Alternatives
Products that do what Vector search and reranking SaaS API does
Hi, https://vecrank.com is an API for indexing data in a vector database and performing vector search with reranking at zero infrastructure cost. We spent a lot of time building a proper semantic search first for another startup. Now we are testing whether it can save time for the developers new to vector search and validating their startup ideas. As a software engineer you can save weeks of building a custom vector search solution with VecRank. It uses Postgres with pgvector, Gemini embeddings and 1.5 Flash for reranking under the hood. Also, it can be integrated in no-code…
- 1
- 2
- 3
- 4
- 5DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
- 6AS
2013 · insightdatascience.com
- 7
- 8
- 9
- 10
- 11
Build smart search experiences in hours instead of months
2020
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19DR
2020 · datorss.com
- 20MA
Hey HN, Matusa here! A friend and I have built Memora. Memora is a vector database with built-in multistage reranking, which can significantly improve search accuracy over semantic search. It also features a proprietary embedding model tailored for RAG use cases — where there's a structural mismatch between the content stored and the query used for searching (hence why HyDE works well). Memora started because we were working on a stealth AI startup where we used an agent that would query into a vector DB, but it would take multiple tries for the agent to find what it needed (20% of the time…
2023 · usememora.app
- 21
- 22SV
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
- 23SS
I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works: Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DB I…
Feb 2026 · ask.rivestack.io
- 24VA
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
Ranked by how close each launch is in meaning, then by votes. Refine with a description →