RAG — Retrieval-Augmented Generation

Hybrid & Advanced Retrieval

This lesson covers the concept of hybrid and advanced retrieval in AI, specifically how to improve the performance of large language models by combining different retrieval algorithms. We will discuss the importance of chunking strategies, combining retrieval algorithms, and evaluating the quality of a retrieval system. This knowledge is crucial for modern AI and LLM systems that rely on efficient and accurate data retrieval.

Why It Matters

In the real world of AI, efficient and accurate data retrieval is critical for large language models to provide high-quality responses. By combining different retrieval algorithms and evaluating their performance, developers can improve the overall quality of their models and provide better user experiences. This topic matters because it directly impacts the performance and reliability of AI systems.

Key Points

• Term-based and Embedding-based Retrieval: There are two main types of retrieval algorithms: term-based and embedding-based. Term-based retrieval uses sparse vectors to represent data, while embedding-based retrieval uses dense vectors to preserve the important properties of the original data.

• Hybrid Search: Combining term-based retrieval and embedding-based retrieval can improve the performance of a retrieval system by leveraging the strengths of both algorithms. This is known as hybrid search.

• Chunking Strategies: The way data is indexed depends on the intended retrieval strategy. Different chunking strategies, such as chunking strategy, reranking, query rewriting, and contextual retrieval, can be used to improve the performance of a retrieval system.

• Evaluation Metrics: To evaluate the quality of a retrieval system, developers should use metrics such as retrieval quality, final RAG outputs, and embeddings (for embedding-based retrieval).

• Vector Databases: Embedding-based retrieval requires a vector database to store and search vectors. A vector database is responsible for finding vectors in the database that are similar to a given query embedding.

• Finetuning: To improve the performance of a retrieval system, developers can finetune the embedding model and the retriever, either separately or together.

• End-to-End Training: Finetuning the whole RAG system end-to-end can improve its performance significantly. This involves training the retriever and the generative model together.

Key Concepts

Sparse Vector

A vector where most of the values are 0.

Embedding

A dense vector that preserves the important properties of the original data.

Hybrid Search

Combining term-based retrieval and embedding-based retrieval to improve performance.

Vector Database

A database that stores and searches vectors.

RAG System

A system that combines a retriever and a generative model to generate responses.

Quick Quiz

1. What is the main difference between term-based and embedding-based retrieval?

A) Term-based uses sparse vectors, while embedding-based uses dense vectors.

B) Term-based uses dense vectors, while embedding-based uses sparse vectors.

C) Term-based uses embeddings, while embedding-based uses sparse vectors.

D) Term-based uses sparse vectors, while embedding-based uses sparse vectors.

2. What is the purpose of a vector database in embedding-based retrieval?

A) To store and search vectors.

B) To finetune the embedding model and the retriever.

C) To evaluate the quality of a retrieval system.

D) To generate responses.

3. What can be done to improve the performance of a retrieval system?

A) Finetune the embedding model and the retriever separately.

B) Finetune the whole RAG system end-to-end.

C) Use only term-based retrieval.

D) Use only embedding-based retrieval.

← Vector Search & Retrieval