src/ai/nl_query.rs

@@ -21,12 +21,6 @@
 //! `geo::forward_geocode`), and person filtering is deferred until a
 //! person→photos resolver exists.
 
-// Phase 1: this module is fully implemented and unit-tested, but its first
-// consumer (the `/photos/search/unified` endpoint) lands in Phase 2. Mirrors
-// llm_client.rs's allow-until-wired pattern so the bin target stays
-// clippy-clean in the interim; remove when the endpoint is added.
-#![allow(dead_code)]
-
 use crate::ai::llm_client::{ChatMessage, LlmClient, Tool, strip_think_blocks};
 use anyhow::{Result, anyhow};
 use serde::{Deserialize, Serialize};

src/clip_search.rs

@@ -124,65 +124,161 @@ fn dot(a: &[f32], b: &[f32]) -> f32 {
     a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
 }
 
-pub async fn search_photos(
-    state: web::Data<AppState>,
-    exif_dao: web::Data<Mutex<Box<dyn ExifDao>>>,
-    query: web::Query<SearchQuery>,
-) -> ActixResult<HttpResponse> {
-    let q_text = query.q.trim().to_string();
-    if q_text.is_empty() {
-        return Ok(HttpResponse::BadRequest().json(SearchError {
-            error: "query parameter `q` is required".into(),
-        }));
-    }
+/// Failure modes of [`score_photos`]. Carries enough to let each caller pick
+/// an appropriate HTTP status (the CLIP service being down is a 502, a
+/// disabled feature is a 503, a rejected query is a 400, a DB failure 500).
+pub enum ScoreError {
+    /// CLIP search isn't configured at all (no Apollo endpoint).
+    Disabled,
+    /// The query was rejected by the encoder (client error).
+    Rejected(String),
+    /// The CLIP service is transiently unavailable (upstream error).
+    Unavailable(String),
+    /// The encoder returned an embedding we couldn't decode.
+    MalformedEmbedding,
+    /// A database / index load failure.
+    Internal(String),
+}
+
+/// Result of scoring the whole library against a query embedding: the
+/// resolved model version, how many embeddings were considered, and every
+/// `(score, content_hash)` above threshold, sorted by descending score.
+/// Pagination and path resolution are the caller's job (see [`resolve_hits`])
+/// so this core can be reused for both the plain search endpoint and the
+/// unified endpoint (which filters by hash before paginating).
+pub struct ScoredPhotos {
+    pub model_version: String,
+    pub considered: usize,
+    /// `(cosine_score, content_hash)` pairs, descending by score.
+    pub hits: Vec<(f32, String)>,
+}
+
+/// Encode `q_text` via CLIP and score it against every stored embedding in
+/// the given library scope. Returns all matches above `threshold`, sorted by
+/// descending similarity. Pure of HTTP concerns so it's shared by
+/// `search_photos` and the unified search endpoint.
+pub async fn score_photos(
+    state: &AppState,
+    exif_dao: &Mutex<Box<dyn ExifDao>>,
+    q_text: &str,
+    library_ids: &[i32],
+    threshold: f32,
+    model_version: Option<&str>,
+) -> Result<ScoredPhotos, ScoreError> {
     if !state.clip_client.is_enabled() {
-        return Ok(HttpResponse::ServiceUnavailable().json(SearchError {
-            error: "CLIP search is disabled (no Apollo CLIP endpoint configured)".into(),
-        }));
+        return Err(ScoreError::Disabled);
     }
 
-    let limit = query.limit.clamp(1, 200);
-    let offset = query.offset;
-    let threshold = query.threshold.clamp(-1.0, 1.0);
-
-    // 1. Encode the query text. Fast — Apollo's text encoder is ~50ms
-    // on CPU. Bail with a clear error message if Apollo's down so the
-    // user sees "service unavailable" rather than empty results.
-    let query_resp = match state.clip_client.encode_text(&q_text).await {
+    // 1. Encode the query text. Fast — Apollo's text encoder is ~50ms on CPU.
+    let query_resp = match state.clip_client.encode_text(q_text).await {
         Ok(r) => r,
-        Err(ClipError::Permanent(e)) => {
-            return Ok(HttpResponse::BadRequest().json(SearchError {
-                error: format!("query rejected: {e}"),
-            }));
-        }
-        Err(ClipError::Transient(e)) => {
-            return Ok(HttpResponse::BadGateway().json(SearchError {
-                error: format!("CLIP service unavailable: {e}"),
-            }));
-        }
-        Err(ClipError::Disabled) => {
-            return Ok(HttpResponse::ServiceUnavailable().json(SearchError {
-                error: "CLIP service disabled".into(),
-            }));
-        }
+        Err(ClipError::Permanent(e)) => return Err(ScoreError::Rejected(e.to_string())),
+        Err(ClipError::Transient(e)) => return Err(ScoreError::Unavailable(e.to_string())),
+        Err(ClipError::Disabled) => return Err(ScoreError::Disabled),
     };
     // decode_embedding works on raw bytes; the wire format is b64.
     let query_bytes = base64::engine::general_purpose::STANDARD
         .decode(query_resp.embedding.as_bytes())
         .unwrap_or_default();
-    let query_vec = match decode_embedding(&query_bytes) {
-        Some(v) => v,
-        None => {
-            return Ok(HttpResponse::BadGateway().json(SearchError {
-                error: "CLIP service returned a malformed query embedding".into(),
-            }));
-        }
-    };
+    let query_vec = decode_embedding(&query_bytes).ok_or(ScoreError::MalformedEmbedding)?;
 
-    // 2. Decide which library scope to search. `library_ids` (multi)
-    // wins over the legacy `library` (single) when both are present;
-    // either / both empty falls back to "every enabled library".
-    let library_ids: Vec<i32> = if let Some(raw) = query.library_ids.as_deref() {
+    // 2. Pull the (hash, embedding) matrix under the dao lock, release
+    // before scoring. The caller-supplied `model_version` (or the live
+    // engine's) forces a strict join so a mid-flight model swap can't mix
+    // geometries.
+    let ctx = opentelemetry::Context::current();
+    let rows: Vec<(String, Vec<u8>)> = {
+        let mut dao = exif_dao.lock().expect("exif dao");
+        dao.list_clip_index(
+            &ctx,
+            library_ids,
+            model_version.or(Some(&query_resp.model_version)),
+        )
+        .map_err(|e| {
+            log::warn!("clip_search: list_clip_index failed: {:?}", e);
+            ScoreError::Internal("failed to load search index".into())
+        })?
+    };
+    let considered = rows.len();
+
+    // 3. Score. Keep all matches and sort at the end (~microseconds at 14k).
+    let mut hits: Vec<(f32, String)> = Vec::with_capacity(considered);
+    for (hash, blob) in rows {
+        let Some(emb) = decode_embedding(&blob) else {
+            continue;
+        };
+        if emb.len() != query_vec.len() {
+            continue;
+        }
+        let sim = dot(&emb, &query_vec);
+        if sim < threshold {
+            continue;
+        }
+        hits.push((sim, hash));
+    }
+    hits.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
+
+    Ok(ScoredPhotos {
+        model_version: query_resp.model_version,
+        considered,
+        hits,
+    })
+}
+
+/// Resolve a page of `(score, content_hash)` pairs back to [`SearchHit`]s
+/// (each carrying `library_id` + `rel_path`). Hashes that no longer resolve
+/// to a row are skipped. Shared by both endpoints.
+pub fn resolve_hits(
+    exif_dao: &Mutex<Box<dyn ExifDao>>,
+    scored: &[(f32, String)],
+) -> Vec<SearchHit> {
+    if scored.is_empty() {
+        return Vec::new();
+    }
+    let ctx = opentelemetry::Context::current();
+    let hashes: Vec<String> = scored.iter().map(|(_, h)| h.clone()).collect();
+    let mut dao = exif_dao.lock().expect("exif dao");
+    let path_map = dao
+        .get_rel_paths_for_hashes(&ctx, &hashes)
+        .unwrap_or_else(|e| {
+            log::warn!("clip_search: get_rel_paths_for_hashes failed: {:?}", e);
+            std::collections::HashMap::new()
+        });
+
+    let mut results = Vec::with_capacity(scored.len());
+    for (score, hash) in scored {
+        let row = match dao.find_by_content_hash(&ctx, hash) {
+            Ok(Some(r)) => r,
+            Ok(None) => continue,
+            Err(e) => {
+                log::warn!("clip_search: find_by_content_hash failed for {hash}: {e:?}");
+                continue;
+            }
+        };
+        // Prefer get_rel_paths_for_hashes's first entry (shares image_exif's
+        // natural order), falling back to the ImageExif row.
+        let rel_path = path_map
+            .get(hash)
+            .and_then(|paths| paths.first().cloned())
+            .unwrap_or(row.file_path);
+        results.push(SearchHit {
+            library_id: row.library_id,
+            rel_path,
+            content_hash: hash.clone(),
+            score: *score,
+        });
+    }
+    results
+}
+
+/// Parse the `library_ids` (multi) / `library` (single) scope params into a
+/// deduped id list. Empty = "every enabled library". Shared so the unified
+/// endpoint scopes CLIP identically.
+pub fn parse_library_scope(
+    library_ids: Option<&str>,
+    library: Option<i32>,
+) -> Result<Vec<i32>, String> {
+    if let Some(raw) = library_ids {
         let mut out: Vec<i32> = Vec::new();
         for piece in raw.split(',') {
             let trimmed = piece.trim();
@@ -195,158 +291,92 @@ pub async fn search_photos(
                         out.push(id);
                     }
                 }
-                Err(_) => {
-                    return Ok(HttpResponse::BadRequest().json(SearchError {
-                        error: format!("invalid library_ids entry: {trimmed:?}"),
-                    }));
-                }
+                Err(_) => return Err(format!("invalid library_ids entry: {trimmed:?}")),
             }
         }
-        out
-    } else if let Some(id) = query.library {
-        vec![id]
+        Ok(out)
+    } else if let Some(id) = library {
+        Ok(vec![id])
     } else {
-        Vec::new()
-    };
+        Ok(Vec::new())
+    }
+}
 
-    // 3. Pull the (hash, embedding) matrix. Lock contention here is
-    // bounded — one big SELECT under a mutex Arc<Mutex<dyn ExifDao>>
-    // and then we release before scoring. If this becomes a hotspot
-    // we'll cache the decoded matrix in AppState with TTL.
-    let ctx = opentelemetry::Context::current();
-    let rows: Vec<(String, Vec<u8>)> = {
-        let mut dao = exif_dao.lock().expect("exif dao");
-        match dao.list_clip_index(
-            &ctx,
-            &library_ids,
-            query
-                .model_version
-                .as_deref()
-                .or(Some(&query_resp.model_version)),
-        ) {
-            Ok(r) => r,
-            Err(e) => {
-                log::warn!("clip_search: list_clip_index failed: {:?}", e);
-                return Ok(HttpResponse::InternalServerError().json(SearchError {
-                    error: "failed to load search index".into(),
-                }));
-            }
-        }
-    };
-    let considered = rows.len();
-    if considered == 0 {
-        return Ok(HttpResponse::Ok().json(SearchResponse {
-            query: q_text,
-            model_version: query_resp.model_version,
-            threshold,
-            considered,
-            total_matching: 0,
-            offset,
-            results: Vec::new(),
+pub async fn search_photos(
+    state: web::Data<AppState>,
+    exif_dao: web::Data<Mutex<Box<dyn ExifDao>>>,
+    query: web::Query<SearchQuery>,
+) -> ActixResult<HttpResponse> {
+    let q_text = query.q.trim().to_string();
+    if q_text.is_empty() {
+        return Ok(HttpResponse::BadRequest().json(SearchError {
+            error: "query parameter `q` is required".into(),
         }));
     }
 
-    // 4. Score. Cap the loop's transient allocation; we keep all scores
-    // and sort at the end. With ~14k entries the sort is microseconds.
-    let mut scored: Vec<(f32, String)> = Vec::with_capacity(considered);
-    for (hash, blob) in rows {
-        let Some(emb) = decode_embedding(&blob) else {
-            continue;
-        };
-        if emb.len() != query_vec.len() {
-            continue;
-        }
-        let sim = dot(&emb, &query_vec);
-        if sim < threshold {
-            continue;
-        }
-        scored.push((sim, hash));
-    }
-    scored.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
-    let total_matching = scored.len();
-    // Pagination — slice the sorted list at `[offset, offset+limit)`.
-    // Offsets past the end produce empty pages rather than an error so
-    // the client can stop fetching naturally on "load more" past the end.
-    let scored: Vec<(f32, String)> = if offset >= total_matching {
+    let limit = query.limit.clamp(1, 200);
+    let offset = query.offset;
+    let threshold = query.threshold.clamp(-1.0, 1.0);
+
+    let library_ids = match parse_library_scope(query.library_ids.as_deref(), query.library) {
+        Ok(ids) => ids,
+        Err(msg) => return Ok(HttpResponse::BadRequest().json(SearchError { error: msg })),
+    };
+
+    let scored = match score_photos(
+        &state,
+        &exif_dao,
+        &q_text,
+        &library_ids,
+        threshold,
+        query.model_version.as_deref(),
+    )
+    .await
+    {
+        Ok(s) => s,
+        Err(e) => return Ok(score_error_response(e)),
+    };
+
+    let total_matching = scored.hits.len();
+    // Pagination — slice the sorted list at `[offset, offset+limit)`. Offsets
+    // past the end produce empty pages so "load more" stops naturally.
+    let page: Vec<(f32, String)> = if offset >= total_matching {
         Vec::new()
     } else {
         let end = (offset + limit).min(total_matching);
-        scored[offset..end].to_vec()
+        scored.hits[offset..end].to_vec()
     };
-
-    if scored.is_empty() {
-        return Ok(HttpResponse::Ok().json(SearchResponse {
-            query: q_text,
-            model_version: query_resp.model_version,
-            threshold,
-            considered,
-            total_matching,
-            offset,
-            results: Vec::new(),
-        }));
-    }
-
-    // 5. Resolve each surviving hash back to a `(library_id, rel_path)`.
-    // `get_rel_paths_by_hash` returns every rel_path; we pick the first
-    // one for the result. Apollo / the UI can fetch alternatives via
-    // /image/metadata when needed.
-    let hashes: Vec<String> = scored.iter().map(|(_, h)| h.clone()).collect();
-    let path_map = {
-        let mut dao = exif_dao.lock().expect("exif dao");
-        match dao.get_rel_paths_for_hashes(&ctx, &hashes) {
-            Ok(m) => m,
-            Err(e) => {
-                log::warn!("clip_search: get_rel_paths_for_hashes failed: {:?}", e);
-                return Ok(HttpResponse::InternalServerError().json(SearchError {
-                    error: "failed to resolve photo paths".into(),
-                }));
-            }
-        }
-    };
-
-    // We need (library_id, rel_path) — get_rel_paths_for_hashes only
-    // returns rel_paths. Cross-reference via find_by_content_hash to
-    // pick the library too. Single call per surviving hash; cheap at
-    // top-20.
-    let mut results = Vec::with_capacity(scored.len());
-    {
-        let mut dao = exif_dao.lock().expect("exif dao");
-        for (score, hash) in scored {
-            let row = match dao.find_by_content_hash(&ctx, &hash) {
-                Ok(Some(r)) => r,
-                Ok(None) => continue,
-                Err(e) => {
-                    log::warn!(
-                        "clip_search: find_by_content_hash failed for {}: {:?}",
-                        hash,
-                        e
-                    );
-                    continue;
-                }
-            };
-            // Prefer get_rel_paths_for_hashes's first entry if it
-            // exists (it shares semantics with `image_exif`'s natural
-            // order), falling back to the ImageExif row.
-            let rel_path = path_map
-                .get(&hash)
-                .and_then(|paths| paths.first().cloned())
-                .unwrap_or(row.file_path);
-            results.push(SearchHit {
-                library_id: row.library_id,
-                rel_path,
-                content_hash: hash,
-                score,
-            });
-        }
-    }
+    let results = resolve_hits(&exif_dao, &page);
 
     Ok(HttpResponse::Ok().json(SearchResponse {
         query: q_text,
-        model_version: query_resp.model_version,
+        model_version: scored.model_version,
         threshold,
-        considered,
+        considered: scored.considered,
         total_matching,
         offset,
         results,
     }))
 }
+
+/// Map a [`ScoreError`] to the HTTP response `search_photos` historically
+/// returned for each failure mode. Reused by the unified endpoint.
+pub fn score_error_response(e: ScoreError) -> HttpResponse {
+    match e {
+        ScoreError::Disabled => HttpResponse::ServiceUnavailable().json(SearchError {
+            error: "CLIP search is disabled (no Apollo CLIP endpoint configured)".into(),
+        }),
+        ScoreError::Rejected(msg) => HttpResponse::BadRequest().json(SearchError {
+            error: format!("query rejected: {msg}"),
+        }),
+        ScoreError::Unavailable(msg) => HttpResponse::BadGateway().json(SearchError {
+            error: format!("CLIP service unavailable: {msg}"),
+        }),
+        ScoreError::MalformedEmbedding => HttpResponse::BadGateway().json(SearchError {
+            error: "CLIP service returned a malformed query embedding".into(),
+        }),
+        ScoreError::Internal(msg) => {
+            HttpResponse::InternalServerError().json(SearchError { error: msg })
+        }
+    }
+}

src/geo.rs

@@ -69,10 +69,6 @@ pub fn gps_bounding_box(lat: f64, lon: f64, radius_km: f64) -> (f64, f64, f64, f
 /// a whole country. We collapse Nominatim's bounding box into the smallest
 /// circle that circumscribes it (see [`bbox_to_circle`]) so "Portland" and
 /// "Italy" both map onto the existing circle filter without a schema change.
-// Phase 1: forward geocoding is implemented and unit-tested here, but its
-// first consumer (the `/photos/search/unified` endpoint) lands in Phase 2.
-// allow-until-wired (mirrors llm_client.rs); remove when the endpoint is added.
-#[allow(dead_code)]
 #[derive(Debug, Clone, PartialEq)]
 pub struct GeoPlace {
     /// Nominatim's canonical name for the match (e.g. "Italia").
@@ -90,7 +86,6 @@ pub struct GeoPlace {
 /// Floor for a geocoded place's radius. Point results (a street address)
 /// come back with a near-zero bounding box; without a floor the circle
 /// filter would match nothing.
-#[allow(dead_code)]
 pub const MIN_PLACE_RADIUS_KM: f64 = 0.5;
 
 /// Collapse a bounding box into the centroid + circumscribing radius.
@@ -105,7 +100,6 @@ pub const MIN_PLACE_RADIUS_KM: f64 = 0.5;
 ///
 /// Pure and exact (no flooring) so it can be unit-tested directly; callers
 /// apply [`MIN_PLACE_RADIUS_KM`] when turning the result into a filter.
-#[allow(dead_code)]
 pub fn bbox_to_circle(south: f64, north: f64, west: f64, east: f64) -> (f64, f64, f64) {
     let center_lat = (south + north) / 2.0;
     let center_lon = (west + east) / 2.0;
@@ -118,7 +112,6 @@ pub fn bbox_to_circle(south: f64, north: f64, west: f64, east: f64) -> (f64, f64
 
 /// Raw Nominatim `/search` result. `lat`/`lon` arrive as strings and
 /// `boundingbox` as a 4-element string array `[south, north, west, east]`.
-#[allow(dead_code)]
 #[derive(Deserialize)]
 struct NominatimSearchResult {
     lat: String,
@@ -136,7 +129,6 @@ struct NominatimSearchResult {
 ///
 /// Nominatim's usage policy requires a `User-Agent` and rate-limits to ~1
 /// request/second; callers doing this interactively should cache results.
-#[allow(dead_code)]
 pub async fn forward_geocode(query: &str) -> Option<GeoPlace> {
     let q = query.trim();
     if q.is_empty() {

src/lib.rs

@@ -35,6 +35,7 @@ pub mod tags;
 #[cfg(test)]
 pub mod testhelpers;
 pub mod thumbnails;
+pub mod unified_search;
 pub mod utils;
 pub mod video;
 

src/main.rs

@@ -54,6 +54,7 @@ mod perceptual_hash;
 mod state;
 mod tags;
 mod thumbnails;
+mod unified_search;
 mod utils;
 mod video;
 mod watcher;
@@ -333,6 +334,13 @@ fn main() -> std::io::Result<()> {
                     web::resource("/photos/search")
                         .route(web::get().to(clip_search::search_photos)),
                 )
+                .service(
+                    // Unified natural-language search: LLM translates the
+                    // query into structured filters + a semantic term, then
+                    // filters constrain and CLIP ranks. See src/unified_search.rs.
+                    web::resource("/photos/search/unified")
+                        .route(web::get().to(unified_search::unified_search::<SqliteTagDao>)),
+                )
                 .service(web::resource("/file/move").post(move_file::<RealFileSystem>))
                 .service(handlers::image::get_image)
                 .service(handlers::image::upload_image)

src/unified_search.rs

@@ -0,0 +1,452 @@
+//! `/photos/search/unified?q=<natural language>` — unified NL photo search.
+//!
+//! One free-text box that composes the two existing engines instead of making
+//! the user pick between them:
+//!  1. A grounded local-LLM call ([`crate::ai::nl_query`]) translates the
+//!     query into a structured filter + a semantic term.
+//!  2. Structured filters (tags / EXIF / geo / date / media-type) define the
+//!     candidate set; the semantic term ranks within it via CLIP.
+//!
+//! Path A (orchestration): we reuse `clip_search`'s scoring core and the
+//! existing `ExifDao` / `TagDao` queries, joining on `content_hash`. EXIF rows
+//! are the universal candidate carrier — each has `(library_id, file_path,
+//! content_hash, date_taken)` — so the structured filter is just a predicate
+//! over them, and the CLIP hits (which key on `content_hash`) intersect by
+//! hash. No new schema, no surgery on `list_photos`.
+//!
+//! Degenerate cases collapse to the existing behavior: semantic-only → plain
+//! CLIP search; filters-only → a date-sorted filtered listing.
+//!
+//! Person filtering is intentionally deferred (no person→photos resolver yet).
+
+use crate::AppState;
+use crate::ai::backend::{BackendKind, SamplingOverrides};
+use crate::ai::nl_query::{StructuredQuery, translate_nl_query};
+use crate::clip_search::{
+    SearchHit, parse_library_scope, resolve_hits, score_error_response, score_photos,
+};
+use crate::data::Claims;
+use crate::database::ExifDao;
+use crate::file_types::{is_image_file, is_video_file};
+use crate::geo::{forward_geocode, gps_bounding_box, haversine_distance};
+use crate::tags::TagDao;
+use actix_web::HttpResponse;
+use actix_web::web::{Data, Query};
+use serde::{Deserialize, Serialize};
+use std::collections::HashSet;
+use std::path::Path;
+use std::sync::Mutex;
+
+#[derive(Debug, Deserialize)]
+pub struct UnifiedQuery {
+    /// Natural-language query. Required; empty triggers 400.
+    pub q: String,
+    #[serde(default = "default_limit")]
+    pub limit: usize,
+    #[serde(default)]
+    pub offset: usize,
+    /// CLIP cosine floor for the semantic ranking stage. Same default as the
+    /// plain search endpoint.
+    #[serde(default = "default_threshold")]
+    pub threshold: f32,
+    /// Legacy single-library scope (see clip_search).
+    pub library: Option<i32>,
+    /// Multi-library scope, comma-separated ids.
+    pub library_ids: Option<String>,
+}
+
+fn default_limit() -> usize {
+    20
+}
+fn default_threshold() -> f32 {
+    0.20
+}
+
+/// A geocoded place echoed back so the client can show / edit the location
+/// filter it actually searched.
+#[derive(Debug, Serialize)]
+struct ResolvedPlace {
+    display_name: String,
+    lat: f64,
+    lon: f64,
+    radius_km: f64,
+}
+
+/// How the server interpreted the NL query — echoed to the client to render
+/// editable filter chips. tag ids map to the client's existing tag list.
+#[derive(Debug, Serialize)]
+struct Interpreted {
+    semantic: Option<String>,
+    tag_ids: Vec<i32>,
+    exclude_tag_ids: Vec<i32>,
+    /// Words the model treated as tags that don't exist in the vocab; folded
+    /// into the semantic term and surfaced here so the UI can explain it.
+    unmatched_tags: Vec<String>,
+    camera_make: Option<String>,
+    camera_model: Option<String>,
+    lens_model: Option<String>,
+    date_from: Option<i64>,
+    date_to: Option<i64>,
+    media_type: Option<String>,
+    place: Option<ResolvedPlace>,
+}
+
+#[derive(Debug, Serialize)]
+struct UnifiedResponse {
+    query: String,
+    interpreted: Interpreted,
+    /// CLIP model version used for ranking; `None` when the query had no
+    /// semantic term (filters-only).
+    model_version: Option<String>,
+    /// Embeddings scored by CLIP (0 when filters-only).
+    considered: usize,
+    /// Matches before pagination.
+    total_matching: usize,
+    offset: usize,
+    results: Vec<SearchHit>,
+}
+
+#[derive(Debug, Serialize)]
+struct ErrorBody {
+    error: String,
+}
+
+fn bad_request(msg: impl Into<String>) -> HttpResponse {
+    HttpResponse::BadRequest().json(ErrorBody { error: msg.into() })
+}
+
+/// Combine the model's semantic term with any tag words that didn't match the
+/// vocab, so a hallucinated/non-vocab tag becomes a soft semantic signal
+/// rather than being dropped.
+fn effective_semantic(sq: &StructuredQuery) -> Option<String> {
+    let mut parts: Vec<String> = Vec::new();
+    if let Some(s) = sq.semantic.as_deref() {
+        parts.push(s.to_string());
+    }
+    parts.extend(sq.unmatched_tags.iter().cloned());
+    if parts.is_empty() {
+        None
+    } else {
+        Some(parts.join(" "))
+    }
+}
+
+pub async fn unified_search<TagD: TagDao>(
+    _: Claims,
+    state: Data<AppState>,
+    exif_dao: Data<Mutex<Box<dyn ExifDao>>>,
+    tag_dao: Data<Mutex<TagD>>,
+    query: Query<UnifiedQuery>,
+) -> HttpResponse {
+    let nl = query.q.trim().to_string();
+    if nl.is_empty() {
+        return bad_request("query parameter `q` is required");
+    }
+
+    let limit = query.limit.clamp(1, 200);
+    let offset = query.offset;
+    let threshold = query.threshold.clamp(-1.0, 1.0);
+
+    let library_ids = match parse_library_scope(query.library_ids.as_deref(), query.library) {
+        Ok(ids) => ids,
+        Err(msg) => return bad_request(msg),
+    };
+
+    let ctx = opentelemetry::Context::current();
+
+    // ── 1. Translate the NL query, grounded on the real tag vocabulary ──
+    let tag_vocab: Vec<(i32, String)> = {
+        let mut dao = tag_dao.lock().expect("tag dao");
+        match dao.get_all_tags(&ctx, None) {
+            Ok(tags) => tags.into_iter().map(|(_, t)| (t.id, t.name)).collect(),
+            Err(e) => {
+                log::warn!("unified_search: get_all_tags failed: {e:?}");
+                Vec::new()
+            }
+        }
+    };
+
+    // Respect env/config for the LLM backend (LLM_BACKEND → ollama or
+    // llama-swap); local only, no hybrid, per the feature's design.
+    let overrides = SamplingOverrides {
+        model: None,
+        num_ctx: None,
+        temperature: None,
+        top_p: None,
+        top_k: None,
+        min_p: None,
+    };
+    let backend = match state
+        .insight_generator
+        .resolve_backend(BackendKind::Local, &overrides)
+        .await
+    {
+        Ok(b) => b,
+        Err(e) => {
+            log::warn!("unified_search: resolve_backend failed: {e:?}");
+            return HttpResponse::ServiceUnavailable().json(ErrorBody {
+                error: "LLM backend unavailable".into(),
+            });
+        }
+    };
+
+    let today = chrono::Utc::now().date_naive();
+    let sq = match translate_nl_query(backend.chat(), &nl, &tag_vocab, today).await {
+        Ok(sq) => sq,
+        Err(e) => {
+            log::warn!("unified_search: translate_nl_query failed: {e:?}");
+            return HttpResponse::BadGateway().json(ErrorBody {
+                error: "could not interpret the query".into(),
+            });
+        }
+    };
+
+    // ── 2. Forward-geocode the place name into a gps circle ──
+    let resolved_place = match sq.place.as_deref() {
+        Some(p) => forward_geocode(p).await.map(|g| ResolvedPlace {
+            display_name: g.display_name,
+            lat: g.lat,
+            lon: g.lon,
+            radius_km: g.radius_km,
+        }),
+        None => None,
+    };
+    let gps = resolved_place.as_ref().map(|p| (p.lat, p.lon, p.radius_km));
+
+    let semantic = effective_semantic(&sq);
+
+    let has_exif_filter = sq.camera_make.is_some()
+        || sq.camera_model.is_some()
+        || sq.lens_model.is_some()
+        || sq.date_from.is_some()
+        || sq.date_to.is_some();
+    let has_struct =
+        has_exif_filter || gps.is_some() || !sq.tag_ids.is_empty() || sq.media_type.is_some();
+
+    // ── 3. Build the structured candidate set (EXIF rows passing every
+    // filter). Skipped entirely for a pure-semantic query. ──
+    let mut candidate: Vec<crate::database::models::ImageExif> = Vec::new();
+    let mut allowed_hashes: HashSet<String> = HashSet::new();
+    if has_struct {
+        // Tag membership set (rel_path only — same cross-library imprecision
+        // as the existing /photos tag listing). ALL-mode: the photo must
+        // carry every named tag.
+        let tag_set: Option<HashSet<String>> = if sq.tag_ids.is_empty() {
+            None
+        } else {
+            let mut dao = tag_dao.lock().expect("tag dao");
+            match dao.get_files_with_all_tag_ids(
+                sq.tag_ids.clone(),
+                sq.exclude_tag_ids.clone(),
+                &ctx,
+            ) {
+                Ok(files) => Some(files.into_iter().map(|f| f.file_name).collect()),
+                Err(e) => {
+                    log::warn!("unified_search: tag filter failed: {e:?}");
+                    Some(HashSet::new())
+                }
+            }
+        };
+
+        // EXIF query handles camera/lens/gps-box/date. With no EXIF filters
+        // it returns the whole table, which we then narrow by the predicates
+        // below (tags / media / scope). Fine at personal-library scale.
+        let gps_bounds = gps.map(|(lat, lon, r)| gps_bounding_box(lat, lon, r));
+        let rows = {
+            let mut dao = exif_dao.lock().expect("exif dao");
+            dao.query_by_exif(
+                &ctx,
+                None, // scope filtered in-Rust to support multi-library
+                sq.camera_make.as_deref(),
+                sq.camera_model.as_deref(),
+                sq.lens_model.as_deref(),
+                gps_bounds,
+                sq.date_from,
+                sq.date_to,
+            )
+            .unwrap_or_else(|e| {
+                log::warn!("unified_search: query_by_exif failed: {e:?}");
+                Vec::new()
+            })
+        };
+
+        candidate = rows
+            .into_iter()
+            .filter(|row| {
+                // Library scope.
+                if !library_ids.is_empty() && !library_ids.contains(&row.library_id) {
+                    return false;
+                }
+                // Precise GPS distance (the EXIF query only did a coarse box).
+                if let Some((lat, lon, radius_km)) = gps {
+                    match (row.gps_latitude, row.gps_longitude) {
+                        (Some(plat), Some(plon)) => {
+                            if haversine_distance(lat, lon, plat as f64, plon as f64) > radius_km {
+                                return false;
+                            }
+                        }
+                        _ => return false,
+                    }
+                }
+                // Media type.
+                if let Some(mt) = sq.media_type.as_deref() {
+                    let p = Path::new(&row.file_path);
+                    let ok = if mt == "video" {
+                        is_video_file(p)
+                    } else {
+                        is_image_file(p)
+                    };
+                    if !ok {
+                        return false;
+                    }
+                }
+                // Tag membership.
+                if let Some(ts) = &tag_set
+                    && !ts.contains(&row.file_path)
+                {
+                    return false;
+                }
+                true
+            })
+            .collect();
+
+        allowed_hashes = candidate
+            .iter()
+            .filter_map(|r| r.content_hash.clone())
+            .collect();
+    }
+
+    // ── 4. Rank ──
+    match semantic {
+        Some(ref sem) => {
+            // Semantic term present: CLIP-rank, then keep only hits that pass
+            // the structured filters (by content_hash).
+            let scored =
+                match score_photos(&state, &exif_dao, sem, &library_ids, threshold, None).await {
+                    Ok(s) => s,
+                    Err(e) => return score_error_response(e),
+                };
+            let hits: Vec<(f32, String)> = if has_struct {
+                scored
+                    .hits
+                    .into_iter()
+                    .filter(|(_, h)| allowed_hashes.contains(h))
+                    .collect()
+            } else {
+                scored.hits
+            };
+            let total_matching = hits.len();
+            let page = paginate(&hits, offset, limit);
+            let results = resolve_hits(&exif_dao, &page);
+            HttpResponse::Ok().json(UnifiedResponse {
+                query: nl,
+                interpreted: interpreted(&sq, resolved_place),
+                model_version: Some(scored.model_version),
+                considered: scored.considered,
+                total_matching,
+                offset,
+                results,
+            })
+        }
+        None => {
+            // Filters-only: no semantic term. Require at least one filter,
+            // then return the candidate set newest-first.
+            if !has_struct {
+                return bad_request("query had no searchable terms");
+            }
+            candidate.sort_by(|a, b| b.date_taken.cmp(&a.date_taken));
+            let total_matching = candidate.len();
+            let end = (offset + limit).min(total_matching);
+            let results: Vec<SearchHit> = if offset >= total_matching {
+                Vec::new()
+            } else {
+                candidate[offset..end]
+                    .iter()
+                    .map(|r| SearchHit {
+                        library_id: r.library_id,
+                        rel_path: r.file_path.clone(),
+                        content_hash: r.content_hash.clone().unwrap_or_default(),
+                        score: 0.0,
+                    })
+                    .collect()
+            };
+            HttpResponse::Ok().json(UnifiedResponse {
+                query: nl,
+                interpreted: interpreted(&sq, resolved_place),
+                model_version: None,
+                considered: 0,
+                total_matching,
+                offset,
+                results,
+            })
+        }
+    }
+}
+
+/// Slice a sorted hit list at `[offset, offset+limit)`, tolerating
+/// out-of-range offsets (empty page).
+fn paginate(hits: &[(f32, String)], offset: usize, limit: usize) -> Vec<(f32, String)> {
+    if offset >= hits.len() {
+        return Vec::new();
+    }
+    let end = (offset + limit).min(hits.len());
+    hits[offset..end].to_vec()
+}
+
+fn interpreted(sq: &StructuredQuery, place: Option<ResolvedPlace>) -> Interpreted {
+    Interpreted {
+        semantic: sq.semantic.clone(),
+        tag_ids: sq.tag_ids.clone(),
+        exclude_tag_ids: sq.exclude_tag_ids.clone(),
+        unmatched_tags: sq.unmatched_tags.clone(),
+        camera_make: sq.camera_make.clone(),
+        camera_model: sq.camera_model.clone(),
+        lens_model: sq.lens_model.clone(),
+        date_from: sq.date_from,
+        date_to: sq.date_to,
+        media_type: sq.media_type.clone(),
+        place,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::ai::nl_query::StructuredQuery;
+
+    #[test]
+    fn effective_semantic_combines_semantic_and_unmatched() {
+        let sq = StructuredQuery {
+            semantic: Some("sunset".into()),
+            unmatched_tags: vec!["golden hour".into()],
+            ..Default::default()
+        };
+        assert_eq!(
+            effective_semantic(&sq).as_deref(),
+            Some("sunset golden hour")
+        );
+    }
+
+    #[test]
+    fn effective_semantic_none_when_empty() {
+        let sq = StructuredQuery::default();
+        assert_eq!(effective_semantic(&sq), None);
+    }
+
+    #[test]
+    fn effective_semantic_unmatched_only() {
+        let sq = StructuredQuery {
+            unmatched_tags: vec!["disco".into()],
+            ..Default::default()
+        };
+        assert_eq!(effective_semantic(&sq).as_deref(), Some("disco"));
+    }
+
+    #[test]
+    fn paginate_handles_out_of_range_offset() {
+        let hits = vec![(0.9, "a".to_string()), (0.8, "b".to_string())];
+        assert_eq!(paginate(&hits, 5, 10).len(), 0);
+        assert_eq!(paginate(&hits, 0, 1).len(), 1);
+        assert_eq!(paginate(&hits, 1, 10).len(), 1);
+    }
+}