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fix: resolve TypeError and improve Pydantic compatibility in async-context-compression v1.2.2

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This commit is contained in:
fujie
2026-01-21 21:51:58 +08:00
parent a75ee555fa
commit 500e090b11
8 changed files with 79 additions and 60 deletions
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2
docs/plugins/filters/async-context-compression.md
View File

@@ -1,7 +1,7 @@
# Async Context Compression
<span class="category-badge filter">Filter</span>
<span class="version-badge">v1.2.1</span>
<span class="version-badge">v1.2.2</span>
Reduces token consumption in long conversations through intelligent summarization while maintaining conversational coherence.

2
docs/plugins/filters/async-context-compression.zh.md
View File

@@ -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">v1.2.1</span>
<span class="version-badge">v1.2.2</span>
通过智能摘要减少长对话的 token 消耗，同时保持对话连贯。

2
docs/plugins/filters/index.md
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@@ -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:** 1.2.1
**Version:** 1.2.2
[:octicons-arrow-right-24: Documentation](async-context-compression.md)

2
docs/plugins/filters/index.zh.md
View File

@@ -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：** 1.2.1
**版本：** 1.2.2
[:octicons-arrow-right-24: 查看文档](async-context-compression.md)

6
plugins/filters/async-context-compression/README.md
View File

@@ -1,9 +1,13 @@
# Async Context Compression Filter
**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.2.1 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **License:** MIT
**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.2.2 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **License:** MIT
This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
## What's new in 1.2.2
- **Critical Fix**: Resolved `TypeError: 'str' object is not callable` caused by variable name conflict in logging function.
- **Compatibility**: Enhanced `params` handling to support Pydantic objects, improving compatibility with different OpenWebUI versions.
## What's new in 1.2.1
- **Smart Configuration**: Automatically detects base model settings for custom models and adds `summary_model_max_context` for independent summary limits.

6
plugins/filters/async-context-compression/README_CN.md
View File

@@ -1,11 +1,15 @@
# 异步上下文压缩过滤器
**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.2.1 | **项目:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **许可证:** MIT
**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.2.2 | **项目:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **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。
## 1.2.2 版本更新
- **严重错误修复**: 解决了因日志函数变量名冲突导致的 `TypeError: 'str' object is not callable` 错误。
- **兼容性增强**: 改进了 `params` 处理逻辑以支持 Pydantic 对象，提高了对不同 OpenWebUI 版本的兼容性。
## 1.2.1 版本更新
- **智能配置增强**: 自动检测自定义模型的基础模型配置，并新增 `summary_model_max_context` 参数以独立控制摘要模型的上下文限制。

60
plugins/filters/async-context-compression/async_context_compression.py
View File

@@ -5,7 +5,7 @@ author: Fu-Jie
author_url: https://github.com/Fu-Jie/awesome-openwebui
funding_url: https://github.com/open-webui
description: Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.
version: 1.2.1
version: 1.2.2
openwebui_id: b1655bc8-6de9-4cad-8cb5-a6f7829a02ce
license: MIT
@@ -839,7 +839,7 @@ class Filter:
except Exception as e:
logger.error(f"Error emitting debug log: {e}")
async def _log(self, message: str, type: str = "info", event_call=None):
async def _log(self, message: str, log_type: str = "info", event_call=None):
"""Unified logging to both backend (print) and frontend (console.log)"""
# Backend logging
if self.valves.debug_mode:
@@ -849,11 +849,11 @@ class Filter:
if self.valves.show_debug_log and event_call:
try:
css = "color: #3b82f6;" # Blue default
if type == "error":
if log_type == "error":
css = "color: #ef4444; font-weight: bold;" # Red
elif type == "warning":
elif log_type == "warning":
css = "color: #f59e0b;" # Orange
elif type == "success":
elif log_type == "success":
css = "color: #10b981; font-weight: bold;" # Green
# Clean message for frontend: remove separators and extra newlines
@@ -999,6 +999,7 @@ class Filter:
# 2. For base models: check messages for role='system'
system_prompt_content = None
# Try to get from DB (custom model)
# Try to get from DB (custom model)
try:
model_id = body.get("model")
@@ -1026,12 +1027,17 @@ class Filter:
# Handle case where params is a JSON string
if isinstance(params, str):
params = json.loads(params)
# Convert Pydantic model to dict if needed
elif hasattr(params, "model_dump"):
params = params.model_dump()
elif hasattr(params, "dict"):
params = params.dict()
# Handle dict or Pydantic object
# Now params should be a dict
if isinstance(params, dict):
system_prompt_content = params.get("system")
else:
# Assume Pydantic model or object
# Fallback: try getattr
system_prompt_content = getattr(params, "system", None)
if system_prompt_content:
@@ -1050,7 +1056,7 @@ class Filter:
if self.valves.show_debug_log and __event_call__:
await self._log(
f"[Inlet] ❌ Failed to parse model params: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -1071,7 +1077,7 @@ class Filter:
if self.valves.show_debug_log and __event_call__:
await self._log(
f"[Inlet] ❌ Error fetching system prompt from DB: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
if self.valves.debug_mode:
@@ -1125,7 +1131,7 @@ class Filter:
if not chat_id:
await self._log(
"[Inlet] ❌ Missing chat_id in metadata, skipping compression",
type="error",
log_type="error",
event_call=__event_call__,
)
return body
@@ -1154,7 +1160,7 @@ class Filter:
else:
await self._log(
f"[Inlet] ⚠️ Invalid Model Configs (Raw: '{raw_config}'): No valid configs parsed. Expected format: 'model_id:threshold:max_context'",
type="warning",
log_type="warning",
event_call=__event_call__,
)
else:
@@ -1258,7 +1264,7 @@ class Filter:
if total_tokens > max_context_tokens:
await self._log(
f"[Inlet] ⚠️ Candidate prompt ({total_tokens} Tokens) exceeds limit ({max_context_tokens}). Reducing history...",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1395,7 +1401,7 @@ class Filter:
await self._log(
f"[Inlet] Applied summary: {system_info} + Head({len(head_messages)} msg, {head_tokens}t) + Summary({summary_tokens}t) + Tail({len(tail_messages)} msg, {tail_tokens}t) = Total({total_section_tokens}t)",
type="success",
log_type="success",
event_call=__event_call__,
)
@@ -1455,7 +1461,7 @@ class Filter:
if total_tokens > max_context_tokens:
await self._log(
f"[Inlet] ⚠️ Original messages ({total_tokens} Tokens) exceed limit ({max_context_tokens}). Reducing history...",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1523,7 +1529,7 @@ class Filter:
if not chat_id:
await self._log(
"[Outlet] ❌ Missing chat_id in metadata, skipping compression",
type="error",
log_type="error",
event_call=__event_call__,
)
return body
@@ -1625,7 +1631,7 @@ class Filter:
if current_tokens >= compression_threshold_tokens:
await self._log(
f"[🔍 Background Calculation] ⚡ Compression threshold triggered (Token: {current_tokens} >= {compression_threshold_tokens})",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1648,7 +1654,7 @@ class Filter:
except Exception as e:
await self._log(
f"[🔍 Background Calculation] ❌ Error: {str(e)}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -1687,7 +1693,7 @@ class Filter:
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[🤖 Async Summary Task] ⚠️ target_compressed_count is None, estimating: {target_compressed_count}",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1734,7 +1740,7 @@ class Filter:
if not summary_model_id:
await self._log(
"[🤖 Async Summary Task] ⚠️ Summary model does not exist, skipping compression",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return
@@ -1765,7 +1771,7 @@ class Filter:
excess_tokens = estimated_input_tokens - max_context_tokens
await self._log(
f"[🤖 Async Summary Task] ⚠️ Middle messages ({middle_tokens} Tokens) + Buffer exceed summary model limit ({max_context_tokens}), need to remove approx {excess_tokens} Tokens",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1822,7 +1828,7 @@ class Filter:
if not new_summary:
await self._log(
"[🤖 Async Summary Task] ⚠️ Summary generation returned empty result, skipping save",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return
@@ -1851,7 +1857,7 @@ class Filter:
await self._log(
f"[🤖 Async Summary Task] ✅ Complete! New summary length: {len(new_summary)} characters",
type="success",
log_type="success",
event_call=__event_call__,
)
await self._log(
@@ -1957,14 +1963,14 @@ class Filter:
except Exception as e:
await self._log(
f"[Status] Error calculating tokens: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
except Exception as e:
await self._log(
f"[🤖 Async Summary Task] ❌ Error: {str(e)}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -2066,7 +2072,7 @@ Based on the content above, generate the summary:
if not model:
await self._log(
"[🤖 LLM Call] ⚠️ Summary model does not exist, skipping summary generation",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return ""
@@ -2133,7 +2139,7 @@ Based on the content above, generate the summary:
await self._log(
f"[🤖 LLM Call] ✅ Successfully received summary",
type="success",
log_type="success",
event_call=__event_call__,
)
@@ -2154,7 +2160,7 @@ Based on the content above, generate the summary:
await self._log(
f"[🤖 LLM Call] ❌ {error_message}",
type="error",
log_type="error",
event_call=__event_call__,
)

59
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/awesome-openwebui
funding_url: https://github.com/open-webui
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: 1.2.1
version: 1.2.2
openwebui_id: 5c0617cb-a9e4-4bd6-a440-d276534ebd18
license: MIT
@@ -787,7 +787,7 @@ class Filter:
except Exception as e:
print(f"Error emitting debug log: {e}")
async def _log(self, message: str, type: str = "info", event_call=None):
async def _log(self, message: str, log_type: str = "info", event_call=None):
"""统一日志输出到后端 (print) 和前端 (console.log)"""
# 后端日志
if self.valves.debug_mode:
@@ -797,11 +797,11 @@ class Filter:
if self.valves.show_debug_log and event_call:
try:
css = "color: #3b82f6;" # 默认蓝色
if type == "error":
if log_type == "error":
css = "color: #ef4444; font-weight: bold;" # 红色
elif type == "warning":
elif log_type == "warning":
css = "color: #f59e0b;" # 橙色
elif type == "success":
elif log_type == "success":
css = "color: #10b981; font-weight: bold;" # 绿色
# 清理前端消息：移除分隔符和多余换行
@@ -948,12 +948,17 @@ class Filter:
# 处理 params 是 JSON 字符串的情况
if isinstance(params, str):
params = json.loads(params)
# 转换 Pydantic 模型为字典
elif hasattr(params, "model_dump"):
params = params.model_dump()
elif hasattr(params, "dict"):
params = params.dict()
# 处理字典或 Pydantic 对象
# 处理字典
if isinstance(params, dict):
system_prompt_content = params.get("system")
else:
# 假设是 Pydantic 模型或对象
# 回退：尝试 getattr
system_prompt_content = getattr(params, "system", None)
if system_prompt_content:
@@ -972,7 +977,7 @@ class Filter:
if self.valves.show_debug_log and __event_call__:
await self._log(
f"[Inlet] ❌ 解析模型参数失败: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -986,7 +991,7 @@ class Filter:
if self.valves.show_debug_log and __event_call__:
await self._log(
f"[Inlet] ❌ 数据库中未找到模型",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -994,7 +999,7 @@ class Filter:
if self.valves.show_debug_log and __event_call__:
await self._log(
f"[Inlet] ❌ 从数据库获取系统提示词错误: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
if self.valves.debug_mode:
@@ -1048,7 +1053,7 @@ class Filter:
if not chat_id:
await self._log(
"[Inlet] ❌ metadata 中缺少 chat_id，跳过压缩",
type="error",
log_type="error",
event_call=__event_call__,
)
return body
@@ -1154,7 +1159,7 @@ class Filter:
if total_tokens > max_context_tokens:
await self._log(
f"[Inlet] ⚠️ 候选提示词 ({total_tokens} Tokens) 超过上限 ({max_context_tokens})。正在缩减历史记录...",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1290,7 +1295,7 @@ class Filter:
await self._log(
f"[Inlet] 应用摘要: {system_info} + Head({len(head_messages)} 条, {head_tokens}t) + Summary({summary_tokens}t) + Tail({len(tail_messages)} 条, {tail_tokens}t) = Total({total_section_tokens}t)",
type="success",
log_type="success",
event_call=__event_call__,
)
@@ -1350,7 +1355,7 @@ class Filter:
if total_tokens > max_context_tokens:
await self._log(
f"[Inlet] ⚠️ 原始消息 ({total_tokens} Tokens) 超过上限 ({max_context_tokens})。正在缩减历史记录...",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1420,7 +1425,7 @@ class Filter:
if not chat_id:
await self._log(
"[Outlet] ❌ metadata 中缺少 chat_id，跳过压缩",
type="error",
log_type="error",
event_call=__event_call__,
)
return body
@@ -1486,7 +1491,7 @@ class Filter:
if current_tokens >= compression_threshold_tokens:
await self._log(
f"[🔍 后台计算] ⚡ 触发压缩阈值 (Token: {current_tokens} >= {compression_threshold_tokens})",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1509,7 +1514,7 @@ class Filter:
except Exception as e:
await self._log(
f"[🔍 后台计算] ❌ 错误: {str(e)}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -1546,7 +1551,7 @@ class Filter:
target_compressed_count = max(0, len(messages) - self.valves.keep_last)
await self._log(
f"[🤖 异步摘要任务] ⚠️ target_compressed_count 为 None，进行估算: {target_compressed_count}",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1593,7 +1598,7 @@ class Filter:
if not summary_model_id:
await self._log(
"[🤖 异步摘要任务] ⚠️ 摘要模型不存在，跳过压缩",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return
@@ -1624,7 +1629,7 @@ class Filter:
excess_tokens = estimated_input_tokens - max_context_tokens
await self._log(
f"[🤖 异步摘要任务] ⚠️ 中间消息 ({middle_tokens} Tokens) + 缓冲超过摘要模型上限 ({max_context_tokens})，需要移除约 {excess_tokens} Token",
type="warning",
log_type="warning",
event_call=__event_call__,
)
@@ -1681,7 +1686,7 @@ class Filter:
if not new_summary:
await self._log(
"[🤖 异步摘要任务] ⚠️ 摘要生成返回空结果，跳过保存",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return
@@ -1710,7 +1715,7 @@ class Filter:
await self._log(
f"[🤖 异步摘要任务] ✅ 完成！新摘要长度: {len(new_summary)} 字符",
type="success",
log_type="success",
event_call=__event_call__,
)
await self._log(
@@ -1821,14 +1826,14 @@ class Filter:
except Exception as e:
await self._log(
f"[Status] 计算 Token 错误: {e}",
type="error",
log_type="error",
event_call=__event_call__,
)
except Exception as e:
await self._log(
f"[🤖 异步摘要任务] ❌ 错误: {str(e)}",
type="error",
log_type="error",
event_call=__event_call__,
)
@@ -1928,7 +1933,7 @@ class Filter:
if not model:
await self._log(
"[🤖 LLM 调用] ⚠️ 摘要模型不存在，跳过摘要生成",
type="warning",
log_type="warning",
event_call=__event_call__,
)
return ""
@@ -1995,7 +2000,7 @@ class Filter:
await self._log(
f"[🤖 LLM 调用] ✅ 成功接收摘要",
type="success",
log_type="success",
event_call=__event_call__,
)
@@ -2016,7 +2021,7 @@ class Filter:
await self._log(
f"[🤖 LLM 调用] ❌ {error_message}",
type="error",
log_type="error",
event_call=__event_call__,
)