The RAG Architecture: End-to-End Process and Data Flow

Advanced

The RAG architecture orchestrates retrieval and generation through a seamless pipeline:

Input (User Query)
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Embed Query ------------------> Embedding Model
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Retrieve Top-k Documents from External Corpus
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Concatenate Query + Documents
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Pass Combined Input to Generator Model
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Generate Response

Step-by-step explanation:

  1. Encoding the query: The user's question is converted into a dense vector using an encoder.
  2. Document retrieval: This vector is compared against document embeddings in a vector database using similarity search, retrieving the most relevant documents.
  3. Input preparation: The query and retrieved documents are combined—often concatenated with special tokens—to form the input for the generator.
  4. Response generation: The generator (e.g., T5, BART) produces a contextually enriched answer conditioned on the combined input.

The entire process can be optimized via pipelines like Hugging Face's transformers and faiss. Proper indexing of documents ensures fast retrieval, critical for real-time applications.

This architecture enables the model to adapt dynamically to different knowledge domains and keeps responses aligned with external, often up-to-date information sources.