工具
This commit is contained in:
608
backend/app/services/builtin_tools.py
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608
backend/app/services/builtin_tools.py
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"""
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内置工具实现
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"""
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from typing import Dict, Any, Optional, List
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import httpx
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import json
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import os
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import logging
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import re
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import math
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from datetime import datetime, timedelta
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import platform
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import sys
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logger = logging.getLogger(__name__)
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async def http_request_tool(
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url: str,
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method: str = "GET",
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headers: Optional[Dict[str, str]] = None,
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body: Any = None
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) -> str:
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"""
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HTTP请求工具
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Args:
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url: 请求URL
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method: HTTP方法 (GET, POST, PUT, DELETE)
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headers: 请求头
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body: 请求体（POST/PUT时使用）
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Returns:
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JSON格式的响应结果
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"""
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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method_upper = method.upper()
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if method_upper == "GET":
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response = await client.get(url, headers=headers)
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elif method_upper == "POST":
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response = await client.post(url, json=body, headers=headers)
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elif method_upper == "PUT":
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response = await client.put(url, json=body, headers=headers)
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elif method_upper == "DELETE":
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response = await client.delete(url, headers=headers)
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else:
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raise ValueError(f"不支持的HTTP方法: {method}")
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# 尝试解析JSON响应
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try:
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response_body = response.json()
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except:
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response_body = response.text
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result = {
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"status_code": response.status_code,
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"headers": dict(response.headers),
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"body": response_body
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}
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return json.dumps(result, ensure_ascii=False)
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except Exception as e:
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logger.error(f"HTTP请求工具执行失败: {str(e)}")
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return json.dumps({"error": str(e)}, ensure_ascii=False)
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async def file_read_tool(file_path: str) -> str:
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"""
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文件读取工具
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Args:
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file_path: 文件路径
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Returns:
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文件内容或错误信息
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"""
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try:
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# 安全检查：限制可读取的文件路径
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# 只允许读取项目目录下的文件
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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abs_path = os.path.abspath(file_path)
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if not abs_path.startswith(project_root):
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return json.dumps({
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"error": f"不允许读取项目目录外的文件: {file_path}"
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}, ensure_ascii=False)
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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return json.dumps({
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"file_path": file_path,
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"content": content,
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"size": len(content)
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}, ensure_ascii=False)
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except FileNotFoundError:
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return json.dumps({
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"error": f"文件不存在: {file_path}"
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"文件读取工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def file_write_tool(file_path: str, content: str, mode: str = "w") -> str:
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"""
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文件写入工具
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Args:
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file_path: 文件路径
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content: 要写入的内容
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mode: 写入模式（w=覆盖，a=追加）
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Returns:
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写入结果
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"""
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try:
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# 安全检查：限制可写入的文件路径
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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abs_path = os.path.abspath(file_path)
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if not abs_path.startswith(project_root):
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return json.dumps({
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"error": f"不允许写入项目目录外的文件: {file_path}"
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}, ensure_ascii=False)
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write_mode = "a" if mode == "a" else "w"
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with open(file_path, write_mode, encoding='utf-8') as f:
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f.write(content)
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return json.dumps({
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"success": True,
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"file_path": file_path,
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"mode": write_mode,
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"content_length": len(content)
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"文件写入工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def text_analyze_tool(text: str, operation: str = "count") -> str:
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"""
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文本分析工具
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Args:
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text: 要分析的文本
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operation: 操作类型（count=统计, keywords=提取关键词, summary=摘要）
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Returns:
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分析结果
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"""
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try:
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if operation == "count":
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# 统计字数、字符数、行数等
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char_count = len(text)
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char_count_no_spaces = len(text.replace(" ", ""))
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word_count = len(text.split())
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line_count = len(text.splitlines())
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paragraph_count = len([p for p in text.split("\n\n") if p.strip()])
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return json.dumps({
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"char_count": char_count,
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"char_count_no_spaces": char_count_no_spaces,
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"word_count": word_count,
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"line_count": line_count,
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"paragraph_count": paragraph_count
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}, ensure_ascii=False)
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elif operation == "keywords":
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# 简单的关键词提取（基于词频）
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words = re.findall(r'\b\w+\b', text.lower())
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word_freq = {}
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for word in words:
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if len(word) > 2: # 忽略太短的词
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word_freq[word] = word_freq.get(word, 0) + 1
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# 取频率最高的10个词
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top_keywords = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:10]
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return json.dumps({
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"keywords": [{"word": k, "frequency": v} for k, v in top_keywords]
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}, ensure_ascii=False)
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elif operation == "summary":
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# 简单的摘要（取前3句）
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sentences = re.split(r'[.!?。！？]\s+', text)
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summary = ". ".join(sentences[:3]) + "."
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return json.dumps({
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"summary": summary,
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"original_length": len(text),
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"summary_length": len(summary)
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}, ensure_ascii=False)
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else:
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return json.dumps({
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"error": f"不支持的操作类型: {operation}"
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"文本分析工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def datetime_tool(operation: str = "now", format: str = "%Y-%m-%d %H:%M:%S",
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timezone: Optional[str] = None) -> str:
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"""
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日期时间工具
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Args:
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operation: 操作类型（now=当前时间, format=格式化, parse=解析）
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format: 时间格式
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timezone: 时区（暂未实现）
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Returns:
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日期时间结果
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"""
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try:
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if operation == "now":
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now = datetime.now()
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return json.dumps({
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"datetime": now.strftime(format),
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"timestamp": now.timestamp(),
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"iso_format": now.isoformat()
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}, ensure_ascii=False)
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elif operation == "format":
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now = datetime.now()
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return json.dumps({
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"formatted": now.strftime(format)
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}, ensure_ascii=False)
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else:
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return json.dumps({
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"error": f"不支持的操作类型: {operation}"
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"日期时间工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def math_calculate_tool(expression: str) -> str:
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"""
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数学计算工具（安全限制：只允许基本数学运算）
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Args:
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expression: 数学表达式（如 "2+2", "sqrt(16)", "sin(0)"）
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Returns:
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计算结果
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"""
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try:
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# 安全检查：只允许数字、基本运算符和安全的数学函数
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allowed_chars = set("0123456789+-*/.() ")
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allowed_functions = ['sqrt', 'sin', 'cos', 'tan', 'log', 'exp', 'abs', 'pow']
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# 检查表达式是否安全
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if not all(c in allowed_chars or any(f in expression for f in allowed_functions) for c in expression):
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return json.dumps({
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"error": "表达式包含不安全的字符"
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}, ensure_ascii=False)
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# 构建安全的数学环境
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safe_dict = {
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"__builtins__": {},
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"abs": abs,
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"sqrt": math.sqrt,
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"sin": math.sin,
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"cos": math.cos,
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"tan": math.tan,
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"log": math.log,
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"exp": math.exp,
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"pow": pow,
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"pi": math.pi,
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"e": math.e
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}
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result = eval(expression, safe_dict)
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return json.dumps({
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"expression": expression,
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"result": result,
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"result_type": type(result).__name__
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"数学计算工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def system_info_tool() -> str:
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"""
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系统信息工具
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Returns:
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系统信息
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"""
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try:
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info = {
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"platform": platform.system(),
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"platform_version": platform.version(),
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"architecture": platform.machine(),
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"processor": platform.processor(),
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"python_version": sys.version,
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"python_version_info": {
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"major": sys.version_info.major,
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"minor": sys.version_info.minor,
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"micro": sys.version_info.micro
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}
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}
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return json.dumps(info, ensure_ascii=False)
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except Exception as e:
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logger.error(f"系统信息工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def json_process_tool(json_string: str, operation: str = "parse") -> str:
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"""
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JSON处理工具
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Args:
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json_string: JSON字符串
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operation: 操作类型（parse=解析, stringify=序列化, validate=验证）
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Returns:
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处理结果
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"""
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try:
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if operation == "parse":
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data = json.loads(json_string)
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return json.dumps({
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"parsed": data,
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"type": type(data).__name__
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}, ensure_ascii=False)
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elif operation == "stringify":
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# 如果输入已经是字符串，尝试解析后再序列化
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try:
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data = json.loads(json_string)
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except:
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data = json_string
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return json.dumps({
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"stringified": json.dumps(data, ensure_ascii=False, indent=2)
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}, ensure_ascii=False)
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elif operation == "validate":
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try:
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json.loads(json_string)
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return json.dumps({
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"valid": True
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}, ensure_ascii=False)
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except json.JSONDecodeError as e:
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return json.dumps({
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"valid": False,
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"error": str(e)
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}, ensure_ascii=False)
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else:
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return json.dumps({
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"error": f"不支持的操作类型: {operation}"
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"JSON处理工具执行失败: {str(e)}")
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return json.dumps({
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"error": str(e)
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}, ensure_ascii=False)
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async def database_query_tool(query: str, database: str = "default") -> str:
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"""
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数据库查询工具（占位实现）
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Args:
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query: SQL查询语句
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database: 数据库名称
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Returns:
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查询结果
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"""
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# TODO: 实现数据库查询逻辑
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return json.dumps({
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"error": "数据库查询工具尚未实现",
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"query": query,
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"database": database
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}, ensure_ascii=False)
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# 工具定义（OpenAI Function格式）
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HTTP_REQUEST_SCHEMA = {
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"type": "function",
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"function": {
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"name": "http_request",
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"description": "发送HTTP请求，支持GET、POST、PUT、DELETE方法。可以用于调用API、获取网页内容等。",
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"parameters": {
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"type": "object",
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"properties": {
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"url": {
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"type": "string",
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"description": "请求的URL地址"
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},
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"method": {
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"type": "string",
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"enum": ["GET", "POST", "PUT", "DELETE"],
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"description": "HTTP请求方法",
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"default": "GET"
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},
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"headers": {
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"type": "object",
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"description": "HTTP请求头（可选）",
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"additionalProperties": {
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||||
"type": "string"
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}
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},
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"body": {
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"type": "object",
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||||
"description": "请求体（POST/PUT时使用，可选）"
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||||
}
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||||
},
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"required": ["url", "method"]
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}
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}
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}
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FILE_READ_SCHEMA = {
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"type": "function",
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"function": {
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"name": "file_read",
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"description": "读取文件内容。只能读取项目目录下的文件，确保文件路径正确。",
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"parameters": {
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"type": "object",
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"properties": {
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"file_path": {
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"type": "string",
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"description": "要读取的文件路径（相对于项目根目录或绝对路径）"
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}
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},
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"required": ["file_path"]
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}
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}
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}
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FILE_WRITE_SCHEMA = {
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"type": "function",
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"function": {
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"name": "file_write",
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"description": "写入文件内容。只能写入项目目录下的文件，确保文件路径正确。",
|
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"parameters": {
|
||||
"type": "object",
|
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"properties": {
|
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"file_path": {
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"type": "string",
|
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"description": "要写入的文件路径（相对于项目根目录或绝对路径）"
|
||||
},
|
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"content": {
|
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"type": "string",
|
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"description": "要写入的内容"
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},
|
||||
"mode": {
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"type": "string",
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"enum": ["w", "a"],
|
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"description": "写入模式：w=覆盖写入，a=追加写入",
|
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"default": "w"
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||||
}
|
||||
},
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||||
"required": ["file_path", "content"]
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||||
}
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||||
}
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||||
}
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|
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TEXT_ANALYZE_SCHEMA = {
|
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"type": "function",
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"function": {
|
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"name": "text_analyze",
|
||||
"description": "分析文本内容，支持统计字数、提取关键词、生成摘要等功能。",
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||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string",
|
||||
"description": "要分析的文本内容"
|
||||
},
|
||||
"operation": {
|
||||
"type": "string",
|
||||
"enum": ["count", "keywords", "summary"],
|
||||
"description": "操作类型：count=统计字数等信息，keywords=提取关键词，summary=生成摘要",
|
||||
"default": "count"
|
||||
}
|
||||
},
|
||||
"required": ["text"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
DATETIME_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "datetime",
|
||||
"description": "获取和处理日期时间信息，支持获取当前时间、格式化时间等。",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"operation": {
|
||||
"type": "string",
|
||||
"enum": ["now", "format"],
|
||||
"description": "操作类型：now=获取当前时间，format=格式化时间",
|
||||
"default": "now"
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"description": "时间格式字符串（如：%Y-%m-%d %H:%M:%S）",
|
||||
"default": "%Y-%m-%d %H:%M:%S"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
MATH_CALCULATE_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "math_calculate",
|
||||
"description": "执行数学计算，支持基本运算和常用数学函数（如sqrt, sin, cos, log等）。",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"expression": {
|
||||
"type": "string",
|
||||
"description": "数学表达式（如：2+2, sqrt(16), sin(0), pow(2,3)）"
|
||||
}
|
||||
},
|
||||
"required": ["expression"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
SYSTEM_INFO_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "system_info",
|
||||
"description": "获取系统信息，包括操作系统、Python版本等。",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
JSON_PROCESS_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "json_process",
|
||||
"description": "处理JSON数据，支持解析、序列化、验证等功能。",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"json_string": {
|
||||
"type": "string",
|
||||
"description": "JSON字符串"
|
||||
},
|
||||
"operation": {
|
||||
"type": "string",
|
||||
"enum": ["parse", "stringify", "validate"],
|
||||
"description": "操作类型：parse=解析JSON，stringify=序列化为JSON，validate=验证JSON格式",
|
||||
"default": "parse"
|
||||
}
|
||||
},
|
||||
"required": ["json_string"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
DATABASE_QUERY_SCHEMA = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "database_query",
|
||||
"description": "执行数据库查询（暂未实现）",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "SQL查询语句"
|
||||
},
|
||||
"database": {
|
||||
"type": "string",
|
||||
"description": "数据库名称",
|
||||
"default": "default"
|
||||
}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,10 +1,15 @@
|
||||
"""
|
||||
LLM服务 - 处理各种LLM提供商的调用
|
||||
"""
|
||||
from typing import Dict, Any, Optional
|
||||
from typing import Dict, Any, Optional, List
|
||||
import json
|
||||
import asyncio
|
||||
import logging
|
||||
from openai import AsyncOpenAI
|
||||
from app.core.config import settings
|
||||
from app.services.tool_registry import tool_registry
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LLMService:
|
||||
@@ -219,6 +224,257 @@ class LLMService:
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"不支持的LLM提供商: {provider}，目前支持: openai, deepseek")
|
||||
|
||||
async def call_openai_with_tools(
|
||||
self,
|
||||
prompt: str,
|
||||
tools: List[Dict[str, Any]],
|
||||
model: str = "gpt-3.5-turbo",
|
||||
temperature: float = 0.7,
|
||||
max_tokens: Optional[int] = None,
|
||||
api_key: Optional[str] = None,
|
||||
base_url: Optional[str] = None,
|
||||
max_iterations: int = 5
|
||||
) -> str:
|
||||
"""
|
||||
调用OpenAI API，支持工具调用
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
tools: 工具定义列表（OpenAI Function格式）
|
||||
model: 模型名称
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
api_key: API密钥
|
||||
base_url: API地址
|
||||
max_iterations: 最大工具调用迭代次数
|
||||
|
||||
Returns:
|
||||
LLM返回的最终文本
|
||||
"""
|
||||
# 获取客户端
|
||||
if api_key is not None or base_url is not None:
|
||||
final_api_key = api_key if api_key else settings.OPENAI_API_KEY
|
||||
final_base_url = base_url if base_url else settings.OPENAI_BASE_URL
|
||||
if not final_api_key:
|
||||
raise ValueError("OpenAI API Key未配置")
|
||||
client = AsyncOpenAI(api_key=final_api_key, base_url=final_base_url)
|
||||
else:
|
||||
if not self.openai_client:
|
||||
if settings.OPENAI_API_KEY:
|
||||
self.openai_client = AsyncOpenAI(
|
||||
api_key=settings.OPENAI_API_KEY,
|
||||
base_url=settings.OPENAI_BASE_URL
|
||||
)
|
||||
else:
|
||||
raise ValueError("OpenAI API Key未配置")
|
||||
client = self.openai_client
|
||||
|
||||
messages = [{"role": "user", "content": prompt}]
|
||||
|
||||
try:
|
||||
for iteration in range(max_iterations):
|
||||
# 准备工具参数（只在第一次调用时传递tools）
|
||||
create_kwargs = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": temperature,
|
||||
"max_tokens": max_tokens
|
||||
}
|
||||
|
||||
if iteration == 0:
|
||||
# 转换工具格式为OpenAI格式
|
||||
openai_tools = []
|
||||
for tool in tools:
|
||||
if isinstance(tool, dict):
|
||||
if "type" in tool and tool["type"] == "function":
|
||||
openai_tools.append(tool)
|
||||
elif "function" in tool:
|
||||
openai_tools.append(tool)
|
||||
else:
|
||||
# 假设是function格式，包装一下
|
||||
openai_tools.append({
|
||||
"type": "function",
|
||||
"function": tool
|
||||
})
|
||||
create_kwargs["tools"] = openai_tools
|
||||
create_kwargs["tool_choice"] = "auto"
|
||||
|
||||
# 调用LLM
|
||||
response = await client.chat.completions.create(**create_kwargs)
|
||||
|
||||
message = response.choices[0].message
|
||||
|
||||
# 添加助手回复到消息历史
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": message.content,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tc.id,
|
||||
"type": tc.type,
|
||||
"function": {
|
||||
"name": tc.function.name,
|
||||
"arguments": tc.function.arguments
|
||||
}
|
||||
} for tc in (message.tool_calls or [])
|
||||
]
|
||||
})
|
||||
|
||||
# 检查是否有工具调用
|
||||
if message.tool_calls and len(message.tool_calls) > 0:
|
||||
logger.info(f"检测到 {len(message.tool_calls)} 个工具调用")
|
||||
# 处理每个工具调用
|
||||
for tool_call in message.tool_calls:
|
||||
tool_name = tool_call.function.name
|
||||
try:
|
||||
tool_args = json.loads(tool_call.function.arguments)
|
||||
except:
|
||||
tool_args = {}
|
||||
|
||||
logger.info(f"执行工具: {tool_name}, 参数: {tool_args}")
|
||||
|
||||
# 执行工具
|
||||
tool_result = await self._execute_tool(tool_name, tool_args)
|
||||
|
||||
# 添加工具结果到消息历史
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call.id,
|
||||
"content": tool_result
|
||||
})
|
||||
else:
|
||||
# 没有工具调用，返回最终回复
|
||||
final_content = message.content or ""
|
||||
if final_content:
|
||||
logger.info("LLM返回最终回复，工具调用完成")
|
||||
return final_content
|
||||
|
||||
# 达到最大迭代次数
|
||||
logger.warning(f"达到最大工具调用迭代次数 ({max_iterations})")
|
||||
last_message = messages[-1] if messages else {}
|
||||
return last_message.get("content", "达到最大工具调用次数")
|
||||
except Exception as e:
|
||||
logger.error(f"工具调用过程中出错: {str(e)}")
|
||||
raise Exception(f"OpenAI工具调用失败: {str(e)}")
|
||||
|
||||
async def call_deepseek_with_tools(
|
||||
self,
|
||||
prompt: str,
|
||||
tools: List[Dict[str, Any]],
|
||||
model: str = "deepseek-chat",
|
||||
temperature: float = 0.7,
|
||||
max_tokens: Optional[int] = None,
|
||||
api_key: Optional[str] = None,
|
||||
base_url: Optional[str] = None,
|
||||
max_iterations: int = 5
|
||||
) -> str:
|
||||
"""
|
||||
调用DeepSeek API，支持工具调用（DeepSeek兼容OpenAI API格式）
|
||||
"""
|
||||
# DeepSeek使用相同的实现
|
||||
return await self.call_openai_with_tools(
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
api_key=api_key or settings.DEEPSEEK_API_KEY,
|
||||
base_url=base_url or settings.DEEPSEEK_BASE_URL,
|
||||
max_iterations=max_iterations
|
||||
)
|
||||
|
||||
async def call_llm_with_tools(
|
||||
self,
|
||||
prompt: str,
|
||||
tools: List[Dict[str, Any]],
|
||||
provider: str = "openai",
|
||||
model: Optional[str] = None,
|
||||
temperature: float = 0.7,
|
||||
max_tokens: Optional[int] = None,
|
||||
**kwargs
|
||||
) -> str:
|
||||
"""
|
||||
通用LLM调用接口（支持工具）
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
tools: 工具定义列表
|
||||
provider: 提供商，支持openai、deepseek
|
||||
model: 模型名称
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
**kwargs: 其他参数
|
||||
|
||||
Returns:
|
||||
LLM返回的最终文本
|
||||
"""
|
||||
if provider == "openai":
|
||||
if not model:
|
||||
model = "gpt-3.5-turbo"
|
||||
return await self.call_openai_with_tools(
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
**kwargs
|
||||
)
|
||||
elif provider == "deepseek":
|
||||
if not model:
|
||||
model = "deepseek-chat"
|
||||
return await self.call_deepseek_with_tools(
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"不支持的LLM提供商: {provider}，目前支持: openai, deepseek")
|
||||
|
||||
async def _execute_tool(self, tool_name: str, tool_args: Dict[str, Any]) -> str:
|
||||
"""
|
||||
执行工具
|
||||
|
||||
Args:
|
||||
tool_name: 工具名称
|
||||
tool_args: 工具参数
|
||||
|
||||
Returns:
|
||||
工具执行结果（JSON字符串）
|
||||
"""
|
||||
# 从注册表获取工具函数
|
||||
tool_func = tool_registry.get_tool_function(tool_name)
|
||||
|
||||
if not tool_func:
|
||||
error_msg = f"工具 {tool_name} 未找到"
|
||||
logger.error(error_msg)
|
||||
return json.dumps({"error": error_msg}, ensure_ascii=False)
|
||||
|
||||
try:
|
||||
logger.info(f"执行工具 {tool_name}，参数: {tool_args}")
|
||||
|
||||
# 执行工具（支持异步函数）
|
||||
if asyncio.iscoroutinefunction(tool_func):
|
||||
result = await tool_func(**tool_args)
|
||||
else:
|
||||
# 同步函数在事件循环中执行
|
||||
result = tool_func(**tool_args)
|
||||
|
||||
# 将结果转换为字符串
|
||||
if isinstance(result, (dict, list)):
|
||||
result_str = json.dumps(result, ensure_ascii=False)
|
||||
else:
|
||||
result_str = str(result)
|
||||
|
||||
logger.info(f"工具 {tool_name} 执行成功，结果长度: {len(result_str)}")
|
||||
return result_str
|
||||
except Exception as e:
|
||||
error_msg = f"工具 {tool_name} 执行失败: {str(e)}"
|
||||
logger.error(error_msg, exc_info=True)
|
||||
return json.dumps({"error": error_msg}, ensure_ascii=False)
|
||||
|
||||
|
||||
# 全局LLM服务实例
|
||||
|
||||
115
backend/app/services/tool_registry.py
Normal file
115
backend/app/services/tool_registry.py
Normal file
@@ -0,0 +1,115 @@
|
||||
"""
|
||||
工具注册表 - 管理所有可用工具
|
||||
"""
|
||||
from typing import Dict, Any, Callable, Optional, List
|
||||
import json
|
||||
import logging
|
||||
from app.models.tool import Tool
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ToolRegistry:
|
||||
"""工具注册表 - 管理所有可用工具"""
|
||||
|
||||
def __init__(self):
|
||||
self._builtin_tools: Dict[str, Callable] = {}
|
||||
self._tool_schemas: Dict[str, Dict[str, Any]] = {}
|
||||
|
||||
def register_builtin_tool(self, name: str, func: Callable, schema: Dict[str, Any]):
|
||||
"""
|
||||
注册内置工具
|
||||
|
||||
Args:
|
||||
name: 工具名称
|
||||
func: 工具函数（可以是同步或异步函数）
|
||||
schema: 工具定义（OpenAI Function格式）
|
||||
"""
|
||||
self._builtin_tools[name] = func
|
||||
self._tool_schemas[name] = schema
|
||||
logger.info(f"注册内置工具: {name}")
|
||||
|
||||
def get_tool_schema(self, name: str) -> Optional[Dict[str, Any]]:
|
||||
"""获取工具定义"""
|
||||
return self._tool_schemas.get(name)
|
||||
|
||||
def get_tool_function(self, name: str) -> Optional[Callable]:
|
||||
"""获取工具函数"""
|
||||
return self._builtin_tools.get(name)
|
||||
|
||||
def get_all_tool_schemas(self) -> List[Dict[str, Any]]:
|
||||
"""获取所有工具定义（用于LLM）"""
|
||||
return list(self._tool_schemas.values())
|
||||
|
||||
def load_tools_from_db(self, db: Session, tool_names: List[str] = None):
|
||||
"""
|
||||
从数据库加载工具
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
tool_names: 工具名称列表（可选，如果为None则加载所有公开工具）
|
||||
"""
|
||||
query = db.query(Tool).filter(Tool.is_public == True)
|
||||
if tool_names:
|
||||
query = query.filter(Tool.name.in_(tool_names))
|
||||
|
||||
tools = query.all()
|
||||
for tool in tools:
|
||||
self._tool_schemas[tool.name] = tool.function_schema
|
||||
# 根据implementation_type加载工具实现
|
||||
if tool.implementation_type == 'builtin':
|
||||
# 从内置工具中查找
|
||||
if tool.name in self._builtin_tools:
|
||||
logger.debug(f"工具 {tool.name} 已在内置工具中注册")
|
||||
else:
|
||||
logger.warning(f"工具 {tool.name} 标记为builtin但未在内置工具中找到")
|
||||
elif tool.implementation_type == 'http':
|
||||
# HTTP工具需要特殊处理
|
||||
self._register_http_tool(tool)
|
||||
elif tool.implementation_type == 'workflow':
|
||||
# 工作流工具
|
||||
self._register_workflow_tool(tool)
|
||||
elif tool.implementation_type == 'code':
|
||||
# 代码执行工具
|
||||
self._register_code_tool(tool)
|
||||
|
||||
logger.info(f"从数据库加载了 {len(tools)} 个工具")
|
||||
|
||||
def _register_http_tool(self, tool: Tool):
|
||||
"""注册HTTP工具"""
|
||||
# TODO: 实现HTTP工具的动态注册
|
||||
logger.warning(f"HTTP工具 {tool.name} 的动态注册尚未实现")
|
||||
|
||||
def _register_workflow_tool(self, tool: Tool):
|
||||
"""注册工作流工具"""
|
||||
# TODO: 实现工作流工具的动态注册
|
||||
logger.warning(f"工作流工具 {tool.name} 的动态注册尚未实现")
|
||||
|
||||
def _register_code_tool(self, tool: Tool):
|
||||
"""注册代码执行工具"""
|
||||
# TODO: 实现代码执行工具的动态注册
|
||||
logger.warning(f"代码执行工具 {tool.name} 的动态注册尚未实现")
|
||||
|
||||
def get_tools_by_names(self, tool_names: List[str]) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
根据工具名称列表获取工具定义
|
||||
|
||||
Args:
|
||||
tool_names: 工具名称列表
|
||||
|
||||
Returns:
|
||||
工具定义列表（OpenAI Function格式）
|
||||
"""
|
||||
tools = []
|
||||
for name in tool_names:
|
||||
schema = self.get_tool_schema(name)
|
||||
if schema:
|
||||
tools.append(schema)
|
||||
else:
|
||||
logger.warning(f"工具 {name} 未找到")
|
||||
return tools
|
||||
|
||||
|
||||
# 全局工具注册表实例
|
||||
tool_registry = ToolRegistry()
|
||||
@@ -725,19 +725,44 @@ class WorkflowEngine:
|
||||
# 记录实际发送给LLM的prompt
|
||||
logger.info(f"[rjb] 准备调用LLM: node_id={node_id}, provider={provider}, model={model}, prompt前200字符='{prompt[:200] if len(prompt) > 200 else prompt}'")
|
||||
|
||||
# 检查是否启用工具调用
|
||||
enable_tools = node_data.get('enable_tools', False)
|
||||
tools_config = node_data.get('tools', []) # 工具名称列表
|
||||
|
||||
# 如果启用了工具，加载工具定义
|
||||
tools = []
|
||||
if enable_tools and tools_config:
|
||||
from app.services.tool_registry import tool_registry
|
||||
# 从注册表加载工具定义
|
||||
tools = tool_registry.get_tools_by_names(tools_config)
|
||||
logger.info(f"[rjb] LLM节点启用工具调用: {len(tools)} 个工具, 工具列表: {tools_config}")
|
||||
|
||||
# 调用LLM服务
|
||||
try:
|
||||
if self.logger:
|
||||
logger.debug(f"[rjb] LLM节点配置: provider={provider}, model={model}, 使用系统默认API Key配置")
|
||||
logger.debug(f"[rjb] LLM节点配置: provider={provider}, model={model}, 使用系统默认API Key配置, 工具调用: {'启用' if tools else '禁用'}")
|
||||
self.logger.info(f"调用LLM服务: {provider}/{model}", node_id=node_id, node_type=node_type)
|
||||
result = await llm_service.call_llm(
|
||||
prompt=prompt,
|
||||
provider=provider,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
# 不传递 api_key 和 base_url，使用系统默认配置
|
||||
)
|
||||
|
||||
# 根据是否启用工具选择不同的调用方式
|
||||
if tools:
|
||||
result = await llm_service.call_llm_with_tools(
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
provider=provider,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
)
|
||||
else:
|
||||
result = await llm_service.call_llm(
|
||||
prompt=prompt,
|
||||
provider=provider,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
# 不传递 api_key 和 base_url，使用系统默认配置
|
||||
)
|
||||
|
||||
exec_result = {'output': result, 'status': 'success'}
|
||||
if self.logger:
|
||||
duration = int((time.time() - start_time) * 1000)
|
||||
@@ -748,6 +773,7 @@ class WorkflowEngine:
|
||||
if self.logger:
|
||||
duration = int((time.time() - start_time) * 1000)
|
||||
self.logger.log_node_error(node_id, node_type, e, duration)
|
||||
logger.error(f"[rjb] LLM节点执行失败: {str(e)}", exc_info=True)
|
||||
return {
|
||||
'output': None,
|
||||
'status': 'failed',
|
||||
@@ -2438,6 +2464,15 @@ class WorkflowEngine:
|
||||
logger.info(f"[rjb] user_input: {user_input[:50]}, output: {str(output)[:50]}, timestamp: {timestamp}")
|
||||
value = eval(value_str, {"__builtins__": {}}, safe_dict)
|
||||
logger.info(f"[rjb] Cache节点 {node_id} value模板执行成功，类型: {type(value)}")
|
||||
|
||||
# 确保conversation_history只保留最近的20条（性能优化）
|
||||
if isinstance(value, dict) and 'conversation_history' in value:
|
||||
if isinstance(value['conversation_history'], list):
|
||||
max_history_length = 20
|
||||
if len(value['conversation_history']) > max_history_length:
|
||||
value['conversation_history'] = value['conversation_history'][-max_history_length:]
|
||||
logger.info(f"[rjb] 对话历史已截断，保留最近 {max_history_length} 条")
|
||||
|
||||
if isinstance(value, dict):
|
||||
logger.info(f"[rjb] keys: {list(value.keys())}")
|
||||
if 'conversation_history' in value:
|
||||
|
||||
Reference in New Issue
Block a user