Emil
128 lines
4.4 KiB
PL/PgSQL
128 lines
4.4 KiB
PL/PgSQL
-- Migration 033: AI Chat Schema
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-- Creates tables for the AI chat assistant extension:
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-- chat_sessions, chat_messages, knowledge_documents, and match_documents RPC
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-- Enable pgvector for embedding storage
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create extension if not exists vector with schema extensions;
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-- ============================================================
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-- chat_sessions
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-- ============================================================
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create table public.chat_sessions (
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id uuid primary key default gen_random_uuid(),
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user_id uuid references auth.users on delete cascade not null,
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title text,
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created_at timestamptz not null default now(),
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updated_at timestamptz not null default now()
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);
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alter table public.chat_sessions enable row level security;
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create policy "chat_sessions_select" on public.chat_sessions
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for select using (auth.uid() = user_id);
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create policy "chat_sessions_insert" on public.chat_sessions
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for insert with check (auth.uid() = user_id);
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create policy "chat_sessions_update" on public.chat_sessions
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for update using (auth.uid() = user_id);
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create policy "chat_sessions_delete" on public.chat_sessions
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for delete using (auth.uid() = user_id);
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create index idx_chat_sessions_user_created on public.chat_sessions (user_id, created_at desc);
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create trigger chat_sessions_updated_at
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before update on public.chat_sessions
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for each row execute function public.update_updated_at_column();
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-- ============================================================
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-- chat_messages
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-- ============================================================
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create table public.chat_messages (
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id uuid primary key default gen_random_uuid(),
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session_id uuid references public.chat_sessions on delete cascade not null,
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user_id uuid references auth.users on delete cascade not null,
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role text not null check (role in ('user', 'assistant')),
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content text not null,
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sources jsonb,
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created_at timestamptz not null default now()
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);
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alter table public.chat_messages enable row level security;
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create policy "chat_messages_select" on public.chat_messages
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for select using (auth.uid() = user_id);
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create policy "chat_messages_insert" on public.chat_messages
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for insert with check (auth.uid() = user_id);
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create policy "chat_messages_update" on public.chat_messages
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for update using (auth.uid() = user_id);
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create policy "chat_messages_delete" on public.chat_messages
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for delete using (auth.uid() = user_id);
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create index idx_chat_messages_session on public.chat_messages (session_id, created_at);
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-- ============================================================
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-- knowledge_documents
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-- ============================================================
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create table public.knowledge_documents (
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id uuid primary key default gen_random_uuid(),
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source_file text not null,
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title text not null,
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section_title text,
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content text not null,
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content_hash text unique not null,
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embedding extensions.vector(1536),
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metadata jsonb default '{}',
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created_at timestamptz not null default now()
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);
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alter table public.knowledge_documents enable row level security;
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-- Knowledge documents are shared — any authenticated user can read
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create policy "knowledge_documents_select" on public.knowledge_documents
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for select using (true);
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create index idx_knowledge_documents_hash on public.knowledge_documents (content_hash);
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-- ============================================================
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-- match_documents RPC (vector similarity search)
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-- ============================================================
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create or replace function public.match_documents(
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query_embedding extensions.vector,
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match_count int default 5,
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match_threshold float default 0.7
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)
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returns table (
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id uuid,
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source_file text,
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title text,
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section_title text,
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content text,
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metadata jsonb,
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similarity float
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)
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language plpgsql
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security definer
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set search_path = public, extensions
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as $$
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begin
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return query
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select
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kd.id,
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kd.source_file,
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kd.title,
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kd.section_title,
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kd.content,
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kd.metadata,
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1 - (kd.embedding <=> query_embedding)::float as similarity
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from public.knowledge_documents kd
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where 1 - (kd.embedding <=> query_embedding) >= match_threshold
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order by kd.embedding <=> query_embedding
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limit match_count;
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end;
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$$;
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grant execute on function public.match_documents(extensions.vector, int, float) to authenticated;
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