Agents of Fortune

Retrieval-Augmented Generation (RAG) is a powerful approach in natural language processing that combines the benefits of information retrieval and text generation. In this article, we will explore six different types of RAG, each offering unique capabilities to improve the quality and accuracy of generated content. https://lnkd.in/dAtnaJBh

Comprehensive Guide to Filtering in Qdrant Filtering is a crucial feature in vector databases like Qdrant, allowing users to refine search results based on specific criteria. This guide will explore how filtering works in Qdrant, its implementation, and best practices for optimal performance. https://lnkd.in/d_ZRB8_7

Advanced Filtering in Neo4j with GraphQL and Vector Embeddings Neo4j, a popular graph database, offers powerful filtering capabilities that can be enhanced when combined with GraphQL and vector embeddings. This guide will explore how to implement advanced filtering techniques in Neo4j, particularly in the context of HybridRAG (Hybrid Retrieval Augmented Generation) systems. https://lnkd.in/d4k4CUyc

LLM Agents in Cybersecurity: Stanford University’s Groundbreaking Benchmark In the rapidly evolving world of artificial intelligence and machine learning, new methods for evaluating the capabilities of language models (LLMs) are constantly emerging. Recently, researchers from Stanford University introduced an intriguing benchmark focused on the abilities of LLM agents in the field of cybersecurity. https://lnkd.in/evMBuTfB

Multi-Stage Vector Querying Using Matryoshka Representation Learning (MRL) in Qdrant Data retrieval is crucial in creating an efficient Retrieval Augmented Generation (RAG) application. Its effectiveness directly impacts the application's performance, accuracy, and reliability. There are various methods of data retrieval from vector databases. Some of the most efficient ones are: - Self-Query Retrieval - Multi-Stage Query - Auto-Merging Retrieval - Hybrid Retrieval In this article, we will explore Multi-Stage Query for data retrieval using Matryoshka Representation Learning (MRL) to increase the efficiency of fetching data from the database. So, let’s first understand what Matryoshka Representation Learning is. https://lnkd.in/epuv5Z3n