🧠 Multimodal Retrieval-Augmented Generation that "weaves" together text and images seamlessly. 🪡

It allows users to upload PDFs and ask questions about the content within these documents.

Metallum/Metal-Archives scrapers, datasets, analysis and recommendations website

Este projeto permite realizar perguntas em linguagem natural sobre o conteúdo de arquivos PDF. Utiliza a abordagem RAG (Retrieval-Augmented Generation)

Efficiently search and retrieve information from PDF documents using a Retrieval-Augmented Generation (RAG) approach. This project leverages DeepSeek-R1 (1.5B) for advanced language understanding, FAISS for high-speed vector search, and Hugging Face’s ecosystem for enhanced NLP capabilities. With an intuitive Streamlit interface and Ollama for mode

This project uses the CrewAI framework to automate stock analysis, enabling AI agents to collaborate and execute complex tasks efficiently. Example stock: Nvidia. Technologies include Python, CrewAI, Unstructured, PyOWM, Tools, Wikipedia, yFinance, SEC-API, tiktoken, faiss-cpu, python-dotenv, langchain-community, langchain-core, and OpenAI.

A sophisticated transformer-based language model with integrated Retrieval-Augmented Generation (RAG) capabilities for intelligent question answering and conversation.

An advanced Retrieval-Augmented Generation (RAG) pipeline with integrated sentiment analysis, user-selectable LLMs (GPT-2 or LLaMA), FAISS vector search, and RLHF-inspired reward scoring. It supports conversational memory with SQLite logging and features a dynamic Gradio UI for end-user interaction.

Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

This is a reasoning AI chatbot that uses Deepseek R1

AI-Powered Document Q&A Bot Stack: Python, LangChain, OpenAI, FAISS, Streamlit, FastAPI Highlights: Upload PDF → Chunk → Vectorize → Search → Answer using GPT Shows LLM, vector DB, chatbot flow Production-quality backend with LangChain and caching

Developed an intelligent AI chatbot utilizing the DeepSeek LLM, designed for efficient interaction with large documents such as textbooks and study materials. Integrated Docling for parsing and processing large files, and implemented a Retrieval-Augmented Generation (RAG) pipeline using FAISS and Sentence Transformers to optimize context retrieval

This is a chatbot finetuned to give answer to medical related questions

A powerful AI chat application built with Streamlit, LangChain, and Ollama, powered by the Gemma3:1B model. Engage in real-time conversations or ask questions about PDF documents with embedded OCR and semantic search capabilities.

FOXO Agentic RAG assistant for document QA, weather-food tips, Fitbit CSV, life & nutrition.

A semantic movie recommendation system using NLP via (sentence-transformers + FAISS index).

AnyBioinfoma is a Streamlit-based application that allows users to interact with a bioinformatics knowledge base. It uses Google Generative AI and FAISS for document embedding and retrieval.

AI-Powered Job Recommendation System An intelligent job recommendation system that analyzes PDF resumes and suggests the best job opportunities using NLP, FAISS, and Sentence Transformers.

It is an ML_Chatbot that explains concepts and terminologies using open-source tools. Used Hugging Face for embeddings, FAISS CPU for vector storage, and Mistral with streamlit for a conversational interface.