What is RAG?

Introduction🚀

RAG is a popular method that improves accuracy and relevance by finding the right information from reliable sources and transforming it into useful answers.

Large Language Models are trained on a fixed dataset, which limits their ability to handle private or recent information. They can sometimes “hallucinate”, providing incorrect yet believable answers. Fine-tuning can help but it is expensive and not ideal for retraining again and again on new data. The Retrieval-Augmented Generation (RAG) framework addresses this issue by using external documents to improve the LLM’s responses through in-context learning. RAG ensures that the information provided by the LLM is not only contextually relevant but also accurate and up-to-date.

There are four main components in RAG:

Indexing: First, documents (in any format) are split into chunks, and embeddings for these chunks are created. These embeddings are then added to a vector store.

Retriever: Then, the retriever finds the most relevant documents based on the user’s query, using techniques like vector similarity from the vector store.

Augment: After that, the Augment part combines the user’s query with the retrieved context into a prompt, ensuring the LLM has the information needed to generate accurate responses.

Generate: Finally, the combined query and prompt are passed to the model, which then generates the final response to the user’s query.

These components of RAG allow the model to access up-to-date, accurate information and generate responses based on external knowledge. However, to ensure RAG systems are functioning effectively, it’s essential to evaluate their performance.

RAG Evaluation📊

Evaluating RAG applications is important for understanding how well these systems work. We can see how effectively they combine information retrieval with generative models by checking their accuracy and relevance. This evaluation helps improve RAG applications in tasks like text summarization, chatbots, and question-answering. It also identifies areas for improvement, ensuring that these systems provide trustworthy responses as information changes.

RAG Techniques⚙️

Technique	Tools	Description	Notebooks
Naive RAG	LangChain, Pinecone, Athina AI	Combines retrieved data with LLMs for simple and effective responses.
Hybrid RAG	LangChain, Chromadb, Athina AI	Combines vector search and traditional methods like BM25 for better information retrieval.
Hyde RAG	LangChain, Weaviate, Athina AI	Creates hypothetical document embeddings to find relevant information for a query.
Parent Document Retriever	LangChain, Chromadb, Athina AI	Breaks large documents into small parts and retrieves the full document if a part matches the query.
RAG fusion	LangChain, LangSmith, Qdrant, Athina AI	Generates sub-queries, ranks documents with Reciprocal Rank Fusion, and uses top results for accurate responses.
Contextual RAG	LangChain, Chromadb, Athina AI	Compresses retrieved documents to keep only relevant details for concise and accurate responses.
Rewrite Retrieve Read	LangChain, Chromadb, Athina AI	Improves query, retrieves better data, and generates accurate answers.
Corrective RAG	LangChain, LangGraph, Chromadb, Athina AI	Refines relevant documents, removes irrelevant ones or does the web search.
Self RAG	LangChain, LangGraph, FAISS, Athina AI	Reflects on retrieved data to ensure accurate and complete responses.
Adaptive RAG	LangChain, LangGraph, FAISS, Athina AI	Adjusts retrieval methods based on query type, using indexed data or web search.