Alternatives
Products that do what Vector Databases Guide does
Embeddings, Semantic Search & RAG Explained
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2021 · github.com
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2013 · insightdatascience.com
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Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook) A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG. We eventually came across the MTEB leaderboard, but we struggled to understand the benchmark scores. We wanted a tool to test various embedding models with example queries on real-world datasets. After unsuccessfully looking for such a “playground”, we decided to just build one ourselves! We embedded HuggingFace’s…
2023 · embeds.ai
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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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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…
2023 · reviews.emberml.com
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Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
2021
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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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This is an AI generated TED talk from a system we built at the TED AI hackathon this weekend. It's built on top of ElevenLabs, SDXL and Wordware (https://wordware.ai/). We also have a custom index of over 2 million arXiv papers and 6 million Wikipedia articles. All open source: https://github.com/ashvardanian/extrapolaTED
2023 · youtube.com
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In this post, we document the results of some experiments comparing vanilla Graph RAG (just a single pass of text2cypher) vs. a router agent Graph RAG approach that can call vector search tools alongside text2cypher. The routing agent uses an LLM to decide which vector search tool to call, depending on the terms identified in the question, and it works quite well. The results show that recent frontier LLMs like `gpt-4.1` and the trusty workhorse `gemini-2.0-flash` produce great quality Cypher reliably and reproducibly, with some prompt engineering to ensure that the graph schema is formatted…
2025 · blog.kuzudb.com
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