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Build a RAG Chatbot in ASP.NET Core with Qdrant (.NET 10)
Chunking, embeddings, Qdrant vector search and a prompt with checked citations, tested against a real Qdrant with no API key.
A customer asks your support bot how long refunds take. It answers “14 days” with total confidence. Your policy says 30. Plain LLM chat is fluent, not informed.
Retrieval-augmented generation (RAG) finds the relevant parts of your documents first and makes the model answer from them, with citations you can check. In this tutorial we’ll build RagChat, a small but real RAG pipeline in ASP.NET Core: chunking, embeddings through Microsoft.Extensions.AI, vector search in Qdrant, a prompt with numbered sources, and two minimal API endpoints, /ingest and /ask.
No API keys needed: the model sits behind IEmbeddingGenerator and IChatClient, so tests use deterministic local fakes, and OpenAI or Ollama is a config change. Qdrant isn't faked: the integration tests run against a real Qdrant 1.19.2 binary. Every output below comes from a real run on .NET SDK 10.0.401 with Qdrant.Client 1.19.0 and Microsoft.Extensions.AI 10.11.0.
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