Retrieval-Augmented Generation (RAG) is the key to building AI agents that don't hallucinate, according to a new guide that outlines how to set up retrieval layers using vector stores like Chroma, FAI…
A new tutorial from StarTrail-org introduces PixelRAG, a pixel-native retrieval-augmented generation pipeline that renders web pages and PDFs as images, divides them into overlapping tiles, and genera…
Milvus 3.0.0, released by the Milvus project on July 29, 2026, extends the open-source vector database with lake-native retrieval, enabling indexing and search over data in open formats like Parquet, …
A senior developer argues that brute-force vector search with NumPy can outperform vector databases for datasets up to about 1 million documents, citing benchmarks from an M4 MacBook Pro showing 79.7 …
A new Damage Cause Encoder proposed by researchers achieves 87.07% test accuracy in classifying 10 damage causes from visible bridge descriptions by chaining knowledge triple extraction, retrieval-aug…
A developer built a Genomic RAG pipeline using LlamaIndex and FAISS to interpret raw DNA data from services like 23andMe. The system retrieves clinical significance from the ClinVar database and gener…
Researchers present Multimodal CoLRAG-TF, a retrieval-augmented generation architecture that integrates dense text embeddings, BM25 keyword matching, knowledge-graph triple filtering, and image-based …
A first-year undergraduate student in Artificial Intelligence Engineering is building a Retrieval-Augmented Generation (RAG) project from scratch, documenting the process in a series of blog posts. In…
A developer advises against passing raw JSON to an LLM for e-commerce chatbots, recommending instead a RAG pipeline that flattens product data into plain English sentences, creates embeddings with Lan…
DS@GT ARC achieved third place in the PlantCLEF 2026 challenge with a multi-scale ViT inference pipeline that blends a fine-tuned DINOv2 ViT-L/14 classifier with FAISS kNN retrieval and habitat-fit pr…
Researchers introduce NeuroGRIP, a retrieval-augmented graph refinement framework that uses external medical knowledge from a large-scale knowledge base derived from clinical guidelines to calibrate n…
A developer forked the archived Kùzu graph database, adding vector search, LLM embeddings, and a full Graph Data Science suite to create Gorgonzola, aiming to make it the central engine for healthcare…
HNSW (Hierarchical Navigable Small World) is a graph-based algorithm that powers vector search in FAISS, pgvector, Qdrant, Weaviate, and Milvus, enabling approximate nearest neighbor search in millise…
A developer built a production-ready semantic search engine for e-commerce using open-source tools: sentence-transformers for embedding, FAISS for vector indexing, and FastAPI for serving. The pipelin…
A developer built ELI, a local AI assistant that avoids trusting the LLM to describe system state. The project features desktop automation, local voice control, layered memory, and sandboxed code exec…
A developer building an AI chapter generator realized they didn't need vector embeddings or a RAG pipeline after all. The project required processing a full transcript in order, not retrieving relevan…
A developer explains how metadata filtering and reranking improve retrieval in RAG systems. Metadata filtering narrows the search space by using chunk attributes like chapter names, while cross-encode…
A developer explains how Retrieval-Augmented Generation (RAG) prevents AI hallucinations by giving models access to real data before answering. The post includes code examples using LangChain and FAIS…
A developer built a centralized vector database service for multi-project Retrieval-Augmented Generation (RAG) systems, allowing each project to have isolated storage while sharing one embedding model…
A developer explains how vector search works under the hood, focusing on the two dominant algorithms: IVF (Inverted File Index) and HNSW (Hierarchical Navigable Small World). The post details why appr…