Amazon Bedrock Knowledge Bases

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.

DataRobot Vector Database is a managed vector store capability within the DataRobot AI Platform that allows users to create, register, deploy, and update vector databases for AI workloads, including RAG and semantic search. It integrates with NVIDIA NIM embeddings and supports both built-in and bring-your-own embeddings for building production-grade vector search solutions.

A managed service and tooling offering from Instaclustr that helps teams operate and optimize vector databases for GenAI and Retrieval-Augmented Generation (RAG) workloads, providing expertise and infrastructure management for production deployments.

A powerful RAG application platform delivering OpenAI-compatible serverless inference APIs for top open-source LLM models. Offers specialized batch processing for large-scale async AI workloads and document extraction capabilities designed for RAG applications, balancing cost-efficiency with high performance.

RAG-powered text-to-SQL framework that enables natural language querying of SQL databases using vector search for retrieval of relevant schema, documentation, and example queries.

Components in LLM frameworks that fetch and parse data from various sources (PDFs, websites, databases) into a standardized format for processing. Essential first step in RAG pipelines for converting raw data into processable documents.