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Embedding models and rerankers
Tengyu Ma
Co-founder & CEO, Voyage
Assistant professor, Stanford
Retrieval Augmented Generation (RAG) is the
Predominant Approach for Enterprise Gen AI
Response
Documents
Embedding
Model
Corpus
Vector
Store
Generative
Model
Vector
Embedding
Model
Query
Documents &
Vectors
Retrieved
Documents
Voyage AI Products and Offerings
Offerings
1. Public API endpoints
2. Zilliz Cloud Pipeline
3. AWS Marketplace Model Package
4. AWS JumpStart
5. On-premise, customer-hosted deployment
6. In-Progress
a. Snowflake Cortex Function
b. Amazon Bedrock
c. Google Cloud Marketplace and Vertex AI
General-purpose
Embedding Model
Code
Embedding Model
Company X
Embedding Model
Embedding models and search
system with continual training
with human feedback
Law
Embedding Model
Finance
Embedding Model
Multilingual
Embedding Model
Voyage Models are State-of-the-Art in Retrieval Quality
voyage-code-2 for Code Retrieval Tasks
voyage-law-2 for Legal Retrieval Tasks
voyage-law-2 on long-context retrieval
voyage-law-2 on other domains
Rerankers
Rerankers
voyage/rerank-lite-1
Early Pilot Partners
Trusted by Partners like
Trusted by Zilliz
Use Voyage
Embeddings on
Zilliz Cloud
Pipeline

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Voyage AI: cutting-edge embeddings and rerankers for search and RAG