DataRobot Vector Databases
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.
About this tool
name: DataRobot Vector Databases slug: datarobot-vector-databases url: https://docs.datarobot.com/en/docs/api/reference/sdk/CHANGES.html vendor: DataRobot category: managed-vector-databases featured: true images:
- https://www.datarobot.com/wp-content/uploads/2023/06/genai-hero.png
- https://www.datarobot.com/wp-content/uploads/2024/01/GenAI-BG.jpg logo: https://www.datarobot.com/wp-content/uploads/2024/01/datarobot-logo.svg labels:
- vector-databases
- rag
- managed-service
Overview
DataRobot Vector Databases provide FAISS-based internal vector stores and managed connections to external vector databases. They are integrated into the DataRobot AI platform to support retrieval-augmented generation (RAG) and other embedding-based AI workloads.
The feature focuses on creating and configuring vector databases, managing versions, and registering and deploying these databases as part of production AI systems.
Features
Vector database types
- Internal FAISS-based vector databases managed directly inside DataRobot.
- External vector database connections, including:
- Pinecone
- Elasticsearch
- Milvus
Creation and configuration
- Create new internal vector databases within the DataRobot platform.
- Configure connection details for supported external vector database providers.
- Associate vector databases with AI projects and deployments.
Data sources and ingestion
- Add internal data sources (data already within DataRobot) to internal vector databases.
- Add and manage external data sources as inputs to vector databases.
- Configure ingestion and update behavior for connected databases (where supported by the underlying system).
Versioning and lifecycle management
- Versioning for internal vector databases, allowing tracking and promotion of specific versions.
- Versioning for connected / external databases, capturing versions of configurations or snapshots as supported.
- Manage multiple versions for experimentation, testing, and rollback.
Registration and deployment
- Register vector databases as reusable assets within the DataRobot platform.
- Deploy vector databases into production environments as part of AI applications.
- Use deployed vector databases as a retrieval layer for models and generative pipelines.
RAG and AI use cases
- Power retrieval-augmented generation (RAG) workflows by providing similarity search over embedded documents.
- Support other embedding-based AI use cases, such as semantic search and recommendation, through the same internal/external vector database abstraction.
Integrations
- Internal: FAISS-based vector store integrated natively with DataRobot.
- External vector databases:
- Pinecone
- Elasticsearch
- Milvus
Pricing
Pricing information is not provided in the available content. Refer to DataRobot’s official pricing or sales materials for details on plans and costs related to vector databases.
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