* fix: support large proxy model endpoint refresh
Large OpenAI-compatible proxy endpoints can expose hundreds of models and make /v1/models slow. Treating those endpoints like local model servers caused model picker opens and background probes to repeatedly hit /models, producing timeouts and making otherwise usable endpoints appear offline.
Make model endpoint discovery cached-first for normal UI usage, add explicit proxy/API classification and refresh policy fields, exclude proxy/API endpoints from aggressive local probing, and preserve cached models when refresh fails.
Manual Test/Add/Refresh actions still fetch the full model list with longer timeouts so users can intentionally import large proxy model lists without blocking normal model picker usage.
* fix: preserve endpoint ping status semantics
Gemma 4 returns reasoning_content in streaming responses via
llama-server, but the model wasn't listed in _THINKING_MODEL_PATTERNS,
causing reasoning tokens to be mishandled. Add "gemma" to the pattern
list and register Gemma 4's 128K context window in KNOWN_CONTEXT_WINDOWS
so the agent loop budgets context correctly.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
KNOWN_CONTEXT_WINDOWS lists 'o1' (200k) before 'o1-mini' (128k), and
_lookup_known returned on the first substring hit — so "o1-mini" matched
'o1' and reported 200000 instead of 128000. Track the longest matching
key instead, so the most specific entry wins regardless of table order.
When running Odysseus in Docker and connecting to a local LLM on the host machine (e.g. `llama.cpp` or `Ollama`), the standard endpoint `http://host.docker.internal` is used to breach the container network.
Because `host.docker.internal` was missing from `_LOCAL_HOSTS`, Odysseus incorrectly treated local self-hosted models as cloud APIs. This triggered the fallback behavior where actual API-reported context limits were being ignored and overridden by hardcoded fallbacks in `KNOWN_CONTEXT_WINDOWS`.
**Changes**
- Added `"host.docker.internal"` to the `_LOCAL_HOSTS` whitelist in `src/model_context.py` so that Dockerized deployments correctly trust and respect the context limits of locally hosted models.
**Checks Ran**
- [x] Syntax check (`python -m py_compile src/model_context.py`)
- [x] Tested manually in Docker (`docker compose up -d --build`) on a Windows host using `llama-server`. The correct API context length is now correctly reported in the UI instead of falling back to the 131k hardcode.
Yuri