chroma VS runpodctl

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:

You can rent a GPU on https://www.runpod.io/, which also has stable diffusion templates so any time you start the GPU SD will be preinstalled for you, ready to use:)

Another option is to use a service like www.runpod.io to rent time on more powerful systems. A few times a week I’ll load up whatever the latest 13B or 20/24B (and even low bpw 70Bs with EXL2) model is on a system with an rtx 3090 for $0.44/hr, and sometimes I’ll treat myself to an A6000 system to run 4bit 70Bs for $0.79/hr. They also offer A4000 systems with 16GB VRAM which is plenty to run a 4bpw 13B EXL2 model or an 8bpw 7B model, and those systems are just $0.36/hr.

Many people use sites like runpod for this.

You can rent time on systems on www.runpod.io with a 48GB A6000 for $0.50/hr spot pricing and $0.79/hr regular pricing. 3090s can be had for $0.29/hr spot pricing and $.44/hr regular.

Keep in mind you can also use www.runpod.io to rent access to systems with a 3090 for about $.45/hr. It might be significantly cheaper ir at least more affordable to do this for a few hours a week instead of dropping $1,000 on a new laptop. This is what I personally do (I generally use it in the evening, and can get an Nvidia A6000 with 48GB VRAM for $.49/hr spot pricing). This lets me play with the latest 33B and 65B models, with really fast replies, and I spend maybe $5-7 a week if I use it a lot.

you can also connect the volume to other cloud storage (pcloud, dropbox, etc) (look for the option cloud sync in the "my pods" console) , or use python libraries to pull/push files from S3 buckets / dropbox / ftps / your NAS / etc, or use their docker tools to transfer files between your local PC and the volumehttps://github.com/runpod/runpodctl/blob/main/README.md