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[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (1)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (2)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (3)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (4)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (5)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (1)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (2)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (3)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (4)
[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel product image (5)

[Source Code] Building a RAG App with DeepSeek R1, Ollama, and Semantic Kernel

$7.88 AUD

In this project, we’ll walk you through creating a Retrieval Augmented Generation (RAG) application that runs locally with DeepSeek R1, Ollama, and the Semantic Kernel.

You’ll see how to embed and query your own documents—like an expense manager—using natural-language prompts. By the end, you’ll know how to combine DeepSeek R1 open-source AI models with custom data to get quick, accurate answers in real time.
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