Both session factories run with expire_on_commit=False, so ORM objects keep their attribute values after commit. Every session.refresh issued right after a commit therefore re-SELECTed a row whose values the session already held, including full chat JSON blobs and user settings, purely to overwrite identical data. Fifty such calls existed across the model layer, covering nearly every write path in the app (chat inserts, title updates, pin/archive toggles, user role and settings updates, tool, prompt, function, model, file, tag, feedback, memory, automation and grant writes).
All fifty are removed. The only refreshes with an actual job were the two update-then-reload paths in tools and skills, where a Core UPDATE statement bypasses the identity map; those now use session.get(..., populate_existing=True), which guarantees a fresh row in one SELECT whether or not the row was already present in the session (the previous code issued get plus refresh, two SELECTs, on the default configuration).
Benchmark (real SQLite DB, per write):
| write path | before | after |
| --- | --- | --- |
| chat title update, ~600 KB chat blob | 2.08 ms | 1.24 ms |
| user role update, small row | 1.21 ms | 0.68 ms |
On Postgres each removed refresh is additionally a network round trip. The chat-blob case also skips re-parsing the entire JSON document per write.
Functionally verified against a fresh database: user insert, role and settings updates, chat insert (including the server-default meta column, which is always provided client-side), title update and pin toggle, tool insert and the Core-update reload path, tag insert and the prompt insert flow that pins version_id after history creation all return correct values and persist correctly.
The get_token_usage_by_user query lacked group_id filtering, while the
companion get_message_count_by_user query already supported it. When an
admin filtered analytics by user group, message counts were correctly
scoped to the group but token usage totals included data from all users.
Add the group_id parameter and subquery filter to get_token_usage_by_user,
matching the pattern used by get_message_count_by_user and other analytics
queries, and pass group_id through from the analytics endpoint.
- Add chat_message table for message-level analytics with usage JSON field
- Add migration to backfill from existing chats
- Add /analytics endpoints: summary, models, users, daily
- Support hourly/daily granularity for time-series data
- Fill missing days/hours in date range