What Happens During the ‘Ingestion Phase’ of a RAG Workflow?
The Ingestion Phase is the “heavy lifting” stage of a Retrieval-Augmented Generation (RAG) system. It transforms your raw, unstructured documents—such as PDFs, Word files, or internal wikis—into a searchable format that AI can understand and use.
Without a solid ingestion process, your chatbot is essentially an empty shell with no access to your specific business data.
The Goal of Ingestion
The primary goal of this phase is to move data from your storage into a vector database. Unlike traditional databases that search for exact words, vector databases allow AI to search for meaning and context.
The Step-by-Step Workflow
To prepare your data for AI, it passes through three critical technical stages:
1. Chunking (Breaking It Down)
Large documents are difficult for AI models to process all at once. If you feed an entire 50-page manual into the system, the specific answer to a user’s question might get lost in the noise.
During chunking, the system breaks text into smaller, overlapping pieces (usually a few sentences or paragraphs each). This ensures that when the AI searches for an answer, it can pinpoint the exact section of a document that is relevant.
2. Embedding (Mathematical Translation)
AI models don’t read words—they process numbers. Once the text is chunked, each piece is sent through an embedding model.
This model translates human language into a vector—a long string of numbers representing the mathematical “coordinates” of that text’s meaning. For example, “car” and “automobile” would result in vectors that are mathematically close to each other, despite their different spellings.
3. Vector Storage
Once text chunks have been converted into vectors, they are stored in a vector database.
This database acts as a specialized filing cabinet, storing both the original text chunk and its corresponding mathematical vector. This setup allows the system to perform similarity searches later, matching a user’s question to the most relevant data chunks in milliseconds.
Why Quality Ingestion Matters
The Ingestion Phase is where you prevent “garbage in, garbage out.” If data is poorly chunked or if the embedding model is weak, the chatbot will struggle to find the right information, regardless of how advanced the AI model is. A clean ingestion pipeline ensures that your self-hosted RAG system remains accurate and reliable.