feat(ai): few-shot exemplars + sticky Ollama preference

- Few-shot injection on /insights/generate/agentic: compresses prior
  training_messages into trajectory blocks (tool calls + result summaries)
  and injects into the system prompt. Hardcoded default ids with optional
  request override.
- New fewshot_source_ids column on photo_insights (+ migration) to track
  which exemplars influenced a given row, for downstream training-set
  filtering. Chat amend rows stamp None with a lineage note.
- Ollama client now remembers which server (primary/fallback) most
  recently succeeded and tries it first on the next call, via a shared
  Arc<AtomicBool>. Avoids re-404ing the primary on every agent iteration
  when the chosen model only lives on the fallback.
- Demote noisy logs: daily_summary "Summary match" lines to debug;
  inner chat_with_tools non-2xx body log from error to warn (outer
  layer owns the terminal-error signal).
- Drift-guard tests for summarize_tool_result covering the success /
  empty / error / unknown shape for every tool.
- Tidy: three pre-existing clippy warnings cleaned up.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/ai/handlers.rs

@@ -4,6 +4,7 @@ use opentelemetry::trace::{Span, Status, Tracer};
 use serde::{Deserialize, Serialize};
 
 use crate::ai::insight_chat::{ChatStreamEvent, ChatTurnRequest};
+use crate::ai::ollama::ChatMessage;
 use crate::ai::{InsightGenerator, ModelCapabilities, OllamaClient};
 use crate::data::Claims;
 use crate::database::{ExifDao, InsightDao};
@@ -12,6 +13,13 @@ use crate::otel::{extract_context_from_request, global_tracer};
 use crate::state::AppState;
 use crate::utils::normalize_path;
 
+/// Hardcoded few-shot exemplars for the agentic endpoint. Populate with the
+/// ids of approved insights whose `training_messages` should be compressed
+/// into trajectory form and injected into the system prompt. Empty = no
+/// change in behavior. Request-level `fewshot_insight_ids` overrides this
+/// when non-empty.
+const DEFAULT_FEWSHOT_INSIGHT_IDS: &[i32] = &[2918, 2908];
+
 #[derive(Debug, Deserialize)]
 pub struct GeneratePhotoInsightRequest {
     pub file_path: String,
@@ -33,6 +41,12 @@ pub struct GeneratePhotoInsightRequest {
     /// OpenRouter chat). Only respected by the agentic endpoint.
     #[serde(default)]
     pub backend: Option<String>,
+    /// Insight ids whose stored `training_messages` should be compressed
+    /// into few-shot trajectories and injected into the system prompt.
+    /// Silently truncated to the first 2. When absent/empty, the handler
+    /// falls back to `DEFAULT_FEWSHOT_INSIGHT_IDS`.
+    #[serde(default)]
+    pub fewshot_insight_ids: Option<Vec<i32>>,
 }
 
 #[derive(Debug, Deserialize)]
@@ -326,6 +340,41 @@ pub async fn generate_agentic_insight_handler(
         span.set_attribute(KeyValue::new("backend", b.clone()));
     }
 
+    // Resolve few-shot ids: request-provided ids take precedence when
+    // non-empty; otherwise fall back to the hardcoded defaults.
+    let fewshot_ids: Vec<i32> = match request.fewshot_insight_ids.as_deref() {
+        Some(ids) if !ids.is_empty() => ids.iter().take(2).copied().collect(),
+        _ => DEFAULT_FEWSHOT_INSIGHT_IDS
+            .iter()
+            .take(2)
+            .copied()
+            .collect(),
+    };
+    span.set_attribute(KeyValue::new("fewshot_count", fewshot_ids.len() as i64));
+
+    let fewshot_examples: Vec<Vec<ChatMessage>> = {
+        let otel_context = opentelemetry::Context::new();
+        let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
+        fewshot_ids
+            .iter()
+            .filter_map(|id| {
+                let insight = dao.get_insight_by_id(&otel_context, *id).ok().flatten()?;
+                let json = insight.training_messages?;
+                match serde_json::from_str::<Vec<ChatMessage>>(&json) {
+                    Ok(msgs) => Some(msgs),
+                    Err(e) => {
+                        log::warn!(
+                            "Few-shot insight {} has malformed training_messages: {}",
+                            id,
+                            e
+                        );
+                        None
+                    }
+                }
+            })
+            .collect()
+    };
+
     let result = insight_generator
         .generate_agentic_insight_for_photo(
             &normalized_path,
@@ -338,6 +387,8 @@ pub async fn generate_agentic_insight_handler(
             request.min_p,
             max_iterations,
             request.backend.clone(),
+            fewshot_examples,
+            fewshot_ids,
         )
         .await;