An AI assistant app that mixes AI features with traditional personal productivity. The AI can work in the background to answer multiple chats, handle tasks, and stream/feed entries.
Here's a demo video (recorded using OpenHermes Mistral 7b with llama.cpp):
nucleo-alpha.mp4
Warnings:
This is "alpha" software. It is new. There are bugs and issues.
Some features (esp the assistant mode) can use a lot of tokens. Be aware when you are trying this with paid APIs. This is an early release and there are bugs and improvements needed.
You should have Python 3 and git installed. I've tried to keep the app light-weight, but RAG requires downloading some large files (about 850mb for the embed and cross encoder).
The app does not include the LLM engine. You can connect it to any openai-compatible endpoint, like llama.cpp, oobabooga with the --api flag, and newer versions of ollama. I use llama-cpp-python and together ai quite a bit.
If you're using Windows, see this install note.
$ git clone git@github.com:AndrewVeee/nucleo-ai.git
# Create python venv, install torch cpu-only and requirements.txt. Copy config file.
$ ./setup.sh
Edit the config file in ./data/config.toml
Find the [llm] block and set your openai_base_url (something like http://localhost:5000/v1) and
the openai_api_key if required. You can also set the secret_key at the top if you want to require auth to
connect to Nucleo.
Run ./start.sh to start the app. The first run will take a bit to download the
SentenceTransformers models for RAG support.
Once the app has started, you will see a line like:
* Running on http://127.0.0.1:4742
Open the link in your browser to start using Nucleo!
Share your initial feedback on r/nucleoai if you have time - it will help improve the app!
Nucleo supports regular chat. You can create multiple chats, and choose saved or temporary (throwaway) chat when you create it.
The Assistant chat mode has the AI make a list of tasks from the request and perform them. It can perform web searches, add to do entries, create docs, and respond.
This is very early, and most models will make mistakes. Mixtral tends to do pretty well. I personally use OpenHermes Mistral 7b for testing, and it has ~50% success rate unless you are very careful with the wording.
In the stream, you can click the "Research" option from "Add to your stream..." to let the AI research a topic in-depth.
It will generate an initial list of research topics, and allow you to configure max depth, max research time, whether it should search the web, and other options.
The researcher will generate a doc with research on the topic, including links it used at the bottom.
You can upload PDFs (I recommend < 5mb right now) in the Docs tab or upload plain text/markdown files. You can create new docs at the top, or paste in a URL to download and convert it to markdown.
With files uploaded, you can enable the Docs chat mode and it should do a decent job of finding relevant content and responding. Of course, this is dependent on your model. For small context models, make sure to set the "Past Messages" chat option low or there won't be much room for RAG results.
The stream tab shows a list of actions the assistant has performed. You can also add one-shot chats to your stream - Assistant Task, Quick Chat, or Message (paste emails, etc to categorize and summarize). When you create entries, you can replay them later.
There is a very basic to do list in the app, so the AI can create entries for you. You can add, edit, or remove entries through the tab.
Right now, I'm primarily looking for feedback and ideas, to know how far it is from a daily driver for others.
I have some ideas I really want to try:
- Researcher: In the stream, enter a topic and let the AI run for a few minutes, choosing subtopics, running web searches, and writing its findings in a "live document".
- Assistant Modes: There are many ways to implement the assistant mode, from quick 1- or 2-shot requests, to complex ReAct LLM loops. I want to experiment with multiple options, some for tiny models, some for large models.
- Stream/Feed: I want to have useful integrations with things like email, calendar, and other personal data sources, so it can all be in one place, and the AI can reference it.
- More Functions: Each function makes the assistant more useful, and I'd like to have a list of popular requests to build.
- Fine Tuned Models: It would be really nice to have a tiny fine-tuned model (like TinyLlama, Phi, etc) that can handle an assistant mode.
My long term goal for Nucleo is a platform that helps developers build and test ideas and make them available to others to try easily. I'm not a fan of most LLM frameworks, so I'd prefer Nucleo as a collection of tools that you can pick and choose as you need.
The code is pretty messy, but here are some key points to play around:
- You can take a look at
app/functions/functions.pyif you want to add your own function. - Nucleo is API-first. You can connect to it at
/api/v1/chat/completionswith an OpenAI client. - Nucleo uses "virtual" models. You can set the model to 'assistant' to chat in assistant mode, 'chat' to chat in basic mode, or 'message' to summarize messages.
- With chat mode, you can also append a message with {'role': 'config', 'content': '{"rag": true}'} to enable RAG in the chat.
- One of the key future features is being able to create your own virtual models, but
it's currently a pretty messy process. Take a look at
backend/app/ai_models/default_model.pyto see the chat model, or assistant_model2.py for a complex example.
There's so much work to do. I'm happy to help if you want to contribute.
Need help?
I've set up a subreddit to discuss issues and ideas: https://www.reddit.com/r/nucleoai/
You can post in the issues tab as well, but the community might give a faster response.