Nicolay Rusnachenko

nicolay-r

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

Recent Activity

reacted to julien-c's post with 🔥 1 day ago

BOOOOM: Today I'm dropping TINY AGENTS the 50 lines of code Agent in Javascript 🔥 I spent the last few weeks working on this, so I hope you will like it. I've been diving into MCP (Model Context Protocol) to understand what the hype was all about. It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs. But while doing that, came my second realization: Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯 ➡️ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents

reacted to as-cle-bert's post with 🔥 1 day ago

Ever dreamt of ingesting into a vector DB that pile of CSVs, Word documents and presentations laying in some remote folders on your PC?🗂️ What if I told you that you can do it within three to six lines of code?🤯 Well, with my latest open-source project, 𝐢𝐧𝐠𝐞𝐬𝐭-𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠 (https://github.com/AstraBert/ingest-anything), you can take all your non-PDF files, convert them to PDF, extract their text, chunk, embed and load them into a vector database, all in one go!🚀 How? It's pretty simple! 📁 The input files are converted into PDF by PdfItDown (https://github.com/AstraBert/PdfItDown) 📑 The PDF text is extracted using LlamaIndex readers 🦛 The text is chunked exploiting Chonkie 🧮 The chunks are embedded thanks to Sentence Transformers models 🗄️ The embeddings are loaded into a Qdrant vector database And you're done!✅ Curious of trying it? Install it by running: 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘪𝘯𝘨𝘦𝘴𝘵-𝘢𝘯𝘺𝘵𝘩𝘪𝘯𝘨 And you can start using it in your python scripts!🐍 Don't forget to star it on GitHub and let me know if you have any feedback! ➡️ https://github.com/AstraBert/ingest-anything

posted an update 1 day ago

🚀 Delighted to share a major milestone in adapting reasoning techniques for data collections augmentation! Introducing bulk-chain 1.0.0 -- the first major release of a no-string API for adapting your LLM for Chain-of-Thought alike reasoning over records with large amount of parameters across large datasets. ⭐ Check it out: https://github.com/nicolay-r/bulk-chain What’s new and why it matters: 📦 Fully no-string API for easy client deployment 🔥 Demos are now standalone projects: Demos: 📺 bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell 📺 tksheet: https://github.com/nicolay-r/bulk-chain-tksheet-client Using nlp-thirdgate to host the supported providers: 🌌 LLM providers: https://github.com/nicolay-r/nlp-thirdgate

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🚀 Delighted to share a major milestone in adapting reasoning techniques for data collections augmentation!
Introducing bulk-chain 1.0.0 -- the first major release of a no-string API for adapting your LLM for Chain-of-Thought alike reasoning over records with large amount of parameters across large datasets.

⭐ Check it out: https://github.com/nicolay-r/bulk-chain

What’s new and why it matters:
📦 Fully no-string API for easy client deployment
🔥 Demos are now standalone projects:

Demos:
📺 bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell
📺 tksheet: https://github.com/nicolay-r/bulk-chain-tksheet-client

Using nlp-thirdgate to host the supported providers:
🌌 LLM providers: https://github.com/nicolay-r/nlp-thirdgate

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593