Knowledge Bases | Fully Managed RAG | DigitalOcean

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Knowledge Bases is a fully managed Retrieval-Augmented Generation (RAG) service that handles embeddings, vector storage, retrieval, reranking, and retrieval— so you can go from documents to intelligent AI apps without managing infrastructure.
Knowledge Bases handles ingestion, chunking, embeddings, retrieval, reranking, in one system — no management of vector DB or retrieval logic required.
Hybrid search, metadata filtering, and opt-in reranking with high-quality reranking models improve relevance without tuning pipelines or writing custom logic.
Test retrieval quality and LLM outputs visually before shipping. Adjust parameters, compare results, and generate production-ready API calls instantly.
Connect any MCP-compatible agent or DigitalOcean AI Agent directly to your knowledge base — no glue code, authentication plumbing, or custom connectors.
Most teams building retrieval pipelines are stuck stitching together tools, slowing down development and making production systems harder to scale. Knowledge Bases simplifies this process so you can focus on building.
This works in early prototypes—but becomes brittle and expensive in production.
With Knowledge Bases, you can:
One platform. From raw data to production-ready RAG—without the overhead.
Stop stitching together vector DBs, embedding models, and retrieval logic. DigitalOcean Knowledge Bases replaces fragmented RAG stacks with a single managed service.
Upload documents, wikis, runbooks, or structured data directly into your Knowledge Base.
We handle chunking, embeddings, indexing, and storage using managed infrastructure.
Enable reranking, test results in the Playground, and connect to agents or APIs in production with one click.
Give AI agents persistent memory by connecting them to structured knowledge sources for more accurate, context-aware responses.
Create and manage isolated knowledge bases per customer or project to power scalable, production-ready AI features.
Enable teams to retrieve and act on internal knowledge across wikis, runbooks, and documentation in real time.
Turn help docs into intelligent assistants that answer customer questions grounded in real product knowledge.
Analyze regulatory documents with metadata filters and generate summarized, auditable answers for compliance workflows.
No. Knowledge Bases includes fully managed vector storage and retrieval infrastructure.
Yes. You can choose from multiple DigitalOcean Serverless Inference embedding models.
Yes. MCP integration allows any compatible agent framework to connect directly.
No. It is optional but recommended for higher-quality retrieval results. Learn more about available embeddings and reranking models for DigitalOcean Knowledge Bases.
Directly in the DigitalOcean console under Data Services, via API, or through the DigitalOcean SDK.
We offer fully-managed data infrastructure, integrated with every layer of the DigitalOcean stack. Check out the Data Services page for more product information.
How to Use MCP with OpenAI Agents
MCP 101: An Introduction to Model Context Protocol
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Build an End-to-End RAG Pipeline for LLM Applications