# 概念性代码，展示推理流程的核心步骤
import tiktoken # OpenAI的tokenizer
from typing import List

class CodeCompletionEngine:
    def __init__(self, model, max_context_length=8192):
        self.model = model  # 加载的代码补全模型
        self.tokenizer = tiktoken.get_encoding("cl100k_base")
        self.max_context = max_context_length

    def _build_context(self, current_file_content: str, cursor_position: int, related_files: List[str]) -> str:
        """构建模型的输入上下文。这是上下文工程的核心。"""
        # 1. 获取当前文件的“前缀”：光标前的所有内容
        prefix = current_file_content[:cursor_position]

        # 2. （高级）检索相关上下文：从当前文件的其他部分或其他相关文件中，
        #    通过向量检索等方式，找到与当前前缀最相关的代码片段。
        #    例如，查找相同函数的调用、同类的方法定义等。
        retrieved_snippets = self.retrieve_relevant_code(prefix, related_files)

        # 3. 组装上下文：通常有固定模板，如：
        #    [相关文件1的摘要]
        #    [相关文件2的摘要]
        #    <文件分隔符>
        #    [当前文件前缀]
        context = self._assemble_context(retrieved_snippets, prefix)

        # 4. Token化并截断，确保不超过模型上下文窗口
        tokens = self.tokenizer.encode(context)
        if len(tokens) > self.max_context:
            # 智能截断策略：优先保留更靠近光标的代码和检索到的高分片段
            tokens = self._smart_truncate(tokens)
        return self.tokenizer.decode(tokens)

    def complete(self, context: str, max_tokens=50) -> str:
        """调用模型进行补全。"""
        # 模型接收上下文，并自回归地生成token序列
        generated_tokens = self.model.generate(context, max_tokens=max_tokens)
        completion = self.tokenizer.decode(generated_tokens)
        # 后处理：例如，将补全内容截断到第一个逻辑结束点（如行尾、分号、闭合括号）
        completion = self._post_process(completion)
        return completion

# 模拟使用
engine = CodeCompletionEngine(model=your_model)
context = engine._build_context(current_file, cursor_pos, [file1, file2])
suggestion = engine.complete(context)
print(f"AI建议: {suggestion}")

# 概念性示例：一个简化版编码Agent的核心循环
import subprocess
import json
from typing import Dict, Any
from some_llm import LLMClient # 假设的LLM客户端

class CodingAgent:
    def __init__(self, llm_client: LLMClient, workspace_path: str):
        self.llm = llm_client
        self.workspace = workspace_path
        self.tools = {
            "read_file": self._read_file,
            "write_file": self._write_file,
            "run_command": self._run_command,
            "run_test": self._run_test,
        }
        self.conversation_history = []

    def _read_file(self, path: str) -> str:
        with open(f"{self.workspace}/{path}", 'r') as f:
            return f.read()

    def _write_file(self, path: str, content: str) -> Dict[str, Any]:
        try:
            with open(f"{self.workspace}/{path}", 'w') as f:
                f.write(content)
            return {"status": "success", "message": f"File {path} written."}
        except Exception as e:
            return {"status": "error", "message": str(e)}

    def _run_command(self, command: str, cwd: str = None) -> Dict[str, Any]:
        try:
            result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=cwd or self.workspace)
            return {
                "status": "success",
                "stdout": result.stdout,
                "stderr": result.stderr,
                "returncode": result.returncode
            }
        except Exception as e:
            return {"status": "error", "message": str(e)}

    def execute_task(self, task_instruction: str) -> str:
        """Agent的核心执行循环：思考 -> 行动 -> 观察 -> 循环"""
        system_prompt = """你是一个资深软件工程师AI助手。你可以使用工具来读写文件、运行命令和测试。
        你的目标是以迭代方式完成用户的任务。每次思考，请决定是使用工具还是直接回答用户。
        使用工具时，请严格按照JSON格式：{"action": "tool_name", "args": {...}}。
        观察工具结果后，继续思考下一步。"""
        
        self.conversation_history = [{"role": "system", "content": system_prompt}]
        self.conversation_history.append({"role": "user", "content": task_instruction})

        max_steps = 10
        for step in range(max_steps):
            # 1. 思考：LLM决定下一步行动
            llm_response = self.llm.chat(self.conversation_history)
            self.conversation_history.append({"role": "assistant", "content": llm_response})

            # 2. 解析行动：是直接回答，还是调用工具？
            if self._looks_like_tool_call(llm_response):
                tool_call = json.loads(llm_response)
                tool_name = tool_call["action"]
                tool_args = tool_call["args"]

                # 3. 执行工具
                if tool_name in self.tools:
                    tool_result = self.tools[tool_name](**tool_args)
                    observation = json.dumps(tool_result, indent=2)
                else:
                    observation = json.dumps({"status": "error", "message": f"Unknown tool: {tool_name}"})

                # 4. 观察结果，并加入到历史中，供下一步思考
                self.conversation_history.append({"role": "user", "content": f"Tool Result:\n{observation}"})
                print(f"[Step {step+1}] Used {tool_name}. Result: {tool_result['status']}")
                
                # 如果任务成功完成（如测试通过），可以提前退出循环
                if tool_name == "run_test" and "all tests passed" in observation.lower():
                    return f"Task completed successfully after {step+1} steps."
            else:
                # LLM直接给出了最终答案
                return llm_response

        return "Task execution reached max steps without completion."

# 模拟运行一个简单任务
agent = CodingAgent(llm_client=LLMClient(), workspace_path="./my_project")
result = agent.execute_task(
    "在项目根目录下创建一个新的Python文件 `utils/helper.py`，"
    "其中包含一个函数 `calculate_average(numbers: List[float]) -> float`，"
    "然后运行现有的单元测试确保没有破坏任何功能。"
)
print(result)