Execute doc search in parallel using asyncio instead of threadpool

src/khoj/routers/helpers.py

@@ -1,3 +1,4 @@
+import asyncio
 import base64
 import concurrent.futures
 import hashlib
@@ -1362,7 +1363,7 @@ async def execute_search(
     user_query = q.strip()
     results_count = n or 5
     t = t or state.SearchType.All
-    search_futures: List[concurrent.futures.Future] = []
+    search_tasks = []
 
     # return cached results, if available
     if user:
@@ -1382,33 +1383,33 @@ async def execute_search(
             search_model = await sync_to_async(get_default_search_model)()
             encoded_asymmetric_query = state.embeddings_model[search_model.name].embed_query(defiltered_query)
 
-    with concurrent.futures.ThreadPoolExecutor() as executor:
-        if t.value in [
-            SearchType.All.value,
-            SearchType.Org.value,
-            SearchType.Markdown.value,
-            SearchType.Github.value,
-            SearchType.Notion.value,
-            SearchType.Plaintext.value,
-            SearchType.Pdf.value,
-        ]:
-            # query markdown notes
-            search_futures += [
-                executor.submit(
-                    text_search.query,
-                    user_query,
-                    user,
-                    t,
-                    question_embedding=encoded_asymmetric_query,
-                    max_distance=max_distance,
-                    agent=agent,
-                )
-            ]
+    # Use asyncio to run searches in parallel
+    if t.value in [
+        SearchType.All.value,
+        SearchType.Org.value,
+        SearchType.Markdown.value,
+        SearchType.Github.value,
+        SearchType.Notion.value,
+        SearchType.Plaintext.value,
+        SearchType.Pdf.value,
+    ]:
+        # query markdown notes
+        search_tasks.append(
+            text_search.query(
+                user_query,
+                user,
+                t,
+                question_embedding=encoded_asymmetric_query,
+                max_distance=max_distance,
+                agent=agent,
+            )
+        )
 
-        # Query across each requested content types in parallel
-        with timer("Query took", logger):
-            for search_future in concurrent.futures.as_completed(search_futures):
-                hits = await search_future.result()
+    # Query across each requested content types in parallel
+    with timer("Query took", logger):
+        if search_tasks:
+            hits_list = await asyncio.gather(*search_tasks)
+            for hits in hits_list:
                 # Collate results
                 results += text_search.collate_results(hits, dedupe=dedupe)