Add SemanticCache vector index config

[omribz156]

Summary

Addresses #602.

• Add vector_index_config to SemanticCache so the cache vector field can be created with HNSW or other supported vector index options instead of always using FLAT.
• Keep the default FLAT schema unchanged, and keep dims, datatype, and distance_metric derived from the vectorizer / semantic-cache COSINE behavior.
• Document the HNSW configuration path in the LLM cache user guide.

Verification

• .venv\Scripts\python.exe -m pytest --noconftest tests/unit/test_llmcache_schema.py
• .venv\Scripts\python.exe -m black --check redisvl/extensions/cache/llm/schema.py redisvl/extensions/cache/llm/semantic.py tests/unit/test_llmcache_schema.py
• .venv\Scripts\python.exe -m compileall redisvl/extensions/cache/llm/schema.py redisvl/extensions/cache/llm/semantic.py tests/unit/test_llmcache_schema.py
• .venv\Scripts\python.exe -c "import json; json.load(open('docs/user_guide/03_llmcache.ipynb', encoding='utf-8')); print('notebook json ok')"
• git diff --check

I also tried the normal pytest command first, but local execution without --noconftest is blocked on this Windows machine because the repo autouse fixture invokes Docker Compose and the Docker CLI is not available here.

This was implemented with Codex assistance, with the patch kept focused on the cache schema/config path and docs.

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Note

Low Risk
Additive constructor option with unchanged default FLAT behavior; risk is mainly misconfigured index settings affecting search performance, not data or auth paths.

Overview
Adds optional vector_index_config on SemanticCache so the Redis vector field for prompt embeddings can use HNSW (or other supported algorithms) instead of always FLAT, while dims, datatype, and cosine distance still come from the vectorizer and are rejected if overridden in config.

SemanticCacheIndexSchema.from_params merges user config onto the default FLAT attrs and passes the result into index creation; the LLM cache user guide documents HNSW setup and notes that the algorithm is fixed after index creation (recreate with overwrite=True to change it).

Unit tests cover default FLAT, HNSW options, invalid algorithms, and blocked overrides of vectorizer-derived fields.

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[Copilot]

Pull request overview

Adds a configurable vector-index configuration path for SemanticCache, allowing the cache’s vector field to be created with HNSW (or other supported RedisVL vector algorithms) instead of being hardcoded to FLAT, and documents the new configuration in the LLM cache user guide.

Changes:

• Extend SemanticCacheIndexSchema.from_params to accept vector_index_config and merge it into the vector field attrs while protecting vectorizer-derived attrs (dims, datatype, distance_metric).
• Add vector_index_config plumbing to SemanticCache constructor so the schema/index can be created with a non-FLAT algorithm (e.g., HNSW).
• Add unit tests for default FLAT behavior, HNSW config acceptance, and invalid config rejection; update the LLM cache notebook with a configuration example.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 1 comment.

File	Description
redisvl/extensions/cache/llm/schema.py	Adds vector_index_config support to the semantic cache index schema builder while preventing overrides of derived attrs.
redisvl/extensions/cache/llm/semantic.py	Wires vector_index_config through SemanticCache initialization into schema/index creation.
tests/unit/test_llmcache_schema.py	Adds unit coverage for FLAT default, HNSW config, and invalid algorithm/override behavior.
docs/user_guide/03_llmcache.ipynb	Documents how to configure the semantic cache’s vector index algorithm (example: HNSW).

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[vishal-bala]

Hi, this is coming along - can you rebase onto main to get changes to allow the CI tests to proceed?