webwright
tldw
| webwright | tldw | |
|---|---|---|
| 3 | 5 | |
| 40 | 736 | |
| - | 17.7% | |
| 9.0 | 9.9 | |
| 3 months ago | 3 days ago | |
| Python | 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.
webwright
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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
https://github.com/MittaAI/webwright
Let me know if you want to go over the code or want to discuss what works and what doesn’t. We had a loop on the action/function call “pipeline” but I changed it to just test if there was a function call or not and then just keep calling.
- Devin is now generally available
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Artifacts are now generally available
Webwright, like some other similar tools, will build and run code in a terminal or container. Just ask it to do a Mandelbrot set. We're currently working on adding ChromaDB to it for history and abstracting the LLM class to support Ollama.
https://github.com/MittaAI/webwright
tldw
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Show HN: Morphik – Open-source RAG that understands PDF images, runs locally
Hey yes, I’m building exactly that.
https://github.com/rmusser01/tldw
I first built a POC in gradio and am now rebuilding it as a FastAPI app. The media processing endpoints work but I’m still tweaking media ingestion to allow for syncing to clients(idea is to allow for client-first design).
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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?
semantifly
wdoc - Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, scalable (?), WIP
open-webui - User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
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.
nango - A single API for all your integrations.
augini - augini: AI-Powered Tabular Data Assistant