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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Unlocking the Power of Vector Quantization: Techniques for Efficient Data Compression and Retrieval
Vector Quantization (VQ) is a data compression technique representing a large set of similar data points with a smaller set of representative vectors, known as centroids.