet(ENTITIES_PATH, index=False) + print(f"entities: 已删除 6 个 FETCH_*，新增 1 个 {FETCH_NEW}，当前共 {len(entities_new)} 条") + else: + print("entities: 未发现 6 个 FETCH_*，跳过合并") + + # 确保 FETCH_STRUCTURES_FROM_DB 的 description 为简短版（与 tools.py 一致） + e2 = pd.read_parquet(ENTITIES_PATH) + idx = e2["title"] == FETCH_NEW + if idx.any(): + e2.loc[idx, "description"] = FETCH_STRUCTURES_FROM_DB_DESCRIPTION + e2.to_parquet(ENTITIES_PATH, index=False) + print(f"entities: 已把 {FETCH_NEW} 的 description 设为 tools.py 简短描述") + + # ---- relationships ---- + r = pd.read_parquet(RELATIONSHIPS_PATH) + if r["source"].isin(FETCH_OLD).any() or r["target"].isin(FETCH_OLD).any(): + r = r.copy() + r["source"] = r["source"].replace(FETCH_OLD, FETCH_NEW) + r["target"] = r["target"].replace(FETCH_OLD, FETCH_NEW) + agg = { + "id": "first", + "human_readable_id": "first", + "description": lambda s: " | ".join(s.dropna().astype(str).unique()) if s.notna().any() else None, + "weight": "sum", + "combined_degree": "max", + "text_unit_ids": "first", + } + rel_merged = r.groupby(["source", "target"], as_index=False).agg(agg) + rel_merged = rel_merged[list(r.columns)] + rel_merged.to_parquet(RELATIONSHIPS_PATH, index=False) + print(f"relationships: 已合并边，当前共 {len(rel_merged)} 条") + else: + print("relationships: 未发现 6 个 FETCH_*，跳过合并") + + # ---- output 下 .txt / .md 中的边名统一替换为 FETCH_STRUCTURES_FROM_DB ---- + for path in OUTPUT_DIR.rglob("*"): + if path.is_file() and path.suffix.lower() in (".txt", ".md"): + try: + text = path.read_text(encoding="utf-8") + except Exception: + continue + new_text = text + for old in FETCH_OLD: + new_text = new_text.replace(old, FETCH_NEW) + if new_text != text: + path.write_text(new_text, encoding="utf-8") + print(f" 已替换边名: {path.relative_to(OUTPUT_DIR)}") + print("done.") + + +if __name__ == "__main__": + main() diff --git a/rag/settings.yaml b/rag/settings.yaml new file mode 100644 index 00000000..978db577 --- /dev/null +++ b/rag/settings.yaml @@ -0,0 +1,159 @@ +### This config file contains required core defaults that must be set, along with a handful of common optional settings. +### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/ + +### LLM settings ### +## There are a number of settings to tune the threading and token limits for LLM calls - check the docs. + +models: + default_chat_model: + type: chat + model_provider: openai + auth_type: api_key + api_key: ${PLAN_MAKE_API_KEY} + api_base: https://dashscope.aliyuncs.com/compatible-mode/v1 + model: qwen3-vl-plus + # 备选：Azure gpt-4o + #model_provider: azure + #api_key: ${AZURE_API_KEY} + #api_base: https://ty-pre-activation-17.openai.azure.com/ + #api_version: 2024-12-01-preview + #model: gpt-4o + model_supports_json: true + concurrent_requests: 10 + async_mode: threaded # or asyncio + retry_strategy: exponential_backoff + max_retries: 10 + tokens_per_minute: null + requests_per_minute: null + default_embedding_model: + type: embedding + model_provider: openai + auth_type: api_key + api_key: ${PLAN_MAKE_API_KEY} + api_base: https://dashscope.aliyuncs.com/compatible-mode/v1 + model: text-embedding-v4 + concurrent_requests: 10 + async_mode: threaded # or asyncio + retry_strategy: exponential_backoff + max_retries: 10 + tokens_per_minute: null + requests_per_minute: null + +### Input settings ### + +input: + storage: + type: file # or blob + base_dir: "input" + file_type: text # [csv, text, json] + +# 工具编排向：尽量一工具一 chunk（每个 input/*.txt 一文件），单文件少被拆；group_by_columns [id] 按文档边界切 +chunks: + size: 3000 + overlap: 150 + group_by_columns: [id] + +### Output/storage settings ### +## If blob storage is specified in the following four sections, +## connection_string and container_name must be provided + +output: + type: file # [file, blob, cosmosdb] + base_dir: "output" + +cache: + type: file # [file, blob, cosmosdb] + base_dir: "cache" + +reporting: + type: file # [file, blob] + base_dir: "logs" + +vector_store: + default_vector_store: + type: lancedb + db_uri: output/lancedb + container_name: default + +### Workflow settings ### + +embed_text: + model_id: default_embedding_model + vector_store_id: default_vector_store + +extract_graph: + model_id: default_chat_model + prompt: "prompts/extract_graph.txt" + # 工具能力图谱：仅抽取工具实体及其依赖关系 + entity_types: [tool] + max_gleanings: 1 + +summarize_descriptions: + model_id: default_chat_model + prompt: "prompts/summarize_descriptions.txt" + max_length: 800 # 增加长度以容纳 applicable_tasks 和 keywords 字段 + +extract_graph_nlp: + text_analyzer: + extractor_type: regex_english # [regex_english, syntactic_parser, cfg] + async_mode: threaded # or asyncio + +# 放宽 prune：出现 1 次就保留、保留更多边、不删单连边节点，以便更多实体进社区、community report 更多 +prune_graph: + min_node_freq: 1 + min_edge_weight_pct: 20 + remove_ego_nodes: false + +# 工具编排向：整条工具链有机会落在同一 community，便于 community report 写出完整 TOOL CHAIN；max_cluster_size 调小可得到更多社区 +cluster_graph: + max_cluster_size: 12 + +extract_claims: + enabled: false + model_id: default_chat_model + prompt: "prompts/extract_claims.txt" + description: "Any claims or facts that could be relevant to information discovery." + max_gleanings: 1 + +community_reports: + model_id: default_chat_model + graph_prompt: "prompts/community_report_graph.txt" + text_prompt: "prompts/community_report_text.txt" + max_length: 2000 + max_input_length: 8000 + +embed_graph: + enabled: false # if true, will generate node2vec embeddings for nodes + +umap: + enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled) + +snapshots: + graphml: false + embeddings: false + +### Query settings ### +## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned. +## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#query + +local_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/local_search_system_prompt.txt" + +global_search: + chat_model_id: default_chat_model + map_prompt: "prompts/global_search_map_system_prompt.txt" + reduce_prompt: "prompts/global_search_reduce_system_prompt.txt" + knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt" + +drift_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/drift_search_system_prompt.txt" + reduce_prompt: "prompts/drift_search_reduce_prompt.txt" + +basic_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/basic_search_system_prompt.txt" diff --git a/scripts/compare_rag_entities_with_tools.py b/scripts/compare_rag_entities_with_tools.py new file mode 100644 index 00000000..0ab2615d --- /dev/null +++ b/scripts/compare_rag_entities_with_tools.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +""" +比对 RAG output 里实体表（entities.parquet）与当前 MatMaster ALL_TOOLS 的差异。 +归一化规则：小写、连字符 - 视为下划线 _，再比较。 + +用法（在项目根 MatMaster-test 下）： + uv run python scripts/compare_rag_entities_with_tools.py +""" +from __future__ import annotations + +import sys +from pathlib import Path + +_root = Path(__file__).resolve().parents[1] +if str(_root) not in sys.path: + sys.path.insert(0, str(_root)) + +import pandas as pd +from agents.matmaster_agent.sub_agents.tools import ALL_TOOLS + + +def to_key(s: str) -> str: + return s.lower().replace("-", "_") + + +def main() -> None: + entities_path = _root / "rag" / "output" / "entities.parquet" + if not entities_path.exists(): + print(f"未找到实体表: {entities_path}") + sys.exit(1) + + df = pd.read_parquet(entities_path) + rag_titles = df["title"].astype(str).str.strip().tolist() + tool_names = list(ALL_TOOLS.keys()) + + rag_by_key = {to_key(t): t for t in rag_titles} + tools_by_key = {to_key(t): t for t in tool_names} + + in_rag_only = sorted([rag_by_key[k] for k in rag_by_key if k not in tools_by_key], key=str.lower) + in_tools_only = sorted([tools_by_key[k] for k in tools_by_key if k not in rag_by_key], key=str.lower) + in_both = sorted([rag_by_key[k] for k in rag_by_key if k in tools_by_key], key=str.lower) + + print("=" * 60) + print("RAG 实体表 vs MatMaster ALL_TOOLS 比对") + print("=" * 60) + print(f"RAG 实体表 (entities.parquet): {len(rag_titles)} 个") + print(f"当前 ALL_TOOLS: {len(tool_names)} 个") + print() + print("--- 仅在 RAG 中、不在 ALL_TOOLS 中 ---") + print(f"共 {len(in_rag_only)} 个:") + for x in in_rag_only: + print(f" {x}") + print() + print("--- 仅在 ALL_TOOLS 中、不在 RAG 实体表中 ---") + print(f"共 {len(in_tools_only)} 个:") + for x in in_tools_only: + print(f" {x}") + print() + print("--- 两边都有（名称可对应）---") + print(f"共 {len(in_both)} 个") + for x in in_both: + print(f" {x}") + + +if __name__ == "__main__": + main() diff --git a/scripts/show_tool_list.py b/scripts/show_tool_list.py new file mode 100644 index 00000000..1fc1961b --- /dev/null +++ b/scripts/show_tool_list.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +""" +查看当前 MatMaster 的 tool list（ALL_TOOLS / ALL_AGENT_TOOLS_LIST）。 +用法（在项目根 MatMaster-test 下）： + uv run python scripts/show_tool_list.py + uv run python scripts/show_tool_list.py --verbose # 输出每个工具的 scene、description 摘要 +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +# 保证项目根在 path 中 +_root = Path(__file__).resolve().parents[1] +if str(_root) not in sys.path: + sys.path.insert(0, str(_root)) + +from agents.matmaster_agent.sub_agents.tools import ALL_TOOLS +from agents.matmaster_agent.sub_agents.mapping import ALL_AGENT_TOOLS_LIST + + +def main() -> None: + parser = argparse.ArgumentParser(description="查看 MatMaster tool list") + parser.add_argument( + "--verbose", + "-v", + action="store_true", + help="输出每个工具的 scene、description 摘要", + ) + args = parser.parse_args() + + print(f"ALL_AGENT_TOOLS_LIST 数量: {len(ALL_AGENT_TOOLS_LIST)}") + print(f"ALL_TOOLS 数量: {len(ALL_TOOLS)}") + print() + + # 工具名列表（与 ALL_AGENT_TOOLS_LIST 一致） + names = sorted(ALL_TOOLS.keys()) + print("工具名列表 (按字母序):") + print("-" * 50) + for i, name in enumerate(names, 1): + print(f" {i:3}. {name}") + print() + + if args.verbose: + print("每个工具详情 (scene + description 首行):") + print("-" * 50) + for name in names: + info = ALL_TOOLS.get(name, {}) + scene = info.get("scene", []) + scene_str = ", ".join(str(s) for s in scene) + desc = info.get("description", "") + first_line = desc.split("\n")[0][:80] + ("..." if len(desc) > 80 else "") + print(f" {name}") + print(f" scene: {scene_str}") + print(f" description: {first_line}") + print() + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock index 6e942de2..d88b609d 100644 --- a/uv.lock +++ b/uv.lock @@ -7,6 +7,9 @@ resolution-markers = [ "python_full_version < '3.13'", ] +[options] +prerelease-mode = "allow" + [[package]] name = "a2a-sdk" version = "0.3.22" @@ -145,6 +148,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b4/63/278a98c715ae467624eafe375542d8ba9b4383a016df8fdefe0ae28382a7/aiohttp-3.13.3-cp314-cp314t-win_amd64.whl", hash = "sha256:44531a36aa2264a1860089ffd4dce7baf875ee5a6079d5fb42e261c704ef7344", size = 499694, upload-time = "2026-01-03T17:32:24.546Z" }, ] +[[package]] +name = "aiolimiter" +version = "1.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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