Jeason
c7224153b1
_process_response 的 analysis_complete 检测已经在上一轮修好了，配合反斜杠修复，YAML 能正确解析出 action: "analysis_complete"，不会再 fallback 到代码执行。
文件选择改为只用最近一次上传的文件 — app.state.last_uploaded_files 记录上传的文件列表，/api/start 优先使用它，不再 glob("uploads/*.csv") 把所有历史文件都拿来分析。
209 lines
7.1 KiB
Python
209 lines
7.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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LLM调用辅助模块
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"""
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import asyncio
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import yaml
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from typing import Optional, Callable, AsyncIterator
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from config.llm_config import LLMConfig
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from config.app_config import app_config
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from utils.fallback_openai_client import AsyncFallbackOpenAIClient
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from utils.cache_manager import LLMCacheManager
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# 初始化LLM缓存管理器
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llm_cache = LLMCacheManager(
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cache_dir=app_config.llm_cache_dir,
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enabled=app_config.llm_cache_enabled
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)
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class LLMHelper:
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"""LLM调用辅助类，支持同步和异步调用"""
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def __init__(self, config: LLMConfig = None):
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self.config = config
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self.client = AsyncFallbackOpenAIClient(
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primary_api_key=config.api_key,
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primary_base_url=config.base_url,
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primary_model_name=config.model
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)
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async def async_call(self, prompt: str, system_prompt: str = None, max_tokens: int = None, temperature: float = None) -> str:
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"""异步调用LLM"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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kwargs = {}
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if max_tokens is not None:
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kwargs['max_tokens'] = max_tokens
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else:
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kwargs['max_tokens'] = self.config.max_tokens
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if temperature is not None:
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kwargs['temperature'] = temperature
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else:
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kwargs['temperature'] = self.config.temperature
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try:
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response = await self.client.chat_completions_create(
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messages=messages,
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**kwargs
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)
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return response.choices[0].message.content
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except Exception as e:
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print(f"LLM调用失败: {e}")
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return ""
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def call(self, prompt: str, system_prompt: str = None, max_tokens: int = None, temperature: float = None) -> str:
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"""同步调用LLM"""
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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import nest_asyncio
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nest_asyncio.apply()
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return loop.run_until_complete(self.async_call(prompt, system_prompt, max_tokens, temperature))
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def parse_yaml_response(self, response: str) -> dict:
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"""解析YAML格式的响应"""
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try:
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# 提取```yaml和```之间的内容
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if '```yaml' in response:
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start = response.find('```yaml') + 7
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end = response.find('```', start)
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yaml_content = response[start:end].strip()
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elif '```' in response:
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start = response.find('```') + 3
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end = response.find('```', start)
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yaml_content = response[start:end].strip()
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else:
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yaml_content = response.strip()
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# Strip language identifier if LLM used ```python instead of ```yaml
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# e.g. "python\naction: ..." → "action: ..."
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import re
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if re.match(r'^[a-zA-Z]+\n', yaml_content):
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yaml_content = yaml_content.split('\n', 1)[1]
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# Fix Windows backslash paths that break YAML double-quoted strings.
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# Replace ALL backslashes inside double-quoted strings with forward slashes.
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# This handles both "D:\code\..." and "outputs\session_..." patterns.
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yaml_content = re.sub(
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r'"([^"]*\\[^"]*)"',
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lambda m: '"' + m.group(1).replace('\\', '/') + '"',
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yaml_content,
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)
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parsed = yaml.safe_load(yaml_content)
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return parsed if parsed is not None else {}
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except Exception as e:
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print(f"YAML解析失败: {e}")
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print(f"原始响应: {response}")
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return {}
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async def close(self):
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"""关闭客户端"""
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await self.client.close()
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async def async_call_with_cache(
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self,
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prompt: str,
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system_prompt: str = None,
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max_tokens: int = None,
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temperature: float = None,
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use_cache: bool = True
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) -> str:
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"""带缓存的异步LLM调用"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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# 生成缓存键
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cache_key = llm_cache.get_cache_key_from_messages(messages, self.config.model)
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# 尝试从缓存获取
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if use_cache and app_config.llm_cache_enabled:
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cached_response = llm_cache.get(cache_key)
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if cached_response:
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print("[CACHE] 使用LLM缓存响应")
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return cached_response
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# 调用LLM
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response = await self.async_call(prompt, system_prompt, max_tokens, temperature)
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# 缓存响应
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if use_cache and app_config.llm_cache_enabled and response:
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llm_cache.set(cache_key, response)
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return response
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def call_with_cache(
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self,
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prompt: str,
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system_prompt: str = None,
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max_tokens: int = None,
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temperature: float = None,
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use_cache: bool = True
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) -> str:
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"""带缓存的同步LLM调用"""
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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import nest_asyncio
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nest_asyncio.apply()
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return loop.run_until_complete(
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self.async_call_with_cache(prompt, system_prompt, max_tokens, temperature, use_cache)
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)
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async def async_call_stream(
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self,
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prompt: str,
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system_prompt: str = None,
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max_tokens: int = None,
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temperature: float = None,
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callback: Optional[Callable[[str], None]] = None
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) -> AsyncIterator[str]:
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"""流式异步LLM调用"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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kwargs = {
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'stream': True,
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'max_tokens': max_tokens or self.config.max_tokens,
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'temperature': temperature or self.config.temperature
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}
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try:
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response = await self.client.chat_completions_create(
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messages=messages,
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**kwargs
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)
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full_response = ""
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async for chunk in response:
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if chunk.choices[0].delta.content:
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content = chunk.choices[0].delta.content
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full_response += content
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# 调用回调函数
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if callback:
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callback(content)
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yield content
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except Exception as e:
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print(f"流式LLM调用失败: {e}")
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yield "" |