Understanding the RAG Process

The RAG process involves five distinct steps.

The user query is converted into a numeric format using a different model than the one used in the ingestion phase.

The system retrieves the top-K documents or passages with the lowest similarity to the query vector.

The RAG process is a three-step process.

The embedding model is used to convert the user query into a natural language format.

The user query is posed directly to the knowledge base.

The RAG process involves the retrieval of contextual documents from an internal dataset.

The system generates a response based on the original input only.

The knowledge base is created during the RAG process.

The similarity between the query vector and vectors in the knowledge base is measured using Euclidean distance.

RAG Process Overview

The RAG process consists of four steps: Retrieval, Augmentation, Generation, and Response.
The process is designed to provide informed responses to user queries by leveraging external contextual documents.

Step 1: Retrieval

Contextual documents are retrieved from an external dataset.
The retrieval process is based on the similarity between the user query and the documents in the dataset.

User Query and Conversion

A user poses a natural language query to the LLM (e.g., "Tell me about the Renaissance period").
The query is converted into a numeric format using an embedding model, creating a vector representation.
The embedding model used for query conversion is the same as the one used for article embedding in the ingestion phase.

Vector Comparison and Retrieval

The query vector is compared to vectors in the knowledge base index using similarity or distance metrics (e.g., cosine similarity).
The system retrieves the top-K documents or passages with the highest similarity to the query vector.

Remaining Steps

Augmentation: The retrieved documents are integrated with the original input to enrich the context.
Generation: The model generates a response based on the augmented input.
Response: The informed response, influenced by the retrieved contextual documents, is delivered to the user.

Learn about the RAG process which involves retrieval, augmentation, generation, and response steps in natural language processing. Explore how contextual documents are integrated to enhance responses in models like LLM.