Introduction

As organizations adopt Large Language Models and semantic search systems, vector embeddings have become a core architectural component. Embeddings allow systems to search by meaning rather than exact keywords. This enables retrieval augmented generation, semantic knowledge bases, and intelligent document search.

The common assumption is that implementing embedding-based retrieval requires a specialized vector database. In practice, many enterprise environments already operate mature SQL Server infrastructures with strong governance, security, and operational controls. Before introducing new infrastructure, an important question should be asked: Can SQL Server reliably store and query vector embeddings for enterprise workloads?

This article introduces the Relational Embedding Retrieval Pattern (RERP), a structured approach for storing and querying embeddings inside SQL Server while maintaining performance and architectural discipline.

Understanding Vector Embeddings in Practical Terms

An embedding is a numeric representation of text generated by a machine learning model. In practice, a system first passes text through a trained embedding model such as OpenAI embeddings, SentenceTransformers, or other transformer-based architectures. These models analyze the semantic structure of the text and convert it into a high-dimensional vector that captures meaning.

For example:

Text: “Reset my password.”

Embedding:

Embedding: [0.12, 0.88, 0.43, 0.77, …]

Each number represents a learned semantic feature. Similar text produces similar vectors. Instead of matching keywords, systems compare vectors using mathematical distance calculations.

One of the most common approaches is cosine similarity, which measures how closely two vectors align in multi-dimensional space. Cosine similarity can be expressed as:

Similarity = DotProduct(A, B) / (Norm(A) * Norm(B))

The closer the similarity value is to 1, the more semantically related the texts are. This mechanism allows systems to retrieve documents that are conceptually similar to a query even when the exact words differ.

The Relational Embedding Retrieval Pattern (RERP)

RERP is a structured architectural approach for embedding retrieval inside relational systems. Rather than treating embeddings as an additional column attached to documents, the pattern defines clear architectural responsibilities for storing and querying vectors.

The pattern is based on five core principles.

• Separation of Document Content and Embeddings: Document content and vector embeddings should be stored in separate tables. Documents typically contain raw text and metadata that may require indexing, auditing, or updates over time. Embeddings, however, are numerical representations used primarily for similarity calculations. Separating these concerns allows each dataset to evolve independently and improves query flexibility.
• Precomputation of Vector Norms: Cosine similarity requires the magnitude of each vector. If this magnitude must be computed during every query, the database repeatedly performs the same calculation. By precomputing the vector norm and storing it alongside the embedding, similarity queries can reuse this value and avoid redundant computation.
• Metadata-Scoped Filtering Before Similarity Computation: Relational databases excel at filtering structured metadata. Categories, document types, product identifiers, or tenant boundaries can all be used to restrict the search space before similarity calculations occur. Reducing the candidate set significantly lowers the number of vector comparisons required.
• Controlled Search Boundaries: Not every embedding system requires global search across the entire dataset. Many enterprise workloads operate within well-defined domains, such as product documentation, support knowledge bases, or internal policy repositories. Restricting search boundaries can dramatically improve performance while maintaining relevant retrieval.
• Explicit Performance Thresholds: Architectural limits should be defined early. Organizations should determine acceptable embedding volumes, latency requirements, and scaling thresholds for relational retrieval. Beyond certain limits, specialized vector systems may become more appropriate.

Rather than treating embeddings as an afterthought column, RERP models them as first-class architectural entities.

Schema Design Under RERP

A recommended schema structure is shown below. The design separates document storage from embedding storage.

The Documents table stores the original text content along with metadata describing the document. This metadata may include category classification, creation timestamps, or any attributes used for filtering and governance. The DocumentEmbeddings table stores the numerical embedding vector associated with each document. It also stores the precomputed vector norm used during similarity calculations.

CREATE TABLE dbo.Documents
(
    DocumentId INT IDENTITY PRIMARY KEY,
    Content NVARCHAR(MAX) NOT NULL,
    Category NVARCHAR(100),
    CreatedAt DATETIME2 DEFAULT SYSDATETIME()
);

CREATE TABLE dbo.DocumentEmbeddings
(
    DocumentId INT PRIMARY KEY,
    Embedding NVARCHAR(MAX) NOT NULL,
    VectorNorm FLOAT NOT NULL,
    CONSTRAINT FK_DocumentEmbeddings_Documents
        FOREIGN KEY (DocumentId)
        REFERENCES dbo.Documents(DocumentId)
);

Embeddings themselves are arrays of floating-point numbers. In SQL Server implementations, they are often stored as JSON arrays inside NVARCHAR columns because JSON storage is transparent and easy to inspect during development.

For example:

[0.12, 0.88, 0.43, 0.77]

For production environments that require greater storage efficiency, embeddings may also be serialized into VARBINARY format. JSON storage, however, is frequently preferred during early implementation because it simplifies debugging and validation.

This schema design provides several benefits. First, separating document content from embeddings allows independent indexing strategies and query plans. Documents may be accessed frequently through relational queries, while embeddings are primarily used for similarity computation. Precomputing the vector norm eliminates repeated magnitude calculations during similarity queries. Since cosine similarity requires vector magnitude, storing the norm once significantly reduces computation during retrieval. Finally, storing metadata within the document table enables relational filtering before similarity calculations occur. By narrowing the candidate search set early, the system can reduce the number of vector comparisons required.

Computing Cosine Similarity in SQL Server

Similarity comparison in SQL Server typically involves three conceptual steps.

1. Parsing stored embedding values
2. Computing the dot product between vectors
3. Dividing by the stored vector norms

Below is a simplified conceptual example.

DECLARE @QueryEmbedding NVARCHAR(MAX) = '[0.10,0.90,0.40,0.75]';
DECLARE @QueryNorm FLOAT = 1.25;  -- Precomputed externally

SELECT TOP 5
    d.DocumentId,
    d.Content,
    (DotProduct / (e.VectorNorm * @QueryNorm)) AS SimilarityScore
FROM dbo.Documents d
JOIN dbo.DocumentEmbeddings e
    ON d.DocumentId = e.DocumentId
ORDER BY SimilarityScore DESC;

In practice, the dot product is computed by parsing the JSON vector values using OPENJSON and combining them with the query embedding using CROSS APPLY.

For moderate datasets, this approach performs adequately when combined with scoped filtering and precomputed norms.

Metadata-Scoped Filtering

One of the most important principles of RERP is filtering candidate documents before performing vector similarity calculations. In traditional vector search systems, similarity calculations may be applied across the entire dataset. Relational databases allow a different strategy. Metadata can be used to narrow the search scope before expensive vector operations are executed.

For example, a support knowledge base may contain documents across multiple categories such as onboarding, troubleshooting, or product configuration. If a query targets support articles, the database can restrict the candidate set using metadata before computing similarity.

WHERE d.Category = 'Support'

This filtering step significantly reduces computational cost. Instead of comparing the query embedding with every stored vector, the database evaluates similarity only against a subset of relevant documents.

This hybrid model, combining relational filtering with vector similarity, is often sufficient for enterprise internal search workloads.

Performance Evaluation

Before deploying relational embedding retrieval in production, organizations should conduct controlled performance evaluations. The objective is to understand how dataset size, embedding dimensionality, and filtering strategies influence query latency.

Typical evaluation activities include measuring similarity query latency, observing CPU utilization during vector comparison, and analyzing how metadata filtering affects candidate set size. A representative test environment might include:

• 10,000 embeddings
• 768-dimensional vectors
• Precomputed norms stored

Observed behavior in moderate hardware environments typically shows several patterns. For small datasets, full-table similarity scans may still produce acceptable latency. As embedding counts increase, however, vector comparisons become more computationally expensive.

Metadata filtering significantly improves performance because similarity calculations are performed on a smaller subset of rows. Precomputed norms further reduce CPU overhead by eliminating repeated magnitude calculations.

When scaling the dataset to around 100,000 embeddings, latency typically increases proportionally. This reinforces the importance of defining architectural boundaries and limiting search scope where possible.

The key insight is that SQL Server can support moderate embedding workloads reliably when disciplined architectural patterns are applied.

Architectural Boundaries of RERP

RERP is well suited for specific categories of enterprise workloads. Many internal systems such as support knowledge bases, documentation portals, or operational playbooks contain embedding collections in the thousands or low hundreds of thousands. In these environments, relational filtering combined with cosine similarity often provides sufficient performance. Latency requirements for these systems are typically measured in tens or hundreds of milliseconds, which SQL Server can support when candidate search sets are constrained.

However, relational embedding retrieval becomes less practical when embedding collections scale into the millions or billions. At that scale, specialized vector databases often rely on approximate nearest neighbor algorithms and distributed indexing techniques optimized for large-scale similarity search.

Recognizing these architectural boundaries is essential. RERP is not intended to replace specialized vector infrastructure for extreme scale workloads. Instead, it provides a disciplined approach for organizations that already operate mature relational environments.

Enterprise Production Observations

In enterprise AI deployments, introducing specialized vector infrastructure too early can increase operational complexity. Most organizations already operate well-established SQL Server environments with existing monitoring systems, security controls, backup strategies, and operational processes. Introducing a separate vector database creates additional infrastructure that must be maintained, secured, and monitored.

In practice, many enterprise systems have found relational embedding retrieval to be sufficient for workloads such as internal document search, support automation platforms, and conversational assistance tools. For example, a support automation system may restrict similarity search to documents associated with a specific product or service category. This scoped search dramatically reduces the number of vectors evaluated during retrieval while maintaining high relevance.

In such scenarios, relational embedding retrieval allows organizations to deploy semantic search capabilities while maintaining existing governance and operational controls. Architectural maturity should guide tool selection, not technology trends.

Conclusion

Vector databases are powerful tools, but they are not always required.

The Relational Embedding Retrieval Pattern (RERP) provides a structured approach for storing embeddings and performing semantic retrieval inside SQL Server. By separating embeddings from document storage, precomputing vector norms, applying metadata filtering, and defining architectural performance thresholds, organizations can support many AI retrieval workloads without introducing additional infrastructure.

Relational systems remain highly relevant in the AI era when architectural patterns are applied thoughtfully and system boundaries are clearly understood.