refactor(ai): read LLM settings from LlmConfig instead of env

Every LLM env reader now goes through ai::config::llm_config():
build_ollama / build_llamacpp / build_openrouter take config sections
(OPENROUTER_API_KEY is still read from env), and the backend switch,
embedding dim + prefixes, Ollama / llama.cpp / TTS timeouts, TTS voice and
ref length, agentic caps, chat turn timeout and the unified-search model
read their sections. LocalLlm::from_env -> from_config; the
test_daily_summary and populate_knowledge bins take Ollama defaults from
the config. env_max_iterations / env_default_num_ctx are renamed to
max_chat_iterations / default_num_ctx.

The picker still uses picker.include (= LLAMA_SWAP_ALLOWED_MODELS) until
discovery lands. Legacy env vars keep working through the config's
override layer; checked against the deployed .env, which resolves to the
same values. The LLM_BACKEND parsing tests move to ai::config.

llm.example.toml now leaves the embed prefixes empty: copying the
nomic prefixes onto a corpus embedded without them breaks search.

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

src/ai/persona_chat.rs

@@ -37,7 +37,7 @@ use uuid::Uuid;
 use crate::ai::backend::{BackendKind, SamplingOverrides};
 use crate::ai::handlers::ReplayQuery;
 use crate::ai::insight_chat::{
-    ChatStreamEvent, ContextFitter, DEFAULT_MAX_ITERATIONS, ForkInfo, env_max_iterations,
+    ChatStreamEvent, ContextFitter, DEFAULT_MAX_ITERATIONS, ForkInfo, max_chat_iterations,
     render_tree_path,
 };
 use crate::ai::insight_generator::InsightGenerator;
@@ -954,7 +954,7 @@ impl PersonaChatSession {
             Some("hybrid") => BackendKind::Hybrid,
             _ => BackendKind::Local,
         };
-        let max_iterations = DEFAULT_MAX_ITERATIONS.clamp(1, env_max_iterations());
+        let max_iterations = DEFAULT_MAX_ITERATIONS.clamp(1, max_chat_iterations());
         let overrides = SamplingOverrides {
             model: req.model.clone().filter(|m| !m.is_empty()),
             num_ctx: req.num_ctx,