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

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Embedding models and rerankers

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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
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