localGPT VS chroma

I think local LLMs are great for tinkerers, and with quantization can run on most modern PCs. I am not comfortable sending over my personal data over to OpenAI/Anthropic, so I've been playing around with https://github.com/PromtEngineer/localGPT/, GPT4All, etc. which keep the data all local.

Sliding window chunking, RAG, etc. seem more sophisticated than the other document LLM tools, so I would love to try this out if you ever add the ability to run LLMs locally!

localGPT can parse PDF into embeddings, see <https://github.com/PromtEngineer/localGPT>.

This is going so fast that it feels like a new thing pops up every day. LocalGPT seems to have gotten a lot of traction though: https://github.com/PromtEngineer/localGPT

i am currently trying to use localGPT (https://github.com/PromtEngineer/localGPT) for a project and i encountered a problem.

I tinker a lot with electronics. I have put datasheets for components, documentation for development boards, documentation for software libraries, etc into a database with localGPT.

There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.

Check out local gpt on git hub. I tried but it had slow response for me. Other developers are fine. https://github.com/PromtEngineer/localGPT

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: