中文大语言模型集成

1. 主流中文大语言模型介绍

1.1 模型概览

• 百度文心一言
• 讯飞星火认知
• 智谱 ChatGLM
• 阿里通义千问
• 商汤日日新

1.2 模型特点比较

from enum import Enum
from typing import Dict, Any

class ModelType(Enum):
    WENXIN = "wenxin"
    SPARK = "spark"
    CHATGLM = "chatglm"
    QIANWEN = "qianwen"
    RIRIXIN = "ririxin"

class ModelFeatures:
    def __init__(self, name: str, features: Dict[str, Any]):
        self.name = name
        self.features = features

# 模型特点示例
model_features = {
    ModelType.WENXIN: ModelFeatures(
        "文心一言",
        {
            "context_length": 2000,
            "streaming": True,
            "multi_modal": True
        }
    )
}

2. API接入实现

2.1 统一接口封装

from abc import ABC, abstractmethod
from typing import Optional, List

class LLMBase(ABC):
    @abstractmethod
    async def chat(self, messages: List[Dict[str, str]]) -> str:
        pass
    
    @abstractmethod
    async def generate(self, prompt: str) -> str:
        pass

class WenxinAPI(LLMBase):
    def __init__(self, api_key: str, secret_key: str):
        self.api_key = api_key
        self.secret_key = secret_key
        self.access_token = None
    
    async def _get_access_token(self) -> str:
        # 实现获取access token的逻辑
        pass
    
    async def chat(self, messages: List[Dict[str, str]]) -> str:
        # 实现文心一言对话接口
        pass
    
    async def generate(self, prompt: str) -> str:
        # 实现文本生成接口
        pass

2.2 错误处理

class LLMError(Exception):
    def __init__(self, message: str, model: str, error_code: Optional[str] = None):
        self.model = model
        self.error_code = error_code
        super().__init__(f"{model}: {message}")

class TokenError(LLMError):
    pass

class QuotaExceededError(LLMError):
    pass

class ModelNotAvailableError(LLMError):
    pass

3. 高级功能实现

3.1 流式响应处理

from typing import AsyncGenerator

class StreamingChat:
    async def stream_chat(
        self,
        messages: List[Dict[str, str]]
    ) -> AsyncGenerator[str, None]:
        async for chunk in self._make_streaming_request(messages):
            yield self._process_chunk(chunk)
    
    async def _make_streaming_request(
        self,
        messages: List[Dict[str, str]]
    ) -> AsyncGenerator[Dict[str, Any], None]:
        # 实现流式请求逻辑
        pass
    
    def _process_chunk(self, chunk: Dict[str, Any]) -> str:
        # 处理响应片段
        pass

3.2 多模态能力

from PIL import Image
from pathlib import Path

class MultiModalLLM:
    async def analyze_image(
        self,
        image: Union[str, Path, Image.Image],
        prompt: str
    ) -> str:
        # 图像分析实现
        pass
    
    async def generate_image(
        self,
        prompt: str,
        size: Tuple[int, int] = (512, 512)
    ) -> Image.Image:
        # 图像生成实现
        pass

4. 性能优化

4.1 并发请求处理

import asyncio
from typing import List, Dict

class BatchProcessor:
    def __init__(self, max_concurrent: int = 5):
        self.semaphore = asyncio.Semaphore(max_concurrent)
    
    async def process_batch(
        self,
        items: List[str],
        llm: LLMBase
    ) -> List[str]:
        async def process_item(item: str) -> str:
            async with self.semaphore:
                return await llm.generate(item)
        
        tasks = [process_item(item) for item in items]
        return await asyncio.gather(*tasks)

4.2 缓存机制

from functools import lru_cache
from typing import Optional

class ResponseCache:
    def __init__(self, capacity: int = 1000):
        self.cache = lru_cache(maxsize=capacity)(self._get_cached_response)
    
    def _get_cached_response(self, key: str) -> Optional[str]:
        # 实现缓存逻辑
        pass
    
    async def get_or_compute(
        self,
        key: str,
        compute_func: Callable[[], Awaitable[str]]
    ) -> str:
        if cached := self.cache(key):
            return cached
        result = await compute_func()
        self.cache(key, result)
        return result

5. 最佳实践

5.1 提示词优化

class PromptTemplate:
    def __init__(self, template: str):
        self.template = template
    
    def format(self, **kwargs) -> str:
        return self.template.format(**kwargs)

# 使用示例
code_review_template = PromptTemplate(
    "请帮我检查以下代码可能存在的问题：\n{code}\n"
    "重点关注：\n1. 代码规范\n2. 性能优化\n3. 安全隐患"
)

5.2 错误重试

from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential
)

class RetryableAPI:
    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=4, max=10)
    )
    async def call_with_retry(self, func: Callable, *args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except (TokenError, QuotaExceededError) as e:
            # 处理特定错误
            raise
        except Exception as e:
            # 处理其他错误
            raise

总结

中文大语言模型的集成需要考虑多个方面，包括API封装、错误处理、性能优化等。通过合理的架构设计和最佳实践，可以构建稳定、高效的AI应用。在实际应用中，需要根据具体需求选择合适的模型和优化策略。