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

docs/plugins/filters/async-context-compression.md

@@ -1,7 +1,7 @@
 # Async Context Compression
 
 <span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.1.2</span>
+<span class="version-badge">v1.1.3</span>
 
 Reduces token consumption in long conversations through intelligent summarization while maintaining conversational coherence.
 
@@ -32,6 +32,8 @@ This is especially useful for:
 - :material-console: **Frontend Debugging**: Debug logs in browser console
 - :material-alert-circle-check: **Enhanced Error Reporting**: Clear error status notifications
 - :material-check-all: **Open WebUI v0.7.x Compatibility**: Dynamic DB session handling
+- :material-account-convert: **Improved Compatibility**: Summary role changed to `assistant`
+- :material-shield-check: **Enhanced Stability**: Resolved race conditions in state management
 
 ---
 

docs/plugins/filters/async-context-compression.zh.md

@@ -1,7 +1,7 @@
 # Async Context Compression（异步上下文压缩）
 
 <span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.1.2</span>
+<span class="version-badge">v1.1.3</span>
 
 通过智能摘要减少长对话的 token 消耗，同时保持对话连贯。
 
@@ -32,6 +32,8 @@ Async Context Compression 过滤器通过以下方式帮助管理长对话的 to
 - :material-console: **前端调试**：支持浏览器控制台日志
 - :material-alert-circle-check: **增强错误报告**：清晰的错误状态通知
 - :material-check-all: **Open WebUI v0.7.x 兼容性**：动态数据库会话处理
+- :material-account-convert: **兼容性提升**：摘要角色改为 `assistant`
+- :material-shield-check: **稳定性增强**：解决状态管理竞态条件
 
 ---
 

docs/plugins/filters/index.md

@@ -22,7 +22,7 @@ Filters act as middleware in the message pipeline:
 
     Reduces token consumption in long conversations through intelligent summarization while maintaining coherence.
 
-    **Version:** 1.1.2
+    **Version:** 1.1.3
 
     [:octicons-arrow-right-24: Documentation](async-context-compression.md)
 

docs/plugins/filters/index.zh.md

@@ -22,7 +22,7 @@ Filter 充当消息管线中的中间件：
 
     通过智能总结减少长对话的 token 消耗，同时保持连贯性。
 
-    **版本：** 1.1.0
+    **版本：** 1.1.3
 
     [:octicons-arrow-right-24: 查看文档](async-context-compression.md)
 

plugins/filters/async-context-compression/README.md

@@ -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.
 
 ---
 

plugins/filters/async-context-compression/README_CN.md

@@ -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。
 
 ---
 

plugins/filters/async-context-compression/async_context_compression.py

@@ -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
+        # Record the target compression progress for the original messages, for use in outlet
-        if chat_id in self.temp_state:
+        # Target is to compress up to the (total - keep_last) message
-            await self._log(
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
-                f"[Inlet] ⚠️ Overwriting unconsumed old state (Chat ID: {chat_id})",
-                type="warning",
-                event_call=__event_call__,
-            )
-
-        self.temp_state[chat_id] = target_compressed_count
 
         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.
+            # If target_compressed_count is not passed (should not happen with new logic), estimate it
-            target_compressed_count = self.temp_state.pop(chat_id, None)
             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__,
                 )

plugins/filters/async-context-compression/async_context_compression_cn.py

@@ -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
+        # 记录原始消息的目标压缩进度，供 outlet 使用
-        if chat_id in self.temp_state:
+        # 目标是压缩到倒数第 keep_last 条之前
-            await self._log(
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
-                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
 
         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
+            # 如果未传递 target_compressed_count（新逻辑下不应发生），则进行估算
-            target_compressed_count = self.temp_state.pop(chat_id, None)
             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__,
                 )