---
title: "Discussion on \"RAG for Microsoft AI Agent through TextSearchProvider and Redis\" | Hashnode"
description: "Discussion on \"RAG for Microsoft AI Agent through TextSearchProvider and Redis\". Implement RAG in Microsoft Agent Framework using Redis and TextSearchProvider for vector search, embeddings and semantic retrieval"
url: "https://hashnode.com/posts/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis/6a8e69cedeab3ab2cc6fd4ac"
---

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# [RAG for Microsoft AI Agent through TextSearchProvider and Redis](https://www.azureguru.net/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis?utm_source=hashnode&utm_medium=feed)

Implement RAG in Microsoft Agent Framework using Redis and TextSearchProvider for vector search, embeddings and semantic retrieval

Aug 26 · 12 min read · azureguru.net  
Updated Sep 16  
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-   [#llm](https://hashnode.com/tag/llm)
-   [#rag](https://hashnode.com/tag/rag)
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-   [#vector-embeddings](https://hashnode.com/tag/vector-embeddings)
-   [#vectorsearch](https://hashnode.com/tag/vectorsearch)

## Written by

[Sachin Nandanwar](https://hashnode.com/@SachinNandanwar)

@SachinNandanwar

  
[View profile](https://hashnode.com/@SachinNandanwar)

From Synapse Analytics, Power BI, Spark, Microsoft Fabric,ASP.NET Core and recently Agentic AI on .NET I try to explore, learn and share all aspects of Microsoft Data Stack in this blog.

## Responses 1

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[Julian Neagu](https://hashnode.com/@julianneagulab)

[Aug 28](https://hashnode.com/posts/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis/6a8e69cedeab3ab2cc6fd4ac/comment/6a913dcefd56c90fdd149205)

Moving embeddings out of memory is one of those changes that matters once real traffic shows up. I like the Redis setup here because it keeps storage separate without making retrieval overly complex.