Spaces:
Running
Running
File size: 13,423 Bytes
f209a8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Callable, Optional, Sequence
from agent_base.model_profiles import ModelProfile
from agent_base.utils import safe_jsonable
COMPACT_MEMORY_PREFIX = (
"Runtime memory summary from earlier turns.\n"
"This is compressed context, not ground truth.\n"
"The workspace files remain authoritative; re-read any file if exact details matter.\n\n"
)
@dataclass
class CompactionOutcome:
status: str
compacted_messages: list[dict[str, Any]]
summary_text: str = ""
error: str = ""
trigger_reason: str = ""
prior_token_estimate: int = 0
new_token_estimate: int = 0
compacted_group_count: int = 0
kept_group_count: int = 0
existing_memory_text: str = ""
summary_request: list[dict[str, Any]] | None = None
summary_response: dict[str, Any] | None = None
pre_messages: list[dict[str, Any]] | None = None
post_messages: list[dict[str, Any]] | None = None
def should_compact_messages(
*,
last_input_tokens: Optional[int],
current_token_estimate: int,
model_profile: ModelProfile,
) -> tuple[bool, str]:
usage_hit = last_input_tokens is not None and int(last_input_tokens) >= model_profile.compact_trigger_tokens
estimate_hit = current_token_estimate >= model_profile.compact_trigger_tokens
if usage_hit and estimate_hit:
return True, "usage+estimate"
if usage_hit:
return True, "usage"
if estimate_hit:
return True, "estimate"
return False, ""
def compact_messages(
*,
messages: Sequence[dict[str, Any]],
original_prompt_text: str,
model_name: str,
model_profile: ModelProfile,
llm_caller: Callable[..., dict[str, Any]],
token_counter: Callable[[Sequence[dict[str, Any]]], int],
runtime_deadline: Optional[float] = None,
) -> CompactionOutcome:
safe_messages = [dict(message) for message in messages]
if len(safe_messages) <= 2:
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
pre_messages=safe_messages,
post_messages=safe_messages,
error="context compaction requires at least one conversational turn beyond the initial prompt",
)
prior_token_estimate = token_counter(safe_messages)
existing_memory_text, eligible_messages = _split_existing_memory_messages(safe_messages[2:])
turn_groups = _turn_groups(eligible_messages)
if not turn_groups:
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
prior_token_estimate=prior_token_estimate,
existing_memory_text=existing_memory_text,
pre_messages=safe_messages,
post_messages=safe_messages,
error="context compaction found no eligible conversational turns",
)
compacted_groups, recent_groups = _split_turn_groups(turn_groups, model_profile)
if not compacted_groups:
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
prior_token_estimate=prior_token_estimate,
existing_memory_text=existing_memory_text,
pre_messages=safe_messages,
post_messages=safe_messages,
error="context compaction did not find any older turns to summarize",
)
history_text = _render_history_text(compacted_groups, model_profile)
prior_memory_block = ""
if existing_memory_text:
prior_memory_block = (
"Previously compressed memory to preserve and refine:\n"
f"{_truncate_summary_text(existing_memory_text, max_chars=max(1200, model_profile.context_window // 3))}\n\n"
)
summary_request = [
{
"role": "system",
"content": (
"You compress older tool-using agent history into short working memory for continued execution. "
"Return plain text only. Do not call tools. Do not invent facts."
),
},
{
"role": "user",
"content": (
"Summarize the earlier conversation history for a tool-using agent.\n\n"
f"Original task:\n{original_prompt_text}\n\n"
"Write a concise working memory with these sections:\n"
"- Goal\n"
"- Constraints\n"
"- Files and artifacts\n"
"- Evidence and results\n"
"- Open issues\n"
"- Next useful actions\n\n"
"Rules:\n"
"- Prefer concrete file paths, numeric results, and grounded facts.\n"
"- Mention uncertainty when details may need to be re-read from files.\n"
"- Merge any prior compressed memory with the newer history below into one refreshed memory.\n"
"- Deduplicate repeated sections and do not repeat earlier summaries verbatim.\n"
"- The workspace remains authoritative.\n\n"
f"{prior_memory_block}"
f"Older history to compress:\n{history_text}"
),
},
]
summary_reply = llm_caller(
summary_request,
runtime_deadline=runtime_deadline,
max_output_tokens=model_profile.compact_summary_max_tokens,
)
if not isinstance(summary_reply, dict) or summary_reply.get("status") != "ok":
error = summary_reply.get("error", "context compaction summary call failed") if isinstance(summary_reply, dict) else str(summary_reply)
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
prior_token_estimate=prior_token_estimate,
existing_memory_text=existing_memory_text,
summary_request=summary_request,
summary_response=safe_jsonable(summary_reply) if isinstance(summary_reply, dict) else {"status": "error", "error": error},
pre_messages=safe_messages,
post_messages=safe_messages,
error=error,
compacted_group_count=len(compacted_groups),
kept_group_count=len(recent_groups),
)
if summary_reply.get("tool_calls"):
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
prior_token_estimate=prior_token_estimate,
existing_memory_text=existing_memory_text,
summary_request=summary_request,
summary_response=safe_jsonable(summary_reply),
pre_messages=safe_messages,
post_messages=safe_messages,
compacted_group_count=len(compacted_groups),
kept_group_count=len(recent_groups),
error="context compaction summary call returned tool calls",
)
summary_text = str(summary_reply.get("content", "") or "").strip()
if not summary_text:
return CompactionOutcome(
status="error",
compacted_messages=safe_messages,
prior_token_estimate=prior_token_estimate,
existing_memory_text=existing_memory_text,
summary_request=summary_request,
summary_response=safe_jsonable(summary_reply),
pre_messages=safe_messages,
post_messages=safe_messages,
compacted_group_count=len(compacted_groups),
kept_group_count=len(recent_groups),
error="context compaction summary call returned empty text",
)
summary_message = {"role": "user", "content": COMPACT_MEMORY_PREFIX + summary_text}
compacted_messages = safe_messages[:2] + [summary_message]
for group in recent_groups:
compacted_messages.extend(group)
new_token_estimate = token_counter(compacted_messages)
return CompactionOutcome(
status="ok",
compacted_messages=compacted_messages,
summary_text=summary_text,
prior_token_estimate=prior_token_estimate,
new_token_estimate=new_token_estimate,
compacted_group_count=len(compacted_groups),
kept_group_count=len(recent_groups),
existing_memory_text=existing_memory_text,
summary_request=summary_request,
summary_response=safe_jsonable(summary_reply),
pre_messages=safe_messages,
post_messages=compacted_messages,
)
def _turn_groups(messages: Sequence[dict[str, Any]]) -> list[list[dict[str, Any]]]:
groups: list[list[dict[str, Any]]] = []
current_group: list[dict[str, Any]] = []
for message in messages:
role = str(message.get("role", ""))
if role == "assistant" and current_group:
groups.append(current_group)
current_group = [message]
continue
current_group.append(message)
if current_group:
groups.append(current_group)
return groups
def _split_existing_memory_messages(messages: Sequence[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
existing_summaries: list[str] = []
remaining_messages: list[dict[str, Any]] = []
preserving_summary_prefix = True
for message in messages:
content = message.get("content", "")
if (
preserving_summary_prefix
and str(message.get("role", "")) == "user"
and isinstance(content, str)
and content.startswith(COMPACT_MEMORY_PREFIX)
):
existing_summaries.append(content[len(COMPACT_MEMORY_PREFIX) :].strip())
continue
preserving_summary_prefix = False
remaining_messages.append(dict(message))
merged_summary = "\n\n".join(summary for summary in existing_summaries if summary).strip()
return merged_summary, remaining_messages
def _split_turn_groups(turn_groups: Sequence[Sequence[dict[str, Any]]], model_profile: ModelProfile) -> tuple[list[list[dict[str, Any]]], list[list[dict[str, Any]]]]:
recent_char_budget = max(400, model_profile.recent_history_budget_tokens * 4)
recent_groups: list[list[dict[str, Any]]] = []
recent_chars = 0
for group in reversed(turn_groups):
rendered = _render_group(group, max_chars_per_message=240)
if recent_groups and recent_chars >= recent_char_budget:
break
recent_groups.insert(0, [dict(message) for message in group])
recent_chars += len(rendered)
if len(recent_groups) >= 4:
break
if len(recent_groups) >= len(turn_groups):
recent_groups = recent_groups[1:]
compacted_count = max(0, len(turn_groups) - len(recent_groups))
compacted_groups = [[dict(message) for message in group] for group in turn_groups[:compacted_count]]
return compacted_groups, recent_groups
def _render_history_text(turn_groups: Sequence[Sequence[dict[str, Any]]], model_profile: ModelProfile) -> str:
max_history_chars = max(600, min(64000, model_profile.context_window * 2))
max_chars_per_message = max(200, min(4000, max_history_chars // 10))
parts: list[str] = []
used = 0
for index, group in enumerate(turn_groups, start=1):
rendered = f"[Turn group {index}]\n{_render_group(group, max_chars_per_message=max_chars_per_message)}"
if parts and used + len(rendered) > max_history_chars:
remaining = max_history_chars - used
if remaining > 80:
parts.append(rendered[: remaining - 40].rstrip() + "\n...[history truncated]")
break
parts.append(rendered)
used += len(rendered)
return "\n\n".join(parts).strip()
def _render_group(group: Sequence[dict[str, Any]], *, max_chars_per_message: int) -> str:
lines: list[str] = []
for message in group:
role = str(message.get("role", ""))
content = _message_excerpt(message, max_chars=max_chars_per_message)
lines.append(f"{role}: {content}")
return "\n".join(lines).strip()
def _message_excerpt(message: dict[str, Any], *, max_chars: int) -> str:
content = message.get("content", "")
text: str
if isinstance(content, str):
text = content
elif isinstance(content, list):
parts: list[str] = []
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
parts.append(str(part.get("text", "")))
elif isinstance(part, dict) and part.get("type") == "image_url":
parts.append("[image_url]")
else:
parts.append(str(part))
text = " ".join(part for part in parts if part)
else:
text = str(content)
tool_calls = message.get("tool_calls")
if tool_calls:
tool_names = []
for tool_call in tool_calls:
function_block = tool_call.get("function", {}) if isinstance(tool_call, dict) else {}
tool_names.append(str(function_block.get("name", "")))
if tool_names:
text = (text + "\nTool calls: " + ", ".join(name for name in tool_names if name)).strip()
compacted = " ".join(text.split())
if len(compacted) <= max_chars:
return compacted
return compacted[: max_chars - 16].rstrip() + "...[truncated]"
def _truncate_summary_text(text: str, *, max_chars: int) -> str:
compacted = " ".join(str(text).split())
if len(compacted) <= max_chars:
return compacted
return compacted[: max_chars - 16].rstrip() + "...[truncated]"
|