{
  "schema_version": 2,
  "id": "develop/ai/redisvl/user_guide/threshold_optimization",
  "title": "Threshold Optimization",
  "url": "https://redis.io/docs/latest/develop/ai/redisvl/0.7.0/user_guide/threshold_optimization/",
  "summary": "",
  "content": "\n\nAfter 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.\n\n**Note:** Threshold optimization relies on `python \u003e 3.9.`\n\n# CacheThresholdOptimizer\n\nLet's say you setup the following semantic cache with a distance_threshold of `X` and store the entries:\n\n- prompt: `what is the capital of france?` response: `paris`\n- prompt: `what is the capital of morocco?` response: `rabat`\n\n\n```python\nfrom redisvl.extensions.cache.llm import SemanticCache\nfrom redisvl.utils.vectorize import HFTextVectorizer\n\nsem_cache = SemanticCache(\n    name=\"sem_cache\",                                       # underlying search index name\n    redis_url=\"redis://localhost:6379\",                     # redis connection url string\n    distance_threshold=0.5,                                 # semantic cache distance threshold\n    vectorizer=HFTextVectorizer(\"redis/langcache-embed-v1\") # embedding model\n)\n\nparis_key = sem_cache.store(prompt=\"what is the capital of france?\", response=\"paris\")\nrabat_key = sem_cache.store(prompt=\"what is the capital of morocco?\", response=\"rabat\")\n\n```\n\n    /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\n      from .autonotebook import tqdm as notebook_tqdm\n\n\n    16:53:13 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n    16:53:13 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: redis/langcache-embed-v1\n    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.\n    \n    \n    \n\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  6.09it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  2.76it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 19.54it/s]\n\n\nThis 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. \n\n\n```python\nsem_cache.check(\"what's the capital of britain?\")\n```\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  3.09it/s]\n\n\n\n\n\n    [{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n      'prompt': 'what is the capital of france?',\n      'response': 'paris',\n      'vector_distance': 0.335606694221,\n      'inserted_at': 1748551995.69,\n      'updated_at': 1748551995.69,\n      'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]\n\n\n\n### Define test_data and optimize\n\nWith the `CacheThresholdOptimizer` you can quickly tune the distance threshold by providing some test data in the form:\n\n```json\n[\n    {\n        \"query\": \"What's the capital of Britain?\",\n        \"query_match\": \"\"\n    },\n    {\n        \"query\": \"What's the capital of France??\",\n        \"query_match\": paris_key\n    },\n    {\n        \"query\": \"What's the capital city of Morocco?\",\n        \"query_match\": rabat_key\n    },\n]\n```\n\nThe 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\n\n\n```python\nfrom redisvl.utils.optimize import CacheThresholdOptimizer\n\ntest_data = [\n    {\n        \"query\": \"What's the capital of Britain?\",\n        \"query_match\": \"\"\n    },\n    {\n        \"query\": \"What's the capital of France??\",\n        \"query_match\": paris_key\n    },\n    {\n        \"query\": \"What's the capital city of Morocco?\",\n        \"query_match\": rabat_key\n    },\n]\n\nprint(f\"Distance threshold before: {sem_cache.distance_threshold} \\n\")\noptimizer = CacheThresholdOptimizer(sem_cache, test_data)\noptimizer.optimize()\nprint(f\"Distance threshold after: {sem_cache.distance_threshold} \\n\")\n```\n\n    Distance threshold before: 0.5 \n    \n\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  3.04it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 21.08it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 21.99it/s]\n\n\n    Distance threshold after: 0.10372881355932204 \n    \n\n\nWe can also see that we no longer match on the incorrect example:\n\n\n```python\nsem_cache.check(\"what's the capital of britain?\")\n```\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 17.70it/s]\n\n\n\n\n\n    []\n\n\n\nBut still match on highly relevant prompts:\n\n\n```python\nsem_cache.check(\"what's the capital city of france?\")\n```\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 19.72it/s]\n\n\n\n\n\n    [{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n      'prompt': 'what is the capital of france?',\n      'response': 'paris',\n      'vector_distance': 0.043138384819,\n      'inserted_at': 1748551995.69,\n      'updated_at': 1748551995.69,\n      'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]\n\n\n\n# RouterThresholdOptimizer\n\nVery similar to the caching case, you can optimize your router.\n\n### Define the routes\n\n\n```python\nfrom redisvl.extensions.router import Route\n\nroutes = [\n        Route(\n            name=\"greeting\",\n            references=[\"hello\", \"hi\"],\n            metadata={\"type\": \"greeting\"},\n            distance_threshold=0.5,\n        ),\n        Route(\n            name=\"farewell\",\n            references=[\"bye\", \"goodbye\"],\n            metadata={\"type\": \"farewell\"},\n            distance_threshold=0.5,\n        ),\n    ]\n```\n\n### Initialize the SemanticRouter\n\n\n```python\nimport os\nfrom redisvl.extensions.router import SemanticRouter\nfrom redisvl.utils.vectorize import HFTextVectorizer\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\n# Initialize the SemanticRouter\nrouter = SemanticRouter(\n    name=\"greeting-router\",\n    vectorizer=HFTextVectorizer(),\n    routes=routes,\n    redis_url=\"redis://localhost:6379\",\n    overwrite=True # Blow away any other routing index with this name\n)\n```\n\n    16:53:26 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n    16:53:26 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  9.90it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00,  6.45it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 51.94it/s]\n\n\n### Provide test_data\n\n\n```python\ntest_data = [\n    # Greetings\n    {\"query\": \"hello\", \"query_match\": \"greeting\"},\n    {\"query\": \"hi\", \"query_match\": \"greeting\"},\n    {\"query\": \"hey\", \"query_match\": \"greeting\"},\n    {\"query\": \"greetings\", \"query_match\": \"greeting\"},\n    {\"query\": \"good morning\", \"query_match\": \"greeting\"},\n    {\"query\": \"good afternoon\", \"query_match\": \"greeting\"},\n    {\"query\": \"good evening\", \"query_match\": \"greeting\"},\n    {\"query\": \"howdy\", \"query_match\": \"greeting\"},\n    {\"query\": \"what's up\", \"query_match\": \"greeting\"},\n    {\"query\": \"yo\", \"query_match\": \"greeting\"},\n    {\"query\": \"hiya\", \"query_match\": \"greeting\"},\n    {\"query\": \"salutations\", \"query_match\": \"greeting\"},\n    {\"query\": \"how's it going\", \"query_match\": \"greeting\"},\n    {\"query\": \"how are you\", \"query_match\": \"greeting\"},\n    {\"query\": \"nice to meet you\", \"query_match\": \"greeting\"},\n    # Farewells\n    {\"query\": \"goodbye\", \"query_match\": \"farewell\"},\n    {\"query\": \"bye\", \"query_match\": \"farewell\"},\n    {\"query\": \"see you later\", \"query_match\": \"farewell\"},\n    {\"query\": \"take care\", \"query_match\": \"farewell\"},\n    {\"query\": \"farewell\", \"query_match\": \"farewell\"},\n    {\"query\": \"have a good day\", \"query_match\": \"farewell\"},\n    {\"query\": \"see you soon\", \"query_match\": \"farewell\"},\n    {\"query\": \"catch you later\", \"query_match\": \"farewell\"},\n    {\"query\": \"so long\", \"query_match\": \"farewell\"},\n    {\"query\": \"peace out\", \"query_match\": \"farewell\"},\n    {\"query\": \"later\", \"query_match\": \"farewell\"},\n    {\"query\": \"all the best\", \"query_match\": \"farewell\"},\n    {\"query\": \"take it easy\", \"query_match\": \"farewell\"},\n    {\"query\": \"have a good one\", \"query_match\": \"farewell\"},\n    {\"query\": \"cheerio\", \"query_match\": \"farewell\"},\n    # Null matches\n    {\"query\": \"what's the capital of britain?\", \"query_match\": \"\"},\n    {\"query\": \"what does laffy taffy taste like?\", \"query_match\": \"\"},\n]\n```\n\n### Optimize\n\nNote: 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. \n\n\n```python\nfrom redisvl.utils.optimize import RouterThresholdOptimizer\n\nprint(f\"Route thresholds before: {router.route_thresholds} \\n\")\noptimizer = RouterThresholdOptimizer(router, test_data)\noptimizer.optimize()\n```\n\n    Route thresholds before: {'greeting': 0.5, 'farewell': 0.5} \n    \n\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 12.61it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 60.26it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 59.99it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 59.22it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 60.21it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 59.79it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 62.56it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 64.33it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 14.11it/s]\n    Batches: 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[00:00\u003c00:00, 52.99it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 54.28it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 44.24it/s]\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 56.39it/s]\n\n\n    Eval metric F1: start 0.438, end 0.562 \n    Ending thresholds: {'greeting': 0.31212121212121235, 'farewell': 0.4414141414141417}\n\n\n### Test it out\n\n\n```python\n# Query the router with a statement\nroute_match = router(\"hi there\")\nroute_match\n```\n\n    Batches: 100%|██████████| 1/1 [00:00\u003c00:00, 54.56it/s]\n\n\n\n\n\n    RouteMatch(name='greeting', distance=0.295984089375)\n\n\n\n## Cleanup\n\n\n```python\nrouter.delete()\nsem_cache.delete()\n```\n",
  "tags": [],
  "last_updated": "0001-01-01T00:00:00Z"
}