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LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are […]
RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval Read More »
In June 2026, Google introduced the Open Knowledge Format (OKF), an open specification for how AI agents organise and exchange knowledge. An OKF bundle is just Markdown files, lightweight YAML metadata, and links between concepts, yet it challenges the assumption that every AI application needs embeddings and vector databases. Because the knowledge base is plain
In this tutorial, we build a RAG-Anything workflow and use it to explore how multimodal retrieval works across text, tables, equations, and images. We start by preparing the Colab environment, installing the required packages, and securely entering our OpenAI API key at runtime to keep the notebook practical and safe to run. We then create
GraphRAG and Vector RAG address different retrieval needs. Vector RAG splits documents into chunks, embeds them, retrieves semantically similar passages, and sends them to an LLM. It is simple, fast to build, and works best when answers sit within one or two relevant chunks. GraphRAG adds structure by extracting entities, relationships, and communities, making it
GraphRAG vs Vector RAG: Which Retrieval Method is Best? Read More »
Google Research team has introduced a new agentic RAG framework. It is built into the Gemini Enterprise Agent Platform. It powers a feature called Cross-Corpus Retrieval, now in public preview. The target is a known failure mode in enterprise search. Standard single-step RAG was not built for multi-source, multi-hop queries. Ask “What are the specs
In this tutorial, we use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to improve retrieval quality. We start by setting up the runtime, loading the reranker, and understanding how it scores query-document pairs. Then, we move from simple pairwise scoring to a practical two-stage retrieve-and-rerank pipeline, where a fast bi-encoder first retrieves candidates and zerank-2 reranks
Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker Read More »
Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single
Gemini API File Search: The Easy Way to Build RAG Read More »
Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer
RAG Without Vectors: How PageIndex Retrieves by Reasoning Read More »
In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming
Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step
