Azure AI Search semantic/hybrid search: conversational query returns results influenced by generic terms (“books”) instead of topic (“anatomy”)

Hi @mona dar ,

Thanks for the thorough post and code samples, this is a classic challenge with conversational queries in hybrid + semantic setups, where common words like "books" or "give" can pull in too many irrelevant matches during the initial retrieval stage.

The core issue is that the keyword/BM25 part of hybrid search (when you pass the full query to SearchAsync(userQuery, ...)) treats every term equally, so generic words compete with your topic ("anatomy"). Semantic reranking helps reorder the top 50 candidates, but it can't fix a noisy initial candidate set.

Recommended fix: separate retrieval from reranking intent

In the .NET SDK (Azure.Search.Documents), use the SemanticSearch.SemanticQuery property to pass the full conversational query only for semantic reranking, captions, and answers. Meanwhile, clean the query on your app side and use that cleaned version for the main search parameter (keyword) and/or vector embedding.

This keeps initial retrieval (BM25 + vector) tightly focused on the topic, while semantic reranking uses the natural-language context to promote the most relevant results.

Example adjustment to your OptimizedSearchAsync:

// Simple app-side cleaning example (expand with regex/NLP as needed)
string cleanedQuery = CleanQuery(userQuery);  // e.g., "give me books about anatomy" → "anatomy books" or just "anatomy"
// Generate vector from full query for semantic similarity
float[] vector = GetEmbedding(userQuery);  // or cleanedQuery, test both
var vectorQuery = new VectorizedQuery(vector.AsMemory())
{
    KNearestNeighborsCount = 50,
    Fields = { "embedding" }
};
var options = new SearchOptions
{
    Size = top,
    IncludeTotalCount = true,
    QueryType = SearchQueryType.Semantic,
    SemanticSearch = new SemanticSearchOptions
    {
        SemanticConfigurationName = "semantic-trial",
        SemanticQuery = userQuery,  // <-- full conversational text here for reranking intent
        QueryCaption = new QueryCaption(QueryCaptionType.None),
        QueryAnswer = new QueryAnswer(QueryAnswerType.None)
    },
    VectorSearch = new VectorSearchOptions
    {
        Queries = { vectorQuery }
    }
};
// Optional: limit keyword search to content-rich fields to reduce generic noise
options.SearchFields.Add("title");
options.SearchFields.Add("description");  // or subjects/categories if you have them
var response = await _searchClient.SearchAsync<BookDocument>(cleanedQuery, options);

This pattern is directly supported: https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-request

Quick answers to your specific questions

• Conversational queries: Yes, preprocess/clean on the app side to strip fillers/greetings (e.g., keep only nouns/topics like "anatomy"). This is a common pattern for better recall/precision in RAG-like scenarios.
• Hybrid + semantic best practices: Use cleaned query for search + vectors, full query for SemanticQuery. Set KNearestNeighborsCount=50 for the vector part, and prioritize content fields in your semantic config (see below).
• Pure vector mode: No, SemanticSearch.RerankerScore won't be populated without a text-based query (semantic reranking requires it). Rely on the vector similarity score (r.Score).
• Semantic configuration fields: For books, order like this (first = highest priority):
  1. Description/summary/content (longer prose for context)
  2. Title
  3. Subjects/categories/genres/keywords Keep to 3-5 fields total due to ~2,000 token limit. Full guide: https://learn.microsoft.com/en-us/azure/search/semantic-how-to-configure
• Other tips: Use English analyzers with stopword removal, searchMode=all if you want stricter term matching, and limit searchFields to avoid metadata noise.

Hybrid overview here: https://learn.microsoft.com/en-us/azure/search/hybrid-search-overview

And .NET SDK reference for SemanticSearchOptions: https://learn.microsoft.com/en-us/dotnet/api/azure.search.documents.models.semanticsearchoptions.semanticquery?view=azure-dotnet

Kindly let us know if the above comment helps or you need further assistance on this issue.

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To address your concerns regarding the Azure AI Search implementation for your books catalog, here are some insights and recommendations based on your queries:

1. Handling Conversational Queries:
   • It is advisable to preprocess user queries on the application side. Normalizing or cleaning the input to focus on the topic (e.g., extracting "anatomy" from "give me books about anatomy") can help in reducing noise from generic terms. This preprocessing can enhance the relevance of the search results.
2. Hybrid + Semantic Best Practices:
   • To ensure that the topic intent dominates in hybrid search, consider using both semanticQuery and vectorQuery. This allows you to leverage the strengths of both semantic understanding and vector similarity. Additionally, ensure that your semantic configuration is well-tuned to prioritize fields that are most relevant to the content of your documents (e.g., title, description).
   • You may also want to experiment with the searchMode parameter, setting it to all to ensure that all terms in the query are considered in the context of the semantic search.
3. Pure Vector + Scoring:
   • In pure vector mode (when searchText is null), the SemanticSearch.RerankerScore should not be expected, as there is no semantic processing occurring. Instead, focus on the vector similarity score for ranking results. This score reflects how closely the vectors match, which is the primary metric in this mode.
4. Configuration Guidance:
   • For a books catalog, prioritize fields such as title, description, and subjects/categories in your semantic configuration. These fields are likely to provide the most context and relevance for user queries.
   • Consider using analyzers that can help filter out common stopwords and irrelevant terms. This can be done by configuring your index to use custom analyzers that are suited for your content type. Additionally, using searchFields to specify which fields to search against can help avoid matches driven by generic terms.

By implementing these strategies, you should be able to improve the relevance of your search results and better align them with user intent.

References:

• Create a semantic hybrid search
• Vector search in Azure AI Search