I’ve been exploring ways to integrate Retrieval-Augmented Generation (RAG) into my website, and I wanted to design a small, event-driven app to learn more about it. My goal was to create something simple yet practical for my site.

Below is a Mermaid diagram I created to model the flow of my application. It highlights the main components of an event-driven RAG system, covering content updates, user queries, and background indexing:

flowchart TD %% Content Update Flow A[Markdown File Update] --> B[File Watcher / Git Hook] B --> C[Trigger Content Update Event] C --> D[Process Content & Generate Embeddings] D --> E[Index/Update Qdrant DB] D --> F[Dynamic Summarization] %% User Query Flow G[User Query via API] --> H[Trigger Query Event] H --> I[Retrieve Related Docs from Qdrant] I --> J[C# Semantic Kernel] J --> K[Generate Answer] K --> L[Return Answer to User] %% Background Processes M[Scheduler Trigger] --> N[Initiate Full Reindexing Pipeline] N --> D %% Notification/Feedback (Optional) E --- O[Monitor Confidence / Low-Match Feedback] O --> P[Trigger Notification or Auto-Enhancement]

How It Works

Content Update Flow

  • Markdown File Update: Whenever I add or modify markdown files for my blog, a file watcher or Git hook detects the change.
  • Trigger Content Update Event: This detection triggers an event to start processing the updated content.
  • Process & Generate Embeddings: The content is processed to extract text, generate embeddings, and optionally create summaries using my C# pipeline.
  • Index/Update Qdrant DB: The embeddings are stored or updated in the Qdrant vector database.

User Query Flow

  • User Query via API: A user submits a query through the web API.
  • Trigger Query Event: The query triggers an event to initiate the search process.
  • Retrieve Related Docs: Relevant documents are fetched from Qdrant using vector similarity search.
  • C# Semantic Kernel: The Semantic Kernel processes the retrieved documents to provide context.
  • Generate Answer: An answer is crafted based on the contextual data.
  • Return Answer: The answer is sent back to the user.

Background Processes

  • Scheduler Trigger: A scheduled event (like a cron job) periodically reindexes all markdown files.
  • Full Reindexing Pipeline: This ensures the database is always up-to-date with the latest embeddings.

Notification/Feedback (Optional)

  • Monitor Confidence: The system monitors for low-confidence matches or ambiguous results.
  • Trigger Notification or Auto-Enhancement: If issues are detected, notifications are sent, or automatic enhancements are triggered to improve the content.

Why This Matters

This event-driven design ensures that my RAG application is efficient, responsive, and always up-to-date. By automating content updates and query handling, I can focus on improving the user experience while the system takes care of the heavy lifting.

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