"""ContextCompressor - 上下文压缩与 Prompt 缓存 长会话自动压缩历史消息，保持 Token 在预算内； 会话内 Prompt 不重复渲染。 """ import hashlib import json import logging from typing import Any, Protocol, runtime_checkable logger = logging.getLogger(__name__) @runtime_checkable class CompressionStrategy(Protocol): """压缩策略协议 — 所有压缩器必须实现此接口""" async def compress(self, messages: list[dict]) -> list[dict]: """压缩消息列表""" ... async def compress_tool_result(self, tool_name: str, result: Any) -> str: """压缩单个工具输出结果，返回压缩后的字符串""" ... def is_available(self) -> bool: """检查压缩器是否可用""" ... class ContextCompressor: """Compress long conversation histories to stay within token budgets""" def __init__( self, llm_gateway: Any = None, max_tokens: int = 4000, keep_recent: int = 3, model: str = "default", ): self._llm_gateway = llm_gateway self._max_tokens = max_tokens self._keep_recent = keep_recent self._model = model def estimate_tokens(self, messages: list[dict]) -> int: """Estimate total tokens in message list (rough: 4 chars = 1 token)""" total = 0 for msg in messages: content = msg.get("content", "") total += len(str(content)) // 4 return total async def compress(self, messages: list[dict], _compression_depth: int = 0) -> list[dict]: """Compress messages if they exceed token budget Strategy: 1. Keep system messages unchanged 2. Keep the most recent N messages unchanged 3. Compress older messages into a summary using LLM """ if self.estimate_tokens(messages) <= self._max_tokens: return messages # Separate system messages, old messages, and recent messages system_msgs = [m for m in messages if m.get("role") == "system"] non_system = [m for m in messages if m.get("role") != "system"] if len(non_system) <= self._keep_recent: return messages # Not enough messages to compress old_msgs = non_system[:-self._keep_recent] recent_msgs = non_system[-self._keep_recent:] # Compress old messages summary = await self._summarize(old_msgs) # Build compressed message list compressed = list(system_msgs) if summary: compressed.append({ "role": "system", "content": f"## Conversation Summary\n{summary}", }) compressed.extend(recent_msgs) # Recursive check: if still over budget, compress again if self.estimate_tokens(compressed) > self._max_tokens: if _compression_depth >= 1: # Depth guard: force truncation instead of infinite recursion return self._truncate(compressed) if len(recent_msgs) > 1: # Try keeping fewer recent messages return await self._compress_aggressive(messages, _compression_depth=_compression_depth + 1) # Last resort: truncate return self._truncate(compressed) return compressed async def _summarize(self, messages: list[dict], max_input_tokens: int = 3200) -> str: """Summarize a list of messages using LLM""" if not self._llm_gateway: # No LLM available, do simple truncation return self._simple_summary(messages) # Build summary prompt conversation_text = "\n".join( f"[{m.get('role', 'unknown')}]: {m.get('content', '')}" for m in messages ) # Pre-truncate if conversation_text exceeds safe token threshold estimated_tokens = len(conversation_text) // 4 if estimated_tokens > max_input_tokens: max_chars = max_input_tokens * 4 conversation_text = conversation_text[:max_chars] + "\n...[truncated]" prompt = ( "Summarize the following conversation history concisely, " "preserving key facts, decisions, and context. " "Focus on information that would be needed for continuing the conversation.\n\n" f"{conversation_text}" ) try: response = await self._llm_gateway.chat( messages=[{"role": "user", "content": prompt}], model=self._model, agent_name="compressor", task_type="summarization", ) return response.content except Exception as e: logger.warning(f"LLM summarization failed, using simple summary: {e}") return self._simple_summary(messages) def _simple_summary(self, messages: list[dict]) -> str: """Simple truncation-based summary when LLM is unavailable""" parts = [] for msg in messages: role = msg.get("role", "unknown") content = str(msg.get("content", ""))[:200] parts.append(f"[{role}]: {content}...") return "\n".join(parts) async def _compress_aggressive(self, messages: list[dict], _compression_depth: int = 0) -> list[dict]: """More aggressive compression when standard compression isn't enough""" system_msgs = [m for m in messages if m.get("role") == "system"] non_system = [m for m in messages if m.get("role") != "system"] # Keep only the last message if non_system: summary = await self._summarize(non_system[:-1]) compressed = list(system_msgs) if summary: compressed.append({ "role": "system", "content": f"## Conversation Summary\n{summary}", }) compressed.append(non_system[-1]) return compressed return messages def _truncate(self, messages: list[dict]) -> list[dict]: """Last resort: truncate long messages""" result = [] for msg in messages: content = str(msg.get("content", "")) if len(content) > self._max_tokens * 4: msg = {**msg, "content": content[:self._max_tokens * 4] + "...[truncated]"} result.append(msg) return result async def compress_tool_result(self, tool_name: str, result: Any) -> str: """默认实现：不做压缩，直接返回字符串表示""" return str(result) def is_available(self) -> bool: """ContextCompressor 始终可用""" return True def create_compressor(config: dict[str, Any] | None = None) -> CompressionStrategy | None: """根据配置创建压缩器实例 Args: config: 压缩配置字典，支持以下字段： - enabled: bool, 是否启用压缩（默认 False） - provider: "headroom" | "summary", 压缩提供者 - max_tokens: int, token 预算（summary 模式） - keep_recent: int, 保留最近 N 条消息（summary 模式） - 其他 provider 特定配置 Returns: CompressionStrategy 实例，或 None（未启用时） """ if not config or not config.get("enabled", False): return None provider = config.get("provider", "summary") if provider == "headroom": try: from agentkit.core.headroom_compressor import HeadroomCompressor compressor = HeadroomCompressor(config) if compressor.is_available(): return compressor logger.warning( "HeadroomCompressor not available (headroom-ai not installed?). " "Falling back to ContextCompressor." ) except ImportError: logger.warning( "HeadroomCompressor module not available. " "Falling back to ContextCompressor." ) # Fallback to summary compressor return ContextCompressor( max_tokens=config.get("max_tokens", 4000), keep_recent=config.get("keep_recent", 3), ) # Default: summary-based compression return ContextCompressor( max_tokens=config.get("max_tokens", 4000), keep_recent=config.get("keep_recent", 3), ) def render_cached(template, variables: dict[str, Any] | None = None) -> list[dict[str, str]]: """Render PromptTemplate with caching - returns cached result for same variables""" cache_key = hashlib.md5( json.dumps(variables or {}, sort_keys=True).encode() ).hexdigest() if not hasattr(template, '_render_cache'): template._render_cache = {} if cache_key in template._render_cache: return template._render_cache[cache_key] result = template.render(variables=variables) template._render_cache[cache_key] = result return result def clear_cache(template) -> None: """Clear the render cache on a PromptTemplate instance""" if hasattr(template, '_render_cache'): template._render_cache.clear()