Oracle AI Vector SearchOracle
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voyage-code-3MongoDB
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Related Products
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About
Oracle AI Vector Search is a capability within Oracle Database designed for AI workloads that enables querying data based on semantics or meaning rather than traditional keyword matching. It allows organizations to search both structured and unstructured data using similarity search, making it possible to retrieve results based on contextual relevance instead of exact values. It uses vector embeddings to represent data such as text, images, or documents, and applies specialized vector indexes and distance functions to efficiently identify similar items. It introduces a native VECTOR data type, along with SQL operators and syntax that allow developers to combine semantic search with relational queries on business data in a single database environment. This eliminates the need for separate vector databases and reduces data fragmentation by keeping AI and operational data unified.
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About
Voyage AI introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Enterprises and developers who need to build AI applications that perform semantic search and generate context-aware results directly on business data
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Audience
AI researchers and developers in search of a solution providing an embedding model for code retrieval
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
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API
Offers API
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API
Offers API
Not Supported
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Screenshots and Videos |
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
No information available.
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
Supported
In Person
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Company InformationOracle
United States
www.oracle.com/database/ai-vector-search/
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Company InformationMongoDB
Founded: 2007
United States
blog.voyageai.com/2024/12/04/voyage-code-3/
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Integrations
Elasticsearch
Not Supported
JSON
Supported
Milvus
Not Supported
My DSO Manager
Supported
Oracle Database
Supported
Qdrant
Not Supported
SQL
Supported
Vespa
Not Supported
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Integrations
Elasticsearch
Supported
JSON
Not Supported
Milvus
Supported
My DSO Manager
Not Supported
Oracle Database
Not Supported
Qdrant
Supported
SQL
Not Supported
Vespa
Supported
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