@suspiro.extremo: Videos de amor #paradedicar #miamorcito #miamor #enamorados #teextraño @mari lecona 🎶

SUSPIRO EXTREMO

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Friday 23 October 2026 21:09:59 GMT

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Ginna Trejo :

[Puntuación perfecta][Corazón rojo][Puntuación perfecta] GRACIAS GRACIAS GRACIAS 🤗☺️☺️☺️[Conmovedor][Conmovedor][Conmovedor]

2026-10-26 00:44:51

1

María :

Bendiciones amigo ❤️

2026-10-26 02:46:06

0

maramartnezmaravi :

que hermoso detalle mil bendiciones siempre amén amén amén amén amén amén

2026-10-25 22:41:21

0

Isabel Gomez :

gracias 🥰🥰

2026-10-26 00:10:26

0

Maria Gonon :

gracias por 👍

2026-10-26 03:24:21

0

Ruthy Merino Aguilar :

@gracias.amor

2026-10-26 00:53:40

0

mariacielo :

@grasiasamigoestanmuybonitasamenamenamenmenamenamenaenmaenamenamen

2026-10-26 00:40:20

0

Rosas ⚘️ :

Muchas gracias por compartir bonito video feliz bendecida noche

2026-10-26 00:10:14

0

camila3437 :

Muchas gracias. por. el. detalle. tan. hermoso. que. tenga. una. bonita. y. bendecida. noche. 🙏🙏🥰🥰🥰🤗🤗

2026-10-26 01:25:09

0

Milena3543 :

2026-10-26 02:47:18

0

Reina. M Sacida. S :

gracias muy amable bendiciones 🙏🙏💐💐💞❤️❤️💞💐💐

2026-10-24 20:41:13

1

Damian maric :

Hola

2026-10-25 17:36:05

0

Ofelia mamani :

muchas gracias por todo mi corazón te extraño mucho mi amor ❤️😍😍😍❤️😂😂😂😂

2026-10-25 23:14:03

0

userp918xp2w72 :

gracias 🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰

2026-10-26 01:15:15

0

Mabel Gomez :

gaciasMarcosqeJususTeColmeDeBendiciones

2026-10-26 00:38:10

0

Maria Ofelia :

Gracias❤️❤️❤️

2026-10-26 00:14:27

0

Jucelia Santos :

2026-10-24 00:50:05

1

sarahfigueroa309 :

2026-10-24 13:52:15

0

Jesús manuel Mendoza arredondo :

🥰🥰🥰

2026-10-25 13:39:43

0

camila3437 :

2026-10-25 07:38:52

0

Azul/ COR. :

bendiciones

2026-10-25 16:48:29

0

Domitila Quiroz :

Muchas gracias

2026-10-25 16:05:49

0

Roci Sarmiento🦉 :

Bello detalle [Plegaria][Plegaria]

2026-10-26 05:13:17

0

Martha Guañuna :

Gracias

2026-10-25 10:38:18

0

Victor manuel Salcedo :

sofia

2026-10-24 21:53:15

0

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pgvector — turn Postgres into a vector database 🐘🧠 Everyone building RAG thinks they need Pinecone or Weaviate. For most apps, you don't. The database you're probably already running — Postgres — can do semantic search with one extension. Your embeddings live right next to your data, no second system to run, sync or pay for. Here's the whole thing 👇 🔹 WHAT IS PGVECTOR? An open-source extension for PostgreSQL. It adds a vector data type and the ability to search by similarity. You store embeddings in a column right beside your normal rows, run nearest-neighbour search in plain SQL, and that's it — no new database to stand up. On Supabase it's one click to enable. The big win: your embeddings live in the SAME table as the data they describe, so there's no separate system to sync, back up or get billed for. 🔹 WHY NOT A DEDICATED VECTOR DB? Pinecone and Weaviate are genuinely great at massive scale. But for most real apps, pgvector wins on simplicity. One database instead of two. Real SQL joins, so you can filter vectors by your own columns. ACID transactions you already trust. No extra per-vector bill. And one backup — your normal DB dump already contains everything. Honest rule of thumb: reach for a dedicated vector DB when you hit tens of millions of vectors. Below that, pgvector is plenty. 🔹 SETUP: ENABLE IT + MAKE A TABLE CREATE EXTENSION vector; CREATE TABLE docs ( id bigserial PRIMARY KEY, content text, embedding vector(1536) ); That vector(1536) dimension MUST match your embedding model's output size exactly — get it wrong and your inserts fail. Store the original text right alongside the embedding so you've got something to hand back. 🔹 FINDING SIMILAR ROWS The whole point. Order by distance to a query vector: SELECT content FROM docs ORDER BY embedding <=> $1 LIMIT 5; The operators: <=> is cosine distance, <-> is Euclidean (L2), <#> is negative inner product. $1 is your user's question, embedded with the same model as your chunks. Those 5 nearest rows become the context you feed to the LLM. 🔹 INDEX OR IT'S SLOW Without an index, every search scans every single row — fine for a demo, painful in production. Add one once you have real data: CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops); HNSW is fast and accurate — the usual pick. IVFFlat is smaller but needs data present to build well. One rule people miss: match the index operator class to your distance operator. Using <=> (cosine) in your query? Build a cosine index (vector_cosine_ops). Mismatch them and the index silently won't be used. 🔹 THE REAL SUPERPOWER: FILTER WHILE YOU SEARCH This is the thing dedicated vector DBs make awkward and Postgres makes trivial — mix vector similarity with a normal WHERE clause: SELECT content FROM docs WHERE tenant_id = $2 ORDER BY embedding <=> $1 LIMIT 5; For multi-tenant SaaS this is gold. One query gives you semantic search scoped to exactly one customer's data — no separate index per tenant, no filtering in application code after the fact. The database does it all. 🔹 PRODUCTION NOTES ✅ Match dimensions — table vector size = model output size ✅ Normalise your vectors — then cosine and inner product line up ✅ Index after loading your data, not before ✅ Keep the source text beside the embedding ✅ Re-embed when content changes — stale vectors = stale answers ✅ Tune ef_search to trade recall against speed per query If you followed my RAG post and wondered where the vectors actually go — this is it. Same stack, zero extra services, all in the database you already run. Save this for your next build 🔖 and follow @panecci.solutions for daily AI engineering, automation and build-in-public tips 🚀 #pgvector #PostgreSQL #RAG #AIEngineering #BuildInPublic

pgvector — turn Postgres into a vector database 🐘🧠 Everyone building RAG thinks they need Pinecone or Weaviate. For most apps, you don't. The database you're probably already running — Postgres — can do semantic search with one extension. Your embeddings live right next to your data, no second system to run, sync or pay for. Here's the whole thing 👇 🔹 WHAT IS PGVECTOR? An open-source extension for PostgreSQL. It adds a vector data type and the ability to search by similarity. You store embeddings in a column right beside your normal rows, run nearest-neighbour search in plain SQL, and that's it — no new database to stand up. On Supabase it's one click to enable. The big win: your embeddings live in the SAME table as the data they describe, so there's no separate system to sync, back up or get billed for. 🔹 WHY NOT A DEDICATED VECTOR DB? Pinecone and Weaviate are genuinely great at massive scale. But for most real apps, pgvector wins on simplicity. One database instead of two. Real SQL joins, so you can filter vectors by your own columns. ACID transactions you already trust. No extra per-vector bill. And one backup — your normal DB dump already contains everything. Honest rule of thumb: reach for a dedicated vector DB when you hit tens of millions of vectors. Below that, pgvector is plenty. 🔹 SETUP: ENABLE IT + MAKE A TABLE CREATE EXTENSION vector; CREATE TABLE docs ( id bigserial PRIMARY KEY, content text, embedding vector(1536) ); That vector(1536) dimension MUST match your embedding model's output size exactly — get it wrong and your inserts fail. Store the original text right alongside the embedding so you've got something to hand back. 🔹 FINDING SIMILAR ROWS The whole point. Order by distance to a query vector: SELECT content FROM docs ORDER BY embedding <=> $1 LIMIT 5; The operators: <=> is cosine distance, <-> is Euclidean (L2), <#> is negative inner product. $1 is your user's question, embedded with the same model as your chunks. Those 5 nearest rows become the context you feed to the LLM. 🔹 INDEX OR IT'S SLOW Without an index, every search scans every single row — fine for a demo, painful in production. Add one once you have real data: CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops); HNSW is fast and accurate — the usual pick. IVFFlat is smaller but needs data present to build well. One rule people miss: match the index operator class to your distance operator. Using <=> (cosine) in your query? Build a cosine index (vector_cosine_ops). Mismatch them and the index silently won't be used. 🔹 THE REAL SUPERPOWER: FILTER WHILE YOU SEARCH This is the thing dedicated vector DBs make awkward and Postgres makes trivial — mix vector similarity with a normal WHERE clause: SELECT content FROM docs WHERE tenant_id = $2 ORDER BY embedding <=> $1 LIMIT 5; For multi-tenant SaaS this is gold. One query gives you semantic search scoped to exactly one customer's data — no separate index per tenant, no filtering in application code after the fact. The database does it all. 🔹 PRODUCTION NOTES ✅ Match dimensions — table vector size = model output size ✅ Normalise your vectors — then cosine and inner product line up ✅ Index after loading your data, not before ✅ Keep the source text beside the embedding ✅ Re-embed when content changes — stale vectors = stale answers ✅ Tune ef_search to trade recall against speed per query If you followed my RAG post and wondered where the vectors actually go — this is it. Same stack, zero extra services, all in the database you already run. Save this for your next build 🔖 and follow @panecci.solutions for daily AI engineering, automation and build-in-public tips 🚀 #pgvector #PostgreSQL #RAG #AIEngineering #BuildInPublic

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