semantifly
tldw
| semantifly | tldw | |
|---|---|---|
| 1 | 4 | |
| 14 | 666 | |
| - | 19.4% | |
| 7.2 | 9.9 | |
| 7 months ago | 4 days ago | |
| Go | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
semantifly
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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
My company (actually our two amazing interns) was working on this over the summer, we abandoned it but it’s 85% of the way to doing what you want it to do: https://github.com/accretional/semantifly
We stopped working on it mostly because we had higher priorities and because I became pretty disillusioned with top-K rag. We had to build out a better workflow system anyway, and with that we could instead just have models write and run specific queries (eg list all .ts files containing the word “DatabaseClient”), and otherwise have their context set by users explicitly.
The problem with RAG is that simplistic implementations distract and slow down models. You probably need an implementation that makes multiple passes to prune the context down to what you need to get good results, but that’s complicated enough that you might want to build something else that gives you more bang for your buck.
tldw
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TL;DW: Too Long; Didn't Watch Distill YouTube Videos to the Relevant Information
You could try my app https://github.com/rmusser01/tldw
Supports arbitrary length videos and also lets you choose what LLM API to use.
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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
Not the person you asked, but it's dependent on what you're trying to chunk. I've written a standalone chunking library for an app I'm building: https://github.com/rmusser01/tldw/blob/main/App_Function_Lib...
It's setup so that you can perform whatever type of chunking you might prefer.
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Meta is killing off its own AI-powered Instagram and Facebook profiles
As someone who's built something like it in their free time as a hobby project ( https://github.com/rmusser01/tldw), could I ask what would make it a professional product vs something an intern came up with? Looking for insights I could possibly apply/learn from to implement in my own project.
One of my goals with my project I ended up taking on was to match/exceed NotebookLMs feature set, to ensure that an open source version would be available to people for free, with ownership of their data.
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Xapian Is an Open Source Search Engine Library
Hey I’m working on exactly this: https://github.com/rmusser01/tldw
It’s still a work in progress but my goal is to make an open source solution for exactly what you describe to help people. (Starting with myself :p)
What are some alternatives?
chonkie - 🦛 CHONK your texts with Chonkie ✨ - The no-nonsense RAG chunking library
wdoc - Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, scalable (?), WIP
webwright - Webwright is an AI-powered terminal emulator that lives within your OS. It eliminates time spent on repetitive tasks, conjures code, summons software, and bends the OS to its will. Are you ready to release the ghost in your shell?
M.I.L.E.S - M.I.L.E.S, a GPT-4-Turbo voice assistant, self-adapts its prompts and AI model, can play any Spotify song, adjusts system and Spotify volume, performs calculations, browses the web and internet, searches global weather, delivers date and time, autonomously chooses and retains long-term memories. Available for macOS and Windows.
augini - augini: AI-Powered Tabular Data Assistant
gptme - Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision.