Hi @mustafa
For example, I’m using supabase postgres table schema like this where I store a 1536-dimension vector embedding in vector_embedding
pgvector column. This gives further granular control on how we chunk, index, and retrieve chunks of information during RAG search.
create table public.source_file_chunks (
id uuid not null default extensions.uuid_generate_v4 (),
source_file_id uuid not null,
chunk_index integer not null,
content text not null,
embed text not null,
vector_embedding public.vector null,
blocks jsonb null,
created_at timestamp with time zone not null default CURRENT_TIMESTAMP,
constraint source_file_chunks_pkey primary key (id),
constraint uq_source_file_chunk unique (source_file_id, chunk_index),
constraint source_file_chunks_source_file_id_fkey foreign KEY (source_file_id) references source_files (id) on delete CASCADE
) TABLESPACE pg_default;
So many of us use supabase for easy backend that eliminates the use of vector DBs thanks to pgvector columns. Would love more support and documentation on this!
(also posted on: Official Documentation & Support for Supabase Integration)