An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

Everything you need to know to build your own RAG application

A python library for creating AI assistants with Vectara, using Agentic RAG

🔥🔥🔥 Simple way to create composable AI agents

Connect to your customer data using any LLM and gain actionable insights. IdentityRAG creates a single comprehensive customer 360 view (golden record) by unifying, consolidating, disambiguating and deduplicating data across multiple sources through identity resolution.

Agents and RAG workflows with little to no code

This repository provides the building blocks for integrating LangChain, LangGraph, and the Tilores entity resolution system.

Interactive LLM Chatbot that constructs direct and transitive software dependencies as a knowledge graph and answers user's questions leveraging RAG and critic-agent approach

A RAG system is just the beginning of harnessing the power of LLM. The next step is creating an intelligent Agent. In Agentic RAG the Agent makes use of available tools, strategies and LLM to generate response in a specialized way. Unlike a simple RAG, an Agent can dynamically choose between tools, routing strategy, etc.

Agentic RAG using Crew AI

Production-ready Agentic AI ChatBot using Llamaindex and Groq-Llama 3.3

This repository provides the building blocks for integrating LangChain, LangGraph, and the Tilores entity resolution system.

GlancyAI is an LLM (like ChatGPT) that you can talk with, and it recommends products and helps you make your educated guess to buy a product.

The revolutionary open-source AgenticAI RAG w/ internet search capability, unleashing the world’s knowledge, empowering humanity with unparalleled intelligence and freedom.

Automated resume generation based on job link using CrewAi

Simple agents are good for 1-to-1 retrieval system. For more complex task we need multi steps reasoning loop. In a reasoning loop the agent can break down a complex task into subtasks and solve them step by step while maintaining a conversational memory.

Conducting literature surveys is time-consuming for researchers and students who must sift through numerous academic papers. This project develops an application that streamlines the process, allowing users to search arXiv for relevant papers by keywords, authors, or topics, receive concise summaries, and interact with the content through Q&A.

Investigating the efficacy of Retrieval-Augmented Generation (RAG) and Corrective Retrieval-Augmented Generation (CRAG) in harnessing external knowledge to improve AI model performance and output quality.

Docker implementation of Llama Index Agentic RAG. Developing a RAG system requires multiple component such as LLM, Vector-DB, UI, etc. In this work we perform containerization of entire system.

Theory as well implementation of various type of rag structures