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fix: sort memory sections for stable system prompt ordering

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Memory sections injected into the system prompt (`[User Memory]`,
`[Memory Neighborhood]`, `[Relevant Context]`) are populated from
vector similarity search, which can return results in different
orders between requests even when the same memories are retrieved.

This causes the system prompt to change on every turn, which
invalidates the LLM's KV cache prefix. On local inference backends
like Ollama, this forces a full prompt re-evaluation on every
follow-up message — turning a ~3 second cache hit into a ~55 second
reprocessing of the entire conversation context.

The fix sorts each section alphabetically before rendering, ensuring
a stable prompt prefix across turns.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Renaud 2026-08-08 15:14:38 +02:00
parent 8faaf2cd1e
commit 5ebf7705c5
1 changed files with 3 additions and 3 deletions
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6
backend/open_webui/utils/memory.py
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@ -361,11 +361,11 @@ async def add_memory_context(request, form_data: dict, user, model: dict | None
parts = []
if sections['user']:
parts.append('[User Memory]\n' + '\n'.join(f'- {memory}' for memory in sections['user']))
parts.append('[User Memory]\n' + '\n'.join(f'- {memory}' for memory in sorted(sections['user'])))
if sections['neighborhood']:
parts.append('[Memory Neighborhood]\n' + '\n'.join(f'- {memory}' for memory in sections['neighborhood']))
parts.append('[Memory Neighborhood]\n' + '\n'.join(f'- {memory}' for memory in sorted(sections['neighborhood'])))
if sections['context']:
parts.append('[Relevant Context]\n' + '\n'.join(f'- {memory}' for memory in sections['context']))
parts.append('[Relevant Context]\n' + '\n'.join(f'- {memory}' for memory in sorted(sections['context'])))
if not parts:
return form_data