Clelia Astra Bertelli

as-cle-bert

https://www.cleliasportfolio.xyz

AI & ML interests

Recent Activity

posted an update 3 days ago

I heard someone saying 𝘃𝗼𝗶𝗰𝗲 assistants are the future, and someone else that 𝗠𝗖𝗣 will rule the AI world... So I decided to combine both!🚀 Meet 𝐓𝐲𝐒𝐕𝐀 (𝗧𝘆pe𝗦cript 𝗩oice 𝗔ssistant, https://github.com/AstraBert/TySVA), your (speaking) AI companion for everyday TypeScript programming tasks!🎙️ TySVA is a skilled TypeScript expert and, to provide accurate and up-to-date responses, she leverages the following workflow: 🗣️ If you talk to her, she converts the audio into a textual prompt, and use it a starting point to answer your questions (if you send a message, she'll use directly that💬) 🧠 She can solve your questions by (deep)searching the web and/or by retrieving relevant information from a vector database containing TypeScript documentation. If the answer is simple, she can also reply directly (no tools needed!) 🛜 To ease her life, TySVA has all the tools she needs available through Model Context Protocol (MCP) 🔊 Once she's done, she returns her answer to you, along with a voice summary of what she did and what solution she found But how does she do that? What are her components?🤨 📖 Qdrant + HuggingFace give her the documentation knowledge, providing the vector database and the embeddings 🌐 Linkup provides her with up-to-date, grounded answers, connecting her to the web 🦙 LlamaIndex makes up her brain, with the whole agentic architecture 🎤 ElevenLabs gives her ears and mouth, transcribing and producing voice inputs and outoputs 📜 Groq provides her with speech, being the LLM provider behind TySVA 🎨 Gradio+FastAPI make up her face and fibers, providing a seamless backend-to-frontend integration If you're now curious of trying her, you can easily do that by spinning her up locally (and with Docker!🐋) from the GitHub repo ➡️ https://github.com/AstraBert/TySVA And feel free to leave any feedback!✨

liked a dataset 8 days ago

lucadillenburg/startup-chatbot

liked a Space 12 days ago

Langflow/Langflow

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as-cle-bert's activity

posted an update 3 days ago

Post

561

I heard someone saying 𝘃𝗼𝗶𝗰𝗲 assistants are the future, and someone else that 𝗠𝗖𝗣 will rule the AI world... So I decided to combine both!🚀

Meet 𝐓𝐲𝐒𝐕𝐀 (𝗧𝘆pe𝗦cript 𝗩oice 𝗔ssistant, https://github.com/AstraBert/TySVA), your (speaking) AI companion for everyday TypeScript programming tasks!🎙️

TySVA is a skilled TypeScript expert and, to provide accurate and up-to-date responses, she leverages the following workflow:
🗣️ If you talk to her, she converts the audio into a textual prompt, and use it a starting point to answer your questions (if you send a message, she'll use directly that💬)
🧠 She can solve your questions by (deep)searching the web and/or by retrieving relevant information from a vector database containing TypeScript documentation. If the answer is simple, she can also reply directly (no tools needed!)
🛜 To ease her life, TySVA has all the tools she needs available through Model Context Protocol (MCP)
🔊 Once she's done, she returns her answer to you, along with a voice summary of what she did and what solution she found

But how does she do that? What are her components?🤨

📖 Qdrant + HuggingFace give her the documentation knowledge, providing the vector database and the embeddings
🌐 Linkup provides her with up-to-date, grounded answers, connecting her to the web
🦙 LlamaIndex makes up her brain, with the whole agentic architecture
🎤 ElevenLabs gives her ears and mouth, transcribing and producing voice inputs and outoputs
📜 Groq provides her with speech, being the LLM provider behind TySVA
🎨 Gradio+FastAPI make up her face and fibers, providing a seamless backend-to-frontend integration

If you're now curious of trying her, you can easily do that by spinning her up locally (and with Docker!🐋) from the GitHub repo ➡️ https://github.com/AstraBert/TySVA

And feel free to leave any feedback!✨

liked a dataset 8 days ago

lucadillenburg/startup-chatbot

Viewer • Updated Jan 18, 2024 • 1.23k • 63 • 7

liked a Space 12 days ago

1.13k

LangFlow

⛓

Build and execute complex language workflows

posted an update 13 days ago

Post

612

Drowning in handouts, documents and presentations from your professors and not knowing where to start?🌊😵‍💫
Well, I might have a tool for you: 𝐏𝐝𝐟𝟐𝐍𝐨𝐭𝐞𝐬 (https://github.com/AstraBert/pdf2notes) is an 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱, 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 solution that lets you turn your unstructured and chaotic PDFs into nice and well-ordered notes in a matter of seconds!📝

𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?
📄 You first upload a document
⚙️ LlamaParse by LlamaIndex extracts the text from the document, using DeepMind's Gemini 2 Flash to perform multi-modal parsing
🧠 Llama-3.3-70B by Groq turns the extracted text into notes!

The notes are not perfect or you want more in-depth insights? No problem:
💬 Send a direct message to the chatbot
⚙️ The chatbot will retrieve the chat history from a Postgres database
🧠 Llama-3.3-70B will produce the answer you need

All of this is nicely wrapped within a seamless backend-to-frontend framework powered by Gradio and FastAPI🎨

And you can even spin it up easily and locally, using Docker🐋

So, what are you waiting for? Go turn your hundreds of pages of chaotic learning material into neat and elegant notes ➡️ https://github.com/AstraBert/pdf2notes

And, if you would like an online demo, feel free to drop a comment - we'll see what we can build🚀

liked 2 models 18 days ago

google/gemma-3-1b-it

Text Generation • Updated 2 days ago • 426k • 273

unsloth/gemma-3-4b-it-GGUF

Image-Text-to-Text • Updated 21 days ago • 71.6k • 59

liked a dataset 21 days ago

keivalya/MedQuad-MedicalQnADataset

Viewer • Updated Oct 11, 2023 • 16.4k • 1.48k • 102

posted an update 25 days ago

Post

1676

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 in as-cle-bert/pdfitdown 26 days ago

Update requirements.txt

#1 opened 27 days ago by

not-lain

posted an update about 1 month ago

Post

2721

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:

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✨

commented on streamlit_supabase_auth_ui about 1 month ago

Hi there, just wanted to reach out also here, so that if people see our conversation know that this feature has been integrated: you can now find it in the v0.1.0 of the package, already installable via pip.
Have fun!

commented on streamlit_supabase_auth_ui about 1 month ago

I did not specify any configuration, but I'm pretty sure we could play around with Supabase and set a login/logout status for the user (like saying: the user last logged in at time X and logged out at time Y; if Y > X, then the user can login in again, else they cannot).
If you want, I can put it in the roadmap for the next release of the package: then I would ask you to open an issue here: https://github.com/AstraBert/streamlit_supabase_auth_ui/issues so that I can add it to the milestone for v0.1.0 :)

commented on streamlit_supabase_auth_ui about 1 month ago

I could not find it either back in the days, when I wanted to suppress it, but my suspicion is that is linked to some not-so-up-to-date portions of the code (the code is based on a repo that used Streamlit 1.34, I believe). Nevertheless, what I did in my personal projects was suppressing all the warnings with:

from warnings import filterwarnings
filterwarnings(action="ignore")
# source -> https://www.geeksforgeeks.org/how-to-disable-python-warnings/

Hope this helps!

commented on streamlit_supabase_auth_ui about 1 month ago

Hi! Yes, the code is open and you can modify it for your projects :)
If you want to change the language of the components, you just need to modify the widget.py script, i.e. https://github.com/AstraBert/streamlit_supabase_auth_ui/blob/main/streamlit_supabase_auth_ui/widgets.py

posted an update about 2 months ago

Post

2395

I built an AI agent app in less than 8 hours🤯
And, believe me, this is 𝗻𝗼𝘁 clickbait❌

GitHub 👉 https://github.com/AstraBert/PapersChat
Demo 👉 as-cle-bert/PapersChat

The app is called 𝐏𝐚𝐩𝐞𝐫𝐬𝐂𝐡𝐚𝐭, and it is aimed at 𝗺𝗮𝗸𝗶𝗻𝗴 𝗰𝗵𝗮𝘁𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗽𝗮𝗽𝗲𝗿𝘀 𝗲𝗮𝘀𝗶𝗲𝗿.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐩𝐩 𝐝𝐨𝐞𝐬:

📄 Parses the papers that you upload thanks to LlamaIndex🦙 (either with LlamaParse or with simpler, local methods)
📄 Embeds documents both with a sparse and with a dense encoder to enable hybrid search
📄 Uploads the embeddings to Qdrant
⚙️ Activates an Agent based on mistralai/Mistral-Small-24B-Instruct-2501 that will reply to your prompt
🧠 Retrieves information relevant to your question from the documents
🧠 If no relevant information is found, it searches PubMed and arXiv databases
🧠 Returns a grounded answer to your prompt

𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐦𝐚𝐧𝐚𝐠𝐞 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐬 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝟖 𝐡𝐨𝐮𝐫𝐬?

Three key points:

- LlamaIndex🦙 provides countless integrations with LLM providers, text embedding models and vectorstore services, and takes care of the internal architecture of the Agent. You just plug it in, and it works!🔌⚡
- Qdrant is a vector database service extremely easy to set up and use: you just need a one-line Docker command😉
- Gradio makes frontend development painless and fast, while still providing modern and responsive interfaces🏗️

And a bonus point:

- Deploying the demo app couldn't be easier if you use Gradio-based Hugging Face Spaces🤗

So, no more excuses: build your own AI agent today and do it fast, (almost) for free and effortlessly🚀

And if you need a starting point, the code for PapersChat is open and fully reproducible on GitHub 👉 https://github.com/AstraBert/PapersChat

updated a Space about 2 months ago

PapersChat

📚

Chatting with scientific papers made easy

liked a Space about 2 months ago

PapersChat

📚

Chatting with scientific papers made easy

published a Space about 2 months ago

PapersChat

📚

Chatting with scientific papers made easy

posted an update about 2 months ago

Post

1399

𝐒𝐜𝐢𝐍𝐞𝐰𝐬𝐁𝐨𝐭 - 𝐑𝐞𝐩𝐨𝐫𝐭 𝐝𝐚𝐢𝐥𝐲 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐧𝐞𝐰𝐬 𝐨𝐧 𝐁𝐥𝐮𝐞𝐒𝐤𝐲

GitHub 👉 https://github.com/AstraBert/SciNewsBot
BlueSky 👉 https://bsky.app/profile/sci-news-bot.bsky.social

Hi there HF Community!🤗
I just created a very simple AI-powered bot that shares fact-checked news about Science, Environment, Energy and Technology on BlueSky :)

The bot takes news from Google News, filters out the sources that are not represented in the Media Bias Fact Check database, and then evaluates the reliability of the source based on the MBFC metrics. After that, it creates a catchy headline for the article and publishes the post on BlueSky📰

The cool thing? SciNewsBot is open-source and is cheap to maintain, as it is based on mistralai/Mistral-Small-24B-Instruct-2501 (via Mistral API). You can reproduce it locally, spinning it up on your machine, and even launch it on cloud through a comfy Docker setup🐋

Have fun and spread Science!✨

posted an update about 2 months ago

Post

2764

𝐏𝐡𝐢𝐐𝐰𝐞𝐧𝐒𝐓𝐄𝐌 - 𝐚 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐒𝐓𝐄𝐌 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧

Demo 👉 https://pqstem.org
GitHub 👉 https://github.com/AstraBert/PhiQwenSTEM

Hello HF community!🤗
Ever struggled with some complex Maths problem or with a very hard Physics question? Well, fear no more, because now you can rely on PhiQwenSTEM, an assistant specialized in answering STEM-related question!
The assistant can count on a knowledge base of 𝟭𝟱𝗸+ 𝘀𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝗦𝗧𝗘𝗠 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻-𝗮𝗻𝘀𝘄𝗲𝗿 𝗽𝗮𝗶𝗿𝘀 spanning the domains of Chemistry, Physics, Matemathics and Biochemistry (from EricLu/SCP-116K). It also relies on the combined power of microsoft/Phi-3.5-mini-instruct and Qwen/QwQ-32B-Preview to produce reliable and reasoned answers.
For the next 30 days, you will be able to try for free the web demo: https://pqstem.org
In the GitHub repo you can find all the information to reproduce PhiQwenSTEM 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗹𝗼𝗰𝗮𝗹 𝗺𝗮𝗰𝗵𝗶𝗻𝗲, 𝗯𝗼𝘁𝗵 𝘃𝗶𝗮 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗼𝗺𝗳𝘆 𝗗𝗼𝗰𝗸𝗲𝗿🐋 𝘀𝗲𝘁𝘂𝗽: https://github.com/AstraBert/PhiQwenSTEM