使用 Terraform 在 Azure 上构建 AI 搜索 RAG:您的第一个 RAG 管道
📄 中文摘要
Azure 的 RAG 模式通过单个 API 调用连接 Blob 存储、AI 搜索和 OpenAI。Terraform 用于配置搜索服务和存储。'On Your Data' API 处理其余部分。通过 Azure OpenAI 端点回答一般问题时,可能会出现幻觉现象,而 RAG 模式通过将模型响应与实际数据相结合来解决这一问题。Azure 的 RAG 架构将三个服务连接在一起:Blob 存储用于保存文档,Azure AI 搜索负责使用向量和关键字搜索对其进行索引,Azure OpenAI 的 'On Your Data' 功能则将这一切整合在一个 API 调用中,简化了检索管道的构建过程。
📄 English Summary
Azure AI Search RAG with Terraform: Your First RAG Pipeline on Azure 🔍
The Azure RAG pattern connects Blob Storage, AI Search, and OpenAI in a single API call, with Terraform provisioning the search service and storage. The 'On Your Data' API manages the remaining processes. When using an Azure OpenAI endpoint to answer general questions, it may generate hallucinations regarding internal documents. The RAG approach mitigates this by grounding model responses in actual data. Azure's RAG architecture integrates three services: Blob Storage for document storage, Azure AI Search for indexing with vector and keyword search, and Azure OpenAI's 'On Your Data' feature that ties everything together in a single API call, simplifying the retrieval pipeline construction.
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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等