Raise chat truncation default num_ctx to 32k, env-overridable

The history-truncation budget assumed an 8192-token context whenever a
chat request omitted num_ctx, while the llama-swap chat slots serve
20k-131k. Replayed transcripts past ~6k tokens were silently gutted
every turn — losing conversation history and destroying llama.cpp
KV-cache prefix reuse (full SWA re-prefill per turn).

Default is now 32768 (real conversations top out around 16k), with
AGENTIC_CHAT_DEFAULT_NUM_CTX to override per deploy, floored at
headroom + 1024. Explicit per-request num_ctx still wins.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

CLAUDE.md

@@ -671,6 +671,11 @@ LLAMA_SWAP_TTS_REQUEST_TIMEOUT_SECONDS=600     # Per-request synth timeout (long
 
 # 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).
 ```
 
 **AI Insights Fallback Behavior:**
@@ -794,14 +799,17 @@ 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), the oldest
+`num_ctx - 2048` tokens (cheap 4-byte/token heuristic; `num_ctx` defaults
-assistant-tool_call + tool_result pairs are dropped until under budget. The
+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.
 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).
 
 **Apollo Places integration (optional):**
 

src/ai/handlers.rs

@@ -8,7 +8,7 @@ use crate::ai::insight_chat::{ChatStreamEvent, ChatTurnRequest};
 use crate::ai::ollama::ChatMessage;
 use crate::ai::{ModelCapabilities, OllamaClient};
 use crate::data::Claims;
-use crate::database::models::{InsightGenerationType, InsightJobStatus};
+use crate::database::models::{InsightGenerationType, InsightJobStatus, PhotoInsight};
 use crate::database::{ExifDao, InsightDao};
 use crate::libraries;
 use crate::otel::{extract_context_from_request, global_tracer};
@@ -273,6 +273,11 @@ pub async fn cancel_generation_handler(
 pub struct RateInsightRequest {
     pub file_path: String,
     pub approved: bool,
+    /// When set, rate this specific insight version by primary key
+    /// (used by the per-file history view to rate superseded versions).
+    /// When omitted, the current insight for `file_path` is rated.
+    #[serde(default)]
+    pub insight_id: Option<i32>,
 }
 
 #[derive(Debug, Deserialize)]
@@ -333,6 +338,31 @@ pub struct PhotoInsightResponse {
     pub persona_id: Option<String>,
 }
 
+impl From<PhotoInsight> for PhotoInsightResponse {
+    fn from(insight: PhotoInsight) -> Self {
+        PhotoInsightResponse {
+            id: insight.id,
+            file_path: insight.file_path,
+            title: insight.title,
+            summary: insight.summary,
+            generated_at: insight.generated_at,
+            model_version: insight.model_version,
+            prompt_eval_count: insight.prompt_eval_count,
+            eval_count: insight.eval_count,
+            approved: insight.approved,
+            has_training_messages: insight.training_messages.is_some(),
+            backend: insight.backend,
+            num_ctx: insight.num_ctx,
+            temperature: insight.temperature,
+            top_p: insight.top_p,
+            top_k: insight.top_k,
+            min_p: insight.min_p,
+            system_prompt: insight.system_prompt,
+            persona_id: insight.persona_id,
+        }
+    }
+}
+
 #[derive(Debug, Serialize)]
 pub struct AvailableModelsResponse {
     pub primary: ServerModels,
@@ -554,29 +584,7 @@ pub async fn get_insight_handler(
     let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
 
     match dao.get_insight_for_paths(&otel_context, &sibling_paths) {
-        Ok(Some(insight)) => {
+        Ok(Some(insight)) => HttpResponse::Ok().json(PhotoInsightResponse::from(insight)),
-            let response = PhotoInsightResponse {
-                id: insight.id,
-                file_path: insight.file_path,
-                title: insight.title,
-                summary: insight.summary,
-                generated_at: insight.generated_at,
-                model_version: insight.model_version,
-                prompt_eval_count: insight.prompt_eval_count,
-                eval_count: insight.eval_count,
-                approved: insight.approved,
-                has_training_messages: insight.training_messages.is_some(),
-                backend: insight.backend,
-                num_ctx: insight.num_ctx,
-                temperature: insight.temperature,
-                top_p: insight.top_p,
-                top_k: insight.top_k,
-                min_p: insight.min_p,
-                system_prompt: insight.system_prompt,
-                persona_id: insight.persona_id,
-            };
-            HttpResponse::Ok().json(response)
-        }
         Ok(None) => HttpResponse::NotFound().json(serde_json::json!({
             "error": "Insight not found"
         })),
@@ -631,26 +639,7 @@ pub async fn get_all_insights_handler(
         Ok(insights) => {
             let responses: Vec<PhotoInsightResponse> = insights
                 .into_iter()
-                .map(|insight| PhotoInsightResponse {
+                .map(PhotoInsightResponse::from)
-                    id: insight.id,
-                    file_path: insight.file_path,
-                    title: insight.title,
-                    summary: insight.summary,
-                    generated_at: insight.generated_at,
-                    model_version: insight.model_version,
-                    prompt_eval_count: insight.prompt_eval_count,
-                    eval_count: insight.eval_count,
-                    approved: insight.approved,
-                    has_training_messages: insight.training_messages.is_some(),
-                    backend: insight.backend,
-                    num_ctx: insight.num_ctx,
-                    temperature: insight.temperature,
-                    top_p: insight.top_p,
-                    top_k: insight.top_k,
-                    min_p: insight.min_p,
-                    system_prompt: insight.system_prompt,
-                    persona_id: insight.persona_id,
-                })
                 .collect();
 
             HttpResponse::Ok().json(responses)
@@ -664,6 +653,39 @@ pub async fn get_all_insights_handler(
     }
 }
 
+/// GET /insights/history?path=/path/to/photo.jpg - Get all insight versions
+/// for a single photo (current plus previously generated/superseded ones),
+/// newest first. Backs the per-file insight history view.
+#[get("/insights/history")]
+pub async fn get_insight_history_handler(
+    _claims: Claims,
+    query: web::Query<GetPhotoInsightQuery>,
+    insight_dao: web::Data<std::sync::Mutex<Box<dyn InsightDao>>>,
+) -> impl Responder {
+    let normalized_path = normalize_path(&query.path);
+    log::debug!("Fetching insight history for {}", normalized_path);
+
+    let otel_context = opentelemetry::Context::new();
+    let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
+
+    match dao.get_insight_history(&otel_context, &normalized_path) {
+        Ok(insights) => {
+            let responses: Vec<PhotoInsightResponse> = insights
+                .into_iter()
+                .map(PhotoInsightResponse::from)
+                .collect();
+
+            HttpResponse::Ok().json(responses)
+        }
+        Err(e) => {
+            log::error!("Failed to fetch insight history ({}): {:?}", &query.path, e);
+            HttpResponse::InternalServerError().json(serde_json::json!({
+                "error": format!("Failed to fetch insight history: {:?}", e)
+            }))
+        }
+    }
+}
+
 /// POST /insights/generate/agentic - Generate insight using agentic tool-calling loop (async)
 #[post("/insights/generate/agentic")]
 pub async fn generate_agentic_insight_handler(
@@ -787,6 +809,21 @@ pub async fn generate_agentic_insight_handler(
         .filter(|s| !s.trim().is_empty())
         .unwrap_or_else(|| "default".to_string());
 
+    // Server-side persona resolution: an explicit client `system_prompt`
+    // wins; otherwise the persona's stored prompt from the persona store;
+    // otherwise None and `build_system_content` applies its neutral
+    // default. Without the lookup, a request carrying only `persona_id`
+    // silently generated in the default voice.
+    let system_prompt = request
+        .system_prompt
+        .clone()
+        .filter(|s| !s.trim().is_empty())
+        .or_else(|| {
+            app_state
+                .insight_generator
+                .persona_system_prompt(user_id, &persona_id)
+        });
+
     let max_iterations: usize = std::env::var("AGENTIC_MAX_ITERATIONS")
         .ok()
         .and_then(|v| v.parse().ok())
@@ -812,7 +849,7 @@ pub async fn generate_agentic_insight_handler(
                 generator_for_task.generate_agentic_insight_for_photo(
                     &path_for_task,
                     request.model.clone(),
-                    request.system_prompt.clone(),
+                    system_prompt,
                     request.num_ctx,
                     request.temperature,
                     request.top_p,
@@ -1012,15 +1049,23 @@ pub async fn rate_insight_handler(
 ) -> impl Responder {
     let normalized_path = normalize_path(&request.file_path);
     log::info!(
-        "Rating insight for {}: approved={}",
+        "Rating insight for {} (id={:?}): approved={}",
         normalized_path,
+        request.insight_id,
         request.approved
     );
 
     let otel_context = opentelemetry::Context::new();
     let mut dao = insight_dao.lock().expect("Unable to lock InsightDao");
 
-    match dao.rate_insight(&otel_context, &normalized_path, request.approved) {
+    // Rate a specific version by id when provided (history view), otherwise
+    // rate the current insight for the path.
+    let result = match request.insight_id {
+        Some(id) => dao.rate_insight_by_id(&otel_context, id, request.approved),
+        None => dao.rate_insight(&otel_context, &normalized_path, request.approved),
+    };
+
+    match result {
         Ok(()) => HttpResponse::Ok().json(serde_json::json!({
             "success": true,
             "message": "Insight rated successfully"

src/ai/insight_chat.rs

@@ -19,7 +19,13 @@ use futures::stream::{BoxStream, StreamExt};
 use uuid::Uuid;
 
 const DEFAULT_MAX_ITERATIONS: usize = 6;
-const DEFAULT_NUM_CTX: i32 = 8192;
+/// Assumed context window when the request doesn't specify `num_ctx`.
+/// The llama-swap chat slots serve 20k-131k contexts and real conversations
+/// rarely pass ~16k tokens, so 32k keeps the truncation pass from gutting
+/// history that the server could comfortably hold (which also destroys the
+/// server's KV-cache prefix reuse). Override per-deploy with
+/// AGENTIC_CHAT_DEFAULT_NUM_CTX if the serving models change shape.
+const DEFAULT_NUM_CTX: i32 = 32768;
 /// Headroom reserved for the model's response, deducted from the context
 /// budget when deciding whether to truncate the replayed history.
 const RESPONSE_HEADROOM_TOKENS: usize = 2048;
@@ -33,6 +39,12 @@ const BYTES_PER_TOKEN: usize = 4;
 /// characters) must NOT be counted as text bytes — doing so dwarfs the entire
 /// text budget and forces spurious truncation on every turn.
 const IMAGE_TOKENS_EACH: usize = 1300;
+/// User prompt injected when the agentic loop exhausts its iteration budget
+/// without producing a tool-free reply. Internal scaffolding only — it is
+/// stripped from the transcript before persistence (see
+/// [`push_synthetic_final_prompt`] / [`remove_synthetic_final_prompt`]).
+const SYNTHETIC_FINAL_ANSWER_PROMPT: &str =
+    "Please write your final answer now without calling any more tools.";
 
 pub type ChatLockMap = Arc<TokioMutex<HashMap<(i32, String), Arc<TokioMutex<()>>>>>;
 
@@ -361,7 +373,7 @@ impl InsightChatService {
 
         // 6. Apply truncation budget. Drops oldest tool_call+tool pairs
         //    (preserves system + first user including any images).
-        let budget_tokens = (req.num_ctx.unwrap_or(DEFAULT_NUM_CTX) as usize)
+        let budget_tokens = (req.num_ctx.unwrap_or_else(env_default_num_ctx) as usize)
             .saturating_sub(RESPONSE_HEADROOM_TOKENS);
         let budget_bytes = budget_tokens.saturating_mul(BYTES_PER_TOKEN);
         let truncated = apply_context_budget(&mut messages, budget_bytes);
@@ -457,9 +469,7 @@ impl InsightChatService {
                 "Chat loop exhausted after {} iterations, requesting final answer",
                 iterations_used
             );
-            messages.push(ChatMessage::user(
+            let synthetic_idx = push_synthetic_final_prompt(&mut messages);
-                "Please write your final answer now without calling any more tools.",
-            ));
             let (final_response, prompt_tokens, eval_tokens) = backend
                 .chat()
                 .chat_with_tools(messages.clone(), vec![])
@@ -468,8 +478,15 @@ impl InsightChatService {
             last_eval_count = eval_tokens;
             final_content = final_response.content.clone();
             messages.push(final_response);
+            // Drop the synthetic prompt before persistence — internal
+            // scaffolding only (mirrors both streaming variants).
+            remove_synthetic_final_prompt(&mut messages, synthetic_idx);
         }
 
+        // Strip any leaked <think> reasoning block from the content we
+        // return / persist as the reply (the raw transcript keeps it).
+        let final_content = crate::ai::llm_client::strip_think_blocks(&final_content);
+
         loop_cx.span().set_status(Status::Ok);
 
         // Drop the per-turn iteration-budget note from the system message
@@ -853,7 +870,7 @@ impl InsightChatService {
             None
         };
 
-        let budget_tokens = (req.num_ctx.unwrap_or(DEFAULT_NUM_CTX) as usize)
+        let budget_tokens = (req.num_ctx.unwrap_or_else(env_default_num_ctx) as usize)
             .saturating_sub(RESPONSE_HEADROOM_TOKENS);
         let budget_bytes = budget_tokens.saturating_mul(BYTES_PER_TOKEN);
         let truncated = apply_context_budget(&mut messages, budget_bytes);
@@ -1039,7 +1056,12 @@ impl InsightChatService {
         );
         let tools = InsightGenerator::build_tool_definitions(gate_opts);
 
-        let persona = resolve_bootstrap_system_prompt(req.system_prompt.as_deref());
+        // Server-side persona resolution: explicit client system_prompt wins;
+        // else the active persona's stored prompt; else the neutral default.
+        let persona_prompt = self
+            .generator
+            .persona_system_prompt(req.user_id, &active_persona);
+        let persona = resolve_bootstrap_system_prompt(req.system_prompt.as_deref(), persona_prompt);
         let system_content = build_bootstrap_system_message(
             &persona,
             &normalized,
@@ -1263,10 +1285,7 @@ impl InsightChatService {
 
         // No-tools fallback
         if final_content.is_empty() {
-            let synthetic_idx = messages.len();
+            let synthetic_idx = push_synthetic_final_prompt(messages);
-            messages.push(ChatMessage::user(
-                "Please write your final answer now without calling any more tools.",
-            ));
             let mut stream = backend
                 .chat()
                 .chat_with_tools_stream(messages.clone(), vec![])
@@ -1294,7 +1313,7 @@ impl InsightChatService {
                 final_message.ok_or_else(|| anyhow!("final stream ended without a Done event"))?;
             final_content = final_response.content.clone();
             messages.push(final_response);
-            messages.remove(synthetic_idx);
+            remove_synthetic_final_prompt(messages, synthetic_idx);
         }
 
         Ok(AgenticLoopOutcome {
@@ -1302,7 +1321,9 @@ impl InsightChatService {
             iterations_used,
             last_prompt_eval_count,
             last_eval_count,
-            final_content,
+            // Strip any leaked <think> reasoning block from the content the
+            // caller persists as title/summary (the raw transcript keeps it).
+            final_content: crate::ai::llm_client::strip_think_blocks(&final_content),
             cancelled: false,
         })
     }
@@ -1431,7 +1452,7 @@ impl InsightChatService {
         };
 
         // Truncate before appending the new user turn.
-        let budget_tokens = (req.num_ctx.unwrap_or(DEFAULT_NUM_CTX) as usize)
+        let budget_tokens = (req.num_ctx.unwrap_or_else(env_default_num_ctx) as usize)
             .saturating_sub(RESPONSE_HEADROOM_TOKENS);
         let budget_bytes = budget_tokens.saturating_mul(BYTES_PER_TOKEN);
         let truncated = apply_context_budget(&mut messages, budget_bytes);
@@ -1648,7 +1669,12 @@ impl InsightChatService {
         // get_sms_messages / reverse_geocode / get_personal_place_at
         // the args they need. In hybrid mode the visual description
         // belongs here for the same reason.
-        let persona = resolve_bootstrap_system_prompt(req.system_prompt.as_deref());
+        // Server-side persona resolution: explicit client system_prompt wins;
+        // else the active persona's stored prompt; else the neutral default.
+        let persona_prompt = self
+            .generator
+            .persona_system_prompt(req.user_id, &active_persona);
+        let persona = resolve_bootstrap_system_prompt(req.system_prompt.as_deref(), persona_prompt);
         let system_content = build_bootstrap_system_message(
             &persona,
             &normalized,
@@ -1866,10 +1892,7 @@ impl InsightChatService {
             // and load_history's user-turn handler doesn't reset
             // pending_tools at this position (wiping the prior tool
             // calls from the final assistant render).
-            let synthetic_idx = messages.len();
+            let synthetic_idx = push_synthetic_final_prompt(messages);
-            messages.push(ChatMessage::user(
-                "Please write your final answer now without calling any more tools.",
-            ));
             let mut stream = backend
                 .chat()
                 .chat_with_tools_stream(messages.clone(), vec![])
@@ -1900,7 +1923,7 @@ impl InsightChatService {
             // Drop the synthetic prompt — internal scaffolding only. The
             // model's final_response (now at the end) was generated with
             // it in context and reads coherently without it on replay.
-            messages.remove(synthetic_idx);
+            remove_synthetic_final_prompt(messages, synthetic_idx);
         }
 
         Ok(AgenticLoopOutcome {
@@ -1908,7 +1931,9 @@ impl InsightChatService {
             iterations_used,
             last_prompt_eval_count,
             last_eval_count,
-            final_content,
+            // Strip any leaked <think> reasoning block from the content the
+            // caller persists as title/summary (the raw transcript keeps it).
+            final_content: crate::ai::llm_client::strip_think_blocks(&final_content),
             cancelled: false,
         })
     }
@@ -1921,15 +1946,21 @@ const BOOTSTRAP_DEFAULT_SYSTEM_PROMPT: &str = "You are a helpful AI assistant an
      Use the available tools to gather context and answer their questions \
      in a conversational tone.";
 
-/// Pick the system prompt for bootstrap. Trimmed-non-empty supplied wins;
+/// Pick the system prompt for bootstrap. Precedence: trimmed-non-empty
-/// otherwise fall back to [`BOOTSTRAP_DEFAULT_SYSTEM_PROMPT`]. Returns an
+/// `supplied` (the client's explicit `system_prompt`) wins; else
-/// owned `String` because the bootstrap caller persists it on the new
+/// `persona_prompt` (the active persona's stored prompt, resolved
-/// insight row.
+/// server-side from the persona store); else
-fn resolve_bootstrap_system_prompt(supplied: Option<&str>) -> String {
+/// [`BOOTSTRAP_DEFAULT_SYSTEM_PROMPT`]. Returns an owned `String` because
+/// the bootstrap caller persists it on the new insight row.
+fn resolve_bootstrap_system_prompt(
+    supplied: Option<&str>,
+    persona_prompt: Option<String>,
+) -> String {
     supplied
         .map(str::trim)
         .filter(|s| !s.is_empty())
         .map(str::to_string)
+        .or_else(|| persona_prompt.filter(|s| !s.trim().is_empty()))
         .unwrap_or_else(|| BOOTSTRAP_DEFAULT_SYSTEM_PROMPT.to_string())
 }
 
@@ -2166,6 +2197,17 @@ fn env_max_iterations() -> usize {
         .max(1)
 }
 
+/// Read AGENTIC_CHAT_DEFAULT_NUM_CTX once per call — the assumed context
+/// window for the truncation budget when the request omits `num_ctx`. Same
+/// no-static-global rationale as `env_max_iterations` above.
+fn env_default_num_ctx() -> i32 {
+    std::env::var("AGENTIC_CHAT_DEFAULT_NUM_CTX")
+        .ok()
+        .and_then(|s| s.parse::<i32>().ok())
+        .unwrap_or(DEFAULT_NUM_CTX)
+        .max(RESPONSE_HEADROOM_TOKENS as i32 + 1024)
+}
+
 /// Append a per-turn iteration-budget reminder to the replayed system
 /// message so the model knows how many tool-calling rounds this turn gets.
 /// Returns the original `content` so the caller can restore it before
@@ -2200,6 +2242,30 @@ fn restore_system_content(messages: &mut [ChatMessage], original: Option<String>
     }
 }
 
+/// Append the synthetic "write your final answer" user prompt, returning the
+/// index the caller must later hand to [`remove_synthetic_final_prompt`].
+/// Used when the agentic loop exhausts its budget: the model gets one more
+/// (tool-free) request, but the nudge itself must never persist — it would
+/// render as a user bubble in the transcript and reset `load_history`'s
+/// pending-tools tracking at that position.
+fn push_synthetic_final_prompt(messages: &mut Vec<ChatMessage>) -> usize {
+    let idx = messages.len();
+    messages.push(ChatMessage::user(SYNTHETIC_FINAL_ANSWER_PROMPT));
+    idx
+}
+
+/// Remove the synthetic prompt inserted by [`push_synthetic_final_prompt`].
+/// Defensive no-op when the message at `idx` isn't the synthetic prompt —
+/// guards against index drift if the surrounding code is reordered.
+fn remove_synthetic_final_prompt(messages: &mut Vec<ChatMessage>, idx: usize) {
+    if messages
+        .get(idx)
+        .is_some_and(|m| m.role == "user" && m.content == SYNTHETIC_FINAL_ANSWER_PROMPT)
+    {
+        messages.remove(idx);
+    }
+}
+
 /// Receipt produced by [`apply_system_prompt_override`] so the caller can
 /// undo the override before persistence. Two variants because we either
 /// replaced an existing system message (need its original content) or
@@ -2467,13 +2533,16 @@ mod tests {
             assistant_text("here is the answer"),
         ];
 
-        // Default budget: (8192 - 2048) * 4 bytes ≈ 24KB. The text easily fits;
+        // Default budget: (32768 - 2048) * 4 bytes ≈ 120KB. The text easily
-        // only the (excluded) image bytes could blow it.
+        // fits; only the (excluded) image bytes could blow it.
         let budget_bytes = (DEFAULT_NUM_CTX as usize - RESPONSE_HEADROOM_TOKENS) * BYTES_PER_TOKEN;
         let original_len = msgs.len();
         let dropped = apply_context_budget(&mut msgs, budget_bytes);
 
-        assert!(!dropped, "short conversation with one image must not truncate");
+        assert!(
+            !dropped,
+            "short conversation with one image must not truncate"
+        );
         assert_eq!(msgs.len(), original_len, "no messages should be dropped");
         // Sanity: the flat image charge is accounted for but stays well under budget.
         assert!(estimate_bytes(&msgs) <= budget_bytes);
@@ -2640,26 +2709,26 @@ mod tests {
 
     #[test]
     fn bootstrap_system_prompt_falls_back_to_default_for_none() {
-        let out = resolve_bootstrap_system_prompt(None);
+        let out = resolve_bootstrap_system_prompt(None, None);
         assert_eq!(out, BOOTSTRAP_DEFAULT_SYSTEM_PROMPT);
     }
 
     #[test]
     fn bootstrap_system_prompt_falls_back_to_default_for_empty_string() {
         // Apollo currently sends `''` when no persona is selected.
-        let out = resolve_bootstrap_system_prompt(Some(""));
+        let out = resolve_bootstrap_system_prompt(Some(""), None);
         assert_eq!(out, BOOTSTRAP_DEFAULT_SYSTEM_PROMPT);
     }
 
     #[test]
     fn bootstrap_system_prompt_falls_back_to_default_for_whitespace() {
-        let out = resolve_bootstrap_system_prompt(Some("   \n\t  "));
+        let out = resolve_bootstrap_system_prompt(Some("   \n\t  "), None);
         assert_eq!(out, BOOTSTRAP_DEFAULT_SYSTEM_PROMPT);
     }
 
     #[test]
     fn bootstrap_system_prompt_uses_supplied_when_non_empty() {
-        let out = resolve_bootstrap_system_prompt(Some("you are a journal"));
+        let out = resolve_bootstrap_system_prompt(Some("you are a journal"), None);
         assert_eq!(out, "you are a journal");
     }
 
@@ -2668,10 +2737,76 @@ mod tests {
         // Trim only happens at the edges — interior newlines and spacing
         // (which Apollo's persona uses for tool listings) must survive.
         let prompt = "line one\nline two\n  bullet";
-        let out = resolve_bootstrap_system_prompt(Some(prompt));
+        let out = resolve_bootstrap_system_prompt(Some(prompt), None);
         assert_eq!(out, prompt);
     }
 
+    #[test]
+    fn bootstrap_system_prompt_explicit_wins_over_persona_store() {
+        let out = resolve_bootstrap_system_prompt(
+            Some("explicit prompt"),
+            Some("stored persona prompt".to_string()),
+        );
+        assert_eq!(out, "explicit prompt");
+    }
+
+    #[test]
+    fn bootstrap_system_prompt_uses_persona_store_when_no_explicit() {
+        // Request carried persona_id but no system_prompt — the persona's
+        // stored prompt must be used, not the neutral default.
+        let out = resolve_bootstrap_system_prompt(None, Some("stored persona prompt".to_string()));
+        assert_eq!(out, "stored persona prompt");
+
+        // Empty explicit prompt behaves like None.
+        let out =
+            resolve_bootstrap_system_prompt(Some(""), Some("stored persona prompt".to_string()));
+        assert_eq!(out, "stored persona prompt");
+    }
+
+    #[test]
+    fn bootstrap_system_prompt_blank_persona_prompt_falls_to_default() {
+        let out = resolve_bootstrap_system_prompt(None, Some("   ".to_string()));
+        assert_eq!(out, BOOTSTRAP_DEFAULT_SYSTEM_PROMPT);
+    }
+
+    // ── Synthetic final-answer prompt scaffolding ──────────────────────
+
+    #[test]
+    fn synthetic_final_prompt_round_trip_leaves_no_scaffolding() {
+        // Exhausted-loop fallback: nudge pushed, model reply appended, nudge
+        // removed — the persisted transcript must contain the reply but not
+        // the synthetic user prompt (all three loop variants rely on this).
+        let mut msgs = vec![
+            ChatMessage::system("sys"),
+            ChatMessage::user("q"),
+            assistant_with_tool_call("lookup"),
+            ChatMessage::tool_result("data"),
+        ];
+        let idx = push_synthetic_final_prompt(&mut msgs);
+        assert_eq!(msgs[idx].content, SYNTHETIC_FINAL_ANSWER_PROMPT);
+
+        msgs.push(assistant_text("final answer"));
+        remove_synthetic_final_prompt(&mut msgs, idx);
+
+        assert_eq!(msgs.len(), 5);
+        assert!(
+            msgs.iter()
+                .all(|m| m.content != SYNTHETIC_FINAL_ANSWER_PROMPT),
+            "synthetic prompt must not persist"
+        );
+        assert_eq!(msgs.last().unwrap().content, "final answer");
+    }
+
+    #[test]
+    fn remove_synthetic_final_prompt_is_noop_on_index_mismatch() {
+        // Defensive guard: if the message at idx isn't the synthetic prompt
+        // (index drift), nothing is removed.
+        let mut msgs = vec![ChatMessage::user("q"), assistant_text("a")];
+        remove_synthetic_final_prompt(&mut msgs, 0);
+        remove_synthetic_final_prompt(&mut msgs, 5);
+        assert_eq!(msgs.len(), 2);
+    }
+
     #[test]
     fn bootstrap_backend_defaults_to_local_when_none() {
         let out = resolve_bootstrap_backend(None).unwrap();

src/ai/insight_generator.rs

@@ -28,6 +28,11 @@ use crate::otel::global_tracer;
 use crate::tags::TagDao;
 use crate::utils::{earliest_fs_time, normalize_path};
 
+/// Max location records rendered by `tool_get_location_history`. The DAO
+/// query is range-bounded, not limited, so the tool caps the rendered list
+/// and labels the truncation via `found_header`.
+const LOCATION_HISTORY_DISPLAY_LIMIT: usize = 20;
+
 /// Parse a "Title: ...\n\n<body>" response into (title, body).
 /// Falls back to the first sentence as the title if the model didn't
 /// follow the format.
@@ -218,15 +223,11 @@ impl InsightGenerator {
     /// be called once per chat turn / generation. `has_vision` is
     /// supplied by the caller because it depends on the model selected
     /// for this turn, not on persistent state.
-    pub fn current_gate_opts(&self, has_vision: bool) -> ToolGateOpts {
+    ///
-        self.current_gate_opts_for_persona(has_vision, None)
+    /// Also resolves the per-persona `allow_agent_corrections` flag.
-    }
+    /// Pass `Some((user_id, persona_id))` when generating in a persona
-
+    /// context (every chat turn and agentic generation does); pass
-    /// Same as `current_gate_opts` but resolves the per-persona
+    /// `None` for callers that don't have one, which defaults the gate
-    /// `allow_agent_corrections` flag too. Pass `Some((user_id,
-    /// persona_id))` when generating in a persona context (every chat
-    /// turn does); pass `None` for callers that don't have one yet
-    /// (cold paths, populate_knowledge bin), which defaults the gate
     /// to closed — the conservative posture.
     pub fn current_gate_opts_for_persona(
         &self,
@@ -277,6 +278,22 @@ impl InsightGenerator {
         }
     }
 
+    /// Resolve the stored system prompt for `(user_id, persona_id)` from
+    /// the persona store. Returns `None` when the persona row doesn't
+    /// exist or its prompt is blank — callers fall back to their own
+    /// default. Used for server-side persona resolution: a request that
+    /// carries `persona_id` but no explicit `system_prompt` should speak
+    /// in the persona's stored voice, not the neutral default.
+    pub(crate) fn persona_system_prompt(&self, user_id: i32, persona_id: &str) -> Option<String> {
+        let cx = opentelemetry::Context::new();
+        let mut pdao = self.persona_dao.lock().expect("Unable to lock PersonaDao");
+        pdao.get_persona(&cx, user_id, persona_id)
+            .ok()
+            .flatten()
+            .map(|p| p.system_prompt)
+            .filter(|s| !s.trim().is_empty())
+    }
+
     /// Resolve `rel_path` against the configured libraries, returning the
     /// first root under which the file exists. Insights may be generated
     /// for any library — the generator itself doesn't know which — so we
@@ -1673,6 +1690,10 @@ Return ONLY the summary, nothing else."#,
                 self.tool_recall_facts_for_photo(arguments, user_id, persona_id, cx)
                     .await
             }
+            "recall_facts_for_entity" => {
+                self.tool_recall_facts_for_entity(arguments, user_id, persona_id, cx)
+                    .await
+            }
             "store_entity" => self.tool_store_entity(arguments, cx).await,
             "store_fact" => {
                 self.tool_store_fact(
@@ -2145,8 +2166,8 @@ Return ONLY the summary, nothing else."#,
                     })
                     .collect();
                 format!(
-                    "Found {} messages:\n{}",
+                    "{}\n{}",
-                    messages.len(),
+                    Self::found_header(messages.len(), formatted.len(), "messages"),
                     formatted.join("\n")
                 )
             }
@@ -2171,7 +2192,8 @@ Return ONLY the summary, nothing else."#,
         let days_radius = args
             .get("days_radius")
             .and_then(|v| v.as_i64())
-            .unwrap_or(7);
+            .unwrap_or(7)
+            .clamp(1, 30);
         let limit = args
             .get("limit")
             .and_then(|v| v.as_i64())
@@ -2250,7 +2272,8 @@ Return ONLY the summary, nothing else."#,
         let days_radius = args
             .get("days_radius")
             .and_then(|v| v.as_i64())
-            .unwrap_or(14);
+            .unwrap_or(14)
+            .clamp(1, 30);
 
         let date = match NaiveDate::parse_from_str(date_str, "%Y-%m-%d") {
             Ok(d) => d,
@@ -2279,7 +2302,7 @@ Return ONLY the summary, nothing else."#,
             Some(locs) if !locs.is_empty() => {
                 let formatted: Vec<String> = locs
                     .iter()
-                    .take(20)
+                    .take(LOCATION_HISTORY_DISPLAY_LIMIT)
                     .map(|loc| {
                         let dt = DateTime::from_timestamp(loc.timestamp, 0)
                             .map(|dt| {
@@ -2305,8 +2328,8 @@ Return ONLY the summary, nothing else."#,
                     })
                     .collect();
                 format!(
-                    "Found {} location records:\n{}",
+                    "{}\n{}",
-                    locs.len(),
+                    Self::found_header(locs.len(), formatted.len(), "location records"),
                     formatted.join("\n")
                 )
             }
@@ -2315,6 +2338,20 @@ Return ONLY the summary, nothing else."#,
         }
     }
 
+    /// Render a `Found N <noun>:` tool-result header, annotating when only
+    /// the first `shown` of `total` items are listed below it. Without the
+    /// annotation the model believes it saw everything ("Found 312
+    /// messages:" followed by 60 lines) and reasons from a silently
+    /// truncated list. `summarize_tool_result` keeps parsing the leading
+    /// count either way.
+    fn found_header(total: usize, shown: usize, noun: &str) -> String {
+        if shown < total {
+            format!("Found {} {} (showing first {}):", total, noun, shown)
+        } else {
+            format!("Found {} {}:", total, noun)
+        }
+    }
+
     /// Tool: get_file_tags — fetch tags for a file path
     async fn tool_get_file_tags(
         &self,
@@ -2669,6 +2706,102 @@ Return ONLY the summary, nothing else."#,
         }
     }
 
+    /// Tool: recall_facts_for_entity — retrieve facts for one entity by id.
+    /// Persona scoping and the reviewed-only-facts filter mirror
+    /// `tool_recall_facts_for_photo` exactly: reads are always Single
+    /// (user_id, persona_id), and strict-mode personas see only
+    /// human-reviewed facts.
+    async fn tool_recall_facts_for_entity(
+        &self,
+        args: &serde_json::Value,
+        user_id: i32,
+        persona_id: &str,
+        cx: &opentelemetry::Context,
+    ) -> String {
+        use crate::database::PersonaFilter;
+        let persona_filter = PersonaFilter::Single {
+            user_id,
+            persona_id: persona_id.to_string(),
+        };
+        let entity_id = match args.get("entity_id").and_then(|v| v.as_i64()) {
+            Some(id) => id as i32,
+            None => return "Error: missing required parameter 'entity_id'".to_string(),
+        };
+        let limit = args
+            .get("limit")
+            .and_then(|v| v.as_i64())
+            .unwrap_or(50)
+            .clamp(1, 100) as usize;
+
+        log::info!(
+            "tool_recall_facts_for_entity: entity_id={}, limit={}",
+            entity_id,
+            limit
+        );
+
+        // Resolve the persona's reviewed-only-mode flag once — identical
+        // fallback semantics to recall_facts_for_photo (missing persona
+        // row → permissive active+reviewed default).
+        let reviewed_only = {
+            let mut pdao = self.persona_dao.lock().expect("Unable to lock PersonaDao");
+            pdao.get_persona(cx, user_id, persona_id)
+                .ok()
+                .flatten()
+                .map(|p| p.reviewed_only_facts)
+                .unwrap_or(false)
+        };
+
+        let mut kdao = self
+            .knowledge_dao
+            .lock()
+            .expect("Unable to lock KnowledgeDao");
+
+        let entity = match kdao.get_entity_by_id(cx, entity_id) {
+            Ok(Some(e)) => e,
+            Ok(None) => return format!("No entity found with ID {}.", entity_id),
+            Err(e) => return format!("Error fetching entity: {:?}", e),
+        };
+
+        let mut output_lines = vec![format!("Entity: {} ({})", entity.name, entity.entity_type)];
+        match kdao.get_facts_for_entity(cx, entity_id, &persona_filter) {
+            Ok(facts) => {
+                // Default scope: active + reviewed. Strict mode trims to
+                // reviewed only — same allow rule as recall_facts_for_photo.
+                let allow = |s: &str| -> bool {
+                    if reviewed_only {
+                        s == "reviewed"
+                    } else {
+                        s == "active" || s == "reviewed"
+                    }
+                };
+                for f in facts.iter().filter(|f| allow(&f.status)).take(limit) {
+                    let obj = if let Some(ref v) = f.object_value {
+                        v.clone()
+                    } else if let Some(oid) = f.object_entity_id {
+                        kdao.get_entity_by_id(cx, oid)
+                            .ok()
+                            .flatten()
+                            .map(|e| format!("{} (entity ID: {})", e.name, e.id))
+                            .unwrap_or_else(|| format!("entity:{}", oid))
+                    } else {
+                        "(unknown)".to_string()
+                    };
+                    output_lines.push(format!("  - {} {}", f.predicate, obj));
+                }
+            }
+            Err(e) => return format!("Error fetching facts: {:?}", e),
+        }
+
+        if output_lines.len() == 1 {
+            format!(
+                "No active knowledge facts found for entity {} (ID: {}).",
+                entity.name, entity_id
+            )
+        } else {
+            format!("Knowledge for this entity:\n{}", output_lines.join("\n"))
+        }
+    }
+
     /// Tool: store_entity — upsert an entity into the knowledge memory.
     /// Embeddings go through the configured local backend (`LLM_BACKEND`),
     /// independent of the per-request chat backend in the caller.
@@ -3063,7 +3196,7 @@ Return ONLY the summary, nothing else."#,
     /// Build the list of tool definitions for the agentic loop, gated by
     /// `opts`. Always-on tools: `search_messages`, `get_sms_messages`,
     /// `get_file_tags`, `reverse_geocode`, `get_current_datetime`, the
-    /// four knowledge-memory tools. Conditional: `describe_photo` (vision
+    /// five knowledge-memory tools. Conditional: `describe_photo` (vision
     /// model), `get_personal_place_at` (Apollo configured), `search_rag`
     /// (daily_summaries populated), `get_calendar_events` (calendar
     /// populated), `get_location_history` (location history populated).
@@ -3280,6 +3413,22 @@ Return ONLY the summary, nothing else."#,
             }),
         ));
 
+        tools.push(Tool::function(
+            "recall_facts_for_entity",
+            "Retrieve all stored facts about one specific entity by its ID. Use to follow up on an entity \
+             surfaced by `recall_entities` (or referenced by another fact's object) that is NOT linked to the \
+             current photo — `recall_facts_for_photo` only covers photo-linked entities. \
+             Example: `{entity_id: 7}` — everything known about entity 7.",
+            serde_json::json!({
+                "type": "object",
+                "required": ["entity_id"],
+                "properties": {
+                    "entity_id": { "type": "integer", "description": "ID of the entity to fetch facts for (from recall_entities, store_entity, or a fact's object reference)." },
+                    "limit":     { "type": "integer", "description": "Max facts to return (default 50, max 100)." }
+                }
+            }),
+        ));
+
         tools.push(Tool::function(
             "store_entity",
             "Upsert a person / place / event / thing into the knowledge memory. Returns the entity id (use it as \
@@ -3543,7 +3692,7 @@ Return ONLY the summary, nothing else."#,
                     format!("{} personal place(s)", n)
                 }
             }
-            "recall_entities" | "recall_facts_for_photo" => {
+            "recall_entities" | "recall_facts_for_photo" | "recall_facts_for_entity" => {
                 let n = raw.lines().skip(1).filter(|l| !l.trim().is_empty()).count();
                 let kind = if tool_name == "recall_entities" {
                     "entities"
@@ -4033,8 +4182,11 @@ Return ONLY the summary, nothing else."#,
 
         // 10. Define tools. describe_photo offered only when the chat model
         //     sees images directly (images_inline); in hybrid mode the visual
-        //     description is already inlined as text.
+        //     description is already inlined as text. Persona-aware so the
-        let gate_opts = self.current_gate_opts(backend.images_inline);
+        //     persona's allow_agent_corrections gate opens here exactly like
+        //     it does for chat turns (insight_chat does the same).
+        let gate_opts =
+            self.current_gate_opts_for_persona(backend.images_inline, Some((user_id, &persona_id)));
         let tools = Self::build_tool_definitions(gate_opts);
 
         // 11. Build initial messages. images_inline → attach base64 to the
@@ -4145,7 +4297,10 @@ Return ONLY the summary, nothing else."#,
             .set_attribute(KeyValue::new("iterations_used", iterations_used as i64));
         loop_cx.span().set_status(Status::Ok);
 
-        // 13. Parse title from the model's inline response.
+        // 13. Strip any leaked <think>…</think> reasoning block (thinking
+        // models emit it ahead of the answer; the raw transcript in
+        // training_messages keeps it), then parse the title.
+        final_content = crate::ai::llm_client::strip_think_blocks(&final_content);
         let (title, body) = parse_title_body(&final_content);
         final_content = body;
 
@@ -4341,6 +4496,7 @@ mod tests {
         assert!(names.contains(&"get_current_datetime"));
         assert!(names.contains(&"recall_entities"));
         assert!(names.contains(&"recall_facts_for_photo"));
+        assert!(names.contains(&"recall_facts_for_entity"));
         assert!(names.contains(&"store_entity"));
         assert!(names.contains(&"store_fact"));
 
@@ -4550,6 +4706,37 @@ mod tests {
         );
     }
 
+    #[test]
+    fn found_header_labels_truncation_honestly() {
+        // Truncated: total exceeds what's listed below the header.
+        assert_eq!(
+            InsightGenerator::found_header(312, 60, "messages"),
+            "Found 312 messages (showing first 60):"
+        );
+        // Not truncated: plain header, no annotation noise.
+        assert_eq!(
+            InsightGenerator::found_header(7, 7, "messages"),
+            "Found 7 messages:"
+        );
+        assert_eq!(
+            InsightGenerator::found_header(40, 20, "location records"),
+            "Found 40 location records (showing first 20):"
+        );
+    }
+
+    #[test]
+    fn summarize_parses_truncated_found_header() {
+        // The "(showing first K)" annotation must not break the few-shot
+        // trajectory summarizer's "Found N" count parsing.
+        assert_eq!(
+            InsightGenerator::summarize_tool_result(
+                "get_sms_messages",
+                "Found 312 messages (showing first 60):\n[2023-08-15 10:00] Sarah: hi"
+            ),
+            "312 messages"
+        );
+    }
+
     fn make_search_hit(
         id: i64,
         contact: &str,
@@ -4869,11 +5056,9 @@ mod tests {
         // Replicate the resolve_backend local-client construction
         // (lines ~3686-3695 of this file).
         let mut lc = base.clone();
-        if let Some(ref m) = overrides_model {
+        if !is_hybrid && let Some(ref m) = overrides_model {
-            if !is_hybrid {
+            lc.primary_model = m.clone();
-                lc.primary_model = m.clone();
+            lc.set_vision_model(m.clone());
-                lc.set_vision_model(m.clone());
-            }
         }
 
         // In hybrid mode the local client must keep its configured slots.

src/ai/llamacpp.rs

@@ -590,10 +590,7 @@ impl LlmClient for LlamaCppClient {
             let mut byte_stream = byte_stream;
             let mut buf: Vec<u8> = Vec::new();
             let mut accumulated_content = String::new();
-            let mut tool_state: std::collections::BTreeMap<
+            let mut tool_state = ToolCallAssembly::new();
-                usize,
-                (Option<String>, Option<String>, String),
-            > = std::collections::BTreeMap::new();
             let mut role = "assistant".to_string();
             let mut prompt_tokens: Option<i32> = None;
             let mut completion_tokens: Option<i32> = None;
@@ -670,32 +667,7 @@ impl LlmClient for LlamaCppClient {
                             yield Ok(LlmStreamEvent::TextDelta(content.to_string()));
                         }
                         if let Some(tcs) = delta.get("tool_calls").and_then(|v| v.as_array()) {
-                            for tc_delta in tcs {
+                            apply_tool_call_deltas(&mut tool_state, tcs);
-                                let idx = tc_delta
-                                    .get("index")
-                                    .and_then(|n| n.as_u64())
-                                    .unwrap_or(0) as usize;
-                                let entry = tool_state
-                                    .entry(idx)
-                                    .or_insert((None, None, String::new()));
-                                if let Some(id) =
-                                    tc_delta.get("id").and_then(|v| v.as_str())
-                                {
-                                    entry.0 = Some(id.to_string());
-                                }
-                                if let Some(func) = tc_delta.get("function") {
-                                    if let Some(name) =
-                                        func.get("name").and_then(|v| v.as_str())
-                                    {
-                                        entry.1 = Some(name.to_string());
-                                    }
-                                    if let Some(args) =
-                                        func.get("arguments").and_then(|v| v.as_str())
-                                    {
-                                        entry.2.push_str(args);
-                                    }
-                                }
-                            }
                         }
                     }
                     if done_seen {
@@ -707,28 +679,7 @@ impl LlmClient for LlamaCppClient {
                 }
             }
 
-            let tool_calls: Option<Vec<ToolCall>> = if tool_state.is_empty() {
+            let tool_calls = finalize_tool_calls(tool_state);
-                None
-            } else {
-                let mut v = Vec::with_capacity(tool_state.len());
-                for (_idx, (id, name, args)) in tool_state {
-                    let arguments: Value = if args.trim().is_empty() {
-                        Value::Object(Default::default())
-                    } else {
-                        serde_json::from_str(&args).unwrap_or_else(|_| {
-                            Value::Object(Default::default())
-                        })
-                    };
-                    v.push(ToolCall {
-                        id,
-                        function: ToolCallFunction {
-                            name: name.unwrap_or_default(),
-                            arguments,
-                        },
-                    });
-                }
-                Some(v)
-            };
 
             if let Some(ref frame) = last_frame {
                 log_timings(frame, prompt_tokens, completion_tokens);
@@ -937,6 +888,58 @@ fn extract_error_detail(parsed: &Value) -> String {
     raw.chars().take(300).collect()
 }
 
+/// Per-index assembly state for streamed OpenAI-style tool-call deltas:
+/// `index → (id, name, concatenated argument fragments)`. BTreeMap so the
+/// finalized calls come out in index order.
+type ToolCallAssembly = std::collections::BTreeMap<usize, (Option<String>, Option<String>, String)>;
+
+/// Fold one SSE frame's `delta.tool_calls` array into the assembly state.
+/// Deltas carrying the same `index` merge into one call (llama.cpp streams a
+/// call's argument JSON in fragments — they concatenate); distinct indexes
+/// accumulate as separate calls.
+fn apply_tool_call_deltas(state: &mut ToolCallAssembly, tcs: &[Value]) {
+    for tc_delta in tcs {
+        let idx = tc_delta.get("index").and_then(|n| n.as_u64()).unwrap_or(0) as usize;
+        let entry = state.entry(idx).or_insert((None, None, String::new()));
+        if let Some(id) = tc_delta.get("id").and_then(|v| v.as_str()) {
+            entry.0 = Some(id.to_string());
+        }
+        if let Some(func) = tc_delta.get("function") {
+            if let Some(name) = func.get("name").and_then(|v| v.as_str()) {
+                entry.1 = Some(name.to_string());
+            }
+            if let Some(args) = func.get("arguments").and_then(|v| v.as_str()) {
+                entry.2.push_str(args);
+            }
+        }
+    }
+}
+
+/// Convert assembled tool-call state into canonical `ToolCall`s, parsing each
+/// call's concatenated argument JSON (empty / malformed → `{}`). `None` when
+/// no tool-call deltas arrived.
+fn finalize_tool_calls(state: ToolCallAssembly) -> Option<Vec<ToolCall>> {
+    if state.is_empty() {
+        return None;
+    }
+    let mut v = Vec::with_capacity(state.len());
+    for (_idx, (id, name, args)) in state {
+        let arguments: Value = if args.trim().is_empty() {
+            Value::Object(Default::default())
+        } else {
+            serde_json::from_str(&args).unwrap_or_else(|_| Value::Object(Default::default()))
+        };
+        v.push(ToolCall {
+            id,
+            function: ToolCallFunction {
+                name: name.unwrap_or_default(),
+                arguments,
+            },
+        });
+    }
+    Some(v)
+}
+
 fn find_double_newline(buf: &[u8]) -> Option<usize> {
     for i in 0..buf.len().saturating_sub(1) {
         if buf[i] == b'\n' && buf[i + 1] == b'\n' {
@@ -1302,4 +1305,68 @@ mod tests {
         let c = LlamaCppClient::new(None, None);
         assert_eq!(c.tts_model, "chatterbox");
     }
+
+    #[test]
+    fn stream_assembly_keeps_two_tool_calls_from_separate_chunks() {
+        // llama.cpp emits one delta per SSE frame; two calls with distinct
+        // `index` values arriving in separate frames must BOTH survive.
+        let mut state = ToolCallAssembly::new();
+        apply_tool_call_deltas(
+            &mut state,
+            &[json!({
+                "index": 0,
+                "id": "call_a",
+                "function": { "name": "get_sms_messages", "arguments": "{\"date\":\"2019-01-01\"}" }
+            })],
+        );
+        apply_tool_call_deltas(
+            &mut state,
+            &[json!({
+                "index": 1,
+                "id": "call_b",
+                "function": { "name": "reverse_geocode", "arguments": "{\"latitude\":1.0,\"longitude\":2.0}" }
+            })],
+        );
+
+        let calls = finalize_tool_calls(state).expect("two calls assembled");
+        assert_eq!(calls.len(), 2);
+        assert_eq!(calls[0].id.as_deref(), Some("call_a"));
+        assert_eq!(calls[0].function.name, "get_sms_messages");
+        assert_eq!(calls[0].function.arguments["date"], "2019-01-01");
+        assert_eq!(calls[1].id.as_deref(), Some("call_b"));
+        assert_eq!(calls[1].function.name, "reverse_geocode");
+        assert_eq!(calls[1].function.arguments["latitude"], 1.0);
+    }
+
+    #[test]
+    fn stream_assembly_concatenates_argument_fragments_for_same_index() {
+        // A single call's argument JSON streamed across frames concatenates
+        // into one parseable document.
+        let mut state = ToolCallAssembly::new();
+        apply_tool_call_deltas(
+            &mut state,
+            &[json!({
+                "index": 0,
+                "id": "call_x",
+                "function": { "name": "search_messages", "arguments": "{\"query\":" }
+            })],
+        );
+        apply_tool_call_deltas(
+            &mut state,
+            &[json!({
+                "index": 0,
+                "function": { "arguments": "\"dinner\"}" }
+            })],
+        );
+
+        let calls = finalize_tool_calls(state).expect("one call assembled");
+        assert_eq!(calls.len(), 1);
+        assert_eq!(calls[0].function.name, "search_messages");
+        assert_eq!(calls[0].function.arguments["query"], "dinner");
+    }
+
+    #[test]
+    fn stream_assembly_empty_state_finalizes_to_none() {
+        assert!(finalize_tool_calls(ToolCallAssembly::new()).is_none());
+    }
 }

src/ai/llm_client.rs

@@ -170,3 +170,55 @@ pub struct ModelCapabilities {
     pub has_vision: bool,
     pub has_tool_calling: bool,
 }
+
+/// Strip a leading `<think>…</think>` reasoning block from model output.
+///
+/// Thinking models sometimes emit chain-of-thought inside think tags before
+/// the real answer. Everything after the first `</think>` is the answer;
+/// when no tag is present — or the text after it is empty — the trimmed
+/// input is returned unchanged. Mirrors the behavior Ollama's
+/// `extract_final_answer` has applied to single-shot generation; shared here
+/// so the tool-calling final-content paths (agentic generation + chat) can
+/// apply the identical cleanup before parsing / persisting.
+pub fn strip_think_blocks(response: &str) -> String {
+    let response = response.trim();
+
+    if let Some(pos) = response.find("</think>") {
+        let answer = response[pos + "</think>".len()..].trim();
+        if !answer.is_empty() {
+            return answer.to_string();
+        }
+    }
+
+    response.to_string()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn strip_think_blocks_removes_leading_think_block() {
+        let raw = "<think>\nLet me reason about this.\n</think>\n\nTitle: A Day Out\n\nThe body.";
+        assert_eq!(strip_think_blocks(raw), "Title: A Day Out\n\nThe body.");
+    }
+
+    #[test]
+    fn strip_think_blocks_passes_through_plain_content() {
+        assert_eq!(strip_think_blocks("  just an answer  "), "just an answer");
+    }
+
+    #[test]
+    fn strip_think_blocks_keeps_content_when_answer_after_tag_is_empty() {
+        // A think block with nothing after it: better to return the trimmed
+        // original than an empty string (matches Ollama's fallback).
+        let raw = "<think>only thoughts</think>";
+        assert_eq!(strip_think_blocks(raw), raw);
+    }
+
+    #[test]
+    fn strip_think_blocks_handles_unclosed_tag() {
+        let raw = "<think>thinking forever";
+        assert_eq!(strip_think_blocks(raw), raw);
+    }
+}

src/ai/mod.rs

@@ -25,7 +25,8 @@ pub use handlers::{
     chat_stream_handler, chat_turn_handler, delete_insight_handler, export_training_data_handler,
     generate_agentic_insight_handler, generate_insight_handler, generation_status_handler,
     get_all_insights_handler, get_available_models_handler, get_insight_handler,
-    get_openrouter_models_handler, rate_insight_handler, turn_async_handler, turn_replay_handler,
+    get_insight_history_handler, get_openrouter_models_handler, rate_insight_handler,
+    turn_async_handler, turn_replay_handler,
 };
 pub use insight_generator::InsightGenerator;
 pub use llamacpp::LlamaCppClient;

src/ai/ollama.rs

@@ -360,18 +360,7 @@ impl OllamaClient {
     /// Extract final answer from thinking model output
     /// Handles <think>...</think> tags and takes everything after
     fn extract_final_answer(&self, response: &str) -> String {
-        let response = response.trim();
+        crate::ai::llm_client::strip_think_blocks(response)
-
-        // Look for </think> tag and take everything after it
-        if let Some(pos) = response.find("</think>") {
-            let answer = response[pos + 8..].trim();
-            if !answer.is_empty() {
-                return answer.to_string();
-            }
-        }
-
-        // Fallback: return the whole response trimmed
-        response.to_string()
     }
 
     async fn try_generate(
@@ -846,11 +835,14 @@ Analyze the image and use specific details from both the visual content and the
                             if !chunk.message.role.is_empty() {
                                 role = chunk.message.role;
                             }
-                            // Ollama only attaches tool_calls on the final chunk.
+                            // Ollama ≥0.8 can stream tool_calls incrementally
+                            // across chunks (older servers attach them all to
+                            // one chunk) — append rather than overwrite so
+                            // calls from earlier chunks survive.
                             if let Some(tcs) = chunk.message.tool_calls
                                 && !tcs.is_empty()
                             {
-                                tool_calls = Some(tcs);
+                                append_streamed_tool_calls(&mut tool_calls, tcs);
                             }
                             if chunk.done {
                                 prompt_eval_count = chunk.prompt_eval_count;
@@ -1329,8 +1321,20 @@ struct OllamaEmbedResponse {
     embeddings: Vec<Vec<f32>>,
 }
 
+/// Accumulate tool calls streamed across NDJSON chunks. Ollama ≥0.8 may
+/// emit each tool call on its own chunk; replacing the accumulator on every
+/// chunk would keep only the last call, so extend instead.
+fn append_streamed_tool_calls(
+    acc: &mut Option<Vec<crate::ai::llm_client::ToolCall>>,
+    new: Vec<crate::ai::llm_client::ToolCall>,
+) {
+    acc.get_or_insert_with(Vec::new).extend(new);
+}
+
 #[cfg(test)]
 mod tests {
+    use super::append_streamed_tool_calls;
+    use crate::ai::llm_client::{ToolCall, ToolCallFunction};
 
     #[test]
     fn generate_photo_description_prompt_is_concise() {
@@ -1341,4 +1345,38 @@ mod tests {
                       Focus on the people, location, and activity.";
         assert!(prompt.len() < 200, "Prompt should be concise");
     }
+
+    fn call(name: &str) -> ToolCall {
+        ToolCall {
+            id: None,
+            function: ToolCallFunction {
+                name: name.to_string(),
+                arguments: serde_json::json!({}),
+            },
+        }
+    }
+
+    #[test]
+    fn streamed_tool_calls_across_chunks_accumulate() {
+        // Two tool calls arriving in two separate stream chunks must BOTH
+        // survive assembly — the old `tool_calls = Some(tcs)` kept only the
+        // last chunk's calls.
+        let mut acc: Option<Vec<ToolCall>> = None;
+        append_streamed_tool_calls(&mut acc, vec![call("get_sms_messages")]);
+        append_streamed_tool_calls(&mut acc, vec![call("reverse_geocode")]);
+
+        let calls = acc.expect("tool calls accumulated");
+        assert_eq!(calls.len(), 2);
+        assert_eq!(calls[0].function.name, "get_sms_messages");
+        assert_eq!(calls[1].function.name, "reverse_geocode");
+    }
+
+    #[test]
+    fn streamed_tool_calls_single_chunk_batch_kept_intact() {
+        // Older Ollama servers attach all calls to one chunk — unchanged.
+        let mut acc: Option<Vec<ToolCall>> = None;
+        append_streamed_tool_calls(&mut acc, vec![call("a"), call("b")]);
+        let calls = acc.expect("tool calls accumulated");
+        assert_eq!(calls.len(), 2);
+    }
 }

src/backfill.rs

@@ -529,16 +529,21 @@ mod tests {
         opentelemetry::Context::new()
     }
 
-    /// Build a tempdir-backed library + DAOs sharing a single in-memory
+    /// Everything `setup` hands back to a test: tempdir, library, shared
-    /// SQLite connection (so cross-table joins like
+    /// connection, and the two DAOs. Aliased to keep clippy's
-    /// `list_unscanned_candidates` see consistent state).
+    /// type-complexity lint satisfied.
-    fn setup() -> (
+    type SetupFixture = (
         TempDir,
         Library,
         Arc<Mutex<diesel::SqliteConnection>>,
         Arc<Mutex<Box<dyn ExifDao>>>,
         Arc<Mutex<Box<dyn FaceDao>>>,
-    ) {
+    );
+
+    /// Build a tempdir-backed library + DAOs sharing a single in-memory
+    /// SQLite connection (so cross-table joins like
+    /// `list_unscanned_candidates` see consistent state).
+    fn setup() -> SetupFixture {
         let tmp = TempDir::new().expect("tempdir");
         let mut conn = in_memory_db_connection();
         // Migration seeds library id=1 with a placeholder root; rewrite it

src/database/insights_dao.rs

@@ -47,7 +47,6 @@ pub trait InsightDao: Sync + Send {
         paths: &[String],
     ) -> Result<Option<PhotoInsight>, DbError>;
 
-    #[allow(dead_code)]
     fn get_insight_history(
         &mut self,
         context: &opentelemetry::Context,
@@ -82,6 +81,17 @@ pub trait InsightDao: Sync + Send {
         approved: bool,
     ) -> Result<(), DbError>;
 
+    /// Rate a specific insight version by primary key, regardless of
+    /// `is_current`. Used by the per-file history view to approve/reject
+    /// previously generated (superseded) versions, which the path-based
+    /// `rate_insight` (current row only) cannot reach.
+    fn rate_insight_by_id(
+        &mut self,
+        context: &opentelemetry::Context,
+        insight_id: i32,
+        approved: bool,
+    ) -> Result<(), DbError>;
+
     fn get_approved_insights(
         &mut self,
         context: &opentelemetry::Context,
@@ -352,6 +362,26 @@ impl InsightDao for SqliteInsightDao {
         .map_err(|e| DbError::log(DbErrorKind::UpdateError, e))
     }
 
+    fn rate_insight_by_id(
+        &mut self,
+        context: &opentelemetry::Context,
+        target_id: i32,
+        is_approved: bool,
+    ) -> Result<(), DbError> {
+        trace_db_call(context, "update", "rate_insight_by_id", |_span| {
+            use schema::photo_insights::dsl::*;
+
+            let mut connection = self.connection.lock().expect("Unable to get InsightDao");
+
+            diesel::update(photo_insights.find(target_id))
+                .set(approved.eq(Some(is_approved)))
+                .execute(connection.deref_mut())
+                .map(|_| ())
+                .map_err(|e| anyhow::anyhow!("Update error: {}", e))
+        })
+        .map_err(|e| DbError::log(DbErrorKind::UpdateError, e))
+    }
+
     fn get_approved_insights(
         &mut self,
         context: &opentelemetry::Context,
@@ -396,3 +426,90 @@ impl InsightDao for SqliteInsightDao {
         .map_err(|e| DbError::log(DbErrorKind::UpdateError, e))
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::database::test::in_memory_db_connection;
+
+    fn dao() -> SqliteInsightDao {
+        let conn = Arc::new(Mutex::new(in_memory_db_connection()));
+        SqliteInsightDao::from_connection(conn)
+    }
+
+    /// Build an insight insert with sensible defaults; tests override the
+    /// fields they care about (path, generated_at, model).
+    fn insert(path: &str, generated_at: i64, model: &str) -> InsertPhotoInsight {
+        InsertPhotoInsight {
+            library_id: 1,
+            file_path: path.to_string(),
+            title: format!("title for {model}"),
+            summary: "summary".to_string(),
+            generated_at,
+            model_version: model.to_string(),
+            is_current: true,
+            training_messages: None,
+            backend: "local".to_string(),
+            fewshot_source_ids: None,
+            content_hash: None,
+            num_ctx: None,
+            temperature: None,
+            top_p: None,
+            top_k: None,
+            min_p: None,
+            system_prompt: None,
+            persona_id: None,
+            prompt_eval_count: None,
+            eval_count: None,
+        }
+    }
+
+    #[test]
+    fn get_insight_history_returns_all_versions_newest_first() {
+        let cx = opentelemetry::Context::new();
+        let mut dao = dao();
+
+        // store_insight flips prior rows to is_current=false, so three
+        // generations for the same path leave a 3-row history.
+        dao.store_insight(&cx, insert("a.jpg", 100, "m1")).unwrap();
+        dao.store_insight(&cx, insert("a.jpg", 200, "m2")).unwrap();
+        dao.store_insight(&cx, insert("a.jpg", 300, "m3")).unwrap();
+        // A different path must not leak into the history.
+        dao.store_insight(&cx, insert("b.jpg", 250, "other"))
+            .unwrap();
+
+        let history = dao.get_insight_history(&cx, "a.jpg").unwrap();
+        assert_eq!(history.len(), 3);
+        assert_eq!(
+            history.iter().map(|i| i.generated_at).collect::<Vec<_>>(),
+            vec![300, 200, 100],
+            "history should be newest-first"
+        );
+        // Exactly one version is current (the latest generation).
+        let current: Vec<_> = history.iter().filter(|i| i.is_current).collect();
+        assert_eq!(current.len(), 1);
+        assert_eq!(current[0].generated_at, 300);
+    }
+
+    #[test]
+    fn rate_insight_by_id_rates_only_the_targeted_version() {
+        let cx = opentelemetry::Context::new();
+        let mut dao = dao();
+
+        dao.store_insight(&cx, insert("a.jpg", 100, "m1")).unwrap();
+        dao.store_insight(&cx, insert("a.jpg", 200, "m2")).unwrap();
+
+        // History is newest-first: [200 (current), 100 (superseded)].
+        let history = dao.get_insight_history(&cx, "a.jpg").unwrap();
+        let old_version = history.iter().find(|i| i.generated_at == 100).unwrap();
+        assert!(!old_version.is_current);
+
+        dao.rate_insight_by_id(&cx, old_version.id, true).unwrap();
+
+        let history = dao.get_insight_history(&cx, "a.jpg").unwrap();
+        let old = history.iter().find(|i| i.generated_at == 100).unwrap();
+        let current = history.iter().find(|i| i.generated_at == 200).unwrap();
+        assert_eq!(old.approved, Some(true), "targeted version is rated");
+        assert_eq!(current.approved, None, "current version is untouched");
+    }
+}

src/libraries.rs

@@ -1052,7 +1052,7 @@ mod tests {
             enabled: true,
             excluded_dirs: Vec::new(),
         };
-        let map = new_health_map(&[lib.clone()]);
+        let map = new_health_map(std::slice::from_ref(&lib));
 
         // First probe: empty dir, no prior data — Online.
         let s1 = refresh_health(&map, &lib, false);

src/main.rs

@@ -351,6 +351,7 @@ fn main() -> std::io::Result<()> {
                 .service(ai::get_insight_handler)
                 .service(ai::delete_insight_handler)
                 .service(ai::get_all_insights_handler)
+                .service(ai::get_insight_history_handler)
                 .service(ai::get_available_models_handler)
                 .service(ai::get_openrouter_models_handler)
                 .service(ai::chat_turn_handler)