diff --git a/daras_ai_v2/vector_search.py b/daras_ai_v2/vector_search.py
index f0fd51df8..806a50c8a 100644
--- a/daras_ai_v2/vector_search.py
+++ b/daras_ai_v2/vector_search.py
@@ -45,7 +45,6 @@
 from daras_ai_v2.embedding_model import create_embeddings_cached, EmbeddingModels
 from daras_ai_v2.exceptions import raise_for_status, call_cmd, UserError
 from daras_ai_v2.functional import (
-    flatmap_parallel,
     map_parallel,
     flatmap_parallel_ascompleted,
 )
@@ -146,9 +145,6 @@ def get_top_k_references(
     else:
         selected_asr_model = google_translate_target = None
 
-    file_url_metas = flatmap_parallel(doc_or_yt_url_to_metadatas, input_docs)
-    file_urls, file_metas = zip(*file_url_metas)
-
     yield "Creating knowledge embeddings..."
 
     embedding_model = EmbeddingModels.get(
@@ -158,9 +154,8 @@ def get_top_k_references(
         ),
     )
     embedded_files: list[EmbeddedFile] = map_parallel(
-        lambda f_url, file_meta: get_or_create_embedded_file(
+        lambda f_url: get_or_create_embedded_file(
             f_url=f_url,
-            file_meta=file_meta,
             max_context_words=request.max_context_words,
             scroll_jump=request.scroll_jump,
             google_translate_target=google_translate_target,
@@ -169,8 +164,7 @@ def get_top_k_references(
             is_user_url=is_user_url,
             current_user=current_user,
         ),
-        file_urls,
-        file_metas,
+        input_docs,
         max_workers=4,
     )
     if not embedded_files:
@@ -314,7 +308,13 @@ def doc_url_to_file_metadata(f_url: str) -> FileMetadata:
     else:
         try:
             if is_user_uploaded_url(f_url):
-                r = requests.head(f_url)
+                name = f.path.segments[-1]
+                return FileMetadata(
+                    name=name,
+                    etag=None,
+                    mime_type=mimetypes.guess_type(name)[0],
+                    total_bytes=0,
+                )
             else:
                 r = requests.head(
                     f_url,
@@ -387,7 +387,6 @@ def yt_dlp_extract_info(url: str) -> dict:
 def get_or_create_embedded_file(
     *,
     f_url: str,
-    file_meta: FileMetadata,
     max_context_words: int,
     scroll_jump: int,
     google_translate_target: str | None,
@@ -402,51 +401,69 @@ def get_or_create_embedded_file(
     """
     lookup = dict(
         url=f_url,
-        metadata__name=file_meta.name,
-        metadata__etag=file_meta.etag,
-        metadata__mime_type=file_meta.mime_type,
-        metadata__total_bytes=file_meta.total_bytes,
         max_context_words=max_context_words,
         scroll_jump=scroll_jump,
         google_translate_target=google_translate_target or "",
         selected_asr_model=selected_asr_model or "",
         embedding_model=embedding_model.name,
     )
+
     file_id = hashlib.sha256(str(lookup).encode()).hexdigest()
     with redis_lock(f"gooey/get_or_create_embeddings/v1/{file_id}"):
         try:
-            return EmbeddedFile.objects.filter(**lookup).order_by("-updated_at")[0]
+            embedded_file = EmbeddedFile.objects.filter(**lookup).order_by(
+                "-updated_at"
+            )[0]
+            if is_user_uploaded_url(f_url):
+                return embedded_file
+
+            if not is_yt_dlp_able_url(f_url):
+                file_meta = doc_or_yt_url_to_metadatas(f_url)[0][1]
+                if file_meta == embedded_file.metadata:
+                    return embedded_file
+            pass
+
         except IndexError:
-            refs = create_embeddings_in_search_db(
-                f_url=f_url,
-                file_meta=file_meta,
-                file_id=file_id,
-                max_context_words=max_context_words,
-                scroll_jump=scroll_jump,
-                google_translate_target=google_translate_target or "",
-                selected_asr_model=selected_asr_model or "",
-                embedding_model=embedding_model,
-                is_user_url=is_user_url,
-            )
-            with transaction.atomic():
-                file_meta.save()
-                embedded_file = EmbeddedFile.objects.get_or_create(
-                    **lookup,
-                    defaults=dict(
-                        metadata=file_meta,
-                        vespa_file_id=file_id,
-                        created_by=current_user,
-                    ),
-                )[0]
-                for ref in refs:
-                    ref.embedded_file = embedded_file
-                EmbeddingsReference.objects.bulk_create(
-                    refs,
-                    update_conflicts=True,
-                    update_fields=["url", "title", "snippet", "updated_at"],
-                    unique_fields=["vespa_doc_id"],
+
+            for leaf_url, file_meta in doc_or_yt_url_to_metadatas(f_url):
+                lookup.update(
+                    metadata__name=file_meta.name,
+                    metadata__etag=file_meta.etag,
+                    metadata__mime_type=file_meta.mime_type,
+                    metadata__total_bytes=file_meta.total_bytes,
+                )
+                file_id = hashlib.sha256(str(lookup).encode()).hexdigest()
+
+                refs = create_embeddings_in_search_db(
+                    f_url=f_url,
+                    file_meta=file_meta,
+                    file_id=file_id,
+                    max_context_words=max_context_words,
+                    scroll_jump=scroll_jump,
+                    google_translate_target=google_translate_target or "",
+                    selected_asr_model=selected_asr_model or "",
+                    embedding_model=embedding_model,
+                    is_user_url=is_user_url,
                 )
-        return embedded_file
+                with transaction.atomic():
+                    file_meta.save()
+                    embedded_file = EmbeddedFile.objects.get_or_create(
+                        **lookup,
+                        defaults=dict(
+                            metadata=file_meta,
+                            vespa_file_id=file_id,
+                            created_by=current_user,
+                        ),
+                    )[0]
+                    for ref in refs:
+                        ref.embedded_file = embedded_file
+                    EmbeddingsReference.objects.bulk_create(
+                        refs,
+                        update_conflicts=True,
+                        update_fields=["url", "title", "snippet", "updated_at"],
+                        unique_fields=["vespa_doc_id"],
+                    )
+            return embedded_file
 
 
 def create_embeddings_in_search_db(