tldw
M.I.L.E.S
| tldw | M.I.L.E.S | |
|---|---|---|
| 5 | 1 | |
| 736 | 211 | |
| 17.7% | 6.2% | |
| 9.9 | 7.8 | |
| 1 day ago | 5 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
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)
M.I.L.E.S
What are some alternatives?
wdoc - Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, scalable (?), WIP
DesktopAssistant - A Virtual Desktop Assistant Written in Python
augini - augini: AI-Powered Tabular Data Assistant
gpt_chatbot - This chatbot lets you use your microphone to communicate with GPT-4. It uses the OpenAI text to speech to respond with a voice. It uses Pinecone to store long term information and retrieves it to create context. API keys for OpenAI and Pinecone required. Tested on Windows
gptme - Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision.
kobold_assistant - Like ChatGPT's voice conversations with an AI, but entirely offline/private/trade-secret-friendly, using local AI models such as LLama 2 and Whisper