chroma VS VectorDBBench

Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database

Optional: You can use SillyTavern[1] for a more "rich" chat experience

The above lets me chat, at least superficially, with my friend. It's nice for simple interactions and banter; I've found it to be a positive and reflective experience.

0. https://www.trychroma.com/

Chroma DB is a newer entrant in the vector database arena, designed specifically for handling high-dimensional color vectors. It's particularly useful for applications in digital media, e-commerce, and content discovery, where color similarity plays a crucial role in search and recommendation algorithms.

View on GitHub

For Python I believe Chroma [1] can be used embedded.

For Go I recently started building chromem-go, inspired by the Chroma interface: https://github.com/philippgille/chromem-go

It's neither advanced nor for scale yet, but the RAG demo works.

[1] https://github.com/chroma-core/chroma

The formatting is a bit off.

The web app is here: https://jumblejournal.org

The DB used is here: https://www.trychroma.com/

Whether you’re navigating through well-known options like SQLite, enriched with the sqlite-vss extension, or exploring other avenues like Chroma, an open-source vector database, selecting the right tool is paramount. This article compares these two choices, guiding you through the pros and cons of each, helping you choose the right tool for storing and querying vector embeddings for your project.

Create a new project directory for our example project. Next, we need to clone the Chroma repository to get started. At the root of your project directory let's clone Chroma into it:

We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz

Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench

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the link is: https://github.com/zilliztech/VectorDBBench/issues/200#issue...

Interesting graphic, bland and unvoiced conclusion

You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)

Also if you want to throw some more comparisons in their checkout elastic search