Product Features - voyage-context-3 可以直接替换标准 embeddings，无需更改下游工作流程 [1] - 只需更改模型名称即可开始使用 [1]

Cost Efficiency - Voyage-context-3 (512维)相比 OpenAI-v3-large (3072维) 向量数据库成本降低 99.48% [1] Retrieval Quality - Voyage-context-3 (binary, 512维) 检索质量提升 0.73% [1]

Model Capabilities - Voyage-context-3 支持 2048, 1024, 512 和 256 维度，并具备量化功能 [1] Cost Efficiency - Voyage-context-3 (int8, 2048 维度) 相比 OpenAI-v3-large (float, 3072 维度) 降低了 83% 的向量数据库成本 [1] Performance - Voyage-context-3 提供了 860% 更好的检索质量 [1]

Performance Summary - Voyage-context-3 outperforms all models across all domains in 93 retrieval datasets spanning nine domains [1] - Voyage-context-3 outperforms OpenAI-v3-large by 1420 basis points (14.2%) [1] - Voyage-context-3 outperforms Cohere-v4 by 789 basis points (7.89%) [1] - Voyage-context-3 outperforms Jina-v3 by 2366 basis points (23.66%) [1]

Technically, unlike traditional chunk embedding, the model processes the entire doc in a single pass to embed each chunk.This way, it sees all the chunks at the same time to generate global document-aware chunk embeddings.This gives semantically aware retrieval in RAG. https://t.co/LFIGCGwjtC ...

Model Description - MongoDB's voyage-context-3 embedding model addresses contextual chunk embedding [1] - The model generates vectors for chunks, capturing the complete document context [1] - It eliminates the need for manual metadata and context augmentation [1] Key Feature - The model is a contextualized chunk embedding model [1]

Despite tuning and balancing tradeoffs, the final chunk embeddings are generated independently with no interaction with each other.This isn't true with real-world docs, which have long-range dependencies.Check this 👇 https://t.co/3pydkHDNCN ...

Chunking Challenges in RAG - Chunking involves determining overlap and generating summaries, which can be complex [1] - Lack of chunking increases token costs [1] - Large chunks may result in loss of fine-grained context [1] - Small chunks may result in loss of global/neighbourhood context [1]

RAG系统分析 - RAG系统性能的80%取决于检索质量，20%取决于生成质量 [1] - RAG系统失效的主要原因是检索问题，根本原因在于分块和嵌入策略 [1] - 上下文分块嵌入模型可以解决检索问题 [1]

Model Performance - A new embedding model outperforms OpenAI and Cohere models [1] - The new model reduces vector database costs by approximately 200 times [1]