Clelia Astra Bertelli

as-cle-bert

https://www.cleliasportfolio.xyz

AI & ML interests

Recent Activity

posted an update 2 days ago

𝐑𝐀𝐆𝐜𝐨𝐨𝐧🦝 - 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝 𝐲𝐨𝐮𝐫 𝐬𝐭𝐚𝐫𝐭𝐮𝐩 GitHub 👉 https://github.com/AstraBert/ragcoon Are you building a startup and you're stuck in the process, trying to navigate hundreds of resources, suggestions and LinkedIn posts?😶‍🌫️ Well, fear no more, because 𝗥𝗔𝗚𝗰𝗼𝗼𝗻🦝 is here to do some of the job for you: 📃 It's built on free resources written by successful founders ⚙️ It performs complex retrieval operations, exploiting "vanilla" hybrid search, query expansion with an 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘁𝗶𝗰𝗮𝗹 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 approach and 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 𝗾𝘂𝗲𝗿𝘆 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 📊 It evaluates the 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 of the retrieved context, and the 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝘆 and 𝗳𝗮𝗶𝘁𝗵𝗳𝘂𝗹𝗻𝗲𝘀𝘀 of its own responses, in an auto-correction effort RAGcoon🦝 is 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 and relies on easy-to-use components: 🔹LlamaIndex is at the core of the agent architecture, provisions the integrations with language models and vector database services, and performs evaluations 🔹 Qdrant is your go-to, versatile and scalable companion for vector database services 🔹Groq provides lightning-fast LLM inference to support the agent, giving it the full power of 𝗤𝘄𝗤-𝟯𝟮𝗕 by Qwen 🔹Hugging Face provides the embedding models used for dense and sparse retrieval 🔹FastAPI wraps the whole backend into an API interface 🔹𝗠𝗲𝘀𝗼𝗽 by Google is used to serve the application frontend RAGcoon🦝 can be spinned up locally - it's 𝗗𝗼𝗰𝗸𝗲𝗿-𝗿𝗲𝗮𝗱𝘆🐋, and you can find the whole code to reproduce it on GitHub 👉 https://github.com/AstraBert/ragcoon But there might be room for an online version of RAGcoon🦝: let me know if you would use it - we can connect and build it together!🚀

new activity 3 days ago

as-cle-bert/pdfitdown:Update requirements.txt

posted an update 7 days ago

I just released a fully automated evaluation framework for your RAG applications!📈 GitHub 👉 https://github.com/AstraBert/diRAGnosis PyPi 👉 https://pypi.org/project/diragnosis/ It's called 𝐝𝐢𝐑𝐀𝐆𝐧𝐨𝐬𝐢𝐬 and is a lightweight framework that helps you 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗥𝗔𝗚 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. You can launch it as an application locally (it's Docker-ready!🐋) or, if you want more flexibility, you can integrate it in your code as a python package📦 The workflow is simple: 🧠 You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere) 🧠 You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI) 📄 You prepare and provide your documents ⚙️ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex 📊 The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions 📊 The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents And the cool thing is that all of this is 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱: you plug it in, and it works!🔌⚡ Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds🦙 And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience🕶️ So now it's your turn: you can either get diRAGnosis from GitHub 👉 https://github.com/AstraBert/diRAGnosis or just run a quick and painless: ```bash uv pip install diragnosis ``` To get the package installed (lightning-fast) in your environment🏃‍♀️ Have fun and feel free to leave feedback and feature/integrations requests on GitHub issues✨

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