Many of the problems developers face with Retrieval-Augmented Generation (RAG) boil down to this: Individual chunks don’t contain sufficient context to be properly used by the retrieval system or the Language Model (LLM). This leads to the inability to answer seemingly simple questions and, more worryingly, hallucinations.
(more…)Category: agents
Multi-Stage Vector Querying Using Matryoshka Representation Learning (MRL) in Qdrant
Data retrieval is a crucial component in creating an efficient Retrieval Augmented Generation (RAG) application. The effectiveness of data retrieval directly impacts the performance, accuracy, and reliability of the application.
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.
(more…)How to Perform Full-fledged RAG for Any Website Using Firecrawl and Korvus
We are excited to present a detailed guide on using the power of RAG (Retrieval Augmented Generation) from Korvus in combination with Firecrawl. This combination allows you to quickly and easily set up a generation system with enhanced search capabilities using data from any website. Our approach demonstrates how to combine efficient web scraping, data processing, and modern machine learning methods in one elegant solution.
(more…)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.
(more…)How Businesses Can Adapt to AI Opportunities: A Practical Guide
With the rapid development of artificial intelligence (AI) technologies and the emergence of powerful language models like ChatGPT, new opportunities are opening up for companies. However, to fully capitalize on these opportunities, businesses need a deliberate approach to adapting their processes. How can companies begin integrating AI right now? Let’s explore the key steps that will help businesses successfully incorporate these new technologies into their operations.
(more…)AI Technologies: RAG Chatbots vs Agent AI – Which Is More Effective?
In the rapidly evolving world of artificial intelligence (AI), new technologies continually emerge, revolutionizing our interaction with machines. Two such technologies – RAG chatbots and Agent AI – have recently garnered significant attention. Let’s delve into what they are and which might be more effective for various tasks.
(more…)LLM-Driven Autonomous Agents
Building autonomous agents powered by Large Language Models (LLMs) as their primary control system is a fascinating concept. Several proof-of-concept demonstrations, such as AutoGPT, GPT-Engineer, and BabyAGI, illustrate this potential. The capabilities of LLMs extend far beyond generating well-crafted text and code; they can be harnessed as formidable general problem solvers.
(more…)How To Build Web UI Generator Agent: An Innovative Approach to User Interface Creation
In the ever-evolving world of web development, new tools and technologies constantly emerge to simplify and accelerate the process of creating user interfaces. One such innovative solution is the Web UI Generator Agent – an intelligent assistant that leverages the capabilities of Large Language Models (LLMs) to generate HTML and CSS code based on design descriptions.
(more…)Building Enterprise AI Applications with Multi-Agent RAG Systems (MARS)
In the rapidly evolving world of artificial intelligence, the emergence of advanced Retrieval-Augmented Generation (RAG) and Multi-Agent Software Engineering (MASE) has opened new horizons for enterprise AI applications. Let’s explore how these technologies converge to create a powerful tool for businesses.
(more…)Revolutionizing AI Development: NVIDIA NIM Microservices and LangChain Integration
In the ever-evolving landscape of artificial intelligence, NVIDIA has once again pushed the boundaries with its latest offering: NVIDIA NIM microservices. This groundbreaking technology, a core component of NVIDIA AI Enterprise, has recently expanded its capabilities to support tool-calling for advanced models like Llama 3.1. Moreover, its seamless integration with LangChain provides developers with a robust, production-ready solution for creating sophisticated agentic workflows.
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