The Power of Hybrid Search in Retrieval-Augmented Generation

Overview

Hybrid search is an innovative approach that combines the strengths of multiple search algorithms. It acts like a dream team, leveraging the best of different techniques to deliver highly relevant results for your queries. This is particularly valuable for applications based on Retrieval-Augmented Generation (RAG), where the system needs to understand a wide range of natural language inquiries.

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Introduction

Hybrid search combines the best of keyword-based and semantic search. Keyword search finds exact matches, perfect for queries like “capital of France” or specific product codes. Semantic search, on the other hand, focuses on meaning and intent. It can understand synonyms and related concepts, even if the exact keywords aren’t used.

For instance, imagine searching for “healthy recipes for vegetarians.” Keyword search might find recipes containing the word “vegetarian,” but it might miss dishes using terms like “plant-based” or “vegan.” Semantic search, understanding the intent to find healthy vegetarian options, would also include these.

Hybrid search bridges this gap by combining both approaches. It ensures precision by finding exact matches through keywords, while semantic search broadens the scope to include relevant synonyms and concepts. This comprehensive approach is ideal for RAG applications. Keywords can target specific entities in a query, like ingredients or dietary restrictions, while semantics can grasp the overall goal of finding healthy vegetarian recipes.

Hybrid Search

Hybrid search shines in several key scenarios:

Open Domain Q&A:

Imagine a system that answers questions on any topic. Hybrid search tackles this challenge by looking through massive document collections across various subjects. Website data, for instance, can cover sustainability, leadership, finances, and more. While semantic search grasps meaning, it might struggle with entirely new topics (out-of-domain) lacking specific entities. Here, keyword search steps in, allowing the system to find those unseen entities and narrow down the results for a more accurate answer.

Contextual Chatbots:

Conversations can take unexpected turns, jumping between topics. Hybrid search helps navigate these open-ended dialogues. It can understand the evolving context and retrieve relevant information, making the chatbot interactions smoother and more natural.

Personalized Search:

Large-scale web searches with diverse content benefit from a hybrid approach. Semantic search tackles popular, well-defined queries, while keywords handle less common, longer, and more specific ones (long-tail queries). This combination ensures comprehensive search coverage.

Sample Code

The following code shows how to use hybrid using the Retrieve API with Boto3:

import boto3 
bedrock_agent_runtime = boto3.client(
    service_name = "bedrock-agent-runtime"
)
 
def retrieve(query, kbId, numberOfResults=5):
    return bedrock_agent_runtime.retrieve(
        retrievalQuery= {
            'text': query
        },
        knowledgeBaseId=kbId,
        retrievalConfiguration= {
            'vectorSearchConfiguration': {
                'numberOfResults': numberOfResults,
                'overrideSearchType': "HYBRID", # optional
            }
        }
    )
response = retrieve(“query”)

import boto3

bedrock_agent_runtime = boto3.client(

service_name = "bedrock-agent-runtime"

)

def retrieve(query, kbId, numberOfResults=5):

return bedrock_agent_runtime.retrieve(

retrievalQuery= {

'text': query

knowledgeBaseId=kbId,

retrievalConfiguration= {

'vectorSearchConfiguration': {

'numberOfResults': numberOfResults,

'overrideSearchType': "HYBRID", # optional

}

)

response = retrieve(“query”)

Hybrid Search on Amazon Bedrock console

To use hybrid and semantic search options on the Amazon Bedrock console, complete the following steps:

On the Amazon Bedrock console, choose Knowledge Base in the navigation pane.
Choose the knowledge base you created.
Choose a Test knowledge base.
Choose the configurations icon.

bedrock

Benefits of a Hybrid Approach

By combining keyword and semantic search, hybrid search unlocks several advantages:

Enhanced Accuracy:

The quality of responses generated by your system (FM) relies heavily on the relevance of the retrieved results. Depending on your data, improving accuracy solely with semantic search can be challenging. Hybrid search tackles this by retrieving more relevant information, ultimately leading to more accurate answers from the FM.

Expanded Search Potential:

Keyword search acts like a wider net, catching potentially relevant documents but lacking a strong semantic structure. This allows you to search based on keywords alongside the text’s meaning, significantly expanding your search capabilities.

Conclusion

Hybrid search is a powerful tool for navigating the ever-expanding sea of information. By combining the strengths of keyword-based and semantic search, it delivers a more nuanced and user-friendly search experience. Hybrid search offers a winning solution for complex tasks like open domain question answering and context-aware chatbots.

It tackles diverse topics, adapts to evolving conversations, and personalizes search results. Additionally, it empowers applications like Retrieval-Augmented Generation (RAG) by providing a richer foundation for accurate and insightful responses.

Drop a query if you have any questions regarding Hybrid search and we will get back to you quickly.

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FAQs

1. Why Hybrid Search?

ANS: –

Improved Accuracy: Hybrid search retrieves more relevant information, leading to more accurate answers in applications like question answering systems.
Expanded Search Capabilities: It allows searching on both keywords and the meaning of the text, providing a wider range of potential results.
Better Handling of Complex Queries: Hybrid search more effectively tackles complex queries with various phrasings and open-ended questions.

2. Which regions of Amazon Bedrock are available to use Hybrid Search?

ANS: – East Coast US: Us East (N. Virginia), West Coast US: Us West (Oregon), Asia Pacific: Asia Pacific (Sydney) – Note: Limited features available (model evaluation and continued pre-training of custom models not supported), Asia Pacific: Asia Pacific (Singapore) – Note: Limited features available (model evaluation and continued pre-training of custom models not supported), for more information please visit website.

3. Is Chat Playground available to practice hybrid search on the console?

ANS: – Yes, it is available.

WRITTEN BY Suresh Kumar Reddy

Suresh is a highly skilled and results-driven Generative AI Engineer with over three years of experience and a proven track record in architecting, developing, and deploying end-to-end LLM-powered applications. His expertise covers the full project lifecycle, from foundational research and model fine-tuning to building scalable, production-grade RAG pipelines and enterprise-level GenAI platforms. Adept at leveraging state-of-the-art models, frameworks, and cloud technologies, Suresh specializes in creating innovative solutions to address complex business challenges.