Revolutionizing Information Retrieval for AI App with Azure Search: Vector, Semantic, Keyword, and Hybrid Search
In a recent video shared on the Microsoft Dynamics YouTube channel, Microsoft explored how to enhance information retrieval with Azure Search. In my previous blog, I showed how you can leverage vector search for your AI app. This blog dives into the key strategies covered from the video: Vector Search, Semantic Search, Keyword Search, and Hybrid Search.
Vector Search: Unleashing Conceptual Similarity
Vector Search is an essential technique in the realm of AI. It involves mapping concepts like sentences or images into high-dimensional vector space. These vectors are assigned based on conceptual similarity, not exact keywords. This approach is highly versatile and ideal for generative AI scenarios.
The core idea is to compute distance metrics between these vectors. Cosine similarity is one of the commonly used metrics for vector similarity, measuring the angle between vectors as a way to gauge their likeness. This approach is particularly robust, as it doesn’t rely on specific keywords or exact matches, but rather focuses on the meaning of the data.
Vector Databases: Scaling Efficiency
To make vector search work efficiently with large datasets containing hundreds of thousands or millions of data points, the vectors must be stored in a vector database. The process typically involves chunking the information into smaller units, encoding them into vectors, and indexing those vectors in the database. During retrieval, the query text is transformed into a query vector and sent to the database to quickly find indexed vectors that closely match it.
Azure Cognitive Search offers a robust solution for vector search, simplifying the process of building and querying vector indexes
Vectors for Text and Images
Vector search isn’t limited to text. It can be applied to images, enabling advanced image-based search. Azure Search seamlessly accommodates various data types, from text to images.
Semantic Memory and Vector Index
In my previous blog on Semantic Memory, I discussed how Semantic Memory offers a straightforward method for converting text to vectors, performing document chunking, and ingesting data into a vector database. Semantic Memory seamlessly integrates with Azure Cognitive Search, simplifying the management of embeddings and document chunking.
Keyword Search: Precision for Exact Matches
While vector search excels in capturing conceptual relationships, there are instances where keyword-based search is more suitable. When you need an exact match for specific terms or phrases, keyword search is the way to go. It’s particularly useful for seeking out precise information like email addresses, reference numbers, or any other data that relies on exact matches.
Azure Search supports keyword search alongside vector search, allowing developers to utilize the strengths of both methods as needed.
The Power of Hybrid Search
Hybrid search is where the real magic happens. By combining both vector and keyword search techniques, developers can achieve the best of both worlds. In this approach, a query is transformed into both a text-based query and a vector-based query, and then results from both are combined to provide a comprehensive set of responses.
Enhancing Precision with Semantic Re-ranking
To further enhance the quality of search results, a semantic re-ranking step can be added. In Azure Search, this is done using a semantic ranker developed in partnership with Bing, which leverages vast amounts of data and machine learning expertise. The re-ranking step helps optimize relevance by ensuring that the most related documents are presented at the top of the list.
Combining Search Methods
The combination of vector, keyword, and hybrid search, along with semantic re-ranking, yields superior search results. These approaches save time, enhance content quality, and reduce computational costs in AI applications.
The result shared by Microsoft below demonstrates that Hybrid Search outperforms other search methods.
Ready to Elevate Your Apps
All the techniques discussed are readily available through Azure Search. Microsoft provides sample code to facilitate adoption, making advanced information retrieval accessible to all. Whether you’re building the next AI application or enhancing existing ones, Azure Search offers the tools to revolutionize your information retrieval.
The future of AI applications is promising, and efficient information retrieval is a crucial part of that journey.
References:
- https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/how-vector-search-and-semantic-ranking-improve-your-gpt-prompts/ba-p/3963293
- YouTube Video on Microsoft Dynamics:
3. https://learn.microsoft.com/en-us/azure/search/vector-search-overview
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