---
type: entity
subtype: tool
title: "Oracle AI Database"
description: "The video's sponsor; a unified AI database with built-in embeddings and semantic, keyword, and knowledge-graph search in one place, used for Cole's agentic RAG demo."
resource: "https://www.oracle.com"
tags: [database, backend, rag, knowledge-graphs]
videos: [the-subagent-era-is-officially-here-learn-this-now]
created: 2026-07-21
updated: 2026-07-21
---

# Oracle AI Database

Oracle AI Database is a database that folds the entire retrieval stack into a single system: embeddings, semantic search, keyword search, and knowledge-graph traversal all live inside the database itself. Cole introduces it as the video's sponsor and as a genuine architectural departure: "The sponsor of today's video is Oracle. And I'm excited for this one because of how they're approaching AI infrastructure with their AI database" [0:07:56].

## The problem it collapses

His framing is the sprawl every [RAG](../../concepts/rag.md) builder recognizes. The conventional architecture is a main application database, plus a separate vector database, plus maybe a separate graph database, plus an embedding API, plus a sync pipeline holding it all together, and "it's not easy to manage all of that." Oracle's pitch is that "everything is unified. So you have the embeddings built right into the database and you can do your semantic keyword and even knowledge graph search all in one place" [0:08:27]. Embeddings are generated in place rather than round-tripped to an external model provider, and the three retrieval modes that normally require three systems become three queries against one. Cole says he had never seen a database do this before.

## How Cole uses it

He built an [agentic RAG](../../concepts/agentic-rag.md) system on top of it and packaged the whole thing as a Jupyter notebook: it loads sample documents, demonstrates each of the search strategies native to the database, then wires them into [LangChain](./langchain.md) and [LangGraph](./langgraph.md) agents. In the live demo the agent picks a strategy per query, runs it inside Oracle, and returns with the relevant documentation before answering. That is the payoff of unification for an agent: [hybrid search](../../concepts/hybrid-search.md) and graph traversal become tool choices the model makes at runtime instead of infrastructure decisions frozen at build time. He submitted the notebook to Oracle's open-source AI developer hub and had the pull request approved, so the demo is runnable end to end.

## Realizes

- [Agentic RAG](../../concepts/agentic-rag.md) - Giving an agent retrieval as a tool so it picks strategies and issues follow-up queries instead of one static lookup.
- [Hybrid Search](../../concepts/hybrid-search.md) - Combining keyword and semantic vector search, here available natively in the same engine.
- [Knowledge Graphs](../../concepts/knowledge-graphs.md) - Entities and relationships an agent can traverse, queryable alongside vectors rather than in a separate store.

## Contrasts with

- [Postgres](./postgres-pgvector.md) - The pgvector approach, where vector storage is bolted onto the operational database and graph search still needs another system.
- [Neo4j](./neo4j.md) - A dedicated graph database, one of the separate services Oracle's unified model is meant to absorb.
- [Qdrant](./qdrant.md) - A standalone vector database, the extra hop plus sync pipeline the unified design removes.
- [Supabase](./supabase.md) - Postgres plus pgvector as one managed platform, Cole's usual default for this job.

## Works with

- [LangChain](./langchain.md) - Framework used to build the retrieval tools over the database in the demo notebook.
- [LangGraph](./langgraph.md) - Orchestrates the agent that chooses among the native search strategies per query.

## Related

- [Retrieval-Augmented Generation (RAG)](../../concepts/rag.md) - Grounding answers in retrieved documents, the workload this database is shaped around.
- [Semantic Search](../../concepts/semantic-search.md) - Embedding-based similarity retrieval, built in rather than bolted on.
- [Keyword Search](../../concepts/keyword-search.md) - Exact-term retrieval that complements semantic matching, especially for identifiers.
- [Vector Embeddings](../../concepts/vector-embeddings.md) - The numeric representations generated inside the database instead of through an external API.
- [Production-Ready RAG](../../concepts/production-ready-rag.md) - RAG that survives growth and updates, which fewer moving parts makes easier.
- [Knowledge Bases](../../concepts/knowledge-bases.md) - Curated bodies of knowledge an agent retrieves from to ground its answers.
- [AI Tech Stack](../../concepts/ai-tech-stack.md) - Choosing a coherent set of services, the decision this consolidates.

## Sources

- [The Subagent Era Is Officially Here - Learn this Now](../../sources/the-subagent-era-is-officially-here-learn-this-now.md) - "[0:07:56] The sponsor of today's video is Oracle. And I'm excited for this one because of how they're approaching AI infrastructure with their AI database."
- [The Subagent Era Is Officially Here - Learn this Now](../../sources/the-subagent-era-is-officially-here-learn-this-now.md) - "[0:08:27] everything is unified. So you have the embeddings built right into the database and you can do your semantic keyword and even knowledge graph search all in one place."
