Agents and RAG workflows with little to no code
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Updated
Jun 9, 2024 - Go
Agents and RAG workflows with little to no code
A python library for creating AI assistants with Vectara, using Agentic RAG
Agentic RAG using Crew AI
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.
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.
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.
Automated resume generation based on job link using CrewAi
A tailored Chatbot to reduce hallucinations and improve factuality.
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.
Multi document Agentic RAG implementation using OpenAI GPT3.5-Turbo
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.
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