frimeet-api-nlp / sql /pgadmin_migrate_fasttext_300.sql
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Add pgAdmin migration and Colab embedding loaders
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-- Ejecutar completo una sola vez desde pgAdmin > Query Tool.
-- Requisitos:
-- 1. Tener un snapshot reciente de RDS.
-- 2. Detener temporalmente el Space y cualquier job de embeddings.
-- 3. Conectarse a la base nlp_vectors como propietario de las tablas o administrador.
--
-- Este script elimina solamente los indices derivados de busqueda. No toca la
-- base transaccional de la API principal. Si las columnas ya no son VECTOR(16),
-- aborta para evitar truncar accidentalmente una migracion ya terminada.
CREATE EXTENSION IF NOT EXISTS vector;
BEGIN;
LOCK TABLE public.place_embeddings IN ACCESS EXCLUSIVE MODE;
LOCK TABLE public.post_embeddings IN ACCESS EXCLUSIVE MODE;
DO $$
DECLARE
place_type TEXT;
post_type TEXT;
BEGIN
SELECT format_type(attribute.atttypid, attribute.atttypmod)
INTO place_type
FROM pg_attribute attribute
WHERE attribute.attrelid = 'public.place_embeddings'::regclass
AND attribute.attname = 'embedding'
AND NOT attribute.attisdropped;
SELECT format_type(attribute.atttypid, attribute.atttypmod)
INTO post_type
FROM pg_attribute attribute
WHERE attribute.attrelid = 'public.post_embeddings'::regclass
AND attribute.attname = 'embedding'
AND NOT attribute.attisdropped;
IF place_type <> 'vector(16)' OR post_type <> 'vector(16)' THEN
RAISE EXCEPTION
'Se esperaba VECTOR(16). Tipos encontrados: places=%, posts=%',
place_type,
post_type;
END IF;
END $$;
-- Las funciones antiguas se eliminan antes del cambio de dimension y se
-- reconstruyen dentro de la misma transaccion.
DROP FUNCTION IF EXISTS public.match_places(vector, integer, jsonb);
DROP FUNCTION IF EXISTS public.match_posts(vector, integer, jsonb);
DROP FUNCTION IF EXISTS public.upsert_place_embedding(
text, text, jsonb, vector, text, text, text, boolean
);
DROP FUNCTION IF EXISTS public.upsert_post_embedding(
text, text, jsonb, vector, text, text, text, boolean
);
DROP INDEX IF EXISTS public.place_embeddings_embedding_hnsw_idx;
DROP INDEX IF EXISTS public.post_embeddings_embedding_hnsw_idx;
-- No existe una conversion valida de los vectores mock de 16 dimensiones a
-- FastText. Ambas tablas son indices derivados y se reconstruiran desde cero.
TRUNCATE TABLE public.place_embeddings, public.post_embeddings;
ALTER TABLE public.place_embeddings
ALTER COLUMN embedding TYPE vector(300);
ALTER TABLE public.post_embeddings
ALTER COLUMN embedding TYPE vector(300);
CREATE INDEX place_embeddings_embedding_hnsw_idx
ON public.place_embeddings USING hnsw (embedding vector_cosine_ops);
CREATE INDEX post_embeddings_embedding_hnsw_idx
ON public.post_embeddings USING hnsw (embedding vector_cosine_ops);
CREATE INDEX IF NOT EXISTS place_embeddings_metadata_gin_idx
ON public.place_embeddings USING gin (metadata);
CREATE INDEX IF NOT EXISTS post_embeddings_metadata_gin_idx
ON public.post_embeddings USING gin (metadata);
CREATE OR REPLACE FUNCTION public.match_places(
query_embedding vector(300),
match_count integer,
filters jsonb DEFAULT '{}'::jsonb
)
RETURNS TABLE (
external_id text,
document text,
metadata jsonb,
score double precision
)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = public
AS $$
SELECT
place.external_id,
place.document,
place.metadata,
1 - (place.embedding <=> query_embedding) AS score
FROM public.place_embeddings place
WHERE place.is_active = true
AND COALESCE((filters->>'is_active')::boolean, true) = true
AND ((filters ? 'city') IS FALSE OR lower(place.metadata->>'city') = lower(filters->>'city'))
AND ((filters ? 'state') IS FALSE OR lower(place.metadata->>'state') = lower(filters->>'state'))
AND ((filters ? 'category') IS FALSE OR lower(place.metadata->>'category') = lower(filters->>'category'))
AND ((filters ? 'price_range') IS FALSE OR place.metadata->>'price_range' = filters->>'price_range')
AND ((filters ? 'occasion') IS FALSE OR place.metadata->>'occasion' ILIKE ('%' || (filters->>'occasion') || '%'))
AND (
(filters ? 'place_ids') IS FALSE
OR place.external_id IN (
SELECT jsonb_array_elements_text(filters->'place_ids')
)
)
ORDER BY place.embedding <=> query_embedding
LIMIT match_count;
$$;
CREATE OR REPLACE FUNCTION public.match_posts(
query_embedding vector(300),
match_count integer,
filters jsonb DEFAULT '{}'::jsonb
)
RETURNS TABLE (
external_id text,
document text,
metadata jsonb,
score double precision
)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = public
AS $$
SELECT
post.external_id,
post.document,
post.metadata,
1 - (post.embedding <=> query_embedding) AS score
FROM public.post_embeddings post
WHERE post.is_active = true
AND COALESCE((filters->>'is_active')::boolean, true) = true
AND ((filters ? 'city') IS FALSE OR lower(post.metadata->>'city') = lower(filters->>'city'))
ORDER BY post.embedding <=> query_embedding
LIMIT match_count;
$$;
CREATE OR REPLACE FUNCTION public.upsert_place_embedding(
p_external_id text,
p_document text,
p_metadata jsonb,
p_embedding vector(300),
p_content_hash text,
p_embedding_model text,
p_embedding_version text,
p_is_active boolean
)
RETURNS void
LANGUAGE sql
SECURITY DEFINER
SET search_path = public
AS $$
INSERT INTO public.place_embeddings (
external_id,
document,
metadata,
embedding,
content_hash,
embedding_model,
embedding_version,
is_active,
updated_at
)
VALUES (
p_external_id,
p_document,
p_metadata,
p_embedding,
p_content_hash,
p_embedding_model,
p_embedding_version,
p_is_active,
now()
)
ON CONFLICT (external_id) DO UPDATE SET
document = EXCLUDED.document,
metadata = EXCLUDED.metadata,
embedding = EXCLUDED.embedding,
content_hash = EXCLUDED.content_hash,
embedding_model = EXCLUDED.embedding_model,
embedding_version = EXCLUDED.embedding_version,
is_active = EXCLUDED.is_active,
updated_at = now();
$$;
CREATE OR REPLACE FUNCTION public.upsert_post_embedding(
p_external_id text,
p_document text,
p_metadata jsonb,
p_embedding vector(300),
p_content_hash text,
p_embedding_model text,
p_embedding_version text,
p_is_active boolean
)
RETURNS void
LANGUAGE sql
SECURITY DEFINER
SET search_path = public
AS $$
INSERT INTO public.post_embeddings (
external_id,
document,
metadata,
embedding,
content_hash,
embedding_model,
embedding_version,
is_active,
updated_at
)
VALUES (
p_external_id,
p_document,
p_metadata,
p_embedding,
p_content_hash,
p_embedding_model,
p_embedding_version,
p_is_active,
now()
)
ON CONFLICT (external_id) DO UPDATE SET
document = EXCLUDED.document,
metadata = EXCLUDED.metadata,
embedding = EXCLUDED.embedding,
content_hash = EXCLUDED.content_hash,
embedding_model = EXCLUDED.embedding_model,
embedding_version = EXCLUDED.embedding_version,
is_active = EXCLUDED.is_active,
updated_at = now();
$$;
CREATE OR REPLACE FUNCTION public.get_place_content_hashes(
p_external_ids text[]
)
RETURNS TABLE (
external_id text,
content_hash text
)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = public
AS $$
SELECT place.external_id, place.content_hash
FROM public.place_embeddings place
WHERE place.external_id = ANY(p_external_ids);
$$;
CREATE OR REPLACE FUNCTION public.get_post_content_hashes(
p_external_ids text[]
)
RETURNS TABLE (
external_id text,
content_hash text
)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = public
AS $$
SELECT post.external_id, post.content_hash
FROM public.post_embeddings post
WHERE post.external_id = ANY(p_external_ids);
$$;
-- Reaplica permisos solo cuando los roles ya existen.
DO $$
BEGIN
IF EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'nlp_reader') THEN
EXECUTE 'GRANT USAGE ON SCHEMA public TO nlp_reader';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.match_places(vector, integer, jsonb) TO nlp_reader';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.match_posts(vector, integer, jsonb) TO nlp_reader';
END IF;
IF EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'nlp_writer') THEN
EXECUTE 'GRANT USAGE ON SCHEMA public TO nlp_writer';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.get_place_content_hashes(text[]) TO nlp_writer';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.get_post_content_hashes(text[]) TO nlp_writer';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.upsert_place_embedding(text, text, jsonb, vector, text, text, text, boolean) TO nlp_writer';
EXECUTE 'GRANT EXECUTE ON FUNCTION public.upsert_post_embedding(text, text, jsonb, vector, text, text, text, boolean) TO nlp_writer';
END IF;
END $$;
COMMIT;
-- Resultado esperado inmediatamente despues de migrar: dimension 300 y cero
-- filas. Las filas apareceran despues de ejecutar los scripts de Colab.
SELECT
table_name,
column_name,
udt_name,
format_type(attribute.atttypid, attribute.atttypmod) AS formatted_type
FROM information_schema.columns column_info
JOIN pg_attribute attribute
ON attribute.attrelid = (
quote_ident(column_info.table_schema) || '.' || quote_ident(column_info.table_name)
)::regclass
AND attribute.attname = column_info.column_name
WHERE column_info.table_schema = 'public'
AND column_info.table_name IN ('place_embeddings', 'post_embeddings')
AND column_info.column_name = 'embedding'
ORDER BY table_name;
SELECT 'place_embeddings' AS table_name, count(*) AS rows
FROM public.place_embeddings
UNION ALL
SELECT 'post_embeddings' AS table_name, count(*) AS rows
FROM public.post_embeddings;