feat: sort action buttons by valve priority
Action buttons under assistant messages were rendered in
non-deterministic order due to set() deduplication. They now
respect the priority field from function Valves, sorted ascending
(lower value = appears first, default 0), matching the existing
filter priority mechanism.
* feat: add LOG_FORMAT env var with JSON formatter for early logging
Introduce LOG_FORMAT environment variable (set to "json" to enable).
When active, logging.basicConfig() uses a JSONFormatter that outputs
single-line JSON objects with fields: ts, level, msg, caller, error,
stacktrace. This covers all log messages emitted during module imports
before Loguru's start_logger() takes over.
* feat: add JSON sink for Loguru when LOG_FORMAT=json
Add _json_sink() as a Loguru sink function that writes single-line JSON
to stdout. In start_logger(), conditionally use the JSON sink instead of
the plain-text stdout_format when LOG_FORMAT is set to "json".
* feat: suppress ASCII banner and fix alembic logging in JSON mode
- Wrap the ASCII art banner print in main.py with a LOG_FORMAT != "json"
guard so JSON output stays machine-parseable.
- Skip alembic's fileConfig() call in migrations/env.py when
LOG_FORMAT=json to prevent it from replacing the JSON log handlers
installed during early startup.
* perf: eliminate 2 redundant full chat deserialization on every message send (#162)
Problem:
Every message send triggered get_chat_by_id_and_user_id which loads the
entire Chat row — including the potentially massive JSON blob containing
the full conversation history — even when the caller only needed a
simple yes/no ownership check or a single column value.
Two call sites in the message-send hot path were doing this:
1. main.py ownership verification: loaded the entire chat object including
all message history JSON, then checked `if chat is None`. The JSON blob
was immediately discarded — only the existence of the row mattered.
2. middleware.py folder check: loaded the entire chat object including all
message history JSON, then read only `chat.folder_id` — a plain column
on the chat table that requires zero JSON parsing.
Fix:
- Added `chat_exists_by_id_and_user_id()`: uses SQL EXISTS subquery which
returns a boolean without loading any row data. The database can satisfy
this from the primary key index alone.
- Added `get_chat_folder_id()`: queries only the `folder_id` column via
`db.query(Chat.folder_id)`, which tells SQLAlchemy to SELECT only that
single column instead of the entire row.
Both new methods preserve the same error handling semantics (return
False/None on exception) and user_id filtering (ownership check) as
the original get_chat_by_id_and_user_id.
Impact:
- Best case (typical): eliminates deserializing 2 full chat JSON blobs per
message send. For long conversations (hundreds of messages with tool
calls, images, file attachments), this blob can be multiple megabytes.
- Worst case: no regression — the new queries are strictly cheaper than
the old ones (less data transferred, less Python object construction,
no Pydantic model_validate overhead).
- The 3 remaining full chat loads in process_chat_payload (load_messages_from_db,
add_file_context, chat_image_generation_handler) are left untouched as
they genuinely need the full history and require separate analysis.
* Address maintainer feedback: rename method and inline call (#166)
- Rename chat_exists_by_id_and_user_id -> is_chat_owner
- Remove intermediate chat_owned variable; call is_chat_owner directly in if condition
feat: add citation sources for fetch_url tool results
URL fetches now produce clickable citation sources in the UI, matching
the existing behavior of search_web and knowledge file tools. When a
model calls fetch_url during native tool calling, the fetched URL
appears as a citable source with a content preview, giving users full
transparency into what pages the model referenced.
When models reference functions (via filterIds/actionIds) that no longer
exist in the database, the /api/models endpoint crashes with a 500 error,
preventing the UI from loading chats entirely. This can happen after
upgrades when built-in functions are removed or when user-created
functions are deleted while still referenced by models.
Instead of raising an exception, log at INFO level and skip the missing
function so the rest of the models load successfully.
Fixes#21464https://claude.ai/code/session_015JRM7m2bNeZPBBmV2Gv4Mj
Co-authored-by: Claude <noreply@anthropic.com>
