Refactor vector search to utilize Qdrant VectorDB

server/src/handlers/workspaces.rs

@@ -618,41 +618,54 @@ impl McpTool for SemanticCodeSearchHandler {
         
         let query_emb = generate_embedding_async(tool_args.query.clone()).await?;
         
-        // For MVP, we search across snippets dynamically. A true background codebase indexer would be a separate subsystem.
         let mut results = Vec::new();
-        let mut texts_to_embed = Vec::new();
-        let mut metadata = Vec::new();
         
-        let snippets = state.snippets.read_with(|snips| snips.clone());
-        for snippet in snippets {
-            let combined = format!("{} {} {}", snippet.name, snippet.description, snippet.code);
-            texts_to_embed.push(combined);
-            metadata.push((snippet.name, snippet.description));
-        }
-        
-        let sticky = state.sticky.read_with(|s| s.clone());
-        for note in sticky {
-            texts_to_embed.push(note.content.clone());
-            metadata.push(("StickyNote".to_string(), note.content.chars().take(200).collect::<String>()));
-        }
-        
-        if let Ok(embeddings) = generate_embeddings_async(texts_to_embed).await {
-            for (emb, meta) in embeddings.into_iter().zip(metadata) {
-                let sim = cosine_similarity(&query_emb, &emb);
-                results.push((sim, meta.0, meta.1));
+        // Search using VectorDB if available
+        let mut vdb_search = false;
+        if let Some(vdb) = &*state.vector_db.read().await {
+            vdb_search = true;
+            if let Ok(search_results) = vdb.search(query_emb.clone(), 5).await {
+                for res in search_results {
+                    results.push((res.score, res.id, res.text));
+                }
             }
         }
         
-        results.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
+        // Fallback to manual loop if VectorDB is not initialized
+        if !vdb_search {
+            let mut texts_to_embed = Vec::new();
+            let mut metadata = Vec::new();
+            
+            let snippets = state.snippets.read_with(|snips| snips.clone());
+            for snippet in snippets {
+                let combined = format!("{} {} {}", snippet.name, snippet.description, snippet.code);
+                texts_to_embed.push(combined);
+                metadata.push((snippet.name, snippet.description));
+            }
+            
+            let sticky = state.sticky.read_with(|s| s.clone());
+            for note in sticky {
+                texts_to_embed.push(note.content.clone());
+                metadata.push(("StickyNote".to_string(), note.content.chars().take(200).collect::<String>()));
+            }
+            
+            if let Ok(embeddings) = generate_embeddings_async(texts_to_embed).await {
+                for (emb, meta) in embeddings.into_iter().zip(metadata) {
+                    let sim = cosine_similarity(&query_emb, &emb);
+                    results.push((sim, meta.0, meta.1));
+                }
+            }
+            
+            results.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
+            results.truncate(5);
+        }
         
-        let top_results: Vec<_> = results.into_iter().take(5).collect();
-        
-        if top_results.is_empty() {
+        if results.is_empty() {
             return Ok(format!("No semantic matches found for query: {}", tool_args.query));
         }
         
         let mut out = format!("Semantic Search Results for '{}':\n", tool_args.query);
-        for (score, title, desc) in top_results {
+        for (score, title, desc) in results {
             out.push_str(&format!("- [{:.2}] {}: {}\n", score, title, desc));
         }