Alternatives
Products that do what A vector database with semantic SQL-like filtering does
Hi HN! It’s always bothered me that there’s no real equivalent of SQL WHERE for vector content. Filtering is one of the cornerstones of a modern database — but vector DBs only support either top-k sort, which is only useful for fuzzy search, or metadata filtering, which isn’t semantic. I’ve found myself wanting all the results matching my semantic query, not just k! Aside from data analysis, it's relevant if you’re trying to do any LLM reasoning: you don’t make good decisions or reach good conclusions by considering a small subset of information. So, we’ve designed a filtering primitive on…
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2021 · github.com
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MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use…
2023 · myscale.com
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2020 · github.com
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Embeddings, Semantic Search & RAG Explained
23d ago · khayyamshah2007.blogspot.com
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Built QueryWeaver, an open-source text2SQL tool that uses a graph to create a semantic layer on top of your existing databases. When you ask "show me customers who bought product X in a certain ‘REGION’ over the last Y period of time," it knows which tables to join and how. When you follow up with "just the ones from Europe," it remembers what you were talking about. Instead of feeding the model a list of tables and columns, we feed it a graph that understands what a customer is, how it connects to orders, which products belong to a campaign, and what "active user" actually means in your…
2025 · github.com
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2014 · pipesql.com
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Hey HN — We're excited to share Trellis — a snowflake for unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the…
2024 · demo.runtrellis.com
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Hey #SQL and #js people, I'm stoked to introduce Linked QL - a “magic wand” query client for your database that simplifies how you query data and manage your schemas. If you've worked with structured data on a real world project, then chances are that you've also had a hair-pulling experience figuring out relational queries, managing schemas and coordinating dozens of migration files. You also may have had to move from a tool like Prisma to one like Drizzle in search of sanity, and then realise that this too inherits everything that makes an ORM and ORM! I am writing Linked QL to be…
2024 · github.com
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2025 · github.com
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Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!
2024 · github.com
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Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…
2025 · aisearch.vpuna.com
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Would appreciate a star (and happy for ideas on improving indexing speed/embedding quality)!
May 2026 · github.com
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Hello HN! I've been working on Atomic Database[1] for almost exactly a month now, having restarted from an earlier Hackathon prototype version. Atomic Database is a deductive database like Datalog that has a useful GUI interface, a natural language query system, smart metadata storage, and gradual typing capabilities. The actual database backend of all this is based on Entity Attribute Value triples currently (I don't forsee changing it, as yet!). This is still very much a prototype, but I feel like I'm at the stage in its evolution where I need some outside input and, possibly, help. To…
2019
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Disclaimer it is a heavily AI assisted project. The goal was not to be the most performative but the kind that's easier to learn from. I wanted to share this in case there are people who had the same idea or wanted to see something like this.
Jun 2026 · github.com
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Build you own query planner guide. I've been developing this guide for some time now, and I'm excited to finally share it with you. This project includes: - a SQL-like language - a fully-functional SQL planner/optimizer - a minimal query executor (for demoing purpose) GitHub: https://github.com/tuannh982/query-planner-guide
2024 · github.com
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2011 · htsql.org
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Hey HN, At Mintplex Labs are building developer tools for AI applications. One area we encountered frustration was the use of Vector Databases like Pinecone, Chroma, QDrant, or Weaviate to "unlock" long-term memory and contextual answers. It is nearly impossible to manage this data when in use for production. The craziest thing was how you cannot atomically CRUD any vectors in most of these vector databases. Let alone easily copy, clone, or migrate data or entire indexes without paying for re-embedding - among other things. With VectorAdmin you get a database level UI with the ability to…
2023 · vectoradmin.com
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2017 · github.com
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When your embedding provider is good, but could be better for your use-case.
2024 · zoplabs.com
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Hey HN! My brothers and I have worked on this for the last 2 weeks. We use OpenAI's `text-embedding-ada-002` model to embed queries and a vector database to search for similar verses / blocks of verses. We'd like to see what you think and appreciate any feedback!
2023 · siliconscripture.org
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We built a structured search engine for Twitter called Bird SQL, available at https://www.perplexity.ai/sql. Our search interface uses OpenAI Codex to translate natural language to SQL. Our backend then verifies the SQL, executes it, and displays the results on the web app. This makes large structured datasets like a scrape of Twitter easy for anyone to explore. As background, while working on text-to-SQL as a general problem, we came to believe one of its most powerful applications is as a search tool because: - SQL is hard to write by hand and prone to errors - It allows you…
2022 · perplexity.ai
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