What Are the Four Essential Workflows for a Self-Hosted RAG Chatbot?
Retrieval-Augmented Generation (RAG) is the gold standard for creating AI chatbots that understand your specific data. While cloud-based services offer convenience, self-hosting a RAG system provides complete control over your privacy and infrastructure.
To build a reliable self-hosted RAG chatbot, you need to think beyond just “chatting.” A robust architecture requires four essential workflows that handle everything from initial setup to daily data management.
1. Bootstrap Workflow (Infrastructure)
The Bootstrap workflow forms your system’s foundation. This is where you set up the underlying environment your chatbot needs to function—think of it as building the house before moving in the furniture.
In a self-hosted environment, this workflow automates the deployment of:
- Your vector database
- The Large Language Model (LLM) orchestration layer
- Any necessary API connections
The Bootstrap workflow’s goal is to ensure all technical components communicate correctly before processing any data.
2. Ingest Workflow (Data Pipeline)
A RAG chatbot is only as intelligent as the data you provide. The Ingest workflow transforms your raw files—PDFs, spreadsheets, or text documents—into a format the AI can understand.
This workflow involves several technical steps:
- Cleaning: Removing unnecessary formatting or noise from files
- Chunking: Breaking long documents into smaller, digestible pieces
- Embedding: Converting text chunks into numerical vectors
- Storage: Saving vectors in your database for later retrieval
3. Deletion and Maintenance Workflow
Data isn’t static. Information becomes outdated, files need replacement, and sometimes you must clear old records. The Deletion and Maintenance workflow prevents your chatbot from suffering from data clutter.
Without dedicated maintenance, your chatbot might retrieve outdated, conflicting information alongside new data, causing confusion or inaccuracies. This workflow enables you to:
- Target specific documents for removal
- Refresh the entire database
- Keep your AI’s knowledge current and clean
4. Responder Workflow (The User Interface)
The Responder workflow is what users actually interact with. This logic governs how the chatbot handles queries.
When a user asks a question, the Responder workflow:
- Searches the vector database for the most relevant information
- Contextualizes by packaging the user’s question with retrieved data
- Generates a natural, evidence-based response by sending the package to the LLM
Conclusion
By separating these four workflows, you create a system that’s easier to troubleshoot, scale, and maintain over the long term. Each workflow serves a distinct purpose, working together to deliver a powerful, self-hosted RAG chatbot that truly understands your data.