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fix(async-context-compression): resolve race condition, update role to assistant, bump to v1.1.3

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fujie
2026-01-12 01:45:58 +08:00
parent d5c099dd15
commit 34b2c3d6cf
8 changed files with 74 additions and 124 deletions
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12
plugins/filters/async-context-compression/README.md
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@@ -1,9 +1,14 @@
# Async Context Compression Filter
**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 1.1.2 | **License:** MIT
**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 1.1.3 | **License:** MIT
This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
## What's new in 1.1.3
- **Improved Compatibility**: Changed summary injection role from `user` to `assistant` for better compatibility across different LLMs.
- **Enhanced Stability**: Fixed a race condition in state management that could cause "inlet state not found" warnings in high-concurrency scenarios.
- **Bug Fixes**: Corrected default model handling to prevent misleading logs when no model is specified.
## What's new in 1.1.2
- **Open WebUI v0.7.x Compatibility**: Resolved a critical database session binding error affecting Open WebUI v0.7.x users. The plugin now dynamically discovers the database engine and session context, ensuring compatibility across versions.
@@ -15,12 +20,7 @@ This filter reduces token consumption in long conversations through intelligent
- **Frontend Debugging**: Added `show_debug_log` option to print debug info to the browser console (F12).
- **Optimized Compression**: Improved token calculation logic to prevent aggressive truncation of history, ensuring more context is retained.
## What's new in 1.1.0
- Reuses Open WebUI's shared database connection by default (no custom engine or env vars required).
- Token-based thresholds (`compression_threshold_tokens`, `max_context_tokens`) for safer long-context handling.
- Per-model overrides via `model_thresholds` for mixed-model workflows.
- Documentation now mirrors the latest async workflow and retention-first injection.
---

12
plugins/filters/async-context-compression/README_CN.md
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@@ -1,11 +1,16 @@
# 异步上下文压缩过滤器
**作者:** [Fu-Jie](https://github.com/Fu-Jie) | **版本:** 1.1.2 | **许可证:** MIT
**作者:** [Fu-Jie](https://github.com/Fu-Jie) | **版本:** 1.1.3 | **许可证:** MIT
> **重要提示**：为了确保所有过滤器的可维护性和易用性，每个过滤器都应附带清晰、完整的文档，以确保其功能、配置和使用方法得到充分说明。
本过滤器通过智能摘要和消息压缩技术，在保持对话连贯性的同时，显著降低长对话的 Token 消耗。
## 1.1.3 版本更新
- **兼容性提升**: 将摘要注入角色从 `user` 改为 `assistant`，以提高在不同 LLM 之间的兼容性。
- **稳定性增强**: 修复了状态管理中的竞态条件，解决了高并发场景下可能出现的“无法获取 inlet 状态”警告。
- **Bug 修复**: 修正了默认模型处理逻辑，防止在未指定模型时产生误导性日志。
## 1.1.2 版本更新
- **Open WebUI v0.7.x 兼容性**: 修复了影响 Open WebUI v0.7.x 用户的严重数据库会话绑定错误。插件现在动态发现数据库引擎和会话上下文，确保跨版本兼容性。
@@ -17,12 +22,7 @@
- **前端调试**: 新增 `show_debug_log` 选项，支持在浏览器控制台 (F12) 打印调试信息。
- **压缩优化**: 优化 Token 计算逻辑，防止历史记录被过度截断，保留更多上下文。
## 1.1.0 版本更新
- 默认复用 OpenWebUI 内置数据库连接，无需自建引擎、无需配置 `DATABASE_URL`。
- 基于 Token 的阈值控制（`compression_threshold_tokens`、`max_context_tokens`），长上下文更安全。
- 支持 `model_thresholds` 为不同模型设置专属阈值，适合混用多模型场景。
- 文档同步最新异步工作流与“先保留再注入”策略。
---

82
plugins/filters/async-context-compression/async_context_compression.py
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@@ -5,7 +5,7 @@ author: Fu-Jie
author_url: https://github.com/Fu-Jie
funding_url: https://github.com/Fu-Jie/awesome-openwebui
description: Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.
version: 1.1.2
version: 1.1.3
openwebui_id: b1655bc8-6de9-4cad-8cb5-a6f7829a02ce
license: MIT
@@ -370,7 +370,10 @@ class Filter:
self.valves = self.Valves()
self._owui_db = owui_db
self._db_engine = owui_engine
self.temp_state = {} # Used to pass temporary data between inlet and outlet
self._db_engine = owui_engine
self._fallback_session_factory = (
sessionmaker(bind=self._db_engine) if self._db_engine else None
)
self._fallback_session_factory = (
sessionmaker(bind=self._db_engine) if self._db_engine else None
)
@@ -638,42 +641,6 @@ class Filter:
return ""
def _inject_summary_to_first_message(self, message: dict, summary: str) -> dict:
"""Injects the summary into the first message (prepended to content)."""
content = message.get("content", "")
summary_block = f"【Historical Conversation Summary】\n{summary}\n\n---\nBelow is the recent conversation:\n\n"
# Handle different content types
if isinstance(content, list): # Multimodal content
# Find the first text part and insert the summary before it
new_content = []
summary_inserted = False
for part in content:
if (
isinstance(part, dict)
and part.get("type") == "text"
and not summary_inserted
):
# Prepend summary to the first text part
new_content.append(
{"type": "text", "text": summary_block + part.get("text", "")}
)
summary_inserted = True
else:
new_content.append(part)
# If no text part, insert at the beginning
if not summary_inserted:
new_content.insert(0, {"type": "text", "text": summary_block})
message["content"] = new_content
elif isinstance(content, str): # Plain text
message["content"] = summary_block + content
return message
async def _emit_debug_log(
self,
__event_call__,
@@ -803,15 +770,9 @@ class Filter:
# Target is to compress up to the (total - keep_last) message
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
# [Optimization] Simple state cleanup check
if chat_id in self.temp_state:
await self._log(
f"[Inlet] ⚠️ Overwriting unconsumed old state (Chat ID: {chat_id})",
type="warning",
event_call=__event_call__,
)
self.temp_state[chat_id] = target_compressed_count
# Record the target compression progress for the original messages, for use in outlet
# Target is to compress up to the (total - keep_last) message
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[Inlet] Recorded target compression progress: {target_compressed_count}",
@@ -844,7 +805,7 @@ class Filter:
f"---\n"
f"Below is the recent conversation:"
)
summary_msg = {"role": "user", "content": summary_content}
summary_msg = {"role": "assistant", "content": summary_content}
# 3. Tail messages (Tail) - All messages starting from the last compression point
# Note: Must ensure head messages are not duplicated
@@ -914,18 +875,29 @@ class Filter:
event_call=__event_call__,
)
return body
model = body.get("model", "gpt-3.5-turbo")
model = body.get("model") or ""
# Calculate target compression progress directly
# Assuming body['messages'] in outlet contains the full history (including new response)
messages = body.get("messages", [])
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
if self.valves.debug_mode or self.valves.show_debug_log:
await self._log(
f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] Response complete",
f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] Response complete\n[Outlet] Calculated target compression progress: {target_compressed_count} (Messages: {len(messages)})",
event_call=__event_call__,
)
# Process Token calculation and summary generation asynchronously in the background (do not wait for completion, do not affect output)
asyncio.create_task(
self._check_and_generate_summary_async(
chat_id, model, body, __user__, __event_emitter__, __event_call__
chat_id,
model,
body,
__user__,
target_compressed_count,
__event_emitter__,
__event_call__,
)
)
@@ -942,6 +914,7 @@ class Filter:
model: str,
body: dict,
user_data: Optional[dict],
target_compressed_count: Optional[int],
__event_emitter__: Callable[[Any], Awaitable[None]] = None,
__event_call__: Callable[[Any], Awaitable[None]] = None,
):
@@ -986,6 +959,7 @@ class Filter:
chat_id,
body,
user_data,
target_compressed_count,
__event_emitter__,
__event_call__,
)
@@ -1015,6 +989,7 @@ class Filter:
chat_id: str,
body: dict,
user_data: Optional[dict],
target_compressed_count: Optional[int],
__event_emitter__: Callable[[Any], Awaitable[None]] = None,
__event_call__: Callable[[Any], Awaitable[None]] = None,
):
@@ -1031,12 +1006,11 @@ class Filter:
)
# 1. Get target compression progress
# Prioritize getting from temp_state (calculated by inlet). If unavailable (e.g., after restart), assume current is full history.
target_compressed_count = self.temp_state.pop(chat_id, None)
# If target_compressed_count is not passed (should not happen with new logic), estimate it
if target_compressed_count is None:
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[🤖 Async Summary Task] ⚠️ Could not get inlet state, estimating progress using current message count: {target_compressed_count}",
f"[🤖 Async Summary Task] ⚠️ target_compressed_count is None, estimating: {target_compressed_count}",
type="warning",
event_call=__event_call__,
)

80
plugins/filters/async-context-compression/async_context_compression_cn.py
View File

@@ -5,7 +5,7 @@ author: Fu-Jie
author_url: https://github.com/Fu-Jie
funding_url: https://github.com/Fu-Jie/awesome-openwebui
description: 通过智能摘要和消息压缩，降低长对话的 token 消耗，同时保持对话连贯性。
version: 1.1.2
version: 1.1.3
openwebui_id: 5c0617cb-a9e4-4bd6-a440-d276534ebd18
license: MIT
@@ -290,7 +290,8 @@ class Filter:
self.valves = self.Valves()
self._db_engine = owui_engine
self._SessionLocal = owui_Session
self.temp_state = {} # 用于在 inlet 和 outlet 之间传递临时数据
self._SessionLocal = owui_Session
self._init_database()
self._init_database()
def _init_database(self):
@@ -471,42 +472,6 @@ class Filter:
"max_context_tokens": self.valves.max_context_tokens,
}
def _inject_summary_to_first_message(self, message: dict, summary: str) -> dict:
"""将摘要注入到第一条消息中（追加到内容前面）"""
content = message.get("content", "")
summary_block = f"【历史对话摘要】\n{summary}\n\n---\n以下是最近的对话：\n\n"
# 处理不同内容类型
if isinstance(content, list): # 多模态内容
# 查找第一个文本部分并在其前面插入摘要
new_content = []
summary_inserted = False
for part in content:
if (
isinstance(part, dict)
and part.get("type") == "text"
and not summary_inserted
):
# 在第一个文本部分前插入摘要
new_content.append(
{"type": "text", "text": summary_block + part.get("text", "")}
)
summary_inserted = True
else:
new_content.append(part)
# 如果没有文本部分，在开头插入
if not summary_inserted:
new_content.insert(0, {"type": "text", "text": summary_block})
message["content"] = new_content
elif isinstance(content, str): # 纯文本
message["content"] = summary_block + content
return message
async def _emit_debug_log(
self,
__event_call__,
@@ -628,15 +593,9 @@ class Filter:
# 目标是压缩到倒数第 keep_last 条之前
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
# [优化] 简单的状态清理检查
if chat_id in self.temp_state:
await self._log(
f"[Inlet] ⚠️ 覆盖未消费的旧状态 (Chat ID: {chat_id})",
type="warning",
event_call=__event_call__,
)
self.temp_state[chat_id] = target_compressed_count
# 记录原始消息的目标压缩进度，供 outlet 使用
# 目标是压缩到倒数第 keep_last 条之前
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[Inlet] 记录目标压缩进度: {target_compressed_count}",
@@ -669,7 +628,7 @@ class Filter:
f"---\n"
f"以下是最近的对话："
)
summary_msg = {"role": "user", "content": summary_content}
summary_msg = {"role": "assistant", "content": summary_content}
# 3. 尾部消息 (Tail) - 从上次压缩点开始的所有消息
# 注意：这里必须确保不重复包含头部消息
@@ -732,18 +691,29 @@ class Filter:
在后台计算 Token 数并触发摘要生成（不阻塞当前响应，不影响内容输出）
"""
chat_id = __metadata__["chat_id"]
model = body.get("model", "gpt-3.5-turbo")
model = body.get("model") or ""
# 直接计算目标压缩进度
# 假设 outlet 中的 body['messages'] 包含完整历史（包括新响应）
messages = body.get("messages", [])
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
if self.valves.debug_mode or self.valves.show_debug_log:
await self._log(
f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] 响应完成",
f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] 响应完成\n[Outlet] 计算目标压缩进度: {target_compressed_count} (消息数: {len(messages)})",
event_call=__event_call__,
)
# 在后台异步处理 Token 计算和摘要生成（不等待完成，不影响输出）
asyncio.create_task(
self._check_and_generate_summary_async(
chat_id, model, body, __user__, __event_emitter__, __event_call__
chat_id,
model,
body,
__user__,
target_compressed_count,
__event_emitter__,
__event_call__,
)
)
@@ -760,6 +730,7 @@ class Filter:
model: str,
body: dict,
user_data: Optional[dict],
target_compressed_count: Optional[int],
__event_emitter__: Callable[[Any], Awaitable[None]] = None,
__event_call__: Callable[[Any], Awaitable[None]] = None,
):
@@ -804,6 +775,7 @@ class Filter:
chat_id,
body,
user_data,
target_compressed_count,
__event_emitter__,
__event_call__,
)
@@ -833,6 +805,7 @@ class Filter:
chat_id: str,
body: dict,
user_data: Optional[dict],
target_compressed_count: Optional[int],
__event_emitter__: Callable[[Any], Awaitable[None]] = None,
__event_call__: Callable[[Any], Awaitable[None]] = None,
):
@@ -847,12 +820,11 @@ class Filter:
await self._log(f"\n[🤖 异步摘要任务] 开始...", event_call=__event_call__)
# 1. 获取目标压缩进度
# 优先从 temp_state 获取（由 inlet 计算），如果获取不到（例如重启后），则假设当前是完整历史
target_compressed_count = self.temp_state.pop(chat_id, None)
# 如果未传递 target_compressed_count（新逻辑下不应发生），则进行估算
if target_compressed_count is None:
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[🤖 异步摘要任务] ⚠️ 无法获取 inlet 状态，使用当前消息数估算进度: {target_compressed_count}",
f"[🤖 异步摘要任务] ⚠️ target_compressed_count 为 None，进行估算: {target_compressed_count}",
type="warning",
event_call=__event_call__,
)