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fix: context compaction never fires during a long tool run

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During a long run of tool calls, context compaction between tool calls judged the conversation's size from a rough character count and ignored the token count the provider reports after every model call. That count leaves out the model's reasoning and undercounts code and command output, so on a code-heavy run it read about half the real size, compaction never started, and the reply ended with the provider's context size error. Compaction now starts from the token count the provider reported for the last model call and only estimates the new tool results on top of it. Providers that report no token counts still get the estimate.

Fixes #32306
This commit is contained in:
Classic298 2026-10-12 23:24:21 +02:00
parent 871d3c4220
commit 4eda1f00df
2 changed files with 10 additions and 6 deletions
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10
backend/open_webui/utils/context_compaction.py
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@ -321,12 +321,10 @@ def _exceeds_token_threshold(messages: list[dict], system_prompt: str, summary:
if threshold <= 0:
return False
# Expanded tool histories include fresh results and rebuilt prompts absent from prior usage.
if not any(message.get('role') == 'tool' for message in messages):
for idx in range(len(messages) - 1, -1, -1):
usage = messages[idx].get('usage') or (messages[idx].get('info') or {}).get('usage')
if isinstance(usage, dict) and (tokens := _usage_token_count(usage)):
return tokens + _estimate_messages_tokens(messages[idx + 1 :]) > threshold
for idx in range(len(messages) - 1, -1, -1):
usage = messages[idx].get('usage') or (messages[idx].get('info') or {}).get('usage')
if isinstance(usage, dict) and (tokens := _usage_token_count(usage)):
return tokens + _estimate_messages_tokens(messages[idx + 1 :]) > threshold
estimated = _estimate_tokens(system_prompt) + _estimate_tokens(summary or '') + _estimate_messages_tokens(messages)
return estimated > threshold

6
backend/open_webui/utils/middleware.py
View file

@ -5917,6 +5917,7 @@ async def streaming_chat_response_handler(response, ctx):
)
tool_call_sources = [] # Track citation sources from tool results
all_tool_call_sources = [] # Accumulated sources across all iterations
attached_usage = None
user_message = get_last_user_message(form_data['messages'])
messages = list(form_data['messages'])
@ -6280,6 +6281,11 @@ async def streaming_chat_response_handler(response, ctx):
image_urls.append(part.get('image_url', ''))
message['content'] = ''.join(text_parts)
# A count reported for the last request covers everything before these results.
if usage is not attached_usage and tool_messages:
tool_messages[0]['usage'] = usage
attached_usage = usage
# Refresh citation context without restoring compacted history.
user_prompt = get_last_user_message_item(form_data['messages'])
for message in messages: