azure-search-openai-demo and entaoai
Both tools provide a sample application for implementing a Retrieval-Augmented Generation (RAG) pattern to chat with custom data using Azure OpenAI, making them direct competitors as they address the same core use case.
About azure-search-openai-demo
Azure-Samples/azure-search-openai-demo
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
Implements document indexing via Azure AI Document Intelligence with support for multiple formats, includes in-UI settings for prompt engineering experimentation, and optionally layers multimodal vision capabilities for image-heavy content analysis. The backend uses Python with Infrastructure-as-Code (Bicep) for reproducible Azure deployments, while offering optional features like persistent chat history via Cosmos DB, speech accessibility, and Microsoft Entra identity integration for role-based data access control.
About entaoai
akshata29/entaoai
Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions
Implements retrieval-augmented generation (RAG) using Azure OpenAI embeddings with pluggable vector stores (Pinecone, Redis, Azure Cognitive Search) for semantic search over enterprise documents. Supports multiple interaction patterns including streaming chat, Q&A with follow-up generation, and SQL natural language queries, with built-in evaluation metrics (groundedness, coherence, similarity) and optional Azure Prompt Flow integration for production deployments.
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