500f0eab4a
- Enhanced Codex model discovery by fetching available models from the API, with fallback to local cache and defaults. - Updated the context compressor's summary target tokens to 2500 for improved performance. - Added external credential detection for Codex CLI to streamline authentication. - Refactored various components to ensure consistent handling of authentication and model selection across the application.
213 lines
9.2 KiB
Python
213 lines
9.2 KiB
Python
"""Automatic context window compression for long conversations.
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Self-contained class with its own OpenAI client for summarization.
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Uses Gemini Flash (cheap/fast) to summarize middle turns while
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protecting head and tail context.
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"""
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import logging
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import os
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from typing import Any, Dict, List
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from agent.auxiliary_client import get_text_auxiliary_client
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from agent.model_metadata import (
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get_model_context_length,
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estimate_messages_tokens_rough,
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)
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logger = logging.getLogger(__name__)
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class ContextCompressor:
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"""Compresses conversation context when approaching the model's context limit.
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Algorithm: protect first N + last N turns, summarize everything in between.
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Token tracking uses actual counts from API responses for accuracy.
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"""
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def __init__(
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self,
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model: str,
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threshold_percent: float = 0.85,
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protect_first_n: int = 3,
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protect_last_n: int = 4,
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summary_target_tokens: int = 2500,
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quiet_mode: bool = False,
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summary_model_override: str = None,
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):
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self.model = model
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self.threshold_percent = threshold_percent
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self.protect_first_n = protect_first_n
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self.protect_last_n = protect_last_n
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self.summary_target_tokens = summary_target_tokens
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self.quiet_mode = quiet_mode
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self.context_length = get_model_context_length(model)
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self.threshold_tokens = int(self.context_length * threshold_percent)
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self.compression_count = 0
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self.last_prompt_tokens = 0
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self.last_completion_tokens = 0
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self.last_total_tokens = 0
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self.client, default_model = get_text_auxiliary_client()
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self.summary_model = summary_model_override or default_model
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def update_from_response(self, usage: Dict[str, Any]):
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"""Update tracked token usage from API response."""
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self.last_prompt_tokens = usage.get("prompt_tokens", 0)
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self.last_completion_tokens = usage.get("completion_tokens", 0)
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self.last_total_tokens = usage.get("total_tokens", 0)
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def should_compress(self, prompt_tokens: int = None) -> bool:
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"""Check if context exceeds the compression threshold."""
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tokens = prompt_tokens if prompt_tokens is not None else self.last_prompt_tokens
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return tokens >= self.threshold_tokens
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def should_compress_preflight(self, messages: List[Dict[str, Any]]) -> bool:
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"""Quick pre-flight check using rough estimate (before API call)."""
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rough_estimate = estimate_messages_tokens_rough(messages)
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return rough_estimate >= self.threshold_tokens
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def get_status(self) -> Dict[str, Any]:
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"""Get current compression status for display/logging."""
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return {
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"last_prompt_tokens": self.last_prompt_tokens,
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"threshold_tokens": self.threshold_tokens,
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"context_length": self.context_length,
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"usage_percent": (self.last_prompt_tokens / self.context_length * 100) if self.context_length else 0,
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"compression_count": self.compression_count,
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}
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def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> str:
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"""Generate a concise summary of conversation turns using a fast model."""
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if not self.client:
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return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed to save space. The assistant performed various actions and received responses."
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parts = []
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for msg in turns_to_summarize:
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role = msg.get("role", "unknown")
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content = msg.get("content", "")
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if len(content) > 2000:
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content = content[:1000] + "\n...[truncated]...\n" + content[-500:]
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tool_calls = msg.get("tool_calls", [])
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if tool_calls:
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tool_names = [tc.get("function", {}).get("name", "?") for tc in tool_calls if isinstance(tc, dict)]
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content += f"\n[Tool calls: {', '.join(tool_names)}]"
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parts.append(f"[{role.upper()}]: {content}")
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content_to_summarize = "\n\n".join(parts)
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prompt = f"""Summarize these conversation turns concisely. This summary will replace these turns in the conversation history.
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Write from a neutral perspective describing:
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1. What actions were taken (tool calls, searches, file operations)
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2. Key information or results obtained
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3. Important decisions or findings
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4. Relevant data, file names, or outputs
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Keep factual and informative. Target ~{self.summary_target_tokens} tokens.
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---
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TURNS TO SUMMARIZE:
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{content_to_summarize}
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---
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Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
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try:
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kwargs = {
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"model": self.summary_model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3,
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"timeout": 30.0,
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}
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# Most providers (OpenRouter, local models) use max_tokens.
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# Direct OpenAI with newer models (gpt-4o, o-series, gpt-5+)
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# requires max_completion_tokens instead.
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try:
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kwargs["max_tokens"] = self.summary_target_tokens * 2
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response = self.client.chat.completions.create(**kwargs)
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except Exception as first_err:
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if "max_tokens" in str(first_err) or "unsupported_parameter" in str(first_err):
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kwargs.pop("max_tokens", None)
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kwargs["max_completion_tokens"] = self.summary_target_tokens * 2
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response = self.client.chat.completions.create(**kwargs)
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else:
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raise
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summary = response.choices[0].message.content.strip()
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if not summary.startswith("[CONTEXT SUMMARY]:"):
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summary = "[CONTEXT SUMMARY]: " + summary
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return summary
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except Exception as e:
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logging.warning(f"Failed to generate context summary: {e}")
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return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed. The assistant performed tool calls and received responses."
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def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None) -> List[Dict[str, Any]]:
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"""Compress conversation messages by summarizing middle turns.
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Keeps first N + last N turns, summarizes everything in between.
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"""
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n_messages = len(messages)
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if n_messages <= self.protect_first_n + self.protect_last_n + 1:
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if not self.quiet_mode:
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print(f"⚠️ Cannot compress: only {n_messages} messages (need > {self.protect_first_n + self.protect_last_n + 1})")
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return messages
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compress_start = self.protect_first_n
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compress_end = n_messages - self.protect_last_n
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if compress_start >= compress_end:
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return messages
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turns_to_summarize = messages[compress_start:compress_end]
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display_tokens = current_tokens if current_tokens else self.last_prompt_tokens or estimate_messages_tokens_rough(messages)
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if not self.quiet_mode:
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print(f"\n📦 Context compression triggered ({display_tokens:,} tokens ≥ {self.threshold_tokens:,} threshold)")
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print(f" 📊 Model context limit: {self.context_length:,} tokens ({self.threshold_percent*100:.0f}% = {self.threshold_tokens:,})")
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# Truncation fallback when no auxiliary model is available
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if self.client is None:
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print("⚠️ Context compression: no auxiliary model available. Falling back to message truncation.")
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# Keep system message(s) at the front and the protected tail;
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# simply drop the oldest non-system messages until under threshold.
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kept = []
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for msg in messages:
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if msg.get("role") == "system":
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kept.append(msg.copy())
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else:
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break
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tail = messages[-self.protect_last_n:]
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kept.extend(m.copy() for m in tail)
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self.compression_count += 1
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if not self.quiet_mode:
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print(f" ✂️ Truncated: {len(messages)} → {len(kept)} messages (dropped middle turns)")
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return kept
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if not self.quiet_mode:
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print(f" 🗜️ Summarizing turns {compress_start+1}-{compress_end} ({len(turns_to_summarize)} turns)")
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summary = self._generate_summary(turns_to_summarize)
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compressed = []
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for i in range(compress_start):
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msg = messages[i].copy()
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if i == 0 and msg.get("role") == "system" and self.compression_count == 0:
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msg["content"] = msg.get("content", "") + "\n\n[Note: Some earlier conversation turns may be summarized to preserve context space.]"
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compressed.append(msg)
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compressed.append({"role": "user", "content": summary})
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for i in range(compress_end, n_messages):
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compressed.append(messages[i].copy())
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self.compression_count += 1
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if not self.quiet_mode:
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new_estimate = estimate_messages_tokens_rough(compressed)
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saved_estimate = display_tokens - new_estimate
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print(f" ✅ Compressed: {n_messages} → {len(compressed)} messages (~{saved_estimate:,} tokens saved)")
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print(f" 💡 Compression #{self.compression_count} complete")
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return compressed
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