diff --git a/apps/roam/src/utils/discourseNodeSearchProviders.ts b/apps/roam/src/utils/discourseNodeSearchProviders.ts
index 8411a614c..a34d1be34 100644
--- a/apps/roam/src/utils/discourseNodeSearchProviders.ts
+++ b/apps/roam/src/utils/discourseNodeSearchProviders.ts
@@ -512,11 +512,12 @@ const runSupabaseSemanticSearch = async ({
   }
 
   const queryEmbedding = await createEmbedding(trimmedQuery);
-  const { data, error } = await supabase.rpc("match_content_embeddings", {
-    query_embedding: JSON.stringify(queryEmbedding),
-    match_threshold: SUPABASE_MATCH_THRESHOLD,
-    match_count: SEARCH_TEST_RESULT_LIMIT,
-  });
+  const { data, error } = await supabase
+    .rpc("match_content_embeddings", {
+      query_embedding: JSON.stringify(queryEmbedding),
+      match_threshold: SUPABASE_MATCH_THRESHOLD,
+    })
+    .limit(SEARCH_TEST_RESULT_LIMIT);
 
   if (error) {
     throw new Error(error.message);
diff --git a/apps/roam/src/utils/hyde.ts b/apps/roam/src/utils/hyde.ts
index 03acdb10b..8a3a7cc5e 100644
--- a/apps/roam/src/utils/hyde.ts
+++ b/apps/roam/src/utils/hyde.ts
@@ -538,11 +538,12 @@ export const findSimilarNodesVectorOnly = async ({
 
     const queryEmbedding = await createEmbedding(text);
 
-    const { data, error } = await supabase.rpc("match_content_embeddings", {
-      query_embedding: JSON.stringify(queryEmbedding),
-      match_threshold: threshold,
-      match_count: limit,
-    });
+    const { data, error } = await supabase
+      .rpc("match_content_embeddings", {
+        query_embedding: JSON.stringify(queryEmbedding),
+        match_threshold: threshold,
+      })
+      .limit(limit);
 
     if (error) {
       console.error("Vector search failed:", error);
diff --git a/packages/database/src/dbTypes.ts b/packages/database/src/dbTypes.ts
index 48342b32c..c37e3a030 100644
--- a/packages/database/src/dbTypes.ts
+++ b/packages/database/src/dbTypes.ts
@@ -1722,7 +1722,6 @@ export type Database = {
       match_content_embeddings: {
         Args: {
           current_document_id?: number
-          match_count: number
           match_threshold: number
           query_embedding: string
         }
diff --git a/packages/database/supabase/migrations/20260622040252_optimize_match_content_embeddings.sql b/packages/database/supabase/migrations/20260622040252_optimize_match_content_embeddings.sql
new file mode 100644
index 000000000..4aea9334a
--- /dev/null
+++ b/packages/database/supabase/migrations/20260622040252_optimize_match_content_embeddings.sql
@@ -0,0 +1,32 @@
+-- Optimize match_content_embeddings by removing LIMIT from function body
+-- This improves query planner performance as the LIMIT parameter was killing the planner
+
+set search_path to public, extensions ;
+
+DROP FUNCTION IF EXISTS public.match_content_embeddings(extensions.vector, double precision, integer, integer) ;
+
+CREATE OR REPLACE FUNCTION public.match_content_embeddings (
+query_embedding extensions.vector,
+match_threshold double precision,
+current_document_id integer DEFAULT NULL::integer)
+RETURNS TABLE (
+content_id bigint,
+roam_uid Text,
+text_content Text,
+similarity double precision)
+SET search_path = 'extensions'
+LANGUAGE sql STABLE
+AS $$
+SELECT
+  c.id AS content_id,
+  c.source_local_id AS roam_uid,
+  c.text AS text_content,
+  1 - (c.vector <=> query_embedding) AS similarity
+FROM public.my_contents_with_embedding_openai_text_embedding_3_small_1536 AS c
+WHERE 1 - (c.vector <=> query_embedding) > match_threshold
+  AND (current_document_id IS NULL OR c.document_id = current_document_id)
+ORDER BY
+  c.vector <=> query_embedding ASC;
+$$ ;
+
+RESET ALL ;
diff --git a/packages/database/supabase/schemas/embedding.sql b/packages/database/supabase/schemas/embedding.sql
index 273fd6eab..0e989e229 100644
--- a/packages/database/supabase/schemas/embedding.sql
+++ b/packages/database/supabase/schemas/embedding.sql
@@ -57,7 +57,6 @@ set search_path to public, extensions ;
 CREATE OR REPLACE FUNCTION public.match_content_embeddings (
 query_embedding extensions.vector,
 match_threshold double precision,
-match_count integer,
 current_document_id integer DEFAULT NULL::integer)
 RETURNS TABLE (
 content_id bigint,
@@ -76,14 +75,12 @@ FROM public.my_contents_with_embedding_openai_text_embedding_3_small_1536 AS c
 WHERE 1 - (c.vector <=> query_embedding) > match_threshold
   AND (current_document_id IS NULL OR c.document_id = current_document_id)
 ORDER BY
-  c.vector <=> query_embedding ASC
-LIMIT match_count;
+  c.vector <=> query_embedding ASC;
 $$ ;
 
 ALTER FUNCTION public.match_content_embeddings (
 query_embedding extensions.vector,
 match_threshold double precision,
-match_count integer,
 current_document_id integer) OWNER TO "postgres" ;
 
 CREATE OR REPLACE FUNCTION public.match_embeddings_for_subset_nodes (