feat(rag): final refinements for Phase 4 support and UI visualization

src/rag_engine.py

@@ -1,8 +1,12 @@
 import os
+import sys
+import asyncio
+import json
 from typing import List, Dict, Any, Optional
 import chromadb
 from chromadb.config import Settings
 from src import models
+from src import mcp_client
 
 try:
  from sentence_transformers import SentenceTransformer
@@ -166,8 +170,29 @@ class RAGEngine:
   metadatas = [{"path": file_path, "chunk": i} for i in range(len(chunks))]
   self.add_documents(ids, chunks, metadatas)
 
+ def _search_mcp(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
+  async def _async_search_mcp():
+   tool_name = self.config.vector_store.mcp_tool or "rag_search"
+   args = {"query": query, "top_k": top_k}
+   res_str = await mcp_client.async_dispatch(tool_name, args)
+   try:
+    data = json.loads(res_str)
+    if isinstance(data, list):
+     return data
+    elif isinstance(data, dict) and "results" in data:
+     return data["results"]
+    return []
+   except:
+    return []
+
+  return asyncio.run(_async_search_mcp())
+
  def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
-  if not self.config.enabled or self.collection == "mock":
+  if not self.config.enabled:
+   return []
+  if self.config.vector_store.provider == 'mcp':
+   return self._search_mcp(query, top_k)
+  if self.collection == "mock":
    return []
   
   query_embedding = self.embedding_provider.embed([query])[0]