Microsoft Semantic Kernel: Restoring Broken Vector Search Across Redis Connectors

Engineering objective

System challenge

Vector search (FT.SEARCH) via the Python Redis connector was broken due to two independent bugs: redisvl 0.5.0 changed the process_results() signature from StorageType to IndexSchema, causing AttributeError/TypeError during result parsing, and requesting include_vectors=False triggered an unhandled KeyError when accessing vector fields.

Constraints

Working within real limits

• ✕Must remain 100% backwards-compatible across both redisvl <0.5.0 and redisvl >=0.5.0
• ✕Zero latency degradation on high-throughput vector similarity retrieval
• ✕Zero unhandled exceptions when vector payloads are omitted
• ✕Must satisfy Microsoft Semantic Kernel strict CI, formatting, and typecheck requirements

Approach

How it was built

1. 01

   Engineered dynamic schema inspection in process_results() to support both StorageType and IndexSchema objects seamlessly.

2. 02

   Added guard conditions ensuring vector fields are omitted cleanly without KeyError when include_vectors=False is specified.

3. 03

   Constructed comprehensive unit and integration tests verifying FT.SEARCH query parsing with both include_vectors=True and include_vectors=False.

Technical validation

Implementation evidence

Observable engineering evidence. Discipline: MERGED UPSTREAM

Public evidence

Review the upstream contribution

Outcome

What was delivered

• Zero Search Downtime: Restored unbroken vector search uptime across high-throughput enterprise Semantic Kernel deployments.

• Backwards Compatibility: 100% compatibility preserved across all redisvl version ranges.

• Upstream Adoption: Merged directly into microsoft/semantic-kernel main branch by Microsoft core maintainer @eavanvalkenburg.

Technologies

Stack and tools

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