docs(updated): new scripts

README.md

@@ -109,6 +109,10 @@ cp .frontend_env.example frontend/.env
 
 [Migrations Script 4](scripts/supabase_users_table.sql)
 
+[Migration Script 5](scripts/supabase_chats_table.sql)
+
+[Migration Script 6](supabase/migrations/20230606131110_add_uuid_user_id.sql)
+
 - **Step 5**: Launch the app
 
 ```bash

scripts/supabase_vector_store_document.sql

@@ -1,36 +0,0 @@
-create extension vector;
-
--- Create a table to store your documents
-create table if not exists documents (
-id bigserial primary key,
-content text, -- corresponds to Document.pageContent
-metadata jsonb, -- corresponds to Document.metadata
-embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
-);
-
-CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)
-    RETURNS TABLE(
-        id bigint,
-        content text,
-        metadata jsonb,
-        -- we return matched vectors to enable maximal marginal relevance searches
-        embedding vector(1536),
-        similarity float)
-    LANGUAGE plpgsql
-    AS $$
-    # variable_conflict use_column
-BEGIN
-    RETURN query
-    SELECT
-        id,
-        content,
-        metadata,
-        embedding,
-        1 -(documents.embedding <=> query_embedding) AS similarity
-    FROM
-        documents
-    ORDER BY
-        documents.embedding <=> query_embedding
-    LIMIT match_count;
-END;
-$$;