Voyage AI
Voyage AI builds embedding models customized to the domain and company, for better retrieval quality.
Voyage AI and Zilliz
Zilliz has partnered with Voyage AI to simplify the conversion of unstructured data into searchable vector embeddings in Zilliz Cloud and to help assess the effectiveness of a RAG system implemented with various embedding models for code-related tasks.
Zilliz and Voyage AI: Revolutionizing RAG applications
Zilliz Cloud Pipelines, a tool for converting unstructured data into high-quality embeddings, supports voyage-2 and voyage-code-2 from VoyageAI as embedding models to achieve the highest retrieval quality on code-related tasks. This integration enables scalable API service for retrieval, ideal for use with tools like LlamaIndex. Get started crafting superior RAG for code-intensive texts with Zilliz Cloud Pipelines and Voyage AI using the integration below. There is no need to create a separate account for the embedding model, everything is turnkey in your Zilliz Cloud account.
Crafting Superior RAG for Code-Intensive Texts with Zilliz Cloud Pipelines and Voyage AI
Learn how to assess the effectiveness of a retrieval augmented generation (RAG) system implemented with various embedding models. See how the voyage-2 and voyage-code-2 embedding models perform compared to BAAI and Open AI in terms of retrieval capability on code datasets. This blog digs into the methodology and shows how the Voyage AI models perform significantly better on code-related retrieval tasks.