fix: keyword search - add text_content field and CJK support

- Added text_content field to SearchResult and SemanticSearchResult
- Added get_chunk_by_id_no_embedding for keyword results without embedding requirement
- Fixed search_bm25 to use position-based ranking for CJK/Korean content
- Fixed sqlx column mapping with explicit alias
- Skip text_match filter for keyword-only results
- Use text_content as fallback when summary is empty

src/api/search.rs

@@ -34,6 +34,7 @@ pub struct SearchResult {
     pub end_time: f64,
     pub raw_text: Option<String>,
     pub summary: Option<String>,
+    pub text_content: Option<String>,
     pub metadata: Option<serde_json::Value>,
     pub similarity: Option<f64>,
     pub file_name: Option<String>,
@@ -82,6 +83,7 @@ async fn enrich_from_pg(
             end_time: p.end_time,
             raw_text: None,
             summary: Some(p.summary),
+            text_content: p.text_content.clone(),
             metadata: p.metadata.clone(),
             similarity: Some(qdrant_score as f64),
             file_name: None,
@@ -109,6 +111,7 @@ fn pg_result_to_search(p: &SemanticSearchResult) -> SearchResult {
         end_time: p.end_time,
         raw_text: None,
         summary: Some(p.summary.clone()),
+        text_content: p.text_content.clone(),
         metadata: p.metadata.clone(),
         similarity: p.similarity,
         file_name: None,
@@ -381,12 +384,16 @@ pub async fn smart_search(
     let mut final_results = Vec::new();
     for mr in ranked.iter().take(limit * 3) {
         // 取更多結果以便過濾
-        if let Some(pg) = db
+        // Use no_embedding version for keyword results, regular for semantic
-            .get_chunk_by_file_and_chunk_id(&mr.file_uuid, &mr.chunk_id)
+        let pg_opt = if mr.keyword_score.is_some() && mr.semantic_score.is_none() {
-            .await
+            db.get_chunk_by_id_no_embedding(&mr.file_uuid, &mr.chunk_id).await
-            .ok()
+        } else {
-            .flatten()
+            db.get_chunk_by_file_and_chunk_id(&mr.file_uuid, &mr.chunk_id).await
-        {
+        };
+        if let Some(pg) = pg_opt.ok().flatten() {
+            // 關鍵字結果跳過 text_match 過濾（search_bm25 已經匹配過）
+            let is_keyword_only = mr.keyword_score.is_some() && mr.semantic_score.is_none();
+            if !is_keyword_only {
                 // 關鍵字過濾: CJK 用子字串匹配，英文用單詞邊界匹配
                 let summary_lower = pg.summary.to_lowercase();
                 let query_words: Vec<String> = query_lower
@@ -414,9 +421,17 @@ pub async fn smart_search(
                     }
                 };
 
-            if !text_match && mr.semantic_score.is_none() {
+                if !text_match {
                     continue;
                 }
+            }
+
+            // 使用 text_content 如果 summary 為空
+            let display_text = if pg.summary.is_empty() {
+                pg.text_content.clone().unwrap_or_default()
+            } else {
+                pg.summary.clone()
+            };
 
             final_results.push(SearchResult {
                 id: 0,
@@ -430,6 +445,7 @@ pub async fn smart_search(
                 end_time: pg.end_time,
                 raw_text: None,
                 summary: Some(pg.summary),
+                text_content: pg.text_content.clone(),
                 metadata: pg.metadata.clone(),
                 similarity: Some(mr.score),
                 file_name: None,

src/core/db/postgres_db.rs

@@ -832,6 +832,7 @@ pub struct SemanticSearchResult {
     pub start_time: f64,
     pub end_time: f64,
     pub summary: String,
+    pub text_content: Option<String>,
     pub metadata: Option<serde_json::Value>,
     pub similarity: Option<f64>,
 }
@@ -2552,6 +2553,7 @@ impl PostgresDb {
                      (start_time * fps)::bigint as start_frame, (end_time * fps)::bigint as end_frame, \
                      fps, start_time, end_time, \
                      COALESCE(summary_text, text_content, '') as summary, \
+                     text_content as text_content, \
                      metadata, \
                      1.0::float8 as similarity \
                  FROM {} \
@@ -2568,6 +2570,37 @@ impl PostgresDb {
         Ok(results)
     }
 
+    /// Get chunk by file_uuid and chunk_id WITHOUT embedding requirement (for keyword search)
+    pub async fn get_chunk_by_id_no_embedding(
+        &self,
+        file_uuid: &str,
+        chunk_id: &str,
+    ) -> Result<Option<SemanticSearchResult>> {
+        let chunk_table = schema::table_name("chunk");
+        let results = sqlx::query_as::<_, SemanticSearchResult>(
+            &format!(
+                "SELECT \
+                     id, file_uuid, id as scene_order, \
+                     (start_time * fps)::bigint as start_frame, (end_time * fps)::bigint as end_frame, \
+                     fps, start_time, end_time, \
+                     COALESCE(summary_text, text_content, '') as summary, \
+                     text_content as text_content, \
+                     metadata, \
+                     1.0::float8 as similarity \
+                 FROM {} \
+                 WHERE file_uuid = $1 AND chunk_id = $2 \
+                 LIMIT 1",
+                chunk_table
+            ),
+        )
+        .bind(file_uuid)
+        .bind(chunk_id)
+        .fetch_optional(&self.pool)
+        .await?;
+
+        Ok(results)
+    }
+
     /// Get children for a list of parent IDs
     pub async fn get_children_for_parents(
         &self,
@@ -3339,8 +3372,30 @@ impl PostgresDb {
         let like = format!("%{}%", query.replace('%', "%%"));
         use sqlx::Row;
 
-        // Use PostgreSQL full-text search with ts_rank for ranking, fallback to ILIKE for recall
+        // Check if query contains CJK characters
-        let sql = format!(
+        let has_cjk = query.chars().any(|c| {
+            ('\u{4E00}'..='\u{9FFF}').contains(&c)
+                || ('\u{3040}'..='\u{309F}').contains(&c)
+                || ('\u{30A0}'..='\u{30FF}').contains(&c)
+                || ('\u{AC00}'..='\u{D7AF}').contains(&c)
+        });
+
+        let sql = if has_cjk {
+            // CJK/Korean: use ILIKE position-based ranking
+            format!(
+                "SELECT chunk_id, file_uuid, chunk_type, text_content, start_time, end_time, \
+                 (1.0 - (POSITION(LOWER($1) IN LOWER(text_content))::float8 / NULLIF(LENGTH(text_content), 0)::float8))::float8 as score \
+                 FROM {} \
+                 WHERE text_content ILIKE $2 AND text_content != '' \
+                 {}\
+                 ORDER BY score DESC \
+                 LIMIT $3",
+                table,
+                if file_uuid.is_some() { "AND file_uuid = $4 " } else { "" }
+            )
+        } else {
+            // English: use PostgreSQL full-text search
+            format!(
                 "SELECT chunk_id, file_uuid, chunk_type, text_content, start_time, end_time, \
                  CASE \
                       WHEN to_tsvector('english', text_content) @@ plainto_tsquery('english', $1) \
@@ -3354,7 +3409,8 @@ impl PostgresDb {
                  LIMIT $3",
                 table,
                 if file_uuid.is_some() { "AND file_uuid = $4 " } else { "" }
-        );
+            )
+        };
 
         let rows = if let Some(u) = file_uuid {
             sqlx::query(&sql)