Everything You Need to Know about Basic RAG
Demystifying the Basics of Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a technique that integrates external knowledge sources into large language models (LLMs) to enhance their response generation capabilities. By ingesting knowledge databases into the LLM, RAG allows the model to access information beyond its training data, leading to more accurate and informative responses.
In this blog post, we'll walk through the fundamental components of a RAG system and how to implement a basic RAG pipeline from scratch. We'll also contrast this approach with using popular frameworks like LangChain and LlamaIndex.
The RAG Architecture
A typical RAG system consists of two main components: the retrieval module and the generation module.
The Retrieval Module:
Data Ingestion: This involves reading documents (PDFs, web pages, etc.), splitting them into smaller text chunks, and embedding these chunks into a vector database for efficient retrieval.
Retrieval: When a user query is received, the retrieval module searches the vector database for the most relevant text chunks based on semantic similarity.
The Generation Module:
Prompt Engineering: The relevant text chunks from the retrieval module are combined with the user query to prompt for the LLM.
Response Generation: The LLM generates a coherent response based on the provided context (relevant text chunks) and the query.
Implementing a Basic RAG Pipeline
Data Ingestion and Embedding:
Extract text from documents (e.g., PDFs)
Split the text into smaller chunks
Embed the text chunks using a suitable embedding model (e.g., Sentence Transformers)
Store the embeddings and associated metadata (e.g., document source) in a vector database (e.g., Qdrant)
Retrieval:
Embed the user query using the same embedding model
Search the vector database for the most relevant text chunks based on semantic similarity between the query embedding and stored embeddings
Response Generation:
Pass the relevant context, query and prompt to the LLM (e.g., OpenAI's GPT-3, a locally hosted LLM like LLaMa3)
The LLM generates a response based on the provided context
Enhancements and Variations
Adding References: Include references in the LLM's response for transparency.
Streaming Responses: Enable real-time streaming of the LLM's response for an improved user experience.
Using Frameworks: While implementing RAG from scratch aids in understanding the concepts, frameworks like LangChain and LlamaIndex offer abstracted and streamlined implementations.
By following this step-by-step guide, you can gain a solid understanding of the core concepts behind RAG and implement a basic RAG pipeline tailored to your specific use case. As you progress, you can explore more advanced techniques, such as query routing, multi-document queries, and multimodal queries, to further enhance the capabilities of your RAG system.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into everything basic RAG, discussing the concepts and contrasting start from scratch implementation vs. LangcChain/LlamaIndex, in the video below!👇
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Source of images/quotes:
• SELF-RAG Explained: Intuitive Guide &...
Github: https://github.com/mallahyari/llm-stuff/blob/main/01_rag_basic.ipynb
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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