Python: Bug: Vector search (`FT.SEARCH`) via the Python Redis connector is broken

Describe the bug

Vector search (FT.SEARCH) via the Python Redis connector is completely broken due to two issues in the search path:

1. RedisCollection._inner_search (line 324) passes a StorageType enum to redisvl's process_results(), which since redisvl 0.5.0 expects an IndexSchema object. Every search call raises AttributeError: 'StorageType' object has no attribute 'index'.

2. RedisHashsetCollection._deserialize_store_models_to_dicts (line 620) unconditionally calls buffer_to_array(rec[field.name], dtype) for every vector field. When include_vectors=False (the default for search), the vector field is absent from the result dict, causing KeyError: 'vector'.

Issue 1 blocks all search calls. Issue 2 blocks hashset search even after issue 1 is fixed, unless the user explicitly passes include_vectors=True.

Expected behavior

collection.search(vector=[...], top=N) should execute FT.SEARCH and return results. With include_vectors=False (default), results should contain data fields but not vector fields.

To Reproduce

Prerequisites: a Redis Stack server reachable on localhost:6379, redis-cli, and a working Python SK dev environment.

1. Install Python SK per python/DEV_SETUP.md:

   cd python && make install-sk PYTHON_VERSION=3.12
   

2. Start Redis Stack on port 6379:

   podman run -d -p 6379:6379 docker.io/redis/redis-stack:latest
   

3. From python/, run the following script:

   # repro_search.py
   import asyncio
   from dataclasses import dataclass, field
   from typing import Annotated
   from uuid import uuid4
   
   from semantic_kernel.connectors.redis import RedisHashsetCollection
   from semantic_kernel.data.vector import VectorStoreField, vectorstoremodel
   
   
   @vectorstoremodel
   @dataclass
   class MyModel:
       vector: Annotated[
           list[float] | None,
           VectorStoreField("vector", index_kind="hnsw", dimensions=3,
                            distance_function="cosine_similarity", type="float"),
       ] = None
       id: Annotated[str, VectorStoreField("key", type="str")] = field(
           default_factory=lambda: str(uuid4())
       )
       content: Annotated[str, VectorStoreField("data", type="str")] = "hello"
   
   
   async def main():
       async with RedisHashsetCollection(
           record_type=MyModel,
           collection_name="repro_search",
           prefix_collection_name_to_key_names=True,
       ) as col:
           await col.ensure_collection_deleted()
           await col.ensure_collection_exists()
           await col.upsert([MyModel(id="1", content="test", vector=[0.1, 0.2, 0.3])])
   
           # This raises AttributeError: 'StorageType' object has no attribute 'index'
           results = await col.search(vector=[0.1, 0.2, 0.3], top=1)
           async for r in results.results:
               print(r.record.id, r.record.content)
   
           await col.ensure_collection_deleted()
   
   
   asyncio.run(main())
   

   REDIS_CONNECTION_STRING="redis://localhost:6379" uv run python repro_search.py
   

   Output:

   VectorSearchExecutionException: An error occurred during the search:
   'StorageType' object has no attribute 'index'
   

Root cause

The connector was written against redisvl 0.4.x where process_results(results, query, storage_type: StorageType) accepted a StorageType enum. Upstream redisvl 0.5.0 changed the third parameter to schema: IndexSchema. The pyproject.toml pin redisvl ~= 0.4 resolves in PEP 440 to >=0.4, <1.0, so uv.lock picks up redisvl 0.15.0 where the API is incompatible. Tightening the pin to <0.5 is not possible because redisvl 0.4.x requires redis <6 while SK requires redis >=6.

The KeyError: 'vector' in the hashset deserializer is a separate code defect: the deserialization loop does not check whether the vector field is present in the result dict before attempting to decode it.

Platform

• Language: Python
• Source: main branch of microsoft/semantic-kernel (SK version 1.41.2)
• redis-py version: 6.4.0
• redisvl version: 0.15.0 (resolved from ~= 0.4 pin)
• Backend tested: Redis Stack 7.4.7 (RediSearch v21020, ReJSON v20809)
• IDE: Kiro
• OS: macOS 25.4.0 (Darwin), arm64

Additional context

This affects every Python user who installs SK from the committed uv.lock or resolves dependencies fresh. The redisvl API break happened at 0.5.0; the committed lockfile resolves 0.15.0. No version of redisvl that is compatible with both the SK code and the SK redis >=6 requirement exists — the code must be updated to the new redisvl >=0.5 API.

The KeyError in the hashset deserializer is independent of the redisvl version and affects any search call with include_vectors=False (the default).

Test coverage gap

Tried to use the Redis connector for vector search and it didn't work. The existing integration tests (tests/integration/memory/test_vector_store.py) only cover single-record upsert → get → delete and never call collection.search(), so these bugs have had zero test coverage. Added more tests covering the full public surface — vector search, batch CRUD, filters, paging, include_vectors, prefix mode, etc. — and that's how these issues were found. The new tests should be included alongside the fix to prevent regression.

Start with RedisCollection._inner_search and RedisHashsetCollection._deserialize_store_models_to_dicts, then inspect pyproject.toml and uv.lock for the redisvl API version. Extend tests/integration/memory/test_vector_store.py or add nearby coverage for vector search with and without included vectors, using the Redis Stack setup from the issue. Done means search returns records for both collections and omits vector fields when include_vectors=False.