Oracle Database 26ai

Measure what language model calls cost and how fast they are from inside Oracle 26ai, then cut cost with smaller models, lean prompts, and caching.

Make internal documents invisible to customers and available to agents in a RAG assistant, with row-level security on retrieval in Oracle 26ai.

See how a planted document reaches a RAG assistant in Oracle AI Database 26ai, block it with a review gate, and build defenses in layers.

Keep personal data out of prompts in Oracle AI Database 26ai with a redaction function, a call log, encrypted credentials, and tight network ACLs.

Keep every prompt on your own hardware by serving a model with Ollama and calling it from Oracle AI Database 26ai, then measure what you trade.

Combine SQL property graphs with vector search in Oracle AI Database 26ai to find who had a similar problem and which customers it affects.

Keep embeddings inside JSON documents in Oracle AI Database 26ai and search them by meaning, in collections or through duality views.

Join, group, and count rows by meaning with VECTOR_DISTANCE in Oracle AI Database 26ai to find knowledge gaps and the right agents.

Combine Oracle Machine Learning with embeddings to classify instantly for free, hand uncertain cases to a language model, and find topics.

Stop a language model's SQL from changing or revealing data with plan checks and a read-only schema, and measure how often it is right.

Let users ask questions in plain language by describing your schema to a language model and generating one SELECT statement in Oracle 26ai.

Check that RAG answers contain the right facts, cite real sources, and claim nothing unsupported, using SQL and a second language model.

Answer questions from your own data with Gemini and Oracle AI Database 26ai, citing sources and saying so when the answer is not there.

Get the details hidden in free text out with a language model in Oracle AI Database 26ai, in the right form and without invented answers.

Sort 400 tickets into categories, priorities, and sentiment with Gemini from Oracle SQL, in batches, and measure how far to trust the labels.

Log every language model call, retry when the provider is busy, see the tokens you pay for, and avoid calling a model for every row of a query.

Send prompts to a language model from SQL and PL/SQL in Oracle AI Database 26ai and control the role, randomness, length, and format of answers.

Find the passage that answers a question in PDF and Word documents with Oracle AI Database 26ai, and test which chunk size works best.