refactor: rename search uuid -> file_uuid

docs_v1.0/API_WORKSPACE/modules/06_search.md

@@ -0,0 +1,135 @@
+<!-- module: search -->
+<!-- description: Vector search, BM25, smart search, universal search, visual search -->
+<!-- depends: 01_auth -->
+
+## Search APIs
+
+### `POST /api/v1/search/smart`
+
+**Auth**: Required
+**Scope**: file-level
+
+Semantic vector search using EmbeddingGemma-300m. Generates a query embedding via EmbeddingGemma (port 11436), then searches pgvector `story_parent` and `llm_parent` chunks by cosine similarity.
+
+#### Request Parameters
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `file_uuid` | string | Yes | — | File UUID to search within |
+| `query` | string | Yes | — | Search text |
+| `page` | integer | No | 1 | Page number |
+| `page_size` | integer | No | 5 | Items per page |
+
+#### Example
+
+```bash
+curl -s -X POST "$API/api/v1/search/smart" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $JWT" \
+  -d '{"file_uuid": "'"$FILE_UUID"'", "query": "Audrey Hepburn"}'
+```
+
+#### Response (200)
+
+```json
+{
+  "query": "Audrey Hepburn",
+  "results": [
+    {
+      "parent_id": 12345,
+      "start_time": 299.0,
+      "end_time": 300.0,
+      "summary": "[299s-300s, 1s] Cast: Audrey Hepburn. Total: 1 lines, 5 words...",
+      "similarity": 0.72
+    }
+  ],
+  "strategy": "semantic_vector_search"
+}
+```
+
+---
+
+### `POST /api/v1/search/universal`
+
+**Auth**: Required
+**Scope**: file-level
+
+Multi-type BM25 full-text search across chunks, frames, and persons. Uses PostgreSQL `tsvector`.
+
+#### Request Parameters
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `query` | string | Yes | — | Search text |
+| `file_uuid` | string | No | — | Restrict to specific file |
+| `types` | string[] | No | `["chunk","frame","person"]` | Search types |
+| `page` | integer | No | 1 | Page number |
+| `page_size` | integer | No | 20 | Items per page |
+
+#### Example
+
+```bash
+curl -s -X POST "$API/api/v1/search/universal" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $JWT" \
+  -d '{"file_uuid": "'"$FILE_UUID"'", "query": "Cary Grant"}'
+```
+
+#### Response (200)
+
+```json
+{
+  "results": [
+    {
+      "type": "chunk",
+      "chunk_id": "uuid_1429",
+      "chunk_type": "story_child",
+      "start_time": 429.16,
+      "end_time": 430.5,
+      "text": "You could have the stamps.",
+      "score": 0.9
+    }
+  ],
+  "total": 20,
+  "took_ms": 18
+}
+```
+
+---
+
+### `POST /api/v1/search/frames`
+
+**Auth**: Required
+**Scope**: file-level
+
+Search face detection frames by identity name or trace ID.
+
+---
+
+### `POST /api/v1/search/identity_text`
+
+**Auth**: Required
+**Scope**: file-level
+
+Search text chunks spoken by a specific identity.
+
+---
+
+### Visual Search
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/api/v1/search/visual` | Search visual chunks |
+| POST | `/api/v1/search/visual/class` | Search by object class |
+| POST | `/api/v1/search/visual/density` | Search by object density |
+| POST | `/api/v1/search/visual/combination` | Search by object combination |
+| POST | `/api/v1/search/visual/stats` | Visual chunk statistics |
+
+#### Embedding Model
+
+| Detail | Value |
+|--------|-------|
+| **Model** | EmbeddingGemma-300m |
+| **Endpoint** | `POST /api/v1/embeddings` on port 11436 |
+| **Dimension** | 768 |
+| **Storage** | pgvector (`chunk.embedding` column) |

src/api/search.rs

@@ -11,7 +11,7 @@ use crate::core::embedding::Embedder;
 
 #[derive(Debug, Deserialize)]
 pub struct SmartSearchRequest {
-    pub uuid: String,
+    pub file_uuid: String,
     pub query: String,
     pub page: Option<usize>,
     pub page_size: Option<usize>,
@@ -82,7 +82,7 @@ pub async fn smart_search(
 
     // 2. Search Database (Drill-Down: Find Parents First)
     let db_parents: Vec<crate::core::db::postgres_db::SemanticSearchResult> = db
-        .search_parent_chunks_semantic(&req.uuid, &embedding, limit)
+        .search_parent_chunks_semantic(&req.file_uuid, &embedding, limit)
         .await
         .map_err(
             |e: anyhow::Error| -> (StatusCode, Json<serde_json::Value>) {

src/api/server.rs

@@ -3916,7 +3916,7 @@ struct ParentChunkResponse {
 /// Search visual chunks based on criteria
 #[derive(Debug, Deserialize)]
 struct VisualChunkSearchRequest {
-    uuid: String,
+    file_uuid: String,
     criteria: visual_chunk_search::VisualChunkSearchCriteria,
 }
 
@@ -3930,8 +3930,8 @@ async fn search_visual_chunks(
     State(state): State<AppState>,
     Json(req): Json<VisualChunkSearchRequest>,
 ) -> Result<Json<VisualChunkSearchResponse>, StatusCode> {
-    let criteria_hash = generate_visual_search_hash(&req.uuid, &req.criteria);
+    let criteria_hash = generate_visual_search_hash(&req.file_uuid, &req.criteria);
-    let cache_key = keys::visual_search(&req.uuid, &criteria_hash);
+    let cache_key = keys::visual_search(&req.file_uuid, &criteria_hash);
     let ttl = state.mongo_cache.ttl_visual_search();
 
     let chunks = state
@@ -3941,7 +3941,7 @@ async fn search_visual_chunks(
                 .await
                 .map_err(|e| anyhow::anyhow!("PG init failed: {}", e))?;
 
-            visual_chunk_search::search_visual_chunks(&db, &req.uuid, &req.criteria)
+            visual_chunk_search::search_visual_chunks(&db, &req.file_uuid, &req.criteria)
                 .await
                 .map_err(|e| anyhow::anyhow!("Visual search failed: {}", e))
         })

src/api/universal_search.rs

@@ -15,7 +15,7 @@ use crate::core::db::{schema, Database, PostgresDb};
 #[derive(Debug, Deserialize)]
 pub struct UniversalSearchRequest {
     pub query: String,
-    pub uuid: Option<String>,
+    pub file_uuid: Option<String>,
     #[serde(default)]
     pub types: Vec<String>, // chunk, frame, person
     pub time_range: Option<[f64; 2]>,
@@ -274,7 +274,7 @@ pub async fn search_persons(
 
 #[derive(Debug, Deserialize)]
 pub struct FrameSearchRequest {
-    pub uuid: Option<String>,
+    pub file_uuid: Option<String>,
     pub object_class: Option<String>,
     pub ocr_text: Option<String>,
     pub face_id: Option<String>,
@@ -292,7 +292,7 @@ pub struct FrameSearchResponse {
 pub struct FrameResult {
     pub frame_number: i64,
     pub timestamp: f64,
-    pub uuid: String,
+    pub file_uuid: String,
     pub objects: Option<Vec<serde_json::Value>>,
     pub ocr_texts: Option<Vec<String>>,
     pub faces: Option<Vec<serde_json::Value>>,
@@ -333,9 +333,9 @@ async fn search_chunks(
     req: &UniversalSearchRequest,
 ) -> Result<Vec<SearchResult>, anyhow::Error> {
     // uuid is required for chunk search - chunk_id is only unique within a video
-    let uuid = match &req.uuid {
+    let uuid = match &req.file_uuid {
         Some(u) => u.replace('\'', "''"),
-        None => return Err(anyhow::anyhow!("uuid is required for chunk search")),
+        None => return Err(anyhow::anyhow!("file_uuid is required for chunk search")),
     };
 
     let chunk_table = schema::table_name("chunk");
@@ -501,7 +501,7 @@ async fn search_frames_internal(
         table, video_table
     );
 
-    if let Some(uuid) = &req.uuid {
+    if let Some(uuid) = &req.file_uuid {
         sql.push_str(&format!(" AND v.file_uuid = '{}'", uuid));
     }
     if let Some(tr) = &req.time_range {
@@ -665,8 +665,8 @@ async fn search_frames_internal_v2(
         table, video_table
     );
 
-    if let Some(uuid) = &req.uuid {
+    if let Some(uuid) = &req.file_uuid {
-        sql.push_str(&format!(" AND v.file_uuid = '{}'", uuid));
+        sql.push_str(&format!(" AND fd.file_uuid = '{}'", uuid));
     }
     if let Some(tr) = &req.time_range {
         sql.push_str(&format!(
@@ -721,7 +721,7 @@ async fn search_frames_internal_v2(
             FrameResult {
                 frame_number,
                 timestamp,
-                uuid,
+                file_uuid: uuid,
                 objects: objects.map(|arr| arr.iter().map(|v| v.clone()).collect()),
                 ocr_texts,
                 faces,