Tech With Tim·2yBuild a Full RAG Based AI App Including Deployment In Under 20 Minutes
Learn how to build a full RAG-based AI app using Vex, a platform that allows you to quickly build and deploy applications. The app demonstrated in the post is a travel assistant that provides personalized recommendations based on travel plans and preferences.
neo4j·2yGraph vs. Vector RAG — Benchmarking, Optimization Levers, and a Financial Analysis Example
Exploring the use of graph and vector search in retrieval-augmented generation (RAG) systems, focusing on their application in financial analysis. Discusses the differences between graph and vector search, optimization levers for graph search, and the combination of both methods in RAG. Highlights the benefits of graph databases for modeling complex relationships and dependencies, as well as the limitations and complementarity of vector search. Demonstrates the application of graph and vector search in a financial report RAG example.
GoPenAI·2yIntroduction to Retrieval-Augmented Generation (RAG): A Beginner’s Guide
Introduction to Retrieval-Augmented Generation (RAG): A Beginner's Guide. RAG combines retrieval and generative AI techniques to ensure accurate and meaningful responses. The RAG process involves document ingestion, retrieval, and response generation. RAG systems provide precise and top-notch text responses, elevating the performance of AI applications.
Towards AI·2yThe Rise of Vector Databases: Understanding Vector Search and RAG Pipeline
This post explains the fundamentals of vectors and vector databases, as well as the implementation of vector searches and Retrieval-Augmented Generation (RAG) using Qdrant Vector Database. It covers the applications of vector databases in NLP, recommendation engines, and image/video searches. The post also provides a step-by-step guide on how to perform vector searches using Qdrant database and how RAG can be used to improve the accuracy of language models.
Medium·2yStream changes from a PostgreSQL Database to a Vector Store
This post explains how to keep a vector store up to date using Change Data Capture (CDC), Python, and Redpanda. It provides a step-by-step guide to building a CDC-powered indexing pipeline that streams changes from a PostgreSQL database to a vector store. Using a prototype application, the post demonstrates how to use Docker, Quix Streams, and other tools to continuously ingest and update vectors for real-time search result optimization. Detailed instructions are provided for setting up and running the pipeline, as well as for understanding the underlying code and architecture.
