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/handlers.rs

@@ -867,10 +867,7 @@ pub async fn generate_agentic_insight_handler(
                 .persona_system_prompt(user_id, &persona_id)
         });
 
-    let max_iterations: usize = std::env::var("AGENTIC_MAX_ITERATIONS")
-        .ok()
-        .and_then(|v| v.parse().ok())
-        .unwrap_or(12);
+    let max_iterations = crate::ai::config::llm_config().agentic.max_iterations;
 
     // Spawn background task with timeout
     let generator = app_state.insight_generator.clone();