vektor VS NeumAI

Compare vektor vs NeumAI and see what are their differences.

vektor

a mini vector database implementation that intends to be educational and interpretable (by notallm)

NeumAI

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale. (by NeumTry)
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vektor NeumAI
4 2
12 785
- 3.9%
7.2 8.7
12 months ago 4 months ago
Python Python
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

vektor

Posts with mentions or reviews of vektor. We have used some of these posts to build our list of alternatives and similar projects.

NeumAI

Posts with mentions or reviews of NeumAI. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-21.

What are some alternatives?

When comparing vektor and NeumAI you can also consider the following projects:

deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

phidata - Memory, knowledge and tools for LLMs

leaptable - Reframe - The fastest way to build, deploy, and manage LLM-powered agents on tabular data (dataframes, SQL tables and Spreadsheets). [Moved to: https://github.com/peterwnjenga/reframe]

versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.

superduperdb - 🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.