Implementing Semantic Search with Azure AI Search and Azure OpenAI Embeddings

Marco Farina
Aug 7, 2025
6 min read

Introduction

Traditional keyword search systems rely on lexical matching. While this approach works well for exact matches, it often fails when users phrase queries differently from how information is stored. Modern applications increasingly require search systems that understand the meaning of a query rather than just its words.

Semantic search solves this problem by representing text as numerical vectors that capture contextual meaning. These vectors allow search engines to retrieve documents that are conceptually similar to a query, even if they do not share the same keywords.

In the Microsoft ecosystem, semantic search can be implemented using Azure AI Search combined with Azure OpenAI embeddings. This architecture enables developers to build highly scalable search systems capable of supporting knowledge bases, document discovery platforms, enterprise copilots, and Retrieval-Augmented Generation (RAG) applications.

This article explores how semantic search works, how vector embeddings are generated, how vector indexes are implemented in Azure AI Search, and how enterprise systems can combine semantic retrieval with traditional search techniques to deliver accurate and relevant results.

The Limitations of Keyword-Based Search

Keyword search works by matching tokens within a document index. Systems such as traditional SQL full-text search or early search engines rely heavily on inverted indexes and ranking algorithms such as TF-IDF or BM25.

While effective in many cases, keyword search presents several limitations.

First, it struggles with synonyms. If a user searches for “car” but the document uses the word “vehicle,” the search engine may fail to retrieve the document.

Second, keyword search lacks contextual understanding. Queries such as “How to scale Azure AI applications” may retrieve documents containing the words “scale” and “Azure,” even if they refer to unrelated concepts.

Third, keyword search cannot effectively capture user intent. Modern users often phrase queries in natural language, expecting systems to interpret meaning rather than exact wording.

Semantic search addresses these issues by embedding text into a high-dimensional vector space where semantically similar content appears close together.

Understanding Embeddings

Embeddings are numerical representations of text generated by machine learning models. Instead of representing text as tokens or keywords, embedding models transform sentences or paragraphs into vectors containing hundreds or thousands of dimensions.

These vectors encode semantic meaning. Texts that share similar meanings are represented by vectors that are close to each other in vector space.

For example, the following phrases may produce similar embeddings:

“deploying AI models in Azure”“running machine learning workloads on Microsoft Azure”

Although the wording differs, both phrases describe the same concept. Their vector representations therefore appear close in the embedding space.

Embedding models available through Azure OpenAI can generate vectors for text passages, queries, and documents. These vectors can then be indexed and searched using vector similarity algorithms.

Azure AI Search as a Vector Database

Azure AI Search provides native support for vector search capabilities. This allows the service to act as both a traditional search engine and a vector database.

A vector index in Azure AI Search stores the embeddings associated with documents or document fragments. When a query is submitted, the system generates an embedding for the query and retrieves the vectors that are closest to it.

The similarity between vectors is typically calculated using cosine similarity or Euclidean distance.

Azure AI Search implements efficient Approximate Nearest Neighbor algorithms that allow vector searches to operate efficiently even on very large datasets containing millions of documents.

This capability makes Azure AI Search suitable for building large-scale semantic search systems and AI-powered knowledge retrieval platforms.

Document Preparation and Indexing

Before documents can be searched semantically, they must go through an ingestion pipeline. This pipeline typically performs several preprocessing steps.

First, documents are collected from enterprise data sources. These sources may include document repositories, content management systems, internal databases, or cloud storage.

Next, text extraction occurs. Structured and unstructured files such as PDFs, Word documents, and HTML pages are converted into plain text.

Once the text is extracted, it is split into smaller segments known as chunks. Chunking is necessary because embedding models perform best when processing manageable pieces of text rather than entire documents.

Typical chunk sizes range between 300 and 800 tokens. Overlapping chunks are often used to preserve context between segments.

After chunking, embeddings are generated for each text segment using Azure OpenAI embedding models. These embeddings are stored together with the original text inside an Azure AI Search index.

The result is a searchable vector database capable of retrieving semantically related content.

Query Execution in a Semantic Search System

When a user submits a search query, the semantic search system follows a sequence of steps.

First, the query text is transformed into an embedding using the same embedding model used during indexing. Maintaining the same embedding model ensures compatibility between query vectors and document vectors.

Next, the query embedding is submitted to Azure AI Search, which performs a vector similarity search against the indexed embeddings.

The search engine retrieves the top matching vectors along with their associated document fragments. These fragments represent the pieces of content that are most semantically relevant to the user’s query.

Finally, the search results are ranked and returned to the application.

In many modern systems, the retrieved content is then passed to a large language model to generate a natural language answer. This pattern is known as Retrieval-Augmented Generation.

Hybrid Search: Combining Vector and Keyword Retrieval

While semantic search is powerful, production systems often combine vector search with traditional keyword-based search. This approach is known as hybrid search.

Hybrid search leverages the strengths of both techniques. Keyword search ensures exact matches for critical terms such as product names or identifiers, while vector search retrieves conceptually related information.

Azure AI Search allows hybrid search queries that combine lexical scoring with vector similarity scoring. The results are merged and re-ranked to produce the most relevant responses.

This technique significantly improves search quality in enterprise environments where both structured terminology and natural language queries are common.

Ranking and Relevance Optimization

Search relevance is a critical factor in semantic search systems. Several techniques can improve ranking quality.

One common technique is re-ranking. After the initial vector retrieval step, a secondary model evaluates the retrieved results and assigns more precise relevance scores.

Another technique involves contextual boosting. Documents that contain important metadata such as document type, publication date, or author can be boosted in ranking.

Additionally, developers can adjust the number of retrieved vectors. Retrieving too few documents may reduce recall, while retrieving too many may introduce noise.

Balancing retrieval depth and ranking accuracy is essential for building high-quality semantic search systems.

Scalability Considerations

Enterprise search systems must handle large volumes of documents and user queries. Azure AI Search provides several capabilities that support scalability.

Indexes can be distributed across multiple partitions and replicas, allowing search workloads to scale horizontally. Replicas increase query throughput, while partitions increase storage capacity.

Embedding generation can also be parallelized during ingestion pipelines. Many organizations implement asynchronous pipelines using Azure Functions or container-based processing to generate embeddings at scale.

Additionally, caching frequently requested queries can significantly reduce latency and compute costs.

Security and Data Governance

When implementing semantic search for enterprise applications, security and compliance must be considered carefully.

Azure provides several mechanisms to protect sensitive data. Private endpoints ensure that services communicate within a secure virtual network. Managed identities allow services to authenticate without storing credentials. Azure Key Vault can store encryption keys and secrets securely.

Role-based access control can also be implemented to ensure that users only retrieve documents they are authorized to access.

In some scenarios, document-level security filters are applied within the search index to enforce access restrictions.

Monitoring and Observability

Production search systems require continuous monitoring to ensure reliability and performance.

Metrics such as query latency, index update frequency, and retrieval accuracy should be tracked. Azure Monitor and Application Insights can provide detailed telemetry about search performance and API usage.

Observability also helps identify cases where semantic retrieval fails to retrieve relevant information. These insights can be used to refine chunking strategies, retrain ranking models, or adjust indexing pipelines.

Real-World Applications of Semantic Search

Semantic search is becoming a core capability in many modern enterprise applications.

Knowledge management systems use semantic search to allow employees to quickly locate relevant documentation. Customer support platforms integrate semantic search to retrieve solutions from knowledge bases. Enterprise copilots rely on semantic retrieval to ground large language models in internal company data.

In software development environments, semantic search enables developers to discover code snippets, documentation, and architecture guidelines without relying on exact keyword matches.

These applications demonstrate how semantic search transforms information retrieval by enabling systems to understand meaning rather than simply matching words.

Conclusion

Semantic search represents a significant advancement over traditional keyword-based retrieval. By leveraging embeddings and vector similarity search, developers can build systems that understand context, intent, and conceptual relationships between documents.

Within the Microsoft ecosystem, the combination of Azure OpenAI embeddings and Azure AI Search provides a powerful platform for implementing semantic search at enterprise scale.

When combined with hybrid retrieval strategies, robust indexing pipelines, and proper monitoring, semantic search becomes a foundational component for modern AI-driven applications.

As organizations continue to adopt generative AI systems and enterprise copilots, semantic search will play an increasingly important role in connecting large language models with reliable and authoritative knowledge sources.