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    DataRobot Vector Databases
    Featured

    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.

    Nuclia

    AI Search and RAG-as-a-Service platform with semantic search capabilities. Features NucliaDB open-source database. Acquired by Progress in 2025, now part of Progress Agentic RAG. This is a commercial service with OSS core (NucliaDB).

    txtai

    All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows. Combines vector indexes (sparse/dense), graph networks and relational databases. This is an OSS framework.

    Instaclustr Vector Database Management

    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.

    Elysia

    Elysia is an open-source, decision-tree-based agentic system built on top of Weaviate that orchestrates tools and vector-search workflows, demonstrating how to build complex AI agents that leverage a vector database as a core component.

    Verba

    Verba is a community-driven, open-source Retrieval-Augmented Generation (RAG) application that provides an end-to-end, user-friendly interface for building RAG workflows on top of a vector database, showcasing practical semantic search and retrieval patterns with Weaviate.

    DataRobot Vector Database

    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.

    DataRobot Vector Databases (GenAI)

    A premium vector database capability within the DataRobot Generative AI platform that stores chunked unstructured text and their embeddings for retrieval-augmented generation (RAG). Users can create vector database objects, connect supported data sources from the DataRobot Data Registry, configure embeddings and chunking, and attach these vector databases to LLM blueprints in the playground to ground model responses in proprietary data.

    Implement two-tower retrieval for large-scale candidate generation

    A Google Cloud reference architecture demonstrating an end-to-end two-tower retrieval system for large-scale candidate generation that uses Vertex AI and vector similarity search concepts to learn and serve semantic similarity between entities.

    HelixDB

    HelixDB is a powerful, open-source graph-vector database built in Rust, designed for intelligent data storage for Retrieval-Augmented Generation (RAG) and AI applications. It combines graph database features with vector search, making it directly relevant to AI and machine learning workflows that require vector data management.

    LangChain

    LangChain is an open-source framework that integrates with various vector databases, including Pinecone, Weaviate, and Chroma, to facilitate retrieval-augmented generation (RAG) and advanced AI workflows.

    Neo4j

    Neo4j is a graph database that has added vector search capabilities, providing unique and effective approaches for retrieval augmented generation (RAG) and other AI applications.

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    All product names, logos, and brands are the property of their respective owners. All company, product, and service names used in this repository, related repositories, and associated websites are for identification purposes only. The use of these names, logos, and brands does not imply endorsement, affiliation, or sponsorship. This directory may include content generated by artificial intelligence.
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