Cosmos DB 向量搜索用于 RAG:Azure 上的 NoSQL 原生 DiskANN 和 Terraform

📄 中文摘要

Azure AI Search 功能强大,但需要管理一个单独的服务。Cosmos DB 的 NoSQL 现在内置了由微软研究院的 DiskANN 驱动的向量搜索,能够将向量和数据一起存储,并实现低于 20 毫秒的延迟。对于许多 RAG 工作负载来说,Azure AI Search 提供的完整检索堆栈虽然具有语义排名、混合搜索和查询分解等功能,但作为一个独立服务,其定价层级可能超出需求。使用 Terraform 设置 Cosmos DB 的向量搜索可以简化基础设施管理,适合 RAG 工作负载的需求。

📄 English Summary

Cosmos DB Vector Search for RAG: NoSQL-Native DiskANN on Azure with Terraform 🔎

Azure AI Search is a powerful tool but requires managing a separate service. Cosmos DB for NoSQL now includes built-in vector search powered by Microsoft's DiskANN, allowing for the storage of vectors and data together with sub-20ms latency. While Azure AI Search offers a complete retrieval stack with features like semantic ranking, hybrid search, and query decomposition, it may be more infrastructure than necessary for many RAG workloads due to its separate service and pricing tiers. Setting up vector search in Cosmos DB using Terraform can streamline infrastructure management, making it suitable for RAG workloads.

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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等