skills/redis-development/rules/semantic-cache-best-practices.md

title	impact	impactDescription	tags	description	alwaysApply
Configure Semantic Cache Properly	MEDIUM	Correct threshold tuning balances hit rate vs accuracy	langcache, cache, threshold, ttl, semantic	Configure Semantic Cache Properly	true

Configure Semantic Cache Properly

Note: LangCache is currently in preview on Redis Cloud. Features and behavior may change.

Tune similarity threshold and cache separation for optimal LangCache results.

Correct: Tune similarity threshold for your use case.

from langcache import LangCache

lang_cache = LangCache(
    server_url=f"https://{os.getenv('HOST')}",
    cache_id=os.getenv("CACHE_ID"),
    api_key=os.getenv("API_KEY")
)

# Stricter matching - fewer false positives (0.95 = very similar)
result = lang_cache.search(
    prompt="What is Redis?",
    similarity_threshold=0.95
)

# Looser matching - higher hit rate (0.8 = somewhat similar)
result = lang_cache.search(
    prompt="What is Redis?",
    similarity_threshold=0.8
)

Correct: Use separate caches for different use cases.

# Create different cache IDs in Redis Cloud for different LLM tasks
support_cache = LangCache(
    server_url=server_url,
    cache_id="support-cache-id",
    api_key=api_key
)

code_cache = LangCache(
    server_url=server_url,
    cache_id="code-cache-id",
    api_key=api_key
)

Incorrect: Using a single cache for all LLM tasks.

# All tasks share one cache - responses may not be relevant
result = lang_cache.search(prompt="How do I reset my password?")
# Could return a code snippet if someone asked a similar coding question

Best practices:

• Start with threshold 0.9, adjust based on your use case
• Use custom attributes to filter results within a single cache
• Monitor cache hit rates to evaluate effectiveness
• Use separate cache IDs for fundamentally different LLM tasks

Reference: LangCache Best Practices