Community
Learn Llama 3.2 and How to Build a RAG Pipeline with Llama and Milvus
introduce Llama 3.1 and 3.2 and explore how to build a RAG app with Llama 3.2 and Milvus.
Community
Building RAG Applications with Milvus, Qwen, and vLLM
In this blog, we will explore Qwen and vLLM and how combining both with the Milvus vector database can be used to build a robust RAG system.
Community
Challenges in Structured Document Data Extraction at Scale with LLMs
In this blog, we’ll dive into the primary challenges of structured document data extraction. We'll also explore how Unstract tackles various scenarios, including its integration with vector databases like Milvus, to bring structure to previously unmanageable data.
Community
Building a RAG Application with Milvus and Databricks DBRX
In this tutorial, we will explore how to build a robust RAG application by combining the capabilities of Milvus, a scalable vector database optimized for similarity search, and DBRX.
Community
Implementing Agentic RAG Using Claude 3.5 Sonnet, LlamaIndex, and Milvus
Learn Agentic RAG, its challenges and benefits, and a guide to building an Agentic RAG with Claude 3.4 Sonnet, LlamaIndex, and Milvus.
Community
Evaluating Safety & Alignment of LLM in Specific Domains
In this blog, we’ll explore how companies like Hydrox AI and AI Alliance are tackling the critical challenges of AI safety and evaluation.
Community
Ensuring Secure and Permission-Aware RAG Deployments
This blog introduces key security considerations for RAG deployments, including data anonymization, strong encryption, input/output validation, and robust access controls, among other critical security measures.
Community
DeepSeek vs. OpenAI: A Battle of Innovation in Modern AI
Compare OpenAI's o1 and o3-mini with DeepSeek R1's open-source alternative. Discover which AI model offers the best balance of reasoning capabilities and cost efficiency.
Community
Evaluating Retrieval-Augmented Generation (RAG): Everything You Should Know
An overview of various RAG pipeline architectures, retrieval and evaluation frameworks, and examples of biases and failures in LLMs.