Traditional RAG architecture: data ingestion, parsing, embeddings, and retrieval.

Traditional RAG Architecture

The Retrieval-Augmented Generation (RAG) architecture combines two key processes: retrieval and generation. This process enhances natural language understanding and generation tasks by efficiently integrating external knowledge sources. The traditional RAG architecture consists of several core components including data ingestion, parsing, embeddings, and retrieval.

Data Ingestion

Data ingestion refers to the process of collecting and importing data from various sources into the RAG system. This can include:

• Text documents
• Web pages
• Databases
• APIs

The goal is to ensure a diverse dataset that accurately reflects the information domain the system will operate within.

Parsing

Once data is ingested, it needs to be parsed. Parsing involves analyzing the text to extract relevant information and structure it for further processing. Key activities in this phase include:

• Tokenization: Breaking text into individual words or phrases.
• Normalization: Converting all text to a standard format.
• Entity recognition: Identifying important entities within the text.

Embeddings

After parsing, the next step is to convert the parsed data into embeddings. Embeddings are dense vector representations that encode the semantic meaning of the text. This is crucial for the following reasons:

• Facilitates comparison between different texts.
• Enables the model to understand context and relationships between concepts.

Common techniques for generating embeddings include Word2Vec, GloVe, and transformer-based models like BERT.

Retrieval

The retrieval phase involves using the embeddings to fetch relevant documents or information from the database that can be used in the generation process. This step typically employs:

1. Similarity search algorithms to identify documents with similar embeddings.
2. Ranking mechanisms to prioritize the most relevant results.

By effectively retrieving contextual information, the RAG model is empowered to generate responses that are not only fluent but also informed by external knowledge.

RAG

Retrieval-Augmented Generation; an architecture that combines retrieval and generative models for enhanced text generation.

Embeddings

A numerical representation of text data in a continuous vector space.