Easily bring AI-powered similarity search to your business data without managing and integrating multiple databases or compromising functionality, security, and consistency. AI Vector Search enables searching both structured and unstructured data by semantics or meaning, and by values, enabling ultra-sophisticated AI search applications. Native AI vector search capabilities can also help large language models (LLMs) deliver more accurate and contextually relevant results for enterprise use cases using retrieval-augmented generation (RAG) on your business data.
Watch how this AI startup went from concept to robust AI offering in record time by leveraging Oracle Autonomous AI Database 26ai.
Oracle AI Database 26ai with AI Vector Search helped the Australia-based company deliver faster, more reliable data and analytics services, increasing its revenue by 30%.
Easily combine similarity search with relational, text, JSON, spatial, and graph data types to enhance your apps—all in a single database. Bring AI to your data – don’t move your data for AI.
Enable natural language search across your private business data using RAG to guide the LLM of your choice better and steer it away from hallucinations.
Use your favorite development tools, AI frameworks, AI models, and programming languages to build AI apps how you want.
Build mission-critical AI apps with ease. Leverage industrial-strength capabilities to achieve scalability, performance, high availability, and security.
Oracle AI Vector Search capabilities include document load, transformation, chunking, embedding, similarity search, and RAG with LLMs is available natively or through APIs within the database.
See how AI-generated vector embeddings enable lightning-fast similarity searches using US National Parks Service’s data.
Oracle AI Database architects AI into the entire data and development stack, helping organizations deliver trusted, AI-powered insights, innovations, and productivity for all their data, everywhere.
Learn how AI Vector Search in Oracle AI Database 26ai combines semantic search on unstructured data with relational search on traditional business data for faster, more relevant, and more secure results.
"Oracle's technology has been instrumental in revolutionizing our disease identification process. Oracle AI Vector Search and Autonomous Database have enabled us to significantly reduce diagnosis time, improve accuracy, and provide better patient care."
Combine AI Vector Search with relational, text, JSON, knowledge graph, and spatial location searches to improve results by focusing on the full meaning of a user’s query when retrieving matching documents, images, videos, audio, and structured data.
Use the native VECTOR data type to store vectors in Oracle AI Database 26ai tables. Generate the vectors using your choice of open source embedding models using the ONNX framework, database APIs to generate vectors from your preferred embedding model provider, or import vectors directly into the database.
Accelerate similarity searches using highly accurate approximate search indexes (vector indexes), such as the in-memory neighbor graph index for maximum performance and neighbor partition indexes for massive data sets. Use hybrid vector indexes to rapidly search combinations of vector and non-vector data.
Use simple, intuitive SQL to perform similarity search on vectors and freely combine vectors with relational, text, JSON, and other data types within the same query.
Take complete control of the search accuracy your application requires by specifying the target accuracy as a simple percentage. Define default accuracy during index creation and override in search queries, if needed.
Accelerate vector index creation and search with Oracle Exadata System Software 25ai optimizations. Gain the high performance, scale, and availability Exadata provides to enterprise databases.
Similarity search is focused on finding related data based on its semantic meaning. Unstructured data is difficult to search directly, so similarity search goes beyond simple keyword searches by considering the underlying text, image, audio, or video data instead of only searching the labels manually applied to it.
The need to identify a match for similar data across large data sets applies to many industries. Examples of similarity search include the following:
RAG uses the results of similarity search to improve the accuracy and contextual relevance of large language model responses to questions about business data. RAG helps identify contextually relevant private data that the LLM may not have been trained on and then uses it to augment user prompts so LLMs can respond with greater accuracy.
The desire to get higher quality answers from LLMs is universal, spanning many industries. Some examples of using RAG for improved accuracy include the following:
RAG helps organizations provide customized answers to business questions without the high cost of retraining or fine-tuning the LLMs.
"We are happy to see AI Vector Search added to Oracle Database. We appreciate that we can run AI Vector Search in the same Oracle Database as our other workloads, which allows us to provide a reliable and secure solution."
Oracle announced the availability of Oracle Database 23ai in May 2024. Oracle AI Database adds to the more than 300 new features available in Oracle Database 23ai, so there's a lot to learn. Dominic Giles highlights several marquee features in his blog post, but one of the most exciting new features of Oracle AI Database is Oracle AI Vector Search.
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With AI Vector Search in Oracle AI Database 26ai, organizations can combine semantic search of their business data with relational queries inside the same database.
Leading industry analysts share how AI Vector Search can help organizations everywhere use business data with GenAI to improve customer experiences and employee productivity.
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