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You can add retrieval-augmented generation (RAG) to an existing ASP.NET Core app with SQL Server, but the documented EF Core vector workflow has a firm compatibility gate: it requires EF Core 10 or later and SQL Server 2025 or later. Microsoft also identifies Azure SQL Database as a vector-data option in its EF Core 10 release notes. Check the app’s actual EF Core provider and deployment database before designing around vector columns.
Check compatibility before changing your data model
Microsoft says vector support was introduced in EF Core 10.0 and is supported by the SQL Server provider only with SQL Server 2025 and above. EF Core 10 release notes also identify Azure SQL Database for vector data and VECTOR_DISTANCE() support. These requirements apply to the documented vector workflow; do not assume that an app using an older EF Core release or a different SQL database can use the same mapping and queries.
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Inventory the application’s .NET and EF Core versions, configured EF Core provider, database engine and version in each deployment environment, migration process, and existing search behavior. The database that matters is the one the app actually connects to in production, not merely the local development instance. If it does not meet the documented requirements, treat that as a design decision to resolve before adding a vector property.
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Represent searchable chunks as durable records
RAG retrieval usually works against passages rather than whole documents. A practical application design is to persist each retrievable chunk as a stable record linked to its source document. Keep the chunk text and useful metadata alongside an embedding. Stable chunk identifiers make it possible to combine results from different retrieval methods and to update or remove passages when the source changes.
This entity shape is an implementation recommendation, not a Microsoft-prescribed schema. Decide what metadata the application needs—for example, source identity or fields used to filter results—based on its corpus and retrieval behavior.
Map the embedding to a dimensioned vector column
Microsoft’s EF Core provider maps a SqlVector<float> property to a SQL vector(n) column. Set n to the number of dimensions produced by the embedding model you choose. Microsoft’s example uses vector(1536); that is a schema example, not a universal model dimension or recommended default. The model’s output and the database column’s dimension must agree.
Generate vectors when content changes
When creating or refreshing chunks, generate and store an embedding for each chunk. At query time, generate an embedding for the user’s question and compare it with stored vectors. Microsoft points to the Microsoft.Extensions.AI IEmbeddingGenerator abstraction and demonstrates GenerateVectorAsync; the application remains responsible for selecting a provider and model.
Plan refresh behavior as part of the data lifecycle: when source text or chunk boundaries change, the stored text and its embedding need to remain in sync. The cited Microsoft guidance does not prescribe a chunk size, overlap, embedding model, or refresh policy. Choose those for the corpus and test them rather than presenting arbitrary values as platform recommendations.
Choose exact or approximate vector retrieval
For a straightforward baseline, use EF.Functions.VectorDistance() to calculate distance between a query vector and stored vectors, then order by distance. Microsoft notes that exact searches scan rows and can become slow at scale, so measure latency and relevance on the application’s workload as the corpus grows.
Approximate nearest-neighbor retrieval is a different trade-off: it can improve retrieval performance but may return approximate rather than exact neighbors. The documented SQL Server path uses a vector index and VectorSearch(); to request approximate search, create the index and call WithApproximate() after Take(). Microsoft lists cosine, Euclidean, and dot metrics for vector indexes. Cosine is commonly used for text embeddings, but the right metric depends on the selected model and workload.
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Start with exact search where it suits the corpus and gives a useful baseline. Consider approximate indexed search only after testing whether its performance and retrieval behavior fit the app. Because Microsoft labels vector indexes and VECTOR_SEARCH() experimental and subject to change, account for that maturity risk in deployment planning.
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Combine keyword and semantic candidates with hybrid ranking
Vector retrieval can find passages that express an idea in different words; lexical search can be stronger for exact terms such as names, identifiers, or distinctive phrases. A hybrid retriever combines both candidate sets instead of asking one method to cover both jobs.
- Retrieve lexical candidates. Use SQL Server full-text search; Microsoft’s example uses
FreeTextTable(). - Retrieve semantic candidates. Generate the query embedding and use
VectorSearch()for vector retrieval. - Bound both result sets. Request a candidate count appropriate to the application, then combine the results by stable chunk ID so the same chunk returned by both methods is treated as one candidate.
- Fuse the rankings. Apply Reciprocal Rank Fusion (RRF) to the lexical and vector result ranks, then return the desired top chunks to the next stage of the RAG pipeline.
Microsoft demonstrates a single EF query pattern combining FreeTextTable() and VectorSearch() with RRF. Candidate counts and ranking behavior still need to be validated against the app’s corpus; the documentation does not establish universal settings.
Validate retrieval against the app’s own questions
Assess retrieval quality and operational behavior before tuning for production. Use representative questions that exercise both exact-term matching and semantic paraphrases, and inspect whether the returned chunks contain the evidence needed to answer them. Measure latency as well as relevance when comparing exact and approximate vector search.
- Include names, product or document identifiers, and other exact strings that matter to users.
- Include paraphrased questions whose wording differs from the source passages.
- Check whether chunk boundaries preserve the context needed to interpret each result.
- Compare lexical-only, vector-only, and hybrid results to see which misses each approach corrects.
- Tune chunking, candidate counts, ranking, and filters against the application’s actual documents and queries.
There is no universal chunk size, overlap, candidate count, or ranking configuration established by the cited platform documentation. The useful settings depend on the corpus, embedding model, search requirements, and deployment constraints.
Quick Recap
Sources
- Microsoft Learn: Vector search in the SQL Server EF Core Provider, updated July 13, 2026.
- Microsoft Learn: What’s New in EF Core 10.
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