Models know how much context they hold

A column rather than a key in capabilities_json, which is rebuilt wholesale
from the submitted checkboxes on every save and would destroy a number
living in it.

0 means unknown, and unknown has to stay tellable from small: the context
percentage and automatic compaction both refuse to act on a figure nobody
supplied. Filled in from /v1/models where the runner advertises it --
OpenRouter, vLLM and llama.cpp each spell it differently, so context_from()
reads the four spellings actually in use, accepts a quoted number but not
"8192 tokens", and rejects anything outside 256..10,000,000. Applied on
discovery only when nothing is set: a refresh must never undo a correction,
since an administrator sets this precisely because the endpoint was wrong.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

src/lembas/services/llm/openai_client.py

@@ -155,6 +155,41 @@ async def list_models(endpoint: Endpoint) -> list[dict[str, Any]]:
     return models
 
 
+# Where the runners that bother to advertise a context length put it. There is
+# no standard field, so this is a list of what the common ones actually emit.
+_CONTEXT_KEYS = ("context_length", "max_model_len", "context_window", "max_context_length")
+
+# Below the first, the number is not a context length; above the second it is a
+# typo or a different unit. Either way, better to record nothing than a wrong
+# figure a percentage would then be computed from.
+MIN_CONTEXT = 256
+MAX_CONTEXT = 10_000_000
+
+
+def context_from(entry: dict[str, Any]) -> int:
+    """A model's context length as advertised by /v1/models, or 0 if it is not.
+
+    Strings are accepted because some servers quote the number, but only when
+    they are digits alone -- "8192 tokens" is a label, not a measurement.
+    """
+    candidates = [entry.get(key) for key in _CONTEXT_KEYS]
+    meta = entry.get("meta")
+    if isinstance(meta, dict):
+        candidates += [meta.get("n_ctx"), *(meta.get(key) for key in _CONTEXT_KEYS)]
+
+    for value in candidates:
+        if isinstance(value, bool):
+            continue
+        if isinstance(value, str):
+            value = value.strip()
+            if not value.isdigit():
+                continue
+            value = int(value)
+        if isinstance(value, int) and MIN_CONTEXT <= value <= MAX_CONTEXT:
+            return value
+    return 0
+
+
 async def stream_chat(
     endpoint: Endpoint,
     payload: dict[str, Any],