Refactor vector search to utilize Qdrant VectorDB

server/Cargo.toml

@@ -18,7 +18,7 @@ tantivy = "0.26.1"
 tokio = { version = "1.53.1", features = ["full"] }
 tracing = "0.1.44"
 tracing-subscriber = "0.3.23"
-uuid = { version = "1.26.0", features = ["v4"] }
+uuid = { version = "1.26.0", features = ["v4", "v5"] }
 tracing-appender = "0.2.5"
 rmcp = { version = "3.4.0", features = ["server"] }
 thiserror = "2.0.20"

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));
         }
         

server/src/main.rs

@@ -13,6 +13,7 @@ mod router;
 mod search;
 pub mod embedding;
 pub mod indexer;
+pub mod vector_db;
 mod state;
 mod store;
 mod tools;
@@ -545,6 +546,18 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     rt.block_on(async {
         let state = Arc::new(MemoryState::new(&base.to_string_lossy()));
+        
+        // Initialize Qdrant VectorDB (default local URL)
+        match crate::vector_db::VectorDB::new("http://localhost:6334", "mcp_memory").await {
+            Ok(vdb) => {
+                tracing::info!("Successfully connected to Qdrant vector database");
+                *state.vector_db.write().await = Some(vdb);
+            }
+            Err(e) => {
+                tracing::warn!("Failed to initialize Qdrant vector database: {}. Vector search will fallback to manual embedding loop. (Is Qdrant running on localhost:6334?)", e);
+            }
+        }
+        
         if let Err(e) = run_server(state).await {
             tracing::error!("Server error: {}", e);
         }

server/src/vector_db.rs

@@ -3,7 +3,7 @@ use qdrant_client::Qdrant;
 use std::sync::Arc;
 use std::error::Error;
 use uuid::Uuid;
-use log::{info, error};
+use tracing::{info, error};
 use serde::{Deserialize, Serialize};
 
 #[derive(Clone)]
@@ -69,10 +69,10 @@ impl VectorDB {
             }
         };
 
-        let mut payload = std::collections::HashMap::new();
-        payload.insert("doc_type".to_string(), doc_type.into());
-        payload.insert("text".to_string(), text.into());
-        payload.insert("original_id".to_string(), id.into());
+        let mut payload: std::collections::HashMap<String, serde_json::Value> = std::collections::HashMap::new();
+        payload.insert("doc_type".to_string(), serde_json::Value::String(doc_type.to_string()));
+        payload.insert("text".to_string(), serde_json::Value::String(text.to_string()));
+        payload.insert("original_id".to_string(), serde_json::Value::String(id.to_string()));
 
         let point = PointStruct::new(point_id, vector, payload);
         
@@ -82,4 +82,46 @@ impl VectorDB {
             
         Ok(())
     }
+
+    pub async fn search(
+        &self,
+        query_vector: Vec<f32>,
+        limit: u64,
+    ) -> Result<Vec<VectorSearchResult>, Box<dyn Error + Send + Sync>> {
+        use qdrant_client::qdrant::SearchPointsBuilder;
+
+        let search_result = self.client
+            .search_points(
+                SearchPointsBuilder::new(&self.collection_name, query_vector, limit)
+                    .with_payload(true)
+            )
+            .await?;
+
+        let mut results = Vec::new();
+        for point in search_result.result {
+            let id = point.payload.get("original_id")
+                .and_then(|v| v.as_str())
+                .map(|s| s.to_string())
+                .unwrap_or_default();
+            
+            let doc_type = point.payload.get("doc_type")
+                .and_then(|v| v.as_str())
+                .map(|s| s.to_string())
+                .unwrap_or_default();
+                
+            let text = point.payload.get("text")
+                .and_then(|v| v.as_str())
+                .map(|s| s.to_string())
+                .unwrap_or_default();
+
+            results.push(VectorSearchResult {
+                id,
+                doc_type,
+                text,
+                score: point.score,
+            });
+        }
+
+        Ok(results)
+    }
 }