185 lines
6.5 KiB
Python
185 lines
6.5 KiB
Python
"""
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统一 LLM 客户端 - 支持多种提供商
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支持的提供商:
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- ollama: 本地运行 (免费，但占用 CPU/内存)
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- deepseek: 云端 API (便宜，¥1/百万 tokens)
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- openai: 云端 API (GPT-4)
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- qwen: 阿里云通义千问 (便宜，国内稳定)
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"""
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import httpx
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from typing import Dict, List, Optional, Any
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import os
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import json
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class LLMClient:
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"""统一 LLM 客户端"""
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def __init__(self):
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self.provider = os.getenv("LLM_PROVIDER", "deepseek").lower()
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self.client = self._create_client()
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def _create_client(self) -> 'LLMProvider':
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"""根据配置创建对应的提供商客户端"""
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if self.provider == "ollama":
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return OllamaClient()
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elif self.provider == "deepseek":
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return OpenAICompatibleClient(
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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base_url=os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com/v1"),
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model=os.getenv("DEEPSEEK_MODEL", "deepseek-chat")
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)
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elif self.provider == "openai":
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return OpenAICompatibleClient(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.edgefn.net/v1"),
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model=os.getenv("OPENAI_MODEL", "MiniMax-M2.5")
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)
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elif self.provider == "qwen":
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return OpenAICompatibleClient(
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api_key=os.getenv("QWEN_API_KEY"),
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base_url=os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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model=os.getenv("QWEN_MODEL", "qwen-plus")
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)
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else:
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raise ValueError(f"不支持的 LLM 提供商: {self.provider}")
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def generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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return self.client.generate(prompt, **kwargs)
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def generate_json(self, prompt: str, **kwargs) -> Dict:
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"""生成 JSON 格式响应"""
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return self.client.generate_json(prompt, **kwargs)
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def get_embedding(self, text: str) -> List[float]:
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"""获取向量嵌入"""
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return self.client.get_embedding(text)
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class LLMProvider:
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"""LLM 提供商基类"""
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def generate(self, prompt: str, **kwargs) -> str:
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raise NotImplementedError
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def generate_json(self, prompt: str, **kwargs) -> Dict:
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raise NotImplementedError
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def get_embedding(self, text: str) -> List[float]:
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raise NotImplementedError
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class OllamaClient(LLMProvider):
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"""Ollama 本地客户端"""
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def __init__(self):
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self.host = os.getenv("OLLAMA_HOST", "http://127.0.0.1:11434").rstrip('/')
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self.chat_model = os.getenv("OLLAMA_CHAT_MODEL", "llama3:8b")
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self.embedding_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
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self.client = httpx.Client(base_url=self.host, timeout=300.0, trust_env=False)
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def generate(self, prompt: str, **kwargs) -> str:
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response = self.client.post("/api/generate", json={
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"model": self.chat_model,
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"prompt": prompt,
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"stream": False,
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**kwargs
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})
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response.raise_for_status()
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return response.json()["response"]
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def generate_json(self, prompt: str, **kwargs) -> Dict:
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response = self.client.post("/api/generate", json={
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"model": self.chat_model,
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"prompt": prompt,
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"stream": False,
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"format": "json",
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**kwargs
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})
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response.raise_for_status()
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content = response.json()["response"]
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# 清理 markdown 代码块
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if content.startswith("```json"):
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content = content[7:]
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if content.startswith("```"):
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content = content[3:]
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if content.endswith("```"):
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content = content[:-3]
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content = content.strip()
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return json.loads(content)
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def get_embedding(self, text: str) -> List[float]:
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response = self.client.post("/api/embeddings", json={
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"model": self.embedding_model,
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"prompt": text
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})
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response.raise_for_status()
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return response.json().get("embedding", [])
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class OpenAICompatibleClient(LLMProvider):
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"""OpenAI 兼容客户端 (适用于 DeepSeek, Qwen 等)"""
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def __init__(self, api_key: str, base_url: str, model: str):
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self.model = model
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self.base_url = base_url
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self.api_key = api_key
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# 延迟初始化客户端，在实际调用时才检查 API_KEY
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self._client = None
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@property
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def client(self):
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"""延迟初始化客户端"""
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if self._client is None:
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if not self.api_key:
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raise ValueError(f"请设置 {self.base_url} 的 API_KEY")
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self._client = httpx.Client(
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base_url=self.base_url,
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timeout=120.0,
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headers={"Authorization": f"Bearer {self.api_key}"}
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)
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return self._client
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def generate(self, prompt: str, **kwargs) -> str:
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response = self.client.post("/chat/completions", json={
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}],
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**kwargs
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})
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response.raise_for_status()
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return response.json()["choices"][0]["message"]["content"]
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def generate_json(self, prompt: str, **kwargs) -> Dict:
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response = self.client.post("/chat/completions", json={
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}],
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"response_format": {"type": "json_object"},
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**kwargs
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})
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response.raise_for_status()
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content = response.json()["choices"][0]["message"]["content"]
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return json.loads(content)
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def get_embedding(self, text: str) -> List[float]:
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# DeepSeek 不支持 embedding，使用替代方案或返回空
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if "deepseek" in self.base_url:
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print("[WARNING] DeepSeek 不支持 embedding API，使用简化向量")
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# 简化方案：基于文本内容生成伪向量（实际使用时应该用其他服务）
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import hashlib
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hash_obj = hashlib.md5(text.encode())
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# 生成 768 维伪向量（与 nomic-embed-text 维度一致）
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return [float((hash_obj.digest()[i % 16] / 255.0) * 2 - 1) for _ in range(768)]
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response = self.client.post("/embeddings", json={
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"model": self.model,
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"input": text
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})
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response.raise_for_status()
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return response.json()["data"][0]["embedding"]
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# 单例实例
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llm_client = LLMClient()
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