core

I haven't personally tried this for anything serious yet, but to get the thread started: Cheshire Cat [0] looks promising. It's a framework for building AI assistants by providing it with documents that it stores as "memories" that can be retrieved later. I'm not sure how well it works yet, but it has an active community on Discord and seems to be developing rapidly. The main perk over the cloud options is that you can point it at any language model, including fully local—my local install pointed at my local Ollama running Mistral. [0] https://github.com/cheshire-cat-ai/core

There are a bunch of little projects that do this. It's on the roadmap for ollama-webui (but not implemented yet), and Ollama published a guest blog post with a simple implementation that can be cloned and run very easily: https://ollama.ai/blog/building-llm-powered-web-apps There's also Cheshire Cat, which is a framework for building chat assistants that use a set of documents as a knowledge base: https://github.com/cheshire-cat-ai/core

A similar idea is been developed in: https://github.com/pieroit/cheshire-cat

The Cheshire Cat is an open source, customizable AI architecture: - language model agnostic (works with OpenAI, Cohere, HuggingFace models, custom)- long term memory - can use external tools (APIs, other models)- can ingest documents (.pdf, .txt) - 100% dockerized- extendible via plugins Waiting for you to try it out and contribute with tutorials, code, and whatever makes you happy GitHub Repo: https://github.com/pieroit/cheshire-cat