chroma VS ollama

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:

Jan goes a step further by integrating with other local engines like LM Studio and ollama.

# install the Ollama curl -fsSL https://ollama.com/install.sh | sh # get the llama3 model ollama pull llama2 # install the MLFlow pip install mlflow

Ollama for running LLMs locally

curl -fsSL https://ollama.com/install.sh | sh

Streaming is not a problem (it's just a simple flag: https://github.com/wiktor-k/llama-chat/blob/main/index.ts#L2...) but I've never used voice input.

The examples show image input though: https://github.com/ollama/ollama/blob/main/docs/api.md#reque...

Maybe you can file an issue here: https://github.com/ollama/ollama/issues

I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.

Ollama for running LLMs locally

There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁

That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.