343 lines
13 KiB
Python
343 lines
13 KiB
Python
# llm_adapters.py
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# -*- coding: utf-8 -*-
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import logging
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from typing import Optional
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from langchain_openai import ChatOpenAI, AzureChatOpenAI
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from google import genai
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from google.genai import types
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from azure.ai.inference import ChatCompletionsClient
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from azure.core.credentials import AzureKeyCredential
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from azure.ai.inference.models import SystemMessage, UserMessage
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from openai import OpenAI
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def check_base_url(url: str) -> str:
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"""
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处理base_url的规则：
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1. 如果url以#结尾，则移除#并直接使用用户提供的url
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2. 否则检查是否需要添加/v1后缀
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"""
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import re
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url = url.strip()
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if not url:
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return url
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if url.endswith('#'):
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return url.rstrip('#')
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if not re.search(r'/v\d+$', url):
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if '/v1' not in url:
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url = url.rstrip('/') + '/v1'
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return url
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class BaseLLMAdapter:
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"""
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统一的 LLM 接口基类，为不同后端（OpenAI、Ollama、ML Studio、Gemini等）提供一致的方法签名。
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"""
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def invoke(self, prompt: str) -> str:
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raise NotImplementedError("Subclasses must implement .invoke(prompt) method.")
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class DeepSeekAdapter(BaseLLMAdapter):
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"""
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适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.base_url = check_base_url(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from DeepSeekAdapter.")
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return ""
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return response.content
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class OpenAIAdapter(BaseLLMAdapter):
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"""
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适配官方/OpenAI兼容接口（使用 langchain.ChatOpenAI）
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.base_url = check_base_url(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from OpenAIAdapter.")
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return ""
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return response.content
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class GeminiAdapter(BaseLLMAdapter):
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"""
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适配 Google Gemini (Google Generative AI) 接口
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"""
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def __init__(self, api_key: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = genai.Client(api_key=self.api_key)
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def invoke(self, prompt: str) -> str:
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try:
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response = self._client.models.generate_content(
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model = self.model_name,
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contents = prompt,
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config = types.GenerateContentConfig(
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max_output_tokens=self.max_tokens,
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temperature=self.temperature,
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),
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timeout=self.timeout # 添加超时参数
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)
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if response and response.text:
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return response.text
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else:
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logging.warning("No text response from Gemini API.")
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return ""
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except Exception as e:
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logging.error(f"Gemini API 调用失败: {e}")
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return ""
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class AzureOpenAIAdapter(BaseLLMAdapter):
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"""
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适配 Azure OpenAI 接口（使用 langchain.ChatOpenAI）
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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import re
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match = re.match(r'https://(.+?)/openai/deployments/(.+?)/chat/completions\?api-version=(.+)', base_url)
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if match:
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self.azure_endpoint = f"https://{match.group(1)}"
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self.azure_deployment = match.group(2)
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self.api_version = match.group(3)
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else:
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raise ValueError("Invalid Azure OpenAI base_url format")
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self.api_key = api_key
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self.model_name = self.azure_deployment
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = AzureChatOpenAI(
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azure_endpoint=self.azure_endpoint,
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azure_deployment=self.azure_deployment,
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api_version=self.api_version,
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api_key=self.api_key,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from AzureOpenAIAdapter.")
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return ""
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return response.content
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class OllamaAdapter(BaseLLMAdapter):
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"""
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Ollama 同样有一个 OpenAI-like /v1/chat 接口，可直接使用 ChatOpenAI。
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.base_url = check_base_url(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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if self.api_key == '':
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self.api_key= 'ollama'
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from OllamaAdapter.")
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return ""
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return response.content
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class MLStudioAdapter(BaseLLMAdapter):
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.base_url = check_base_url(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = ChatOpenAI(
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model=self.model_name,
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api_key=self.api_key,
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base_url=self.base_url,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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try:
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response = self._client.invoke(prompt)
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if not response:
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logging.warning("No response from MLStudioAdapter.")
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return ""
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return response.content
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except Exception as e:
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logging.error(f"ML Studio API 调用超时或失败: {e}")
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return ""
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class AzureAIAdapter(BaseLLMAdapter):
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"""
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适配 Azure AI Inference 接口，用于访问Azure AI服务部署的模型
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使用 azure-ai-inference 库进行API调用
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"""
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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import re
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# 匹配形如 https://xxx.services.ai.azure.com/models/chat/completions?api-version=xxx 的URL
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match = re.match(r'https://(.+?)\.services\.ai\.azure\.com(?:/models)?(?:/chat/completions)?(?:\?api-version=(.+))?', base_url)
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if match:
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# endpoint需要是形如 https://xxx.services.ai.azure.com/models 的格式
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self.endpoint = f"https://{match.group(1)}.services.ai.azure.com/models"
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# 如果URL中包含api-version参数，使用它；否则使用默认值
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self.api_version = match.group(2) if match.group(2) else "2024-05-01-preview"
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else:
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raise ValueError("Invalid Azure AI base_url format. Expected format: https://<endpoint>.services.ai.azure.com/models/chat/completions?api-version=xxx")
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self.base_url = self.endpoint # 存储处理后的endpoint URL
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = ChatCompletionsClient(
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endpoint=self.endpoint,
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credential=AzureKeyCredential(self.api_key),
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model=self.model_name,
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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timeout=self.timeout
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)
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def invoke(self, prompt: str) -> str:
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try:
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response = self._client.complete(
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messages=[
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SystemMessage("You are a helpful assistant."),
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UserMessage(prompt)
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]
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)
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if response and response.choices:
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return response.choices[0].message.content
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else:
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logging.warning("No response from AzureAIAdapter.")
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return ""
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except Exception as e:
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logging.error(f"Azure AI Inference API 调用失败: {e}")
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return ""
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# 火山引擎实现
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class VolcanoEngineAIAdapter(BaseLLMAdapter):
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def __init__(self, api_key: str, base_url: str, model_name: str, max_tokens: int, temperature: float = 0.7, timeout: Optional[int] = 600):
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self.base_url = check_base_url(base_url)
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self.api_key = api_key
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.timeout = timeout
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self._client = OpenAI(
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base_url=base_url,
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api_key=api_key,
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timeout=timeout # 添加超时配置
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)
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def invoke(self, prompt: str) -> str:
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try:
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response = self._client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "system", "content": "你是DeepSeek，是一个 AI 人工智能助手"},
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{"role": "user", "content": prompt},
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],
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timeout=self.timeout # 添加超时参数
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)
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if not response:
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logging.warning("No response from DeepSeekAdapter.")
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return ""
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return response.choices[0].message.content
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except Exception as e:
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logging.error(f"火山引擎API调用超时或失败: {e}")
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return ""
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def create_llm_adapter(
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interface_format: str,
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base_url: str,
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model_name: str,
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api_key: str,
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temperature: float,
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max_tokens: int,
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timeout: int
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) -> BaseLLMAdapter:
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"""
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工厂函数：根据 interface_format 返回不同的适配器实例。
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"""
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fmt = interface_format.strip().lower()
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if fmt == "deepseek":
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return DeepSeekAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "openai":
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return OpenAIAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "azure openai":
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return AzureOpenAIAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "azure ai":
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return AzureAIAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "ollama":
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return OllamaAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "ml studio":
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return MLStudioAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "gemini":
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# base_url 对 Gemini 暂无用处，可忽略
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return GeminiAdapter(api_key, model_name, max_tokens, temperature, timeout)
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elif fmt == "阿里云百炼":
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return OpenAIAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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elif fmt == "火山引擎":
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return VolcanoEngineAIAdapter(api_key, base_url, model_name, max_tokens, temperature, timeout)
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else:
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raise ValueError(f"Unknown interface_format: {interface_format}")
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