fix: 🐛 search (#2045)

fixed public brains avaiable

# Description

Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.

## Checklist before requesting a review

Please delete options that are not relevant.

- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged

## Screenshots (if appropriate):

backend/modules/chat/controller/chat/brainful_chat.py

@@ -64,7 +64,7 @@ class BrainfulChat(ChatInterface):
             else:
                 embeddings = OpenAIEmbeddings()
             vector_store = CustomSupabaseVectorStore(
-                supabase_client, embeddings, table_name="vectors"
+                supabase_client, embeddings, table_name="vectors", user_id=user_id
             )
             # Get the first question from the chat_question
 
@@ -72,7 +72,7 @@ class BrainfulChat(ChatInterface):
             history = chat_service.get_chat_history(chat_id)
             if history:
                 question = history[0].user_message
-            list_brains = vector_store.find_brain_closest_query(question)
+            list_brains = vector_store.find_brain_closest_query(user_id, question)
             if list_brains:
                 brain_id_to_use = list_brains[0]["id"]
             else:

backend/vectorstore/supabase.py

@@ -13,6 +13,7 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
     """A custom vector store that uses the match_vectors table instead of the vectors table."""
 
     brain_id: str = "none"
+    user_id: str = "none"
 
     def __init__(
         self,
@@ -20,12 +21,15 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
         embedding: Embeddings,
         table_name: str,
         brain_id: str = "none",
+        user_id: str = "none",
     ):
         super().__init__(client, embedding, table_name)
         self.brain_id = brain_id
+        self.user_id = user_id
 
     def find_brain_closest_query(
         self,
+        user_id: str,
         query: str,
         k: int = 6,
         table: str = "match_brain",
@@ -33,11 +37,14 @@ class CustomSupabaseVectorStore(SupabaseVectorStore):
     ) -> [dict]:
         vectors = self._embedding.embed_documents([query])
         query_embedding = vectors[0]
+        logger.info("🤯🤯")
+
         res = self._client.rpc(
             table,
             {
                 "query_embedding": query_embedding,
                 "match_count": k,
+                "p_user_id": str(self.user_id),
             },
         ).execute()
 

supabase/migrations/20240121195523_fix-public.sql

@@ -0,0 +1,34 @@
+create type "public"."thumbs" as enum ('up', 'down');
+
+drop function if exists "public"."match_brain"(query_embedding vector, match_count integer);
+
+alter table "public"."chat_history" add column "user_feedback" thumbs;
+
+set check_function_bodies = off;
+
+CREATE OR REPLACE FUNCTION public.match_brain(query_embedding vector, match_count integer, p_user_id uuid)
+ RETURNS TABLE(id uuid, name text, similarity double precision)
+ LANGUAGE plpgsql
+AS $function$
+BEGIN
+  RETURN QUERY
+  SELECT
+    b.brain_id,
+    b.name,
+    1 - (b.meaning <=> query_embedding) as similarity
+  FROM
+    brains b
+  LEFT JOIN
+    brains_users bu ON b.brain_id = bu.brain_id
+  WHERE
+    (b.status = 'public') OR 
+    (bu.user_id = p_user_id AND bu.rights IN ('Owner', 'Editor', 'Viewer'))
+  ORDER BY
+    b.meaning <=> query_embedding
+  LIMIT
+    match_count;
+END;
+$function$
+;
+
+