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.

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.

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.

A tailored Chatbot to reduce hallucinations and improve factuality.

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.

Agentic RAG using Crew AI

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.

AI Rate My Professor is an AI-powered chatbot that utilizes Agentic RAG AI to help users find detailed information about professors by name or university.

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.

Automated resume generation based on job link using CrewAi

Demo repository from Hiflylabs to showcase Langgraph and Llamaindex agentic behavior.

Essential LLM abilities required for task automation, and infrastructures for agent development.

An agentic RAG implementation of the Bosch VTA Project

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

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

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.

Following the LangGraph tutorial: https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/#nodes-and-edges Using only opensource! Using llama.cpp with LangChain

A Multi Agent RAG Application built with LangGraph, CrewAI and Pathway. The App simulates the working of an Indian Legal Court.

Agentic RAG workflow purposely build for domain specific search task with context driven iterative reasoning

AI-enabled Intelligent Assistant to Improve Reading and Comprehension Skills in English Language. [Ongoing]