Open Source AI Made Simple

js client for R2R: production-ready RAG engine with a sh*t ton of features.

Perform intelligent research over document collections using hybrid search and LLMs.

Implementation of MLLM-based Self-Vision-RAG models

Client-side retrieval firewall for RAG systems — blocks prompt injection and secret leaks, re-ranks stale or untrusted content, and keeps all data inside your environment.

A collection of 10+ chatbot types, from keyword-based and rule-based to AI-powered models. Explore various implementations for building intelligent chat interfaces and virtual assistants.

Pattern Based Question and Answer

Implementação de um pipeline de Retrieval-Augmented Generation (RAG) com Node.js, React e LanceDB.

A robust Retrieval-Augmented Generation (RAG) system for noisy, multi-intent queries using LLM-based query understanding. Implemented in Python with PostgreSQL and OpenSearch for retrieval and storage.

This project applies AI techniques including LLMs, retrieval-augmented generation, prompt engineering, and distillation to understand unstructured text and generate structured, readable output. It focuses on extracting, organizing, and clarifying complex content using applied NLP techniques.

Customized LangChain Azure Document Intelligence loader for table extraction and summarization