Clelia Astra Bertelli

as-cle-bert

https://www.cleliasportfolio.xyz

AI & ML interests

Recent Activity

posted an update about 22 hours ago

Finding a job that matches with our resume shouldn't be difficult, especially now that we have AI... And still, we're drowning in unclear announcements, jobs whose skill requirements might not really fit us, and tons of material😵‍💫 That's why I decided to build 𝐑𝐞𝐬𝐮𝐦𝐞 𝐌𝐚𝐭𝐜𝐡𝐞𝐫 (https://github.com/AstraBert/resume-matcher), a fully open-source application that scans your resume and searches the web for jobs that match with it!🎉 The workflow is very simple: 🦙 A LlamaExtract agent parses the resume and extracts valuable data that represent your profile 🗄️The structured data are passed on to a Job Matching Agent (built with LlamaIndex😉) that uses them to build a web search query based on your resume 🌐 The web search is handled by Linkup, which finds the top matches and returns them to the Agent 🔎 The agent evaluates the match between your profile and the jobs, and then returns a final answer to you So, are you ready to find a job suitable for you?💼 You can spin up the application completely locally and with Docker, starting from the GitHub repo ➡️ https://github.com/AstraBert/resume-matcher Feel free to leave your feedback and let me know in the comments if you want an online version of Resume Matcher as well!✨

updated a Space 4 days ago

as-cle-bert/pdfitdown

replied to their post 11 days ago

Llama-4 is out and I couldn't resist but to cook something with it... So I came up with 𝐋𝐥𝐚𝐦𝐚𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 (https://llamaresearcher.com), your deep-research AI companion!🔎 The workflow behind 𝗟𝗹𝗮𝗺𝗮𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿 is simple: 💬 You submit a query 🛡️ Your query is evaluated by Llama 3 guard model, which deems it safe or unsafe 🧠 If your query is safe, it is routed to the Researcher Agent ⚙️ The Researcher Agent expands the query into three sub-queries, with which to search the web 🌐 The web is searched for each of the sub-queries 📊 The retrieved information is evaluated for relevancy against your original query ✍️ The Researcher Agent produces an essay based on the information it gathered, paying attention to referencing its sources The agent itself is also built with easy-to-use and intuitive blocks: 🦙 LlamaIndex provides the agentic architecture and the integrations with the language models ⚡Groq makes Llama-4 available with its lightning-fast inference 🔎 Linkup allows the agent to deep-search the web and provides sourced answers 💪 FastAPI does the heavy loading with wrapping everything within an elegant API interface ⏱️ Redis is used for API rate limiting 🎨 Gradio creates a simple but powerful user interface Special mention also to Lovable, which helped me build the first draft of the landing page for LlamaResearcher!💖 If you're curious and you want to try LlamaResearcher, you can - completely for free and without subscription - for 30 days from now ➡️ https://llamaresearcher.com And if you're like me, and you like getting your hands in code and build stuff on your own machine, I have good news: this is all open-source, fully reproducible locally and Docker-ready🐋 Just go to the GitHub repo: https://github.com/AstraBert/llama-4-researcher and don't forget to star it, if you find it useful!⭐ As always, have fun and feel free to leave your feedback✨

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as-cle-bert's activity

posted an update about 22 hours ago

Post

1474

Finding a job that matches with our resume shouldn't be difficult, especially now that we have AI... And still, we're drowning in unclear announcements, jobs whose skill requirements might not really fit us, and tons of material😵‍💫
That's why I decided to build 𝐑𝐞𝐬𝐮𝐦𝐞 𝐌𝐚𝐭𝐜𝐡𝐞𝐫 (https://github.com/AstraBert/resume-matcher), a fully open-source application that scans your resume and searches the web for jobs that match with it!🎉
The workflow is very simple:
🦙 A LlamaExtract agent parses the resume and extracts valuable data that represent your profile
🗄️The structured data are passed on to a Job Matching Agent (built with LlamaIndex😉) that uses them to build a web search query based on your resume
🌐 The web search is handled by Linkup, which finds the top matches and returns them to the Agent
🔎 The agent evaluates the match between your profile and the jobs, and then returns a final answer to you

So, are you ready to find a job suitable for you?💼 You can spin up the application completely locally and with Docker, starting from the GitHub repo ➡️ https://github.com/AstraBert/resume-matcher
Feel free to leave your feedback and let me know in the comments if you want an online version of Resume Matcher as well!✨

updated a Space 4 days ago

Pdfitdown

📚

Convert (almost) everything to PDF!

replied to their post 11 days ago

I used good old Canva (pro :)

posted an update 15 days ago

Post

2910

Llama-4 is out and I couldn't resist but to cook something with it... So I came up with 𝐋𝐥𝐚𝐦𝐚𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 (https://llamaresearcher.com), your deep-research AI companion!🔎

The workflow behind 𝗟𝗹𝗮𝗺𝗮𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿 is simple:
💬 You submit a query
🛡️ Your query is evaluated by Llama 3 guard model, which deems it safe or unsafe
🧠 If your query is safe, it is routed to the Researcher Agent
⚙️ The Researcher Agent expands the query into three sub-queries, with which to search the web
🌐 The web is searched for each of the sub-queries
📊 The retrieved information is evaluated for relevancy against your original query
✍️ The Researcher Agent produces an essay based on the information it gathered, paying attention to referencing its sources

The agent itself is also built with easy-to-use and intuitive blocks:
🦙 LlamaIndex provides the agentic architecture and the integrations with the language models
⚡Groq makes Llama-4 available with its lightning-fast inference
🔎 Linkup allows the agent to deep-search the web and provides sourced answers
💪 FastAPI does the heavy loading with wrapping everything within an elegant API interface
⏱️ Redis is used for API rate limiting
🎨 Gradio creates a simple but powerful user interface

Special mention also to Lovable, which helped me build the first draft of the landing page for LlamaResearcher!💖

If you're curious and you want to try LlamaResearcher, you can - completely for free and without subscription - for 30 days from now ➡️ https://llamaresearcher.com
And if you're like me, and you like getting your hands in code and build stuff on your own machine, I have good news: this is all open-source, fully reproducible locally and Docker-ready🐋
Just go to the GitHub repo: https://github.com/AstraBert/llama-4-researcher and don't forget to star it, if you find it useful!⭐

As always, have fun and feel free to leave your feedback✨

2 replies

posted an update 19 days ago

Post

729

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 24 days ago

lucadillenburg/startup-chatbot

Viewer • Updated Jan 18, 2024 • 1.23k • 55 • 7

liked a Space 28 days ago

1.14k

LangFlow

⛓

Build and execute complex language workflows

posted an update 30 days ago

Post

627

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 about 1 month ago

google/gemma-3-1b-it

Text Generation • Updated 18 days ago • 1.81M • 331

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

Image-Text-to-Text • Updated 1 day ago • 42.8k • 71

liked a dataset about 1 month ago

keivalya/MedQuad-MedicalQnADataset

Viewer • Updated Oct 11, 2023 • 16.4k • 2.67k • 102

posted an update about 1 month ago

Post

1682

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 about 1 month ago

Update requirements.txt

#1 opened about 1 month ago by

not-lain

posted an update about 2 months ago

Post

2737

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 2 months 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 2 months 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 2 months 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 2 months 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 2 months ago

Post

2400

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 2 months ago

PapersChat

📚

Chatting with scientific papers made easy