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Description
Oracle AI Vector Search is an innovative feature integrated into Oracle Database, specifically tailored for AI applications, which enables the querying of data based on its semantic meaning rather than relying solely on conventional keyword searches. This functionality empowers organizations to conduct similarity searches across both structured and unstructured datasets, allowing for retrieval of results that prioritize contextual relevance over precise matches. Employing vector embeddings to represent various forms of data—including text, images, and documents—it utilizes advanced vector indexing and distance metrics to quickly locate similar items. Moreover, it introduces a unique VECTOR data type along with SQL operators and syntax that enable developers to merge semantic searches with relational queries within a single database framework. As a result, this integration streamlines the data management process by negating the necessity for separate vector databases, ultimately minimizing data fragmentation and fostering a cohesive environment for both AI and operational data. The enhanced capability not only simplifies the architecture but also enhances the overall efficiency of data retrieval and analysis in complex AI workloads.
Description
Weaviate is an open-source vector database built for the AI era, giving teams one platform for vector search, retrieval-augmented generation, and agent memory. Store data objects together with embeddings from your preferred machine learning models and scale effortlessly to billions of objects. Import your own vectors or rely on Weaviate's built-in vectorization, then search across vector, keyword, and hybrid methods to get highly relevant results, even when filters are applied. By connecting to today's leading large language models, Weaviate helps you build grounded search and question-answering over your own data. The platform reaches well beyond storage: its Query Agent translates plain-language questions into accurate queries with citations, Engram delivers managed long-term memory for AI agents, and Weaviate Embeddings removes the work of running your own embedding pipeline. Available as self-hosted open source or fully managed Weaviate Cloud across AWS, GCP, and Azure, backed by SOC 2 Type II, native multi-tenancy, replication, and role-based access control. From semantic search to recommendation to fully agentic applications, Weaviate is the foundation to ship AI products faster.
API Access
Has API
API Access
Has API
Integrations
Airbyte
Amazon Bedrock
Amazon Web Services (AWS)
Anthropic
Anyscale
Azure Marketplace
Context Data
Databricks
DocumentDB for VS Code
Dynamiq
Integrations
Airbyte
Amazon Bedrock
Amazon Web Services (AWS)
Anthropic
Anyscale
Azure Marketplace
Context Data
Databricks
DocumentDB for VS Code
Dynamiq
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Open source (free); free Weaviate Cloud tier; paid Cloud plans from $45/mo.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Oracle
Country
United States
Website
www.oracle.com/database/ai-vector-search/
Vendor Details
Company Name
Weaviate
Founded
2019
Country
The Netherlands
Website
weaviate.io