Ve

VectorAdmin – An open-source vector database management system

Hacker News

VectorAdmin – An open-source vector database management system

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 easily search for embeddings and atomically manage them on top of being a general tool suite for those using vector databases with LLMs. Some things we have unlocked with Vector Admin: - Upload data directly into the vector db via a text doc or PDF - One click sync of entire existing vector databases - Migrating entire db's to another provider to escape vendor lock in - Ability to duplicate collections/namespaces to create dev-environments off production data at no cost - Be able to finally reset a vector database (provider agnostic) and soon, be able to detect "drift" in semantic search results and catch it early before your production system starts providing wild context snippets. VectorAdmin is open-source or hosted and has a 3-day trial. We really want HN's feedback on the issues or problem you are having wrangling the actual data in a vector database while building any LLM application.

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, open · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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