Completely refactor the LLM integration method to easily replace the LLM used by each module and optimize the retransmission mechanism.
This commit is contained in:
666ghj
@@ -1,11 +1,8 @@
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"""
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LLM调用模块
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支持多种大语言模型的统一接口
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LLM module
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Provides a unified OpenAI-compatible client for the Insight Engine.
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"""
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from .base import BaseLLM
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from .deepseek import DeepSeekLLM
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from .openai_llm import OpenAILLM
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from .kimi import KimiLLM
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from .base import LLMClient
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__all__ = ["BaseLLM", "DeepSeekLLM", "OpenAILLM", "KimiLLM"]
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__all__ = ["LLMClient"]
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+78
-50
@@ -1,61 +1,89 @@
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"""
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LLM基础抽象类
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定义所有LLM实现需要遵循的接口标准
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Unified OpenAI-compatible LLM client for the Insight Engine, with retry support.
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"""
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from abc import ABC, abstractmethod
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from typing import Optional, Dict, Any
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import os
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import sys
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from typing import Any, Dict, Optional
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from openai import OpenAI
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(os.path.dirname(current_dir))
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utils_dir = os.path.join(project_root, "utils")
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if utils_dir not in sys.path:
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sys.path.append(utils_dir)
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try:
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from retry_helper import with_retry, LLM_RETRY_CONFIG
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except ImportError:
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def with_retry(config=None):
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def decorator(func):
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return func
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return decorator
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LLM_RETRY_CONFIG = None
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class BaseLLM(ABC):
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"""LLM基础抽象类"""
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def __init__(self, api_key: str, model_name: Optional[str] = None):
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"""
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初始化LLM客户端
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Args:
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api_key: API密钥
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model_name: 模型名称，如果不指定则使用默认模型
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"""
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class LLMClient:
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"""Minimal wrapper around the OpenAI-compatible chat completion API."""
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def __init__(self, api_key: str, model_name: str, base_url: Optional[str] = None):
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if not api_key:
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raise ValueError("Insight Engine LLM API key is required.")
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if not model_name:
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raise ValueError("Insight Engine model name is required.")
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self.api_key = api_key
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self.base_url = base_url
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self.model_name = model_name
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@abstractmethod
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self.provider = model_name
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timeout_fallback = os.getenv("LLM_REQUEST_TIMEOUT") or os.getenv("INSIGHT_ENGINE_REQUEST_TIMEOUT") or "180"
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try:
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self.timeout = float(timeout_fallback)
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except ValueError:
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self.timeout = 300.0
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client_kwargs: Dict[str, Any] = {
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"api_key": api_key,
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"max_retries": 0,
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}
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if base_url:
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client_kwargs["base_url"] = base_url
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self.client = OpenAI(**client_kwargs)
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@with_retry(LLM_RETRY_CONFIG)
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def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
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"""
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调用LLM生成回复
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Args:
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system_prompt: 系统提示词
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user_prompt: 用户输入
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**kwargs: 其他参数，如temperature、max_tokens等
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Returns:
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LLM生成的回复文本
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"""
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pass
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@abstractmethod
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def get_default_model(self) -> str:
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"""
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获取默认模型名称
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Returns:
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默认模型名称
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"""
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pass
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def validate_response(self, response: str) -> str:
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"""
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验证和清理响应内容
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Args:
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response: LLM原始响应
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Returns:
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清理后的响应内容
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty", "stream"}
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extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
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timeout = kwargs.pop("timeout", self.timeout)
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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timeout=timeout,
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**extra_params,
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)
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if response.choices and response.choices[0].message:
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return self.validate_response(response.choices[0].message.content)
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return ""
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@staticmethod
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def validate_response(response: Optional[str]) -> str:
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if response is None:
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return ""
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return response.strip()
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def get_model_info(self) -> Dict[str, Any]:
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return {
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"provider": self.provider,
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"model": self.model_name,
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"api_base": self.base_url or "default",
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}
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@@ -1,119 +0,0 @@
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"""
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DeepSeek LLM实现
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使用DeepSeek API进行文本生成
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"""
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import os
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from typing import Optional, Dict, Any
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from openai import OpenAI
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from .base import BaseLLM
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import sys
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DEFAULT_DEEPSEEK_BASE_URL = "https://api.deepseek.com"
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# 添加utils目录到Python路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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root_dir = os.path.dirname(os.path.dirname(current_dir))
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utils_dir = os.path.join(root_dir, 'utils')
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if utils_dir not in sys.path:
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sys.path.append(utils_dir)
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try:
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from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
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except ImportError:
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# 如果无法导入重试模块，使用空装饰器
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def with_retry(config):
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def decorator(func):
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return func
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return decorator
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LLM_RETRY_CONFIG = None
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class DeepSeekLLM(BaseLLM):
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"""DeepSeek LLM实现类"""
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def __init__(self, api_key: Optional[str] = None, model_name: Optional[str] = None, base_url: Optional[str] = None):
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"""
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初始化DeepSeek客户端
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Args:
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api_key: DeepSeek API密钥，如果不提供则从环境变量读取
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model_name: 模型名称，默认使用deepseek-chat
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base_url: DeepSeek API基础地址
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"""
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if api_key is None:
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api_key = os.getenv("DEEPSEEK_API_KEY")
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if not api_key:
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raise ValueError("DeepSeek API Key未找到！请设置DEEPSEEK_API_KEY环境变量或在初始化时提供")
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super().__init__(api_key, model_name)
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self.base_url = base_url or os.getenv("DEEPSEEK_BASE_URL") or DEFAULT_DEEPSEEK_BASE_URL
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# 初始化OpenAI客户端，使用DeepSeek的endpoint
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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self.default_model = model_name or self.get_default_model()
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def get_default_model(self) -> str:
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"""获取默认模型名称"""
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return "deepseek-chat"
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@with_retry(LLM_RETRY_CONFIG)
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def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
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"""
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调用DeepSeek API生成回复
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Args:
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system_prompt: 系统提示词
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user_prompt: 用户输入
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**kwargs: 其他参数，如temperature、max_tokens等
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Returns:
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DeepSeek生成的回复文本
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"""
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try:
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# 构建消息
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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# 设置默认参数
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params = {
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"model": self.default_model,
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"messages": messages,
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"temperature": kwargs.get("temperature", 0.7),
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"max_tokens": kwargs.get("max_tokens", 4000),
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"stream": False
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}
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# 调用API
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response = self.client.chat.completions.create(**params)
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# 提取回复内容
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if response.choices and response.choices[0].message:
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content = response.choices[0].message.content
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return self.validate_response(content)
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else:
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return ""
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except Exception as e:
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print(f"DeepSeek API调用错误: {str(e)}")
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raise e
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def get_model_info(self) -> Dict[str, Any]:
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"""
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获取当前模型信息
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Returns:
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模型信息字典
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"""
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return {
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"provider": "DeepSeek",
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"model": self.default_model,
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"api_base": self.base_url
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}
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@@ -1,167 +0,0 @@
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"""
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Kimi LLM实现
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使用Moonshot AI的Kimi API进行文本生成
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"""
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import os
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import sys
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from typing import Optional, Dict, Any
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from openai import OpenAI
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# 假设 .base 模块和 BaseLLM 类已存在
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from .base import BaseLLM
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DEFAULT_KIMI_BASE_URL = "https://api.moonshot.cn/v1"
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# 添加utils目录到Python路径并导入重试模块
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try:
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current_dir = os.path.dirname(os.path.abspath(__file__))
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root_dir = os.path.dirname(os.path.dirname(current_dir))
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utils_dir = os.path.join(root_dir, 'utils')
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if utils_dir not in sys.path:
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sys.path.append(utils_dir)
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from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
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except ImportError:
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# 如果无法导入重试模块，使用空装饰器避免报错
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def with_retry(config):
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def decorator(func):
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return func
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return decorator
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LLM_RETRY_CONFIG = None
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class KimiLLM(BaseLLM):
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"""Kimi LLM实现类"""
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def __init__(self, api_key: Optional[str] = None, model_name: Optional[str] = None, base_url: Optional[str] = None):
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"""
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初始化Kimi客户端
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Args:
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api_key: Kimi API密钥，如果不提供则从环境变量读取
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model_name: 模型名称，默认使用kimi-k2-0711-preview
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base_url: Kimi API基础地址
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"""
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if api_key is None:
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api_key = os.getenv("KIMI_API_KEY")
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if not api_key:
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raise ValueError("Kimi API Key未找到！请设置KIMI_API_KEY环境变量或在初始化时提供")
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super().__init__(api_key, model_name)
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self.base_url = base_url or os.getenv("KIMI_BASE_URL") or DEFAULT_KIMI_BASE_URL
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# 初始化OpenAI客户端，使用Kimi的endpoint
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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self.default_model = model_name or self.get_default_model()
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def get_default_model(self) -> str:
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"""获取默认模型名称"""
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return "kimi-k2-0711-preview"
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@with_retry(LLM_RETRY_CONFIG)
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def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
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"""
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调用Kimi API生成回复
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Args:
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system_prompt: 系统提示词
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user_prompt: 用户输入
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**kwargs: 其他参数，如temperature、max_tokens等
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Returns:
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Kimi生成的回复文本
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"""
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try:
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# 构建消息
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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# 智能计算max_tokens - 根据输入长度自动调整输出长度
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input_length = len(system_prompt) + len(user_prompt)
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if input_length > 100000: # 超长文本
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default_max_tokens = 81920
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elif input_length > 50000: # 超长文本
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default_max_tokens = 40960
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elif input_length > 20000: # 长文本
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default_max_tokens = 16384
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elif input_length > 5000: # 中等文本
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default_max_tokens = 8192
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else: # 短文本
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default_max_tokens = 4096
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# 设置默认参数，针对长文本处理优化
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params = {
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"model": self.default_model,
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"messages": messages,
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"temperature": kwargs.get("temperature", 0.6), # Kimi建议使用0.6
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"max_tokens": kwargs.get("max_tokens", default_max_tokens), # 智能调整token限制
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"stream": False
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}
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# 添加其他可选参数
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if "top_p" in kwargs:
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params["top_p"] = kwargs["top_p"]
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if "presence_penalty" in kwargs:
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params["presence_penalty"] = kwargs["presence_penalty"]
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if "frequency_penalty" in kwargs:
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params["frequency_penalty"] = kwargs["frequency_penalty"]
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if "stop" in kwargs:
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params["stop"] = kwargs["stop"]
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# 输出调试信息（仅在使用Kimi时）
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print(f"[Kimi] 输入长度: {input_length}, 使用max_tokens: {params['max_tokens']}")
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# 调用API
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response = self.client.chat.completions.create(**params)
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# 提取回复内容
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if response.choices and response.choices[0].message:
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content = response.choices[0].message.content
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return self.validate_response(content)
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else:
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return ""
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except Exception as e:
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print(f"Kimi API调用错误: {str(e)}")
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raise e
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def get_model_info(self) -> Dict[str, Any]:
|
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"""
|
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获取当前模型信息
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Returns:
|
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模型信息字典
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"""
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return {
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"provider": "Kimi",
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"model": self.default_model,
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"api_base": self.base_url,
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"max_context_length": "长文本支持（200K+ tokens）"
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}
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# ==================== 代码修改部分 ====================
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def invoke_long_context(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
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"""
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专门用于长文本处理的调用方法 (作为invoke的兼容接口)。
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此方法通过设置推荐的默认参数，然后调用通用的invoke方法来处理请求。
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|
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Args:
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system_prompt: 系统提示词
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user_prompt: 用户输入
|
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**kwargs: 其他参数
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|
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Returns:
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Kimi生成的回复文本
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"""
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# 为长文本场景，设置一个慷慨的默认 max_tokens，仅当用户未指定时生效。
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# 您原有的16384是一个非常合理的值。
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kwargs.setdefault("max_tokens", 16384)
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# 直接调用核心的invoke方法，将所有参数（包括预设的默认值）传递给它。
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return self.invoke(system_prompt, user_prompt, **kwargs)
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@@ -1,108 +0,0 @@
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"""
|
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OpenAI LLM实现
|
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使用OpenAI API进行文本生成
|
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"""
|
||||
|
||||
import os
|
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import sys
|
||||
from typing import Optional, Dict, Any
|
||||
from openai import OpenAI
|
||||
from .base import BaseLLM
|
||||
|
||||
# 添加utils目录到Python路径并导入重试模块
|
||||
try:
|
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current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
root_dir = os.path.dirname(os.path.dirname(current_dir))
|
||||
utils_dir = os.path.join(root_dir, 'utils')
|
||||
if utils_dir not in sys.path:
|
||||
sys.path.append(utils_dir)
|
||||
from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
|
||||
except ImportError:
|
||||
# 如果无法导入重试模块，使用空装饰器避免报错
|
||||
def with_retry(config):
|
||||
def decorator(func):
|
||||
return func
|
||||
return decorator
|
||||
LLM_RETRY_CONFIG = None
|
||||
|
||||
|
||||
class OpenAILLM(BaseLLM):
|
||||
"""OpenAI LLM实现类"""
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None, model_name: Optional[str] = None):
|
||||
"""
|
||||
初始化OpenAI客户端
|
||||
|
||||
Args:
|
||||
api_key: OpenAI API密钥，如果不提供则从环境变量读取
|
||||
model_name: 模型名称，默认使用gpt-4o-mini
|
||||
"""
|
||||
if api_key is None:
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
if not api_key:
|
||||
raise ValueError("OpenAI API Key未找到！请设置OPENAI_API_KEY环境变量或在初始化时提供")
|
||||
|
||||
super().__init__(api_key, model_name)
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
self.client = OpenAI(api_key=self.api_key)
|
||||
self.default_model = model_name or self.get_default_model()
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
"""获取默认模型名称"""
|
||||
return "gpt-4o-mini"
|
||||
|
||||
@with_retry(LLM_RETRY_CONFIG)
|
||||
def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
|
||||
"""
|
||||
调用OpenAI API生成回复
|
||||
|
||||
Args:
|
||||
system_prompt: 系统提示词
|
||||
user_prompt: 用户输入
|
||||
**kwargs: 其他参数，如temperature、max_tokens等
|
||||
|
||||
Returns:
|
||||
OpenAI生成的回复文本
|
||||
"""
|
||||
try:
|
||||
# 构建消息
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt}
|
||||
]
|
||||
|
||||
# 设置默认参数
|
||||
params = {
|
||||
"model": self.default_model,
|
||||
"messages": messages,
|
||||
"temperature": kwargs.get("temperature", 0.7),
|
||||
"max_tokens": kwargs.get("max_tokens", 4000)
|
||||
}
|
||||
|
||||
# 调用API
|
||||
response = self.client.chat.completions.create(**params)
|
||||
|
||||
# 提取回复内容
|
||||
if response.choices and response.choices[0].message:
|
||||
content = response.choices[0].message.content
|
||||
return self.validate_response(content)
|
||||
else:
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
print(f"OpenAI API调用错误: {str(e)}")
|
||||
raise e
|
||||
|
||||
def get_model_info(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取当前模型信息
|
||||
|
||||
Returns:
|
||||
模型信息字典
|
||||
"""
|
||||
return {
|
||||
"provider": "OpenAI",
|
||||
"model": self.default_model,
|
||||
"api_base": "https://api.openai.com"
|
||||
}
|
||||
Reference in New Issue
Block a user