Maximizing LLM RAG Retrieval with Hybrid Search: A Step-by-Step Guide

Author(s): Dr. Leon Eversberg

TL;DR: Use Hybrid Search for improved LLM RAG retrieval. Combine dense embeddings and BM25 to create an advanced local LLM RAG pipeline.

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Introduction

In today’s world, data retrieval and analysis have become crucial for businesses and organizations to make informed decisions. Legal document retrieval, in particular, is a complex and time-consuming process, especially for lawyers and legal professionals. However, with the advancement of technology, hybrid search techniques have emerged, making the retrieval process more efficient and accurate. In this blog post, we will discuss how hybrid search can be used for better LLM RAG retrieval and how to build an advanced local LLM RAG pipeline by combining dense embeddings with BM25.

Understanding Hybrid Search

Hybrid search is a combination of two or more search techniques to retrieve relevant information from a large dataset. In the legal domain, hybrid search combines the traditional keyword-based search with advanced techniques such as natural language processing (NLP) and machine learning (ML). This combination allows for a more comprehensive and accurate retrieval of legal documents, making it an ideal solution for lawyers and legal professionals.

Using Hybrid Search for LLM RAG Retrieval

LLM RAG retrieval refers to the process of retrieving relevant legal documents based on a specific legal concept or topic. Traditional keyword-based search techniques often fail to capture the nuances and complexities of legal language, leading to inaccurate results. Hybrid search, on the other hand, utilizes NLP and ML techniques to understand the context and meaning of legal terms, resulting in more precise and relevant document retrieval.

Building an Advanced Local LLM RAG Pipeline

To build an advanced local LLM RAG pipeline, we can combine dense embeddings with BM25. Dense embeddings, also known as deep learning-based embeddings, are powerful techniques that represent words or phrases in a high-dimensional vector space. On the other hand, BM25 is a ranking algorithm that takes into account the relevance and importance of words in a document. By combining these two techniques, we can build a more advanced and accurate LLM RAG pipeline that can handle complex legal language and retrieve relevant documents.

Steps to Build an Advanced Local LLM RAG Pipeline

To build an advanced local LLM RAG pipeline, we need to follow these steps:
1. Preprocess the legal documents: The first step is to preprocess the documents by removing stop words, punctuation, and converting them into lowercase.
2. Generate dense embeddings: Next, we need to generate dense embeddings for each document using deep learning techniques such as Word2Vec or BERT.
3. Calculate BM25 scores: Then, we need to calculate the BM25 scores for each document based on the legal concept or topic we want to retrieve

In conclusion, combining dense embeddings with BM25 can greatly improve the performance of local LLM RAG retrieval. Hybrid search techniques allow for a more comprehensive approach to searching, resulting in more accurate and relevant results. By implementing this advanced pipeline, users can expect to see a significant improvement in their LLM RAG retrieval process. This method is user-friendly and highly effective, making it a valuable tool for anyone looking to optimize their search process.

Crafted using generative AI from insights found on Towards Data Science.

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