Move data from any vector DB to any other vector DB

Hacker News

While building semantic search and LLM-powered apps, one needs to try out various vector databases because they differ widely in feature set, cost and other characteristics. The Vector-io library introduces a universal open format for storing vector datasets (Vectors, along with their metadata), along with import and export scripts for a wide range of vector databases: - Pinecone - Qdrant - Milvus - GCP Vertex AI Vector Search - KDB.AI - LanceDB - DataStax Astra DB - Chroma - Turbopuffer This will allow easier backup, snapshots, sharing of vector datasets and managing data across different vector DBs. I'm also curating a list of publicly available datasets in this format, which can be loaded directly from HuggingFace into your favorite VectorDB: https://huggingface.co/collections/aintech/vector-io-compati... If you have data in a vector DB, please try it out and let me know if you have feedback. Thanks!

Visit product View on Hacker NewsMay 13, 2024

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3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, open · Missing: mac, agents, macos

74%74% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, 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 · Strong signals: introduce · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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