📍 Store text locations in vector databases for QA tasks, enhancing citation accuracy and retrieval precision in RAG systems.

🧠 Enhance LLMs with Memlayer for intelligent, contextual memory and fast retrieval—streamline agents with just 3 lines of code.

🚀 Optimize AI context retrieval with OrionGraphDB, a powerful engine that respects token budgets and delivers diverse, relevant information seamlessly.

🔍 Empower efficient retrieval with PageIndex, a reasoning-based system that eliminates the need for vector databases and chunking for human-like results.

🔍 Validate AI-generated content accuracy in fintech and compliance, ensuring safer information by checking claims against verified knowledge bases.

🔍 Optimize RAG systems by exploring Lexical, Semantic, and Hybrid Search methods for better context retrieval and improved LLM responses.

# 🔍 Semantic Article RecommenderThis project offers a simple way to find articles that are similar in meaning. It uses advanced techniques like Hugging Face embeddings and FAISS for efficient searching. 🛠️

MTEB: Massive Text Embedding Benchmark

Data for the MTEB leaderboard

End-to-end healthcare RAG pipeline built with Streamlit and ChromaDB — includes LLM-based retrieval, SQLite drug DB, and contextual evidence reasoning.

This repository is designed for absolute beginners who want to master RAG from the ground up in just 10 days

A realtime serving engine for Data-Intensive Generative AI Applications

Advanced RVC Inference for quicker and effortless model downloads

Customizable Case-Based Reasoning (CBR) toolkit for Python with a built-in API and CLI.

Tool to retrieve various Janet repositories

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

capybaradb - a toy Vector DB implementation from scratch in Python. Explore Vector DB internals.

Retrieval-Augmented Generation

[SIL-C] Policy Compatible Skill Incremental Learning via Lazy Learning Interface