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Explore CoRAG, a novel retrieval-augmented generation method that refines queries iteratively to improve multi-hop reasoning and factual answers.


Chain-of-Retrieval Augmented Generation

Learn the right ETL tools for unstructured data to power AI. Explore key challenges, tool comparisons, and integrations with Milvus for vector search.


Selecting the Right ETL Tools for Unstructured Data to Prepare for AI

Discover how audio transformers enhance sound processing. Explore their principles, selection criteria, popular models, applications, and key challenges.


Choosing the Right Audio Transformer: An In-depth Comparison

Discover audio data, its characteristics, processing techniques, and key challenges. Learn how to tackle them effectively in AI applications.


Getting Started with Audio Data: Processing Techniques and Key Challenges

 Explore DeepSeek-VL2, the open-source MoE vision-language model. Discover its architecture, efficient training pipeline, and top-tier performance.


DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Learn how to build scalable multimodal AI pipelines using DataVolo and Milvus. Discover best practices for handling unstructured data and implementing RAG systems.

Multimodal Pipelines for AI Applications

We will discuss how the Gandalf project revealed LLMs' vulnerabilities to adversarial attacks. Additionally, we will address the role of vector databases in AI security.


Insights into LLM Security from the World’s Largest Red Team

This blog will discuss the growing need to detect machine-generated text, past detection methods, and a new approach: Binoculars. 

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

Many-Shot In-Context Learning is an NLP technique where a model generates predictions by observing multiple examples within the input context. 

Unlocking the Power of Many-Shot In-Context Learning in LLMs

The Goldfish Loss technique prevents the verbatim reproduction of training data in LLM output by modifying the standard next-token prediction training objective.

Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

AXN (Adaptive Cross-Encoder Nearest Neighbor Search) uses a sparse matrix of CE scores to approximate k-NN results, reducing computation while maintaining high accuracy.

Next-Gen Retrieval: How Cross-Encoders and Sparse Matrix Factorization Redefine k-NN Search

Knowledge distillation is a machine learning technique in which the knowledge of a large, complex model (teacher) is transferred to a smaller, simpler model (student). 

Knowledge Distillation: Transferring Knowledge from Large, Computationally Expensive LLMs to Smaller Ones Without Sacrificing Validity

Voyager: a new open-source library for fast nearest-neighbor searches. Voyager uses the HNSW algorithm, outperforming its previous library, Annoy. 

Getting Started with Voyager: Spotify's Nearest-Neighbor Search Library

ALIGN (A Large-scale ImaGe and Noisy-text embedding) model is designed to learn visual and language representations from noisy image-alt-text pairs.

ALIGN Explained: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Mamba is a new architecture for sequence modeling, designed to offer an alternative to the Transformer models commonly used in machine learning. 

The Potential Transformer Replacement: Mamba

Mixture of Experts (MoE): a neural network architecture to improve model efficiency and scalability by selecting specialized experts for different tasks.


What is Mixture of Experts (MoE)? 

Object detection is a computer vision technique that uses neural networks to classify and locate objects, such as humans, buildings, or cars, in images or video. 

What is Object Detection? A Comprehensive Guide

DistilBERT maintains 97% of BERT's language understanding capabilities while being 40% small and 60% faster.


DistilBERT: A Distilled Version of BERT

A knowledge graph is a data structure representing information as a network of entities and their relationships.

What is a Knowledge Graph (KG)? 

RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an improved version of BERT designed to address its limitations. 

RoBERTa: An Optimized Method for Pretraining Self-supervised NLP Systems

Despite advances in LLMs like ChatGPT, search still matters. Combining GenAI with search and vector databases enhances search accuracy and experience.

Search Still Matters: Enhancing Information Retrieval with Generative AI and Vector Databases

Information retrieval (IR) is the process of efficiently retrieving relevant information from large collections of unstructured or semi-structured data. 

What is Information Retrieval?

Vision Transformers (ViTs) are neural network models that use transformers to perform computer vision tasks like object detection and image classification. 

A Beginner's Guide to Understanding Vision Transformers (ViT)

HyDE (Hypothetical Document Embeddings) is a retrieval method that uses "fake" documents to improve the answers of LLM and RAG. 

Improving Information Retrieval and RAG with Hypothetical Document Embeddings (HyDE)

Using LLMware and the Milvus vector database, we can combine the power of vector similarity search and LLMs to ask questions on our private documents.

Safeguarding Data Integrity: On-Prem RAG Deployment with LLMware and Milvus 

This article discussed the popular voyage AI embedding models and rerankers and their integration with Zilliz Cloud. 

Voyage AI Embeddings and Rerankers for Search and RAG 

Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.

Image Embeddings for Enhanced Image Search: An In-depth Explainer

Integrating the Milvus vector database and MemGPT has taken one step further in streamlining the development of AI Agents with connections to external data sources. In this post, we share an example demonstrating how to use this integration to build a chatbot with external memories. 


Introduction to MemGPT and Its Integration with Milvus

In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.


An Introduction to Vector Embeddings: What They Are and How to Use Them 

Understand how vector databases process data to enhance customer experience and drive business growth. 

Enhancing Customer Experience with Vector Databases: A Strategic Approach

Learn the mechanics, applications, and benefits of lexical and semantic search and how to perform it in Zilliz.

Navigating the Nuances of Lexical and Semantic Search with Zilliz

Explore different ranking algorithms, their usage, and best practices. Uncover the real-world success stories of ranking algorithms and their evolution over time.


Ranking Models: What Are They and When to Use Them?  

Multimodal AI learning can get input and understand information from various modalities like text, images, and audio together, leading to a deeper understanding of the world. Learn more about OpenAI's CLIP (Contrastive Language-Image Pre-training), a popular multimodal model for text and image data.

Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning

Mastering color histograms is indispensable for anyone involved in image processing and analysis. By understanding the nuances of color distributions and leveraging advanced techniques, practitioners can unlock the full potential of color histograms in various imaging projects and research endeavors.

 Demystifying Color Histograms: A Guide to Image Processing and Analysis

The softmax function is a mathematical function used in machine learning, particularly in the context of classification tasks. 

Decoding Softmax Activation Function

Understand Locality Sensitive Hashing as an effective similarity search technique. Learn practical applications, challenges, and Python implementation of LSH.

Mastering Locality Sensitive Hashing: A Comprehensive Tutorial and Use Cases

Understand Open AI Whisper and follow this step-by-step article to implement it in projects that can significantly enhance the efficiency of speech-to-text tasks.

OpenAI Whisper: Transforming Speech-to-Text with Advanced AI

BERTopic is a novel topic modeling technique that allows for easily interpretable topics while keeping important words in the topic descriptions. 

Exploring BERTopic: A New Era of Neural Topic Modeling

SPLADE is a technique that uses pre-trained transformer models to process sparse data. This post explores SPLADE, its benefits, and real-world apps. 

Discover SPLADE: Revolutionizing Sparse Data Processing

Cross Entropy loss is used for training classification models.

Cross-Entropy Loss: Unraveling its Role in Machine Learning

This article explores the memory capabilities of modern LLMs, using LangChain modules to establish memory buffers and build conversational AI applications. 

LangChain Memory: Enhancing AI Conversational Capabilities

Understand Information Retrieval Metrics and learn how to apply these metrics to evaluate your systems. 

Information Retrieval Metrics

This post explores various data modeling techniques for optimizing the performance of vector databases. 