From 53fb98e44eac11871fdf5143f0413c96e517d8f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9C=B1=E6=BD=AE?= Date: Thu, 16 Apr 2026 17:55:34 +0800 Subject: [PATCH 1/2] Retrieval Policy --- prompt/system_prompt.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/prompt/system_prompt.md b/prompt/system_prompt.md index 6607564..c0c8be9 100644 --- a/prompt/system_prompt.md +++ b/prompt/system_prompt.md @@ -69,9 +69,9 @@ When creating scripts in `executable_code/`, follow these organization rules: - Temporary script (when needed): `{agent_dir_path}/executable_code/tmp/test.py` - Downloaded file: `{agent_dir_path}/download/report.pdf` -# Retrieval Policy (Priority & Fallback) +# Retrieval Policy -### 1. Retrieval Source Priority and Tool Selection +### 1. Retrieval Order and Tool Selection - Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations. - Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve` > local filesystem retrieval. - Do NOT answer from model knowledge first. From e1bf685314f752243ca709150bdf529f07436ae7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9C=B1=E6=BD=AE?= Date: Thu, 16 Apr 2026 19:38:13 +0800 Subject: [PATCH 2/2] add rag_retrieve autoload --- mcp/mcp_settings.json | 11 +- prompt/system_prompt.md | 54 ---- .../rag-retrieve/.claude-plugin/plugin.json | 22 ++ skills_autoload/rag-retrieve/README.md | 153 +++++++++++ .../rag-retrieve/hooks/pre_prompt.py | 20 ++ .../rag-retrieve/hooks/retrieval-policy.md | 53 ++++ skills_autoload/rag-retrieve/mcp_common.py | 251 ++++++++++++++++++ .../rag-retrieve}/rag_retrieve_server.py | 0 .../rag-retrieve}/rag_retrieve_tools.json | 0 utils/multi_project_manager.py | 38 ++- 10 files changed, 525 insertions(+), 77 deletions(-) create mode 100644 skills_autoload/rag-retrieve/.claude-plugin/plugin.json create mode 100644 skills_autoload/rag-retrieve/README.md create mode 100644 skills_autoload/rag-retrieve/hooks/pre_prompt.py create mode 100644 skills_autoload/rag-retrieve/hooks/retrieval-policy.md create mode 100644 skills_autoload/rag-retrieve/mcp_common.py rename {mcp => skills_autoload/rag-retrieve}/rag_retrieve_server.py (100%) rename {mcp/tools => skills_autoload/rag-retrieve}/rag_retrieve_tools.json (100%) diff --git a/mcp/mcp_settings.json b/mcp/mcp_settings.json index ddf9962..3aa61ae 100644 --- a/mcp/mcp_settings.json +++ b/mcp/mcp_settings.json @@ -1,14 +1,5 @@ [ { - "mcpServers": { - "rag_retrieve": { - "transport": "stdio", - "command": "python", - "args": [ - "./mcp/rag_retrieve_server.py", - "{bot_id}" - ] - } - } + "mcpServers": {} } ] diff --git a/prompt/system_prompt.md b/prompt/system_prompt.md index c0c8be9..7888edf 100644 --- a/prompt/system_prompt.md +++ b/prompt/system_prompt.md @@ -69,60 +69,6 @@ When creating scripts in `executable_code/`, follow these organization rules: - Temporary script (when needed): `{agent_dir_path}/executable_code/tmp/test.py` - Downloaded file: `{agent_dir_path}/download/report.pdf` -# Retrieval Policy - -### 1. Retrieval Order and Tool Selection -- Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations. -- Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve` > local filesystem retrieval. -- Do NOT answer from model knowledge first. -- Do NOT skip directly to local filesystem retrieval when an earlier retrieval source may answer the question. -- When a suitable skill-enabled knowledge retrieval tool is available, use it first. -- If no suitable skill-enabled retrieval tool is available, or if its result is insufficient, continue with `rag_retrieve` or `table_rag_retrieve`. -- Use `table_rag_retrieve` first for values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, extraction, lists, tables, name lookup, historical coverage, mixed questions, and unclear cases. -- Use `rag_retrieve` first only for clearly pure concept, definition, workflow, policy, or explanation questions without structured data needs. -- After each retrieval step, evaluate sufficiency before moving to the next source. Do NOT run these retrieval sources in parallel. - -### 2. Query Preparation -- Do NOT pass the raw user question unless it already works well for retrieval. -- Rewrite for recall: extract entity, time scope, attributes, and intent. -- Add useful variants: synonyms, aliases, abbreviations, related titles, historical names, and category terms. -- Expand list-style, extraction, overview, historical, roster, timeline, and archive queries more aggressively. -- Preserve meaning. Do NOT introduce unrelated topics. - -### 3. Retrieval Breadth (`top_k`) -- Apply `top_k` only to `rag_retrieve`. Use the smallest sufficient value, then expand only if coverage is insufficient. -- Use `30` for simple fact lookup. -- Use `50` for moderate synthesis, comparison, summarization, or disambiguation. -- Use `100` for broad recall, such as comprehensive analysis, scattered knowledge, multiple entities or periods, or list / catalog / timeline / roster / overview requests. -- Raise `top_k` when keyword branches are many or results are too few, repetitive, incomplete, sparse, or too narrow. -- Use this expansion order: `30 -> 50 -> 100`. If unsure, use `100`. - -### 4. Result Evaluation -- Treat results as insufficient if they are empty, start with `Error:`, say `no excel files found`, are off-topic, miss the core entity or scope, or provide no usable evidence. -- Also treat results as insufficient when they cover only part of the request, or when full-list, historical, comparison, or mixed data + explanation requests return only partial or truncated coverage. - -### 5. Fallback and Sequential Retry -- If the first retrieval result is insufficient, call the next retrieval source in the default order before replying. -- If the first RAG tool is insufficient, call the other RAG tool next before moving to local filesystem retrieval. -- If `table_rag_retrieve` is insufficient or empty, continue with `rag_retrieve`. -- If `rag_retrieve` is insufficient or empty, continue with `table_rag_retrieve`. -- If both `rag_retrieve` and `table_rag_retrieve` are insufficient, continue with local filesystem retrieval. -- Say no relevant information was found only after all applicable skill-enabled retrieval tools, both `rag_retrieve` and `table_rag_retrieve`, and local filesystem retrieval have been tried and still do not provide enough evidence. -- Do NOT reply that no relevant information was found before the final local filesystem fallback has also been tried. - -### 6. Table RAG Result Handling -- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` content in `table_rag_retrieve` results. -- If results are truncated, explicitly tell the user total matches (`N+M`), displayed count (`N`), and omitted count (`M`). -- Cite data sources using filenames from `file_ref_table`. - -### 7. Citation Requirements for Retrieved Knowledge -- When using knowledge from `rag_retrieve` or `table_rag_retrieve`, you MUST generate `` tags. -- Follow the citation format returned by each tool. -- Place citations immediately after the paragraph or bullet list that uses the knowledge. -- Do NOT collect citations at the end. -- Use 1-2 citations per paragraph or bullet list when possible. -- If learned knowledge is used, include at least 1 ``. - # System Information Working directory: {agent_dir_path} diff --git a/skills_autoload/rag-retrieve/.claude-plugin/plugin.json b/skills_autoload/rag-retrieve/.claude-plugin/plugin.json new file mode 100644 index 0000000..b57fdf3 --- /dev/null +++ b/skills_autoload/rag-retrieve/.claude-plugin/plugin.json @@ -0,0 +1,22 @@ +{ + "name": "rag-retrieve", + "description": "rag-retrieve and table-rag-retrieve", + "hooks": { + "PrePrompt": [ + { + "type": "command", + "command": "python hooks/pre_prompt.py" + } + ] + }, + "mcpServers": { + "rag_retrieve": { + "transport": "stdio", + "command": "python", + "args": [ + "./skills_autoload/rag-retrieve/rag_retrieve_server.py", + "{bot_id}" + ] + } + } +} diff --git a/skills_autoload/rag-retrieve/README.md b/skills_autoload/rag-retrieve/README.md new file mode 100644 index 0000000..9177f2a --- /dev/null +++ b/skills_autoload/rag-retrieve/README.md @@ -0,0 +1,153 @@ +# User Context Loader + +用户上下文加载器示例 Skill，演示 Claude Plugins 模式的 hooks 机制。 + +## 功能说明 + +本 Skill 演示了三种 Hook 类型： + +### PrePrompt Hook +在 system_prompt 加载时执行，动态注入用户上下文信息。 +- 文件: `hooks/pre_prompt.py` +- 用途: 查询用户信息、偏好设置、历史记录等，注入到 prompt 中 + +### PostAgent Hook +在 agent 执行完成后执行，用于后处理。 +- 文件: `hooks/post_agent.py` +- 用途: 记录分析数据、触发异步任务、发送通知等 + +### PreSave Hook +在消息保存前执行，用于内容处理。 +- 文件: `hooks/pre_save.py` +- 用途: 内容过滤、敏感信息脱敏、格式转换等 + +## 目录结构 + +``` +user-context-loader/ +├── README.md # Skill 说明文档 +├── .claude-plugin/ +│ └── plugin.json # Hook 和 MCP 配置文件 +└── hooks/ + ├── pre_prompt.py # PrePrompt hook 脚本 + ├── post_agent.py # PostAgent hook 脚本 + └── pre_save.py # PreSave hook 脚本 +``` + +## plugin.json 格式 + +```json +{ + "name": "user-context-loader", + "description": "用户上下文加载器示例 Skill", + "hooks": { + "PrePrompt": [ + { + "type": "command", + "command": "python hooks/pre_prompt.py" + } + ], + "PostAgent": [ + { + "type": "command", + "command": "python hooks/post_agent.py" + } + ], + "PreSave": [ + { + "type": "command", + "command": "python hooks/pre_save.py" + } + ] + }, + "mcpServers": { + "server-name": { + "command": "node", + "args": ["path/to/server.js"], + "env": { + "API_KEY": "${API_KEY}" + } + } + } +} +``` + +## Hook 脚本格式 + +Hook 脚本通过子进程执行，通过环境变量接收参数，通过 stdout 返回结果。 + +### 可用环境变量 + +| 环境变量 | 说明 | 适用于 | +|---------|------|--------| +| `ASSISTANT_ID` | Bot ID | 所有 hook | +| `USER_IDENTIFIER` | 用户标识 | 所有 hook | +| `SESSION_ID` | 会话 ID | 所有 hook | +| `LANGUAGE` | 语言代码 | 所有 hook | +| `HOOK_TYPE` | Hook 类型 | 所有 hook | +| `CONTENT` | 消息内容 | PreSave | +| `ROLE` | 消息角色 | PreSave | +| `RESPONSE` | Agent 响应 | PostAgent | +| `METADATA` | 元数据 JSON | PostAgent | + +### PrePrompt 示例 + +```python +#!/usr/bin/env python3 +import os +import sys + +def main(): + user_identifier = os.environ.get('USER_IDENTIFIER', '') + bot_id = os.environ.get('ASSISTANT_ID', '') + + # 输出要注入到 prompt 中的内容 + print(f"## User Context\n\n用户: {user_identifier}") + return 0 + +if __name__ == '__main__': + sys.exit(main()) +``` + +### PreSave 示例 + +```python +#!/usr/bin/env python3 +import os +import sys + +def main(): + content = os.environ.get('CONTENT', '') + + # 处理内容并输出 + print(content) # 输出处理后的内容 + return 0 + +if __name__ == '__main__': + sys.exit(main()) +``` + +### PostAgent 示例 + +```python +#!/usr/bin/env python3 +import os +import sys + +def main(): + response = os.environ.get('RESPONSE', '') + session_id = os.environ.get('SESSION_ID', '') + + # 记录日志（输出到 stderr） + print(f"Session {session_id}: Response length {len(response)}", file=sys.stderr) + return 0 + +if __name__ == '__main__': + sys.exit(main()) +``` + +## 使用场景 + +1. **PrePrompt**: 用户登录时自动加载其偏好设置、历史订单等 +2. **PostAgent**: 记录对话分析数据，触发后续业务流程 +3. **PreSave**: 敏感信息脱敏后再存储，如手机号、邮箱等 diff --git a/skills_autoload/rag-retrieve/hooks/pre_prompt.py b/skills_autoload/rag-retrieve/hooks/pre_prompt.py new file mode 100644 index 0000000..11f445d --- /dev/null +++ b/skills_autoload/rag-retrieve/hooks/pre_prompt.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 +""" +PreMemoryPrompt Hook - 用户上下文加载器示例 + +在记忆提取提示词（FACT_RETRIEVAL_PROMPT）加载时执行， +读取同目录下的 memory_prompt.md 作为自定义记忆提取提示词模板。 +""" +import sys +from pathlib import Path + + +def main(): + prompt_file = Path(__file__).parent / "retrieval-policy.md" + if prompt_file.exists(): + print(prompt_file.read_text(encoding="utf-8")) + return 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/skills_autoload/rag-retrieve/hooks/retrieval-policy.md b/skills_autoload/rag-retrieve/hooks/retrieval-policy.md new file mode 100644 index 0000000..6527185 --- /dev/null +++ b/skills_autoload/rag-retrieve/hooks/retrieval-policy.md @@ -0,0 +1,53 @@ +# Retrieval Policy + +### 1. Retrieval Order and Tool Selection +- Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations. +- Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve` > local filesystem retrieval. +- Do NOT answer from model knowledge first. +- Do NOT skip directly to local filesystem retrieval when an earlier retrieval source may answer the question. +- When a suitable skill-enabled knowledge retrieval tool is available, use it first. +- If no suitable skill-enabled retrieval tool is available, or if its result is insufficient, continue with `rag_retrieve` or `table_rag_retrieve`. +- Use `table_rag_retrieve` first for values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, extraction, lists, tables, name lookup, historical coverage, mixed questions, and unclear cases. +- Use `rag_retrieve` first only for clearly pure concept, definition, workflow, policy, or explanation questions without structured data needs. +- After each retrieval step, evaluate sufficiency before moving to the next source. Do NOT run these retrieval sources in parallel. + +### 2. Query Preparation +- Do NOT pass the raw user question unless it already works well for retrieval. +- Rewrite for recall: extract entity, time scope, attributes, and intent. +- Add useful variants: synonyms, aliases, abbreviations, related titles, historical names, and category terms. +- Expand list-style, extraction, overview, historical, roster, timeline, and archive queries more aggressively. +- Preserve meaning. Do NOT introduce unrelated topics. + +### 3. Retrieval Breadth (`top_k`) +- Apply `top_k` only to `rag_retrieve`. Use the smallest sufficient value, then expand only if coverage is insufficient. +- Use `30` for simple fact lookup. +- Use `50` for moderate synthesis, comparison, summarization, or disambiguation. +- Use `100` for broad recall, such as comprehensive analysis, scattered knowledge, multiple entities or periods, or list / catalog / timeline / roster / overview requests. +- Raise `top_k` when keyword branches are many or results are too few, repetitive, incomplete, sparse, or too narrow. +- Use this expansion order: `30 -> 50 -> 100`. If unsure, use `100`. + +### 4. Result Evaluation +- Treat results as insufficient if they are empty, start with `Error:`, say `no excel files found`, are off-topic, miss the core entity or scope, or provide no usable evidence. +- Also treat results as insufficient when they cover only part of the request, or when full-list, historical, comparison, or mixed data + explanation requests return only partial or truncated coverage. + +### 5. Fallback and Sequential Retry +- If the first retrieval result is insufficient, call the next retrieval source in the default order before replying. +- If the first RAG tool is insufficient, call the other RAG tool next before moving to local filesystem retrieval. +- If `table_rag_retrieve` is insufficient or empty, continue with `rag_retrieve`. +- If `rag_retrieve` is insufficient or empty, continue with `table_rag_retrieve`. +- If both `rag_retrieve` and `table_rag_retrieve` are insufficient, continue with local filesystem retrieval. +- Say no relevant information was found only after all applicable skill-enabled retrieval tools, both `rag_retrieve` and `table_rag_retrieve`, and local filesystem retrieval have been tried and still do not provide enough evidence. +- Do NOT reply that no relevant information was found before the final local filesystem fallback has also been tried. + +### 6. Table RAG Result Handling +- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` content in `table_rag_retrieve` results. +- If results are truncated, explicitly tell the user total matches (`N+M`), displayed count (`N`), and omitted count (`M`). +- Cite data sources using filenames from `file_ref_table`. + +### 7. Citation Requirements for Retrieved Knowledge +- When using knowledge from `rag_retrieve` or `table_rag_retrieve`, you MUST generate `` tags. +- Follow the citation format returned by each tool. +- Place citations immediately after the paragraph or bullet list that uses the knowledge. +- Do NOT collect citations at the end. +- Use 1-2 citations per paragraph or bullet list when possible. +- If learned knowledge is used, include at least 1 ``. diff --git a/skills_autoload/rag-retrieve/mcp_common.py b/skills_autoload/rag-retrieve/mcp_common.py new file mode 100644 index 0000000..5bf5935 --- /dev/null +++ b/skills_autoload/rag-retrieve/mcp_common.py @@ -0,0 +1,251 @@ +#!/usr/bin/env python3 +""" +MCP服务器通用工具函数 +提供路径处理、文件验证、请求处理等公共功能 +""" + +import json +import os +import sys +import asyncio +from typing import Any, Dict, List, Optional, Union +import re + +def get_allowed_directory(): + """获取允许访问的目录""" + # 优先使用命令行参数传入的dataset_dir + if len(sys.argv) > 1: + dataset_dir = sys.argv[1] + return os.path.abspath(dataset_dir) + + # 从环境变量读取项目数据目录 + project_dir = os.getenv("PROJECT_DATA_DIR", "./projects/data") + return os.path.abspath(project_dir) + + +def resolve_file_path(file_path: str, default_subfolder: str = "default") -> str: + """ + 解析文件路径，支持 folder/document.txt 和 document.txt 两种格式 + + Args: + file_path: 输入的文件路径 + default_subfolder: 当只传入文件名时使用的默认子文件夹名称 + + Returns: + 解析后的完整文件路径 + """ + # 如果路径包含文件夹分隔符，直接使用 + if '/' in file_path or '\\' in file_path: + clean_path = file_path.replace('\\', '/') + + # 移除 projects/ 前缀（如果存在） + if clean_path.startswith('projects/'): + clean_path = clean_path[9:] # 移除 'projects/' 前缀 + elif clean_path.startswith('./projects/'): + clean_path = clean_path[11:] # 移除 './projects/' 前缀 + else: + # 如果只有文件名，添加默认子文件夹 + clean_path = f"{default_subfolder}/{file_path}" + + # 获取允许的目录 + project_data_dir = get_allowed_directory() + + # 尝试在项目目录中查找文件 + full_path = os.path.join(project_data_dir, clean_path.lstrip('./')) + if os.path.exists(full_path): + return full_path + + # 如果直接路径不存在，尝试递归查找 + found = find_file_in_project(clean_path, project_data_dir) + if found: + return found + + # 如果是纯文件名且在default子文件夹中不存在，尝试在根目录查找 + if '/' not in file_path and '\\' not in file_path: + root_path = os.path.join(project_data_dir, file_path) + if os.path.exists(root_path): + return root_path + + raise FileNotFoundError(f"File not found: {file_path} (searched in {project_data_dir})") + + +def find_file_in_project(filename: str, project_dir: str) -> Optional[str]: + """在项目目录中递归查找文件""" + # 如果filename包含路径，只搜索指定的路径 + if '/' in filename: + parts = filename.split('/') + target_file = parts[-1] + search_dir = os.path.join(project_dir, *parts[:-1]) + + if os.path.exists(search_dir): + target_path = os.path.join(search_dir, target_file) + if os.path.exists(target_path): + return target_path + else: + # 纯文件名，递归搜索整个项目目录 + for root, dirs, files in os.walk(project_dir): + if filename in files: + return os.path.join(root, filename) + return None + + +def load_tools_from_json(tools_file_name: str) -> List[Dict[str, Any]]: + """从 JSON 文件加载工具定义""" + try: + tools_file = os.path.join(os.path.dirname(__file__), tools_file_name) + if os.path.exists(tools_file): + with open(tools_file, 'r', encoding='utf-8') as f: + return json.load(f) + else: + # 如果 JSON 文件不存在，使用默认定义 + return [] + except Exception as e: + print(f"Warning: Unable to load tool definition JSON file: {str(e)}") + return [] + + +def create_error_response(request_id: Any, code: int, message: str) -> Dict[str, Any]: + """创建标准化的错误响应""" + return { + "jsonrpc": "2.0", + "id": request_id, + "error": { + "code": code, + "message": message + } + } + + +def create_success_response(request_id: Any, result: Any) -> Dict[str, Any]: + """创建标准化的成功响应""" + return { + "jsonrpc": "2.0", + "id": request_id, + "result": result + } + + +def create_initialize_response(request_id: Any, server_name: str, server_version: str = "1.0.0") -> Dict[str, Any]: + """创建标准化的初始化响应""" + return { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": { + "tools": {} + }, + "serverInfo": { + "name": server_name, + "version": server_version + } + } + } + + +def create_ping_response(request_id: Any) -> Dict[str, Any]: + """创建标准化的ping响应""" + return { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "pong": True + } + } + + +def create_tools_list_response(request_id: Any, tools: List[Dict[str, Any]]) -> Dict[str, Any]: + """创建标准化的工具列表响应""" + return { + "jsonrpc": "2.0", + "id": request_id, + "result": { + "tools": tools + } + } + + +def is_regex_pattern(pattern: str) -> bool: + """检测字符串是否为正则表达式模式""" + # 检查 /pattern/ 格式 + if pattern.startswith('/') and pattern.endswith('/') and len(pattern) > 2: + return True + + # 检查 r"pattern" 或 r'pattern' 格式 + if pattern.startswith(('r"', "r'")) and pattern.endswith(('"', "'")) and len(pattern) > 3: + return True + + # 检查是否包含正则特殊字符 + regex_chars = {'*', '+', '?', '|', '(', ')', '[', ']', '{', '}', '^', '$', '\\', '.'} + return any(char in pattern for char in regex_chars) + + +def compile_pattern(pattern: str) -> Union[re.Pattern, str, None]: + """编译正则表达式模式，如果不是正则则返回原字符串""" + if not is_regex_pattern(pattern): + return pattern + + try: + # 处理 /pattern/ 格式 + if pattern.startswith('/') and pattern.endswith('/'): + regex_body = pattern[1:-1] + return re.compile(regex_body) + + # 处理 r"pattern" 或 r'pattern' 格式 + if pattern.startswith(('r"', "r'")) and pattern.endswith(('"', "'")): + regex_body = pattern[2:-1] + return re.compile(regex_body) + + # 直接编译包含正则字符的字符串 + return re.compile(pattern) + except re.error as e: + # 如果编译失败，返回None表示无效的正则 + print(f"Warning: Regular expression '{pattern}' compilation failed: {e}") + return None + + +async def handle_mcp_streaming(request_handler): + """处理MCP请求的标准主循环""" + try: + while True: + # Read from stdin + line = await asyncio.get_event_loop().run_in_executor(None, sys.stdin.readline) + if not line: + break + + line = line.strip() + if not line: + continue + + try: + request = json.loads(line) + response = await request_handler(request) + + # Write to stdout + sys.stdout.write(json.dumps(response, ensure_ascii=False) + "\n") + sys.stdout.flush() + + except json.JSONDecodeError: + error_response = { + "jsonrpc": "2.0", + "error": { + "code": -32700, + "message": "Parse error" + } + } + sys.stdout.write(json.dumps(error_response, ensure_ascii=False) + "\n") + sys.stdout.flush() + + except Exception as e: + error_response = { + "jsonrpc": "2.0", + "error": { + "code": -32603, + "message": f"Internal error: {str(e)}" + } + } + sys.stdout.write(json.dumps(error_response, ensure_ascii=False) + "\n") + sys.stdout.flush() + + except KeyboardInterrupt: + pass diff --git a/mcp/rag_retrieve_server.py b/skills_autoload/rag-retrieve/rag_retrieve_server.py similarity index 100% rename from mcp/rag_retrieve_server.py rename to skills_autoload/rag-retrieve/rag_retrieve_server.py diff --git a/mcp/tools/rag_retrieve_tools.json b/skills_autoload/rag-retrieve/rag_retrieve_tools.json similarity index 100% rename from mcp/tools/rag_retrieve_tools.json rename to skills_autoload/rag-retrieve/rag_retrieve_tools.json diff --git a/utils/multi_project_manager.py b/utils/multi_project_manager.py index be29923..a5bca91 100644 --- a/utils/multi_project_manager.py +++ b/utils/multi_project_manager.py @@ -320,6 +320,12 @@ def create_robot_project(dataset_ids: List[str], bot_id: str, force_rebuild: boo str: 机器人项目目录路径 """ + skills = list(skills or []) + has_rag_retrieve = any(Path(skill.lstrip("@")).name == "rag-retrieve" for skill in skills) + if dataset_ids and not has_rag_retrieve: + skills.append("@skills_autoload/rag-retrieve") + logger.info("Auto loaded skill '@skills_autoload/rag-retrieve' because dataset_ids is not empty") + logger.info(f"Ensuring robot project exists: {bot_id}, skills: {skills}") # 创建机器人目录结构（如果不存在） @@ -375,27 +381,27 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path) - 如果是简单名称（如 "rag-retrieve"），从以下目录按优先级顺序查找： 1. projects/uploads/{bot_id}/skills/ 2. skills/ + - 如果是以 @ 开头的仓库相对路径（如 "@skills_autoload/rag-retrieve"），则从仓库根目录直接解析 搜索目录优先级：先搜索 projects/uploads/{bot_id}/skills/，再搜索 skills/ Args: bot_id: 机器人 ID - skills: 技能文件名列表（如 ["rag-retrieve", "projects/uploads/{bot_id}/skills/rag-retrieve"]） + skills: 技能文件名列表（如 ["rag-retrieve", "@skills_autoload/rag-retrieve", "projects/uploads/{bot_id}/skills/rag-retrieve"]） project_path: 项目路径 """ - import zipfile - # skills 源目录（按优先级顺序） + repo_root = Path(__file__).resolve().parent.parent skills_source_dirs = [ project_path / "uploads" / bot_id / "skills", - Path("skills"), + repo_root / "skills", ] skills_target_dir = project_path / "robot" / bot_id / "skills" skills_target_dir.mkdir(parents=True, exist_ok=True) logger.info(f"Copying skills to {skills_target_dir}") # 清理不在列表中的多余 skill 文件夹 - expected_skill_names = {os.path.basename(skill) for skill in skills} + expected_skill_names = {Path(skill.lstrip("@")).name for skill in skills} if skills_target_dir.exists(): for item in skills_target_dir.iterdir(): if item.is_dir() and item.name not in expected_skill_names: @@ -403,7 +409,8 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path) shutil.rmtree(item) for skill in skills: - target_dir = skills_target_dir / os.path.basename(skill) + skill_name = Path(skill.lstrip("@")).name + target_dir = skills_target_dir / skill_name # 如果目标目录已存在，跳过复制 if target_dir.exists(): @@ -412,20 +419,25 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path) source_dir = None - # 简单名称：按优先级顺序在多个目录中查找 - for base_dir in skills_source_dirs: - candidate_dir = base_dir / skill + if skill.startswith("@"): + candidate_dir = repo_root / skill.lstrip("@") if candidate_dir.exists(): source_dir = candidate_dir - logger.info(f" Found skill '{skill}' in {base_dir}") - break + logger.info(f" Found skill '{skill}' at {candidate_dir}") + + # 简单名称：按优先级顺序在多个目录中查找 + if source_dir is None: + for base_dir in skills_source_dirs: + candidate_dir = base_dir / skill + if candidate_dir.exists(): + source_dir = candidate_dir + logger.info(f" Found skill '{skill}' in {base_dir}") + break if source_dir is None: logger.warning(f" Skill directory '{skill}' not found in any source directory: {[str(d) for d in skills_source_dirs]}") continue - target_dir = skills_target_dir / os.path.basename(skill) - try: shutil.copytree(source_dir, target_dir) logger.info(f" Copied: {source_dir} -> {target_dir}")