When the selected model fails before producing output, stream_llm_with_fallback
quietly switches to the next candidate and the reply is shown under the
originally selected model's name, so a misconfigured provider looks like it
works. (Concretely: a Bedrock gateway that 400s every Anthropic/Claude request
appears fine because another model silently answers under the Claude label.)
Emit a `fallback` SSE event ({selected_model, answered_by, reason}) the first
time a non-primary candidate produces output, forward it through the agent loop
and both chat-route paths, stamp the response metrics with the model that
actually answered, and show a notice + relabel the reply in the UI.
Tested: python -m pytest tests/test_llm_core_fallback.py (3 pass);
python -m py_compile src/llm_core.py src/agent_loop.py routes/chat_routes.py;
node --check static/js/chat.js.
In Compare each pane renders into a sandboxed <iframe>. A file dropped on
a pane was handled by the iframe (browser default), so the browser loaded
the file *inside* the pane — appearing 'behind' the app — instead of
attaching it. The existing #chat-container drop handler never sees the
event because drag events don't bubble out of an iframe.
While a file drag is active in Compare, raise a single full-window drop
shield above the panes/iframes so the drop lands on the parent document,
then route the files into the shared composer (the same pending-files
pipeline the file picker and paste already use). Scoped to Compare via the
.compare-active class, so normal chat and the tool dropzones (gallery, RAG,
document editor, …) are unaffected.
Verified with a headless-Chromium integration test: synthetic file
dragover raises the shield, drop attaches the file to the composer, and
non-Compare mode is unaffected. Also ran node --check static/app.js.
When an agent turn uses native (OpenAI-style) function calling and the model
returns only tool calls with no prose, _append_tool_results built the follow-up
assistant message with content "" (empty string).
Google Gemini's OpenAI-compatible endpoint and Ollama both reject an assistant
message that carries tool_calls alongside an empty-string content with HTTP 400.
Because that message feeds the tool results back to the model, every tool-using
turn on these providers dies at the second round: the tool runs, but the agent
never produces a result.
Use None (JSON null) instead, which is the spec-correct form the OpenAI SDK
itself emits and which OpenAI and Anthropic accept too. Adds tests covering the
native tool-call content shaping.
James Arslan