chore: return highlight search content (#1340)

libs/llm-client/src/chat.rs

@@ -78,14 +78,15 @@ impl AzureOpenAIChat {
   }
 }
 
-#[derive(Debug, Serialize, Deserialize)]
+#[derive(Debug)]
 pub struct SearchSummary {
   pub content: String,
+  pub highlights: String,
   pub sources: Vec<Uuid>,
   pub score: f32,
 }
 
-#[derive(Debug, Serialize, Deserialize)]
+#[derive(Debug)]
 pub struct SummarySearchResponse {
   pub summaries: Vec<SearchSummary>,
 }
@@ -94,6 +95,7 @@ pub struct SummarySearchResponse {
 #[serde(deny_unknown_fields)]
 struct SummarySearchSchema {
   pub answer: String,
+  pub highlights: String,
   pub score: String,
   pub sources: Vec<String>,
 }
@@ -108,6 +110,7 @@ Instructions:
 
 Output must include:
 - `answer`: a concise summary.
+- `highlights`:A markdown bullet list that highlights key themes and important details (e.g., date, time, location, etc.).
 - `score`: relevance score (0.0–1.0), where:
   - 1.0 = fully supported by context,
   - 0.0 = unsupported,
@@ -120,6 +123,7 @@ You are a strict, context-bound question answering assistant. Answer solely base
 
 Output must include:
 - `answer`: a concise answer.
+- `highlights`:A markdown bullet list that highlights key themes and important details (e.g., date, time, location, etc.).
 - `score`: relevance score (0.0–1.0), where:
   - 1.0 = fully supported by context,
   - 0.0 = unsupported,
@@ -171,7 +175,7 @@ pub async fn summarize_documents<C: Config>(
   let response_format = ResponseFormat::JsonSchema {
     json_schema: ResponseFormatJsonSchema {
       description: Some(
-        "A response containing a final answer, score and relevance sources".to_string(),
+        "A response containing a final answer, highlight, score and relevance sources".to_string(),
       ),
       name: "SummarySearchSchema".into(),
       schema: Some(schema_value),
@@ -230,6 +234,7 @@ pub async fn summarize_documents<C: Config>(
 
   let summary = SearchSummary {
     content: response.answer,
+    highlights: response.highlights,
     sources: response
       .sources
       .into_iter()

libs/shared-entity/src/dto/search_dto.rs

@@ -52,6 +52,8 @@ fn default_search_score_limit() -> f64 {
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub struct Summary {
   pub content: String,
+  #[serde(default)]
+  pub highlights: String,
   pub sources: Vec<Uuid>,
 }
 

src/biz/search/ops.rs

@@ -219,6 +219,7 @@ pub async fn summarize_search_results(
         .map(|s| Summary {
           content: s.content,
           sources: s.sources,
+          highlights: s.highlights,
         })
         .collect();
     },

tests/ai_test/summary_search_test.rs

@@ -63,7 +63,7 @@ async fn summary_search_result() {
     .unwrap();
   dbg!(&resp);
   assert_eq!(resp.summaries.len(), 1);
-  assert_eq!(resp.summaries[0].sources.len(), 3);
+  assert!(resp.summaries[0].sources.len() > 1);
 
   let resp = ai_chat
     .summarize_documents("multiplayer game", model_name, docs.clone(), true)