Parsing-free RAG supported by VLMs

Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.

Vietnamese long form question answering system with documents retrieval.

pgvector + embeddings API

[VLSP 2025] ViDRILL is a Vietnamese document retrieval system for VLSP 2025. It combines dense and sparse retrieval, reranking, and optional LLM-based query rewriting and reasoning to support high-accuracy information retrieval and future LLM-enhanced pipelines.

Implementation of ECIR 2022 Paper: How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

Retrieves the top 10 documents from the Wikipedia corpus for a user inputted free-text query

Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings

The Intelligent "ASKDOC" project combines the power of Langchain, Azure, OpenAI models, and Python to deliver an intelligent question-answering system, that scans your PDF documents and answer queries based on its contents. It can be queried using Human Natural Language.

Code and dataset for the paper "Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness"

"LangChat Explorer: Your intuitive document companion. Effortlessly explore vast information with natural language conversations. Simplify queries, gain insights, and embark on a seamless journey of knowledge discovery. Unleash the power of language with LangChat Explorer."

A comprehensive multimodal OCR application that supports both image and video document processing using state-of-the-art vision-language models. This application provides an intuitive Gradio interface for extracting text, converting documents to markdown, and performing advanced document analysis.

Neural text summarization for document retrieval

A Python-based tool for context-based search across text documents using OpenAI embeddings and Chroma vector storage. This system enables efficient querying of document collections by generating vector embeddings, storing them persistently, and retrieving relevant results based on textual queries.

Doc-VLMs-v2-Localization is a demo app for the Camel-Doc-OCR-062825 model, fine-tuned from Qwen2.5-VL-7B-Instruct for advanced document retrieval, extraction, and analysis. It enhances document understanding and also integrates other notable Hugging Face models.

CodeXpert: A cutting-edge AI-powered code analysis tool leveraging CodeLlama, FAISS, and HuggingFace for efficient code understanding, explanation, and optimization. 🚀✨

A two-stage information retrieval model using baseline TF-IDF model and refined BM25.

a minimal local embedding database.

An RAG-Chatbot developed for a business-oriented-game at the JADE HOCHSCHULE