ShapedQL – A SQL engine for multi-stage ranking and RAG
Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
In plain words
ShapedQL is a SQL engine designed to simplify building recommendation systems and retrieval-augmented generation (RAG) applications. It consolidates the typical stack of vector databases, feature stores, and inference services into a single SQL-based system that compiles queries into multi-stage ranking pipelines. The tool is aimed at developers building personalized feeds, recommendation systems, or RAG platforms who need efficient ranking capabilities without managing multiple infrastructure components.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic and reranking. We built an engine that consolidates this into a single SQL dialect. It compiles declarative queries into high-performance, multi-stage ranking pipelines. HOW IT WORKS: Instead of just SELECT , ShapedQL operates in four stages native to recommendation systems: RETRIEVE: Fetch candidates via Hybrid Search (Keywords + Vectors) or Collaborative Filtering. FILTER: Apply hard constraints (e.g., "inventory > 0"). SCORE: Rank results using real-time models (e.g., p(click) or p(relevance)). REORDER: Apply diversity logic so your Agent/User doesn’t see 10 nearly identical results. THE SYNTAX: Here is what a RAG query looks like. This replaces about 500 lines of standard Python/LangChain code: SELECT item_id, description, price FROM -- Retrieval: Hybrid search across multiple indexes search_flights("$param.user_prompt", "$param.context"), search_hotels("$param.user_prompt", "$param.context") WHERE -- Filtering: Hard business constraints price <= "$param.budget" AND is_available("$param.dates") ORDER BY -- Scoring: Real-time reranking (Personalization + Relevance) 0.5 * preference_score(user, item) + 0.3 * relevance_score(item, "$param.user_prompt") LIMIT 20 If you don’t like SQL, you can also use our Python and Typescript SDKs. I’d love to know what you think of the syntax and the abstraction layer!
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