feat: Identity Agent query Qdrant for face embeddings

Phase 1.4: Modify match_faces_iterative to use Qdrant

Changes:
- match_faces_iterative() now queries FaceEmbeddingDb
- Fallback to PostgreSQL if Qdrant is empty
- Group embeddings by trace_id from Qdrant payload
- Sample 3-angle embeddings (front, mid, back)
- Match against TMDb seeds (threshold=0.50)
- Propagate to unmatched traces
- Update face_detections.identity_id in PostgreSQL

New functions:
- match_faces_iterative() - Qdrant-based matching
- match_faces_iterative_pg() - PostgreSQL fallback

Flow:
1. Load TMDb identities with face_embedding
2. Query Qdrant for file embeddings
3. Sample 3 embeddings per trace
4. Match against TMDb seeds
5. Propagate matches iteratively
6. Update identity_id in PostgreSQL

src/api/identity_agent_api.rs

@@ -619,10 +619,13 @@ fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
     }
 }
 
-/// 迭代多角度 face embedding 比對 + 傳播
+/// 迭代多角度 face embedding 比對 + 傳播 (Qdrant version)
-/// Round 1: 用 TMDb seed face_embedding 比對 face_detections (threshold 0.50)
+/// Round 1: 用 TMDb seed face_embedding 比對 Qdrant embeddings (threshold 0.50)
 /// Round 2+: 用已匹配 trace 的所有 face 作為 seed，傳播到未匹配 trace
 async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
+    use crate::core::db::face_embedding_db::FaceEmbeddingDb;
+    use std::collections::HashMap;
+
     // Step 1: 載入 TMDb identities (source='tmdb' 且有 face_embedding)
     let identities_table = schema::table_name("identities");
     let tmdb_rows = sqlx::query_as::<_, (i32, String, Vec<f32>)>(
@@ -635,12 +638,167 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
         return Ok(0);
     }
     tracing::info!(
-        "[FaceMatch] Loaded {} TMDb seed identities",
+        "[FaceMatch-Qdrant] Loaded {} TMDb seed identities",
+        tmdb_rows.len()
+    );
+
+    // Step 2: Load embeddings from Qdrant
+    let face_db = FaceEmbeddingDb::new();
+    let qdrant_embeddings = face_db.get_all_embeddings_for_file(file_uuid).await?;
+
+    if qdrant_embeddings.is_empty() {
+        tracing::warn!("[FaceMatch-Qdrant] No face embeddings in Qdrant for {}", file_uuid);
+        return match_faces_iterative_pg(pool, file_uuid).await; // Fallback to PG
+    }
+
+    // Group: trace_id → Vec<(frame, embedding)>
+    let mut trace_faces_raw: HashMap<i32, Vec<(i64, Vec<f32>)>> = HashMap::new();
+    for (_, emb, payload) in &qdrant_embeddings {
+        trace_faces_raw
+            .entry(payload.trace_id)
+            .or_default()
+            .push((payload.frame, emb.clone()));
+    }
+
+    // Sample 3 embeddings per trace (front, mid, back)
+    let mut trace_samples: HashMap<i32, Vec<Vec<f32>>> = HashMap::new();
+    for (tid, mut faces) in trace_faces_raw {
+        faces.sort_by_key(|(frame, _)| *frame);
+        let n = faces.len();
+        let indices = if n <= 3 {
+            (0..n).collect::<Vec<_>>()
+        } else {
+            vec![0, n / 2, n - 1]
+        };
+        let samples: Vec<Vec<f32>> = indices.iter().map(|&i| faces[i].1.clone()).collect();
+        trace_samples.insert(tid, samples);
+    }
+
+    let total_traces = trace_samples.len();
+    let sample_count: usize = trace_samples.values().map(|v| v.len()).sum();
+    tracing::info!(
+        "[FaceMatch-Qdrant] Loaded {} traces, sampled {} embeddings",
+        total_traces,
+        sample_count
+    );
+
+    // Step 3: Match against TMDb seeds
+    const TH: f32 = 0.50;
+    let tmdb_seeds: Vec<(i32, String, Vec<f32>)> = tmdb_rows;
+    let mut matched: HashMap<i32, String> = HashMap::new();
+
+    for (&tid, samples) in &trace_samples {
+        let mut best_name = String::new();
+        let mut best_sim = 0.0f32;
+        for (_, ref name, ref tmdb_emb) in &tmdb_seeds {
+            for face_emb in samples {
+                let s = cosine_similarity(face_emb, tmdb_emb);
+                if s > best_sim {
+                    best_sim = s;
+                    best_name = name.clone();
+                }
+            }
+        }
+        if best_sim >= TH {
+            matched.insert(tid, best_name);
+        }
+    }
+    tracing::info!(
+        "[FaceMatch-Qdrant] Round 1: matched {} traces (threshold={})",
+        matched.len(),
+        TH
+    );
+
+    // Round 2+: Propagate
+    let mut round = 2;
+    while matched.len() < trace_samples.len() {
+        let prev_count = matched.len();
+
+        // Collect new matches in separate HashMap
+        let mut new_matches: HashMap<i32, String> = HashMap::new();
+
+        for (&tid, samples) in &trace_samples {
+            if matched.contains_key(&tid) {
+                continue;
+            }
+
+            for (matched_tid, matched_name) in &matched {
+                if let Some(matched_embs) = trace_samples.get(matched_tid) {
+                    for face_emb in samples {
+                        for ref_emb in matched_embs {
+                            let s = cosine_similarity(face_emb, ref_emb);
+                            if s >= TH {
+                                new_matches.insert(tid, matched_name.clone());
+                                break;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        // Merge new matches
+        matched.extend(new_matches);
+
+        if matched.len() == prev_count {
+            break;
+        }
+        tracing::info!(
+            "[FaceMatch-Qdrant] Round {}: matched {} total",
+            round,
+            matched.len()
+        );
+        round += 1;
+    }
+
+    // Update face_detections.identity_id
+    let fd_table = schema::table_name("face_detections");
+    let identities_map: HashMap<String, i32> = tmdb_seeds
+        .iter()
+        .map(|(id, name, _)| (name.clone(), *id))
+        .collect();
+
+    let mut updated = 0usize;
+    for (tid, name) in &matched {
+        let identity_id = identities_map.get(name);
+        if let Some(id) = identity_id {
+            let rows = sqlx::query(&format!(
+                "UPDATE {} SET identity_id = $1 WHERE file_uuid = $2 AND trace_id = $3",
+                fd_table
+            ))
+            .bind(*id)
+            .bind(file_uuid)
+            .bind(*tid)
+            .execute(pool)
+            .await?
+            .rows_affected();
+            updated += rows as usize;
+        }
+    }
+
+    tracing::info!("[FaceMatch-Qdrant] Updated {} face_detections", updated);
+    Ok(updated)
+}
+
+/// Fallback: PostgreSQL-based matching (original implementation)
+async fn match_faces_iterative_pg(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
+    // Step 1: 載入 TMDb identities (source='tmdb' 且有 face_embedding)
+    let identities_table = schema::table_name("identities");
+    let tmdb_rows = sqlx::query_as::<_, (i32, String, Vec<f32>)>(
+        &format!("SELECT id, name, face_embedding::real[] FROM {} WHERE source='tmdb' AND face_embedding IS NOT NULL", identities_table)
+    )
+    .fetch_all(pool).await?;
+
+    if tmdb_rows.is_empty() {
+        tracing::warn!("[FaceMatch-PG] No TMDb identities with face embeddings");
+        return Ok(0);
+    }
+    tracing::info!(
+        "[FaceMatch-PG] Loaded {} TMDb seed identities",
         tmdb_rows.len()
     );
 
     // Step 2: 載入所有 face_detections（含 frame_number），按 trace_id 分組
-    // frame_number is BIGINT (i64) in database
     let fd_table = schema::table_name("face_detections");
     let fd_rows = sqlx::query_as::<_, (i32, i64, Vec<f32>)>(&format!(
         "SELECT trace_id, frame_number, embedding FROM {} \
@@ -653,7 +811,7 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
     .await?;
 
     if fd_rows.is_empty() {
-        tracing::warn!("[FaceMatch] No face detections with embeddings");
+        tracing::warn!("[FaceMatch-PG] No face detections with embeddings");
         return Ok(0);
     }
 
@@ -668,7 +826,6 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
     }
 
     // 從每個 trace 選取不同角度的 3 個 face embedding
-    // 策略：按 frame_number 排序，取前中後各 1 個
     let mut trace_samples: HashMap<i32, Vec<Vec<f32>>> = HashMap::new();
     for (tid, mut faces) in trace_faces_raw {
         faces.sort_by_key(|(frame, _)| *frame);
@@ -686,7 +843,7 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
     let total_traces = trace_samples.len();
     let sample_count: usize = trace_samples.values().map(|v| v.len()).sum();
     tracing::info!(
-        "[FaceMatch] Loaded {} traces, sampled {} embeddings (3-angle)",
+        "[FaceMatch-PG] Loaded {} traces, sampled {} embeddings (3-angle)",
         total_traces,
         sample_count
     );
@@ -699,7 +856,6 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
     let mut matched: HashMap<i32, String> = HashMap::new(); // trace_id → identity_name
 
     // Round 1: 用 3-angle samples 比對 TMDb
-    // 每個 trace 選 3 個不同角度 face，取最高 similarity
     for (&tid, samples) in &trace_samples {
         let mut best_name = String::new();
         let mut best_sim = 0.0f32;