--- linkTitle: Threshold optimization title: Threshold Optimization weight: 09 url: '/develop/ai/redisvl/0.7.0/user_guide/threshold_optimization/' --- After setting up `SemanticRouter` or `SemanticCache` it's best to tune the `distance_threshold` to get the most performance out of your system. RedisVL provides helper classes to make this light weight optimization easy. **Note:** Threshold optimization relies on `python > 3.9.` # CacheThresholdOptimizer Let's say you setup the following semantic cache with a distance_threshold of `X` and store the entries: - prompt: `what is the capital of france?` response: `paris` - prompt: `what is the capital of morocco?` response: `rabat` ```python from redisvl.extensions.cache.llm import SemanticCache from redisvl.utils.vectorize import HFTextVectorizer sem_cache = SemanticCache( name="sem_cache", # underlying search index name redis_url="redis://localhost:6379", # redis connection url string distance_threshold=0.5, # semantic cache distance threshold vectorizer=HFTextVectorizer("redis/langcache-embed-v1") # embedding model ) paris_key = sem_cache.store(prompt="what is the capital of france?", response="paris") rabat_key = sem_cache.store(prompt="what is the capital of morocco?", response="rabat") ``` /Users/tyler.hutcherson/Library/Caches/pypoetry/virtualenvs/redisvl-VnTEShF2-py3.13/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm 16:53:13 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps 16:53:13 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: redis/langcache-embed-v1 16:53:13 sentence_transformers.SentenceTransformer WARNING You try to use a model that was created with version 4.1.0, however, your version is 3.4.1. This might cause unexpected behavior or errors. In that case, try to update to the latest version. Batches: 100%|██████████| 1/1 [00:00<00:00, 6.09it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 2.76it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 19.54it/s] This works well but we want to make sure the cache only applies for the appropriate questions. If we test the cache with a question we don't want a response to we see that the current distance_threshold is too high. ```python sem_cache.check("what's the capital of britain?") ``` Batches: 100%|██████████| 1/1 [00:00<00:00, 3.09it/s] [{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3', 'prompt': 'what is the capital of france?', 'response': 'paris', 'vector_distance': 0.335606694221, 'inserted_at': 1748551995.69, 'updated_at': 1748551995.69, 'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}] ### Define test_data and optimize With the `CacheThresholdOptimizer` you can quickly tune the distance threshold by providing some test data in the form: ```json [ { "query": "What's the capital of Britain?", "query_match": "" }, { "query": "What's the capital of France??", "query_match": paris_key }, { "query": "What's the capital city of Morocco?", "query_match": rabat_key }, ] ``` The threshold optimizer will then efficiently execute and score different threshold against the what is currently populated in your cache and automatically update the threshold of the cache to the best setting ```python from redisvl.utils.optimize import CacheThresholdOptimizer test_data = [ { "query": "What's the capital of Britain?", "query_match": "" }, { "query": "What's the capital of France??", "query_match": paris_key }, { "query": "What's the capital city of Morocco?", "query_match": rabat_key }, ] print(f"Distance threshold before: {sem_cache.distance_threshold} \n") optimizer = CacheThresholdOptimizer(sem_cache, test_data) optimizer.optimize() print(f"Distance threshold after: {sem_cache.distance_threshold} \n") ``` Distance threshold before: 0.5 Batches: 100%|██████████| 1/1 [00:00<00:00, 3.04it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 21.08it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 21.99it/s] Distance threshold after: 0.10372881355932204 We can also see that we no longer match on the incorrect example: ```python sem_cache.check("what's the capital of britain?") ``` Batches: 100%|██████████| 1/1 [00:00<00:00, 17.70it/s] [] But still match on highly relevant prompts: ```python sem_cache.check("what's the capital city of france?") ``` Batches: 100%|██████████| 1/1 [00:00<00:00, 19.72it/s] [{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3', 'prompt': 'what is the capital of france?', 'response': 'paris', 'vector_distance': 0.043138384819, 'inserted_at': 1748551995.69, 'updated_at': 1748551995.69, 'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}] # RouterThresholdOptimizer Very similar to the caching case, you can optimize your router. ### Define the routes ```python from redisvl.extensions.router import Route routes = [ Route( name="greeting", references=["hello", "hi"], metadata={"type": "greeting"}, distance_threshold=0.5, ), Route( name="farewell", references=["bye", "goodbye"], metadata={"type": "farewell"}, distance_threshold=0.5, ), ] ``` ### Initialize the SemanticRouter ```python import os from redisvl.extensions.router import SemanticRouter from redisvl.utils.vectorize import HFTextVectorizer os.environ["TOKENIZERS_PARALLELISM"] = "false" # Initialize the SemanticRouter router = SemanticRouter( name="greeting-router", vectorizer=HFTextVectorizer(), routes=routes, redis_url="redis://localhost:6379", overwrite=True # Blow away any other routing index with this name ) ``` 16:53:26 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps 16:53:26 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2 Batches: 100%|██████████| 1/1 [00:00<00:00, 9.90it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 6.45it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 51.94it/s] ### Provide test_data ```python test_data = [ # Greetings {"query": "hello", "query_match": "greeting"}, {"query": "hi", "query_match": "greeting"}, {"query": "hey", "query_match": "greeting"}, {"query": "greetings", "query_match": "greeting"}, {"query": "good morning", "query_match": "greeting"}, {"query": "good afternoon", "query_match": "greeting"}, {"query": "good evening", "query_match": "greeting"}, {"query": "howdy", "query_match": "greeting"}, {"query": "what's up", "query_match": "greeting"}, {"query": "yo", "query_match": "greeting"}, {"query": "hiya", "query_match": "greeting"}, {"query": "salutations", "query_match": "greeting"}, {"query": "how's it going", "query_match": "greeting"}, {"query": "how are you", "query_match": "greeting"}, {"query": "nice to meet you", "query_match": "greeting"}, # Farewells {"query": "goodbye", "query_match": "farewell"}, {"query": "bye", "query_match": "farewell"}, {"query": "see you later", "query_match": "farewell"}, {"query": "take care", "query_match": "farewell"}, {"query": "farewell", "query_match": "farewell"}, {"query": "have a good day", "query_match": "farewell"}, {"query": "see you soon", "query_match": "farewell"}, {"query": "catch you later", "query_match": "farewell"}, {"query": "so long", "query_match": "farewell"}, {"query": "peace out", "query_match": "farewell"}, {"query": "later", "query_match": "farewell"}, {"query": "all the best", "query_match": "farewell"}, {"query": "take it easy", "query_match": "farewell"}, {"query": "have a good one", "query_match": "farewell"}, {"query": "cheerio", "query_match": "farewell"}, # Null matches {"query": "what's the capital of britain?", "query_match": ""}, {"query": "what does laffy taffy taste like?", "query_match": ""}, ] ``` ### Optimize Note: by default route distance threshold optimization will use a random search to find the best threshold since, unlike caching, there are many thresholds to optimize concurrently. ```python from redisvl.utils.optimize import RouterThresholdOptimizer print(f"Route thresholds before: {router.route_thresholds} \n") optimizer = RouterThresholdOptimizer(router, test_data) optimizer.optimize() ``` Route thresholds before: {'greeting': 0.5, 'farewell': 0.5} Batches: 100%|██████████| 1/1 [00:00<00:00, 12.61it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 60.26it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 59.99it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 59.22it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 60.21it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 59.79it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 62.56it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 64.33it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 14.11it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 60.54it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 47.83it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 12.93it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 13.26it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 56.03it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 57.56it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 63.66it/s] Batches: 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100%|██████████| 1/1 [00:00<00:00, 57.75it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 56.59it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 55.40it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 57.41it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 52.99it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 54.28it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 44.24it/s] Batches: 100%|██████████| 1/1 [00:00<00:00, 56.39it/s] Eval metric F1: start 0.438, end 0.562 Ending thresholds: {'greeting': 0.31212121212121235, 'farewell': 0.4414141414141417} ### Test it out ```python # Query the router with a statement route_match = router("hi there") route_match ``` Batches: 100%|██████████| 1/1 [00:00<00:00, 54.56it/s] RouteMatch(name='greeting', distance=0.295984089375) ## Cleanup ```python router.delete() sem_cache.delete() ```