* fix: normalize usage tokens + migration streaming/batching
- Migration: replace .fetchall() with yield_per streaming, replace per-message INSERT+SAVEPOINT with batched inserts (5k/batch) with fallback to row-by-row on error, add progress logging
- Write path: call normalize_usage() in upsert_message() before saving to ensure input_tokens/output_tokens always present
- Read path: analytics queries now COALESCE across input_tokens/prompt_tokens and output_tokens/completion_tokens so historical data with OpenAI-format keys is visible
* fix: restore defensive timestamp conversion in migration
Re-add try/except around int(float(timestamp)) that was accidentally dropped. Without this, a non-numeric timestamp string would cause a TypeError on the subsequent comparison, breaking the entire upgrade.
* revert: remove changes to chat_messages.py
The non-streaming response handler was saving assistant messages without
their usage/token data. While the streaming handler correctly extracted
and saved usage information, the non-streaming path discarded it entirely.
This caused assistant messages from non-streaming completions to have
NULL usage in the chat_message table, making them invisible to the
analytics token aggregation queries and contributing to the '0 tokens'
display in Admin Panel Analytics.
Extract and normalize the usage data from the API response and include
it in the database upsert, matching the pattern already used by the
streaming handler.
* perf(models): batch-fetch function valves to eliminate N+1 queries
get_action_priority() called Functions.get_function_valves_by_id()
individually for every action on every model — an N+1 query pattern
that issued one DB round-trip per (action x model) pair.
Add Functions.get_function_valves_by_ids() that fetches all valves in
a single WHERE IN query, then look up each action's valves from the
pre-fetched dict inside get_action_priority().
No functional change — same priority resolution, same sort order.
* Update models.py
* Update models.py
The Guidelines section instructed LLMs to return "a JSON array of strings"
while the Output section showed a JSON object with a "follow_ups" key.
This mismatch caused some models to return a top-level array, which the
frontend parser cannot handle (it looks for `{ }` delimiters and the
`follow_ups` key). Updated the guideline to consistently request a JSON
object matching the expected format.
Fixes#22187
The SQLCipher engine used a dummy sqlite:// URL with a creator function,
which caused SQLAlchemy to auto-select SingletonThreadPool. This pool
non-deterministically closes in-use connections when thread count exceeds
pool_size (default 5), leading to use-after-free segfaults (exit code 139)
in the native sqlcipher3 C library during multi-threaded operations like
user signup.
Now defaults to NullPool (each operation creates/closes its own connection)
for maximum safety with the native C extension. Also respects the
DATABASE_POOL_SIZE setting: if explicitly set >0, QueuePool is used with
the configured pool parameters, matching the behavior of other DB paths.
Fixes#22258
Backend emits terminal events for write_file, replace_file_content,
and run_command. Frontend showFileNavDir subscriber uses startsWith
path matching to smartly refresh only when the event is relevant:
- write_file/replace_file_content: refresh if path is in current view
- run_command: always refresh (uses root '/' which matches everything)
- Also adds copy-to-clipboard button and code preview full-height fix
