Research paper on operating and managing Retrieval-Augmented Generation (RAG) pipelines at scale, covering production infrastructure patterns, monitoring, microservices decomposition, and multi-model architecture for enterprise embedding systems.

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.

DeepLearning.AI course teaching six practical vector database applications using Pinecone, including RAG for LLMs, recommender systems, and hybrid search combining images and text.

Advanced retrieval approach combining dense vectors, sparse vectors, and reranking in a multi-stage pipeline, achieving up to 48% better performance than single-method retrieval.