fix(chat): budget chat history against the model's served context window

Long insight/persona chats on llama-swap failed with "exceeds the
available context size" even after raising Context in the app:

- llama-server fixes its window at launch (-c) and ignores num_ctx, but
  the history budget trusted the requested num_ctx, so raising it kept
  MORE history than the server could hold. LlmClient::context_window()
  now reports the served window (llama-swap: /upstream/<model>/props,
  cached 10 min) and the budget uses min(requested, served).
- apply_context_budget could only drop tool-call pairs, so plain Q&A
  chats never shrank. It now also drops whole earlier exchanges (keeping
  user/assistant alternation) and, last, the current turn's tool results,
  never the in-progress question.
- The budget only ran once, before the tool loop. ContextFitter now
  fits every LLM call (on a copy; the stored transcript is untouched)
  and, on a context-overflow error, shrinks the budget from the server's
  reported token counts and retries once.

GET /insights/models reports each llama-swap model's context_window when
it's known without loading the model (cached, or currently running), for
the app's Context picker.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

CLAUDE.md

@@ -798,10 +798,11 @@ TTS_PRONUNCIATIONS_PATH=tts_pronunciations.json # JSON map of pronunciation over
 # Insight Chat Continuation
 AGENTIC_CHAT_MAX_ITERATIONS=6                  # Cap on tool-calling iterations per chat turn (default 6)
 AGENTIC_CHAT_DEFAULT_NUM_CTX=32768             # Assumed context window for the history-truncation budget
-                                               # when a chat request omits num_ctx (default 32768). Size to
-                                               # the smallest context among the chat models actually served;
-                                               # too small silently guts replayed history every turn (and
-                                               # destroys llama.cpp KV-cache prefix reuse).
+                                               # when a chat request omits num_ctx AND the backend can't
+                                               # report its served window (llama-swap reports it via /props,
+                                               # so this mostly matters for Ollama/OpenRouter). Too small
+                                               # silently guts replayed history every turn (and destroys
+                                               # llama.cpp KV-cache prefix reuse).
 ```
 
 **AI Insights Fallback Behavior:**
@@ -924,18 +925,29 @@ Persistence:
 Per-`(library_id, file_path)` async mutex (`AppState.insight_chat.chat_locks`)
 serialises concurrent turns on the same insight so the JSON blob doesn't race.
 
-Context management is a soft bound: if the serialized history exceeds
-`num_ctx - 2048` tokens (cheap 4-byte/token heuristic; `num_ctx` defaults
-to `AGENTIC_CHAT_DEFAULT_NUM_CTX`, 32768, when the request omits it), the
-oldest assistant-tool_call + tool_result pairs are dropped until under budget. The
-initial user message (with any images) and system prompt are always preserved.
+Context management (`ContextFitter` in `insight_chat.rs`): the history
+budget is `min(num_ctx, served window) - 2048` tokens (cheap 4-byte/token
+heuristic). The served window comes from `LlmClient::context_window()`:
+llama-swap models fix it at launch (`-c`) and ignore the request's
+`num_ctx`, so `LlamaCppClient` reads it from `/upstream/<model>/props`
+(cached 10 min). When neither is known the budget uses
+`AGENTIC_CHAT_DEFAULT_NUM_CTX` (32768). Over budget, `apply_context_budget`
+drops, oldest first: tool-call scaffolding in completed exchanges, then
+whole earlier exchanges (keeps user/assistant alternation), then the
+current turn's own tool results. The system prompt, the first user message
+(with any images) and the insight reply are always preserved, as is the
+in-progress question. Every LLM call in the loop is re-fitted (tool results
+grow mid-turn) on a copy, so the stored transcript is never trimmed. If the
+server still rejects the prompt as too long, the budget is shrunk from its
+reported token counts and the call retried once.
 The `truncated` event / flag is surfaced to the client when a drop occurred.
 
 Configurable env:
 - `AGENTIC_CHAT_MAX_ITERATIONS` — cap on tool-calling iterations per turn
   (default 6). Per-request `max_iterations` is clamped to this cap.
 - `AGENTIC_CHAT_DEFAULT_NUM_CTX` — assumed context window for the truncation
-  budget when the request omits `num_ctx` (default 32768).
+  budget when the request omits `num_ctx` and the backend can't report its
+  served window (default 32768).
 
 **Apollo Places integration (optional):**