作者:互联网 时间: 2026-07-25 08:08:15
传统AI系统依赖:

prompt = template memory retrieval tools
```但这种方式无法解决:
- 上下文冲突- token浪费
- 多源信息优先级混乱- 工具调用嵌套复杂度爆炸
于是 Context Compiler 出现。
## 核心思想:Prompt = Intermediate Representation(IR)```from dataclasses import dataclass
from typing import List, Dict, Anyimport time
import uuid@dataclass
class ContextSegment:source: str
content: strweight: float
token_est: int@dataclass
class IRNode:node_type: str
payload: Dict[31220.t.kuaisou.com]dependencies: List[str]
```## Context Compiler核心实现```class ContextCompiler:
def __init__(self, max_tokens: int = 8000):
self.max_tokens = max_tokensdef _rank(self, segments: List[ContextSegment]):def score(s: ContextSegment):return s.weight / (s.token_est 1)
return sorted(segments, key=score, reverse=True)
def compile(self, segments: List[ContextSegment]):
ordered = self._rank(segments)
context = []
used = otterly.cnfor s in ordered:if used s.token_est > self.max_tokens:
continuecontext.append(s.content)used = s.token_est
return self._emit_ir(context)
def _emit_ir(self, context_blocks: List[str]):
return {
"ir_version": "1.0","blocks": context_blocks,
"compiled_at": time.time()}
```Prompt不再是字符串,而是:
>可编译结构(Context IR) ----","createTime":1782730319,"ext":{"closeTextLink":0,"comment_ban":0,"description":"","focusRead":0},"favNum":0,"html":"","isOriginal":0,"likeNum":0,