Auto Added by WPeMatico
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
According to Stack Overflow and Atlassian, developers lose between 6 and 10 hours every week searching for information or clarifying unclear documentation. For a 50-developer team, that adds up to $675,000–$1.1 million in wasted productivity every year. This is not just a tooling issue. It is a retrieval problem.Enterprises have plenty of data but lack
Rethinking Enterprise Search: How Cortex Search Turns Data into Business Impact Read More »
AI demos often look impressive, delivering fast responses, polished communication, and strong performance in controlled environments. But once real users interact with the system, issues surface like hallucinations, inconsistent tone, and answers that should never be given. What seemed ready for production quickly creates friction and exposes the gap between demo success and real-world reliability.
When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades. It scores documents by looking at three things:
How BM25 and RAG Retrieve Information Differently? Read More »
What if the way we build AI document chatbots today is flawed? Most systems use RAG. They split documents into chunks, create embeddings, and retrieve answers using similarity search. It works in demos but often fails in real use. It misses obvious answers or picks the wrong context. Now there is a new approach called
PageIndex vs Traditional RAG: A Better Way to Build Document Chatbots Read More »
