Simple RAG

A Simple Retrieval Augmented Generation (RAG) Framework

Retrieval-Augmented Generation (RAG) enhances language models by integrating external knowledge sources, improving their ability to provide accurate and up-to-date information. A basic RAG system comprises two main components:

Retrieval Model:

• Fetches relevant information from external sources like databases or search engines.

Language Model:

• Generates responses based on the retrieved information.

To build a simple RAG system in Python using the ollama library, follow these steps:

1. Indexing Phase

• Chunking: Divide the dataset into smaller segments (e.g., sentences or paragraphs).
• Embedding: Convert each chunk into a vector representation using a pre-trained embedding model.
• Storage: Store these embeddings in a vector database for efficient retrieval.

2. Retrieval Phase

• Query Embedding: Convert the user's query into an embedding.
• Similarity Search: Compare the query embedding with stored embeddings to find the most relevant chunks using metrics like cosine similarity.

3. Generation Phase

• Prompt Construction: Combine the retrieved information with the user's query to form a prompt.
• Response Generation: Use a language model to generate a response based on this prompt.

This is a basic RAG system that retrieves relevant information from a dataset and generates responses accordingly. For more advanced implementations, consider exploring modular RAG systems that incorporate routing, scheduling, and fusion mechanisms to create flexible and reconfigurable frameworks.