Instruction 03

Explaining Filters in Hybrid Search

Filtering is a key aspect of hybrid search with Azure AI Search. Filters are applied to the content of filterable vector or non-vector fields. By default, filters are applied before the query is executed, reducing the query surface. You can change this to apply filters after the query is executed, reducing the result set instead.

Relevance Tuning

You can tune the results for a hybrid search using scoring profiles, semantic ranking, and fine-tuning query parameters.

Scoring profiles increase the search score if matches are found in a specific field. There’s also static boosting and dynamic scoring — static boosting assigns a constant weight, whereas dynamic scoring adjusts the scores based on set conditions.

You can also tune the search configuration to suit your use case. This usually involves altering the scoring scheme to favor the rankings for certain responses. Typically, you’d tune it to prioritize documents that closely match the semantic meaning of your queries for your specific use case.

Using Lucene syntax, you can boost specific terms in your search query, or specify special characters to configure boosting, scoring profiles, or other tunable properties.

Exploring Advantages of Hybrid Search

Hybrid search in Azure AI Search builds on the strengths of its individual search types to produce a more comprehensive solution. It delivers the speed and accuracy of keyword search and the versatility of vector search in a single, highly efficient query.

Hybrid search significantly enhances vector search. Benchmarks indicate that hybrid searches produce more accurate results than basic semantic search.

Hybrid search is particularly beneficial for RAGs, enabling a superior chat experience over custom data. You get highly accurate results whether searching for specific data, such as product names or IDs, or using less precise, more conversational queries. For example, in a chat app, a user may not always articulate their messages clearly. A hybrid search-backed RAG app built with Azure AI Search will ensure the user receives relevant responses, helping to keep the conversation flowing smoothly.

Hybrid search is effective for queries over large datasets where accuracy is crucial. In applications like document retrieval for legal or medical research, where some queries rely on specific terms (e.g., “clinical trial 2023”), while others need semantic interpretation (e.g., “recent advancements in malaria treatment”), hybrid search can improve both recall and precision.

Organizations can leverage hybrid search to improve document search across various departments and applications. In these cases, hybrid search enables broader query coverage by balancing semantic meaning with specific keywords, helping employees to find accurate and relevant documents faster.

Exploring Scenarios Where Hybrid Search May Not Be Effective

While hybrid search offers many advantages, there are situations where it might not be the most effective solution:

• Highly structured data: If the search query is based on highly specialized codes, serial numbers, or structured data — such as dates and people’s names — hybrid search might not offer a significant advantage over traditional keyword search. In these cases, precise term matching using keyword-based methods alone can yield better performance.
• Small datasets: In scenarios with small datasets or highly structured data, the added complexity of vector search might not provide enough value. For example, if you’re searching a small database with simple queries, the overhead of maintaining a vector-based index can outweigh its benefits.
• Resource constraints: Vector search can be computationally expensive, especially when handling large datasets, or complex queries. In environments with limited resources, or low latency requirements, relying on traditional keyword search will likely be more efficient.

Continue to conclude this lesson.