Oracle AI Vector SearchOracle
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voyage-code-3MongoDB
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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
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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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
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
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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Categories |
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Integrations
Elasticsearch
JSON
Milvus
My DSO Manager
Oracle Database
Qdrant
SQL
Vespa
Weaviate
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Integrations
Elasticsearch
JSON
Milvus
My DSO Manager
Oracle Database
Qdrant
SQL
Vespa
Weaviate
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