RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines

An evaluation framework for LLM applications including RAG systems, providing observability, debugging, and guardrails. TruLens tracks retrieval quality, LLM performance, and hallucinations with detailed tracing.

Research paper introducing LANNS, a web-scale approximate nearest neighbor lookup system developed at Facebook (Meta). Published as an arXiv preprint in 2020, it describes techniques for serving ANN search at massive scale in production systems.

A research paper examining the intersection of long-context LLMs and Retrieval-Augmented Generation, focusing on the challenges of combining long-context windows with RAG pipelines, including the 'hard negatives' problem where irrelevant retrieved documents can degrade LLM output quality.

An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

The DataRobot vector databases feature provides FAISS-based internal vector databases and connections to external vector databases such as Pinecone, Elasticsearch, and Milvus. It supports creating and configuring vector databases, adding internal and external data sources, versioning internal and connected databases, and registering and deploying vector databases within the DataRobot AI platform to power retrieval-augmented generation and other AI use cases.

Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.