Implement Azure Managed Redis vector indexing for similarity search

• Azure Managed Redis enables vector search through the RediSearch module and an FT.CREATE index
• A VECTOR field declares the algorithm (FLAT or HNSW), TYPE, DIM, and DISTANCE_METRIC
• FLAT is exact brute-force search; HNSW is an approximate graph index for large, low-latency workloads
• HNSW recall and latency are tuned by M, EF_CONSTRUCTION, and EF_RUNTIME
• A KNN vector query uses FT.SEARCH with the =>[KNN k @field $vec] syntax and DIALECT 2
• The query vector is passed as raw bytes through PARAMS, and SORTBY is what orders results by distance
• Hybrid queries put a RediSearch filter in front of the KNN clause, and Redis decides how to apply it
• RediSearch supports three vector distance metrics: COSINE, L2 (Euclidean), and IP (inner product)
• DISTANCE_METRIC, TYPE, and DIM are fixed at index creation and must match the stored and query vectors
• COSINE ranks by orientation rather than magnitude, the usual choice for text embeddings
• Embeddings are stored in HASH fields as raw float32 bytes or in JSON as numeric arrays, matching the index ON type
• A key PREFIX defines which keys are indexed; matching keys are indexed automatically on write
• redis-py exposes vector search via its search commands (VectorField, Query) over FT.CREATE/FT.SEARCH
• RediSearch on Azure Managed Redis requires the Enterprise clustering policy and is not offered on every tier

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