// 大模型配置示例
const modelConfig = {
    model: "codex-32k",
    maxTokens: 32768,
    temperature: 0.7,
    topP: 0.95,
    frequencyPenalty: 0.0,
    presencePenalty: 0.0,
    stopSequences: ["\\n\\n"],
    streaming: true
};

# Python代码理解与生成示例
def generate_optimization_code(problem_description):
    """
    根据问题描述生成优化代码
    """
    # 模型能够理解算法逻辑并生成优化实现
    if "排序" in problem_description:
        return """
def optimized_sort(arr):
    # 使用Timsort算法，时间复杂度O(n log n)
    return sorted(arr)
        """
    elif "搜索" in problem_description:
        return """
def binary_search(arr, target):
    # 二分查找，时间复杂度O(log n)
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1
        """

// 少样本学习示例
const fewShotPrompt = `
将用户问题转换为SQL查询：

示例1：
用户：显示所有用户信息
SQL：SELECT * FROM users;

示例2：
用户：找出年龄大于25岁的用户
SQL：SELECT * FROM users WHERE age > 25;

用户：统计每个部门的人数
SQL：`;

// 模型输出：SELECT department, COUNT(*) as count FROM employees GROUP BY department;`

# 创建虚拟环境
python -m venv codex-env
source codex-env/bin/activate

# 安装核心依赖
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers accelerate bitsandbytes
pip install openai tiktoken
pip install fastapi uvicorn python-dotenv

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

def setup_codex_model(model_name="codex-base"):
    """
    配置并加载Codex模型
    """
    # 加载分词器
    tokenizer = AutoTokenizer.from_pretrained(f"bigcode/{model_name}")
    
    # 配置模型加载参数
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    load_config = {
        "torch_dtype": torch.float16,
        "device_map": "auto",
        "load_in_8bit": True,  # 8位量化，节省显存
    }
    
    # 加载模型
    model = AutoModelForCausalLM.from_pretrained(
        f"bigcode/{model_name}",
        **load_config
    )
    
    return model, tokenizer

# 初始化模型
model, tokenizer = setup_codex_model("codex-cushle-001")

# 模型优化设置
def optimize_model_performance(model):
    """
    优化模型性能和内存使用
    """
    # 启用梯度检查点，节省显存
    model.gradient_checkpointing_enable()
    
    # 配置Flash Attention（如果支持）
    if hasattr(model, "enable_flash_attention"):
        model.enable_flash_attention()
    
    # 设置torch编译优化
    model = torch.compile(model, mode="reduce-overhead")
    
    return model

# 应用优化
model = optimize_model_performance(model)

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel
import torch
from typing import Optional
import asyncio

app = FastAPI(title="Codex API Service", version="1.0.0")

class CompletionRequest(BaseModel):
    prompt: str
    max_tokens: int = 100
    temperature: float = 0.7
    top_p: float = 0.95
    stop: Optional[list] = None

class CompletionResponse(BaseModel):
    id: str
    object: str = "text_completion"
    created: int
    model: str
    choices: list
    usage: dict

# 依赖注入，确保模型已加载
def get_model():
    return model, tokenizer

@app.post("/v1/completions", response_model=CompletionResponse)
async def create_completion(
    request: CompletionRequest,
    model_tuple: tuple = Depends(get_model)
):
    model, tokenizer = model_tuple
    
    try:
        # 编码输入
        inputs = tokenizer(
            request.prompt,
            return_tensors="pt",
            return_attention_mask=True
        ).to(model.device)
        
        # 生成配置
        gen_config = {
            "max_new_tokens": request.max_tokens,
            "temperature": request.temperature,
            "top_p": request.top_p,
            "do_sample": True,
            "pad_token_id": tokenizer.eos_token_id,
        }
        
        if request.stop:
            gen_config["stop_strings"] = request.stop
        
        # 生成文本
        with torch.no_grad():
            outputs = model.generate(**inputs, **gen_config)
        
        # 解码输出
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # 构建响应
        return CompletionResponse(
            id=f"cmpl-{asyncio.get_event_loop().time()}",
            created=int(asyncio.get_event_loop().time()),
            model="codex-cushle-001",
            choices=[{
                "text": generated_text[len(request.prompt):],
                "index": 0,
                "logprobs": None,
                "finish_reason": "length"
            }],
            usage={
                "prompt_tokens": len(inputs.input_ids[0]),
                "completion_tokens": len(outputs[0]) - len(inputs.input_ids[0]),
                "total_tokens": len(outputs[0])
            }
        )
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# 启动服务
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

from fastapi.responses import StreamingResponse
import json

@app.post("/v1/completions/stream")
async def create_completion_stream(
    request: CompletionRequest,
    model_tuple: tuple = Depends(get_model)
):
    model, tokenizer = model_tuple
    
    async def generate_stream():
        try:
            inputs = tokenizer(request.prompt, return_tensors="pt").to(model.device)
            
            # 配置流式生成
            gen_config = {
                "max_new_tokens": request.max_tokens,
                "temperature": request.temperature,
                "top_p": request.top_p,
                "do_sample": True,
                "pad_token_id": tokenizer.eos_token_id,
                "num_return_sequences": 1,
            }
            
            # 使用TextStreamer实现流式输出
            from transformers import TextStreamer
            
            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
            
            # 在后台线程中运行生成
            import threading
            
            def generate_in_thread():
                model.generate(**inputs, streamer=streamer, **gen_config)
            
            thread = threading.Thread(target=generate_in_thread)
            thread.start()
            
            # 流式返回数据
            for token in streamer:
                yield f"data: {json.dumps({'token': token})}\n\n"
            
            yield "data: [DONE]\n\n"
            
        except Exception as e:
            yield f"data: {json.dumps({'error': str(e)})}\n\n"
    
    return StreamingResponse(generate_stream(), media_type="text/event-stream")

import asyncio
from concurrent.futures import ThreadPoolExecutor

class BatchProcessor:
    def __init__(self, model, tokenizer, max_workers=4):
        self.model = model
        self.tokenizer = tokenizer
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        self.semaphore = asyncio.Semaphore(max_workers)
    
    async def process_batch(self, prompts, max_tokens=100):
        """
        批量处理多个prompt
        """
        async def process_single(prompt):
            async with self.semaphore:
                loop = asyncio.get_event_loop()
                return await loop.run_in_executor(
                    self.executor,
                    self._generate_sync,
                    prompt,
                    max_tokens
                )
        
        tasks = [process_single(prompt) for prompt in prompts]
        return await asyncio.gather(*tasks)
    
    def _generate_sync(self, prompt, max_tokens):
        """同步生成函数"""
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=0.7,
                top_p=0.95,
                do_sample=True
            )
        
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

# 使用示例
batch_processor = BatchProcessor(model, tokenizer)

@app.post("/v1/batch_completions")
async def create_batch_completion(prompts: list[str], max_tokens: int = 100):
    results = await batch_processor.process_batch(prompts, max_tokens)
    return {"results": results}

from bitsandbytes.nn import Linear8bitLt
import torch.nn as nn

def quantize_model(model):
    """
    8位量化模型
    """
    for name, module in model.named_modules():
        if isinstance(module, nn.Linear):
            # 替换为8位线性层
            module = Linear8bitLt(
                module.in_features,
                module.out_features,
                bias=module.bias is not None,
                has_fp32_weights=False,
                threshold=6.0
            )
    
    return model

# 量化模型
model = quantize_model(model)

import functools
import time
from collections import OrderedDict

class ModelCache:
    def __init__(self, max_size=1000, ttl=3600):
        self.cache = OrderedDict()
        self.max_size = max_size
        self.ttl = ttl
    
    def get(self, key):
        if key in self.cache:
            value, timestamp = self.cache[key]
            if time.time() - timestamp < self.ttl:
                # 更新访问顺序
                self.cache.move_to_end(key)
                return value
            else:
                del self.cache[key]
        return None
    
    def set(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = (value, time.time())
        
        # 限制缓存大小
        if len(self.cache) > self.max_size:
            self.cache.popitem(last=False)

# 创建全局缓存
model_cache = ModelCache()

def cached_generate(prompt, max_tokens=100):
    """带缓存的生成函数"""
    cache_key = f"{prompt}:{max_tokens}"
    
    # 尝试从缓存获取
    cached_result = model_cache.get(cache_key)
    if cached_result:
        return cached_result
    
    # 生成新结果
    result = generate_completion(prompt, max_tokens)
    
    # 存入缓存
    model_cache.set(cache_key, result)
    
    return result

import psutil
import torch
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ModelMonitor:
    @staticmethod
    def get_memory_usage():
        """获取内存使用统计"""
        memory_info = {
            "ram": psutil.virtual_memory()._asdict(),
            "swap": psutil.swap_memory()._asdict(),
        }
        
        if torch.cuda.is_available():
            memory_info["gpu"] = {
                "allocated": torch.cuda.memory_allocated(),
                "cached": torch.cuda.memory_cached(),
                "max_allocated": torch.cuda.max_memory_allocated(),
            }
        
        return memory_info
    
    @staticmethod
    def log_performance_metrics(start_time, end_time, tokens_generated):
        """记录性能指标"""
        duration = end_time - start_time
        tokens_per_second = tokens_generated / duration if duration > 0 else 0
        
        metrics = {
            "total_time": duration,
            "tokens_per_second": tokens_per_second,
            "memory_usage": ModelMonitor.get_memory_usage(),
        }
        
        logger.info(f"性能指标: {metrics}")
        return metrics

# 使用示例
import time

def monitored_generate(prompt, max_tokens=100):
    start_time = time.time()
    
    result = cached_generate(prompt, max_tokens)
    
    end_time = time.time()
    tokens_count = len(result.split())
    
    metrics = ModelMonitor.log_performance_metrics(
        start_time, end_time, tokens_count
    )
    
    return result, metrics

FROM python:3.10-slim

# 设置工作目录
WORKDIR /app

# 安装系统依赖
RUN apt-get update && apt-get install -y \
    build-essential \
    curl \
    && rm -rf /var/lib/apt/lists/*

# 复制依赖文件
COPY requirements.txt .

# 安装Python依赖
RUN pip install --no-cache-dir -r requirements.txt

# 复制应用代码
COPY . .

# 创建模型缓存目录
RUN mkdir -p /app/model_cache

# 环境变量
ENV PYTHONUNBUFFERED=1
ENV MODEL_CACHE_DIR=/app/model_cache
ENV CUDA_VISIBLE_DEVICES=0

# 暴露端口
EXPOSE 8000

# 启动命令
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]

version: '3.8'

services:
  codex-api:
    build: .
    container_name: codex-api
    ports:
      - "8000:8000"
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - MODEL_NAME=codex-cushle-001
      - MAX_WORKERS=4
    volumes:
      - ./model_cache:/app/model_cache
      - ./logs:/app/logs
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 300s

apiVersion: apps/v1
kind: Deployment
metadata:
  name: codex-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: codex-api
  template:
    metadata:
      labels:
        app: codex-api
    spec:
      containers:
      - name: codex-api
        image: codex-api:latest
        ports:
        - containerPort: 8000
        env:
        - name: MODEL_NAME
          value: "codex-cushle-001"
        - name: MAX_WORKERS
          value: "4"
        resources:
          requests:
            memory: "32Gi"
            nvidia.com/gpu: 1
          limits:
            memory: "64Gi"
            nvidia.com/gpu: 1
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 300
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: codex-api-service
spec:
  selector:
    app: codex-api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer

class IntelligentCodeAssistant:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer
        self.context_window = []
        self.max_context_length = 4000
    
    def add_to_context(self, code_snippet, description):
        """添加代码片段到上下文"""
        context_item = {
            "code": code_snippet,
            "description": description,
            "timestamp": time.time()
        }
        self.context_window.append(context_item)
        
        # 维护上下文长度
        self._maintain_context_length()
    
    def _maintain_context_length(self):
        """维护上下文长度在合理范围内"""
        while len(self.context_window) > 10:
            self.context_window.pop(0)
    
    async def generate_with_context(self, current_code, instruction):
        """基于上下文的代码生成"""
        # 构建上下文提示
        context_prompt = "基于以下代码上下文：\\n"
        for item in self.context_window:
            context_prompt += f"代码：{item['code'][:200]}...\\n"
            context_prompt += f"说明：{item['description']}\\n\\n"
        
        context_prompt += f"当前代码：{current_code}\\n"
        context_prompt += f"指令：{instruction}\\n"
        context_prompt += "请生成相应的代码："
        
        # 调用模型生成
        result = await self._generate_code(context_prompt)
        return result
    
    async def _generate_code(self, prompt):
        """生成代码实现"""
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
        
        gen_config = {
            "max_new_tokens": 500,
            "temperature": 0.2,
            "top_p": 0.95,
            "do_sample": True,
            "pad_token_id": self.tokenizer.eos_token_id,
        }
        
        with torch.no_grad():
            outputs = self.model.generate(**inputs, **gen_config)
        
        generated_code = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return generated_code[len(prompt):]

class TestGenerator:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer
    
    def generate_test_cases(self, function_code, language="python"):
        """为函数生成测试用例"""
        test_prompt = f"""
为以下{language}函数生成完整的单元测试：

```{language}
{function_code}
```

请生成：
1. 边界条件测试
2. 正常情况测试
3. 异常情况测试
4. 性能测试（如果适用）

测试代码：
"""
        
        test_code = self._generate(test_prompt, max_tokens=1000)
        return test_code
    
    def _generate(self, prompt, max_tokens=1000):
        """生成文本"""
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=0.1,
                top_p=0.9,
                do_sample=True
            )
        
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)