remove not ready updates

README.md

@@ -95,8 +95,6 @@ cp .frontend_env.example frontend/.env
 
 [Migration Script 2](scripts/supabase_usage_table.sql)
 
-[Migration Script 3](scripts/supabase_vector_store_document.sql)
-
 - **Step 5**: Launch the app
 
 ```bash

scripts/supabase_vector_store_summary.sql

@@ -1,38 +0,0 @@
--- Create a table to store your summaries
-create table if not exists summaries (
-    id bigserial primary key,
-    document_id bigint references documents(id),
-    content text, -- corresponds to the summarized content
-    metadata jsonb, -- corresponds to Document.metadata
-    embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
-);
-
-CREATE OR REPLACE FUNCTION match_summaries(query_embedding vector(1536), match_count int, match_threshold float)
-    RETURNS TABLE(
-        id bigint,
-        document_id bigint,
-        content text,
-        metadata jsonb,
-        -- we return matched vectors to enable maximal marginal relevance searches
-        embedding vector(1536),
-        similarity float)
-    LANGUAGE plpgsql
-    AS $$
-    # variable_conflict use_column
-BEGIN
-    RETURN query
-    SELECT
-        id,
-        document_id,
-        content,
-        metadata,
-        embedding,
-        1 -(summaries.embedding <=> query_embedding) AS similarity
-    FROM
-        summaries
-    WHERE 1 - (summaries.embedding <=> query_embedding) > match_threshold
-    ORDER BY
-        summaries.embedding <=> query_embedding
-    LIMIT match_count;
-END;
-$$;