diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index f2349633..c174973a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -105,7 +105,11 @@ jobs: AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} run: | docker run -d --name redis -p 6379:6379 redis/redis-stack-server:latest - make test-notebooks + if [[ "${{ matrix.python-version }}" > "3.9" ]]; then + make test-notebooks + else + poetry run test-notebooks --ignore ./docs/user_guide/09_threshold_optimization.ipynb + fi docs: runs-on: ubuntu-latest diff --git a/README.md b/README.md index 1fb2bee6..20712e4f 100644 --- a/README.md +++ b/README.md @@ -222,6 +222,11 @@ embeddings = co.embed_many( ### Rerankers [Integrate with popular reranking providers](https://docs.redisvl.com/en/stable/user_guide/06_rerankers.html) to improve the relevancy of the initial search results from Redis +### Threshold Optimization +[Optimize distance thresholds for cache and router](https://docs.redisvl.com/en/stable/user_guide/09_threshold_optimization.html) with the utility `ThresholdOptimizer` classes. + +**Note:** only available for `python > 3.9`. + ## 💫 Extensions diff --git a/docs/user_guide/09_threshold_optimization.ipynb b/docs/user_guide/09_threshold_optimization.ipynb new file mode 100644 index 00000000..2dbac38f --- /dev/null +++ b/docs/user_guide/09_threshold_optimization.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Threshold Optimization\n", + "\n", + "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.\n", + "\n", + "> **Note:** Threshold optimization relies on `python > 3.9.`\n", + "\n", + "# CacheThresholdOptimizer\n", + "\n", + "Let'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`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from redisvl.extensions.llmcache import SemanticCache\n", + "\n", + "sem_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", + ")\n", + "\n", + "paris_key = sem_cache.store(prompt=\"what is the capital of france?\", response=\"paris\")\n", + "rabat_key = sem_cache.store(prompt=\"what is the capital of morocco?\", response=\"rabat\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n", + " 'prompt': 'what is the capital of france?',\n", + " 'response': 'paris',\n", + " 'vector_distance': 0.421104669571,\n", + " 'inserted_at': 1741039231.99,\n", + " 'updated_at': 1741039231.99,\n", + " 'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sem_cache.check(\"what's the capital of britain?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define test_data and optimize\n", + "\n", + "With 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", + "\n", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance threshold before: 0.5 \n", + "\n", + "Distance threshold after: 0.13050847457627118 \n", + "\n" + ] + } + ], + "source": [ + "from redisvl.utils.optimize import CacheThresholdOptimizer\n", + "\n", + "test_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", + "\n", + "print(f\"Distance threshold before: {sem_cache.distance_threshold} \\n\")\n", + "optimizer = CacheThresholdOptimizer(sem_cache, test_data)\n", + "optimizer.optimize()\n", + "print(f\"Distance threshold after: {sem_cache.distance_threshold} \\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also see that we no longer match on the incorrect example:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sem_cache.check(\"what's the capital of britain?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But still match on highly relevant prompts:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n", + " 'prompt': 'what is the capital of france?',\n", + " 'response': 'paris',\n", + " 'vector_distance': 0.0835866332054,\n", + " 'inserted_at': 1741039231.99,\n", + " 'updated_at': 1741039231.99,\n", + " 'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sem_cache.check(\"what's the capital city of france?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RouterThresholdOptimizer\n", + "\n", + "Very similar to the caching case, you can optimize your router.\n", + "\n", + "### Define the routes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from redisvl.extensions.router import Route\n", + "\n", + "routes = [\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", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the SemanticRouter" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from redisvl.extensions.router import SemanticRouter\n", + "from redisvl.utils.vectorize import HFTextVectorizer\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "\n", + "# Initialize the SemanticRouter\n", + "router = 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", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Provide test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "test_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", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimize\n", + "\n", + "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. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Route thresholds before: {'greeting': 0.5, 'farewell': 0.5} \n", + "\n", + "Eval metric F1: start 0.438, end 0.719 \n", + "Ending thresholds: {'greeting': 1.0858585858585856, 'farewell': 0.5545454545454545}\n" + ] + } + ], + "source": [ + "from redisvl.utils.optimize import RouterThresholdOptimizer\n", + "\n", + "print(f\"Route thresholds before: {router.route_thresholds} \\n\")\n", + "optimizer = RouterThresholdOptimizer(router, test_data)\n", + "optimizer.optimize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test it out" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RouteMatch(name='greeting', distance=0.295984119177)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query the router with a statement\n", + "route_match = router(\"hi there\")\n", + "route_match" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "router.delete()\n", + "sem_cache.delete()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "redisvl-Q9FZQJWe-py3.11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/user_guide/router.yaml b/docs/user_guide/router.yaml index 38aecfdd..b743aaf6 100644 --- a/docs/user_guide/router.yaml +++ b/docs/user_guide/router.yaml @@ -7,7 +7,7 @@ routes: - what's trending in tech? metadata: category: tech - priority: '1' + priority: 1 distance_threshold: 1.0 - name: sports references: @@ -18,7 +18,7 @@ routes: - basketball and football metadata: category: sports - priority: '2' + priority: 2 distance_threshold: 0.5 - name: entertainment references: @@ -27,12 +27,11 @@ routes: - what's new in the entertainment industry? metadata: category: entertainment - priority: '3' + priority: 3 distance_threshold: 0.7 vectorizer: type: hf model: sentence-transformers/all-mpnet-base-v2 routing_config: - distance_threshold: 0.5 max_k: 3 aggregation_method: min diff --git a/poetry.lock b/poetry.lock index a86a3523..85c682e7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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version = ">=4.44", optional = true } mistralai = { version = ">=1.0.0", optional = true } voyageai = { version = ">=0.2.2", optional = true } -boto3 = { version = "^1.36.0", optional = true, extras = ["bedrock"] } +ranx = { version = "^0.3.0", python=">=3.10", optional = true } +boto3 = {version = "1.36.0", optional = true, extras = ["bedrock"]} [tool.poetry.extras] openai = ["openai"] @@ -53,6 +54,7 @@ vertexai = ["google_cloud_aiplatform", "protobuf"] cohere = ["cohere"] mistralai = ["mistralai"] voyageai = ["voyageai"] +ranx = ["ranx"] bedrock = ["boto3"] [tool.poetry.group.dev.dependencies] diff --git a/redisvl/extensions/llmcache/semantic.py b/redisvl/extensions/llmcache/semantic.py index 41e6e214..9652a335 100644 --- a/redisvl/extensions/llmcache/semantic.py +++ b/redisvl/extensions/llmcache/semantic.py @@ -2,6 +2,7 @@ import weakref from typing import Any, Dict, List, Optional +import numpy as np from redis import Redis from redisvl.extensions.constants import ( @@ -23,6 +24,7 @@ from redisvl.index import AsyncSearchIndex, SearchIndex from redisvl.query import RangeQuery from redisvl.query.filter import FilterExpression +from redisvl.query.query import BaseQuery from redisvl.redis.connection import RedisConnectionFactory from redisvl.utils.log import get_logger from redisvl.utils.utils import ( diff --git a/redisvl/extensions/router/semantic.py b/redisvl/extensions/router/semantic.py index 4a7e72c3..5335d724 100644 --- a/redisvl/extensions/router/semantic.py +++ b/redisvl/extensions/router/semantic.py @@ -160,6 +160,16 @@ def update_routing_config(self, routing_config: RoutingConfig): """ self.routing_config = routing_config + def update_route_thresholds(self, route_thresholds: Dict[str, Optional[float]]): + """Update the distance thresholds for each route. + + Args: + route_thresholds (Dict[str, float]): Dictionary of route names and their distance thresholds. + """ + for route in self.routes: + if route.name in route_thresholds: + route.distance_threshold = route_thresholds[route.name] # type: ignore + def _route_ref_key(self, route_name: str, reference: str) -> str: """Generate the route reference key.""" reference_hash = hashify(reference) @@ -263,18 +273,16 @@ def _get_route_matches( aggregation_method: DistanceAggregationMethod, max_k: int = 1, ) -> List[RouteMatch]: - """Get the route matches for a given vector and aggregation method.""" + """Get route response from vector db""" - thresholds = [route.distance_threshold for route in self.routes] - if thresholds: - distance_threshold = max(thresholds) - else: - raise ValueError("No distance thresholds provided for the semantic router") + # what's interesting about this is that we only provide one distance_threshold for a range query not multiple + # therefore you might take the max_threshold and further refine from there. + distance_threshold = max(route.distance_threshold for route in self.routes) vector_range_query = RangeQuery( vector=vector, vector_field_name=ROUTE_VECTOR_FIELD_NAME, - distance_threshold=distance_threshold, + distance_threshold=float(distance_threshold), return_fields=["route_name"], ) @@ -329,7 +337,7 @@ def _classify_multi_route( ) -> List[RouteMatch]: """Classify to multiple routes, up to max_k (int), using a vector.""" - route_matches = self._get_route_matches(vector, aggregation_method, max_k) + route_matches = self._get_route_matches(vector, aggregation_method, max_k=max_k) # process route matches top_route_matches: List[RouteMatch] = [] diff --git a/redisvl/utils/optimize/__init__.py b/redisvl/utils/optimize/__init__.py new file mode 100644 index 00000000..c420610f --- /dev/null +++ b/redisvl/utils/optimize/__init__.py @@ -0,0 +1,12 @@ +from redisvl.utils.optimize.base import BaseThresholdOptimizer, EvalMetric +from redisvl.utils.optimize.cache import CacheThresholdOptimizer +from redisvl.utils.optimize.router import RouterThresholdOptimizer +from redisvl.utils.optimize.schema import TestData + +__all__ = [ + "CacheThresholdOptimizer", + "RouterThresholdOptimizer", + "EvalMetric", + "BaseThresholdOptimizer", + "TestData", +] diff --git a/redisvl/utils/optimize/base.py b/redisvl/utils/optimize/base.py new file mode 100644 index 00000000..daa8be60 --- /dev/null +++ b/redisvl/utils/optimize/base.py @@ -0,0 +1,45 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any, Callable, Dict, List, TypeVar + +from redisvl.utils.optimize.utils import _validate_test_dict + + +class EvalMetric(str, Enum): + """Evaluation metrics for threshold optimization.""" + + F1 = "f1" + PRECISION = "precision" + RECALL = "recall" + + +T = TypeVar("T") # Type variable for the optimizable object (Cache or Router) + + +class BaseThresholdOptimizer(ABC): + """Abstract base class for threshold optimizers.""" + + def __init__( + self, + optimizable: T, + test_dict: List[Dict], + opt_fn: Callable, + eval_metric: str = "f1", + ): + """Initialize the optimizer. + + Args: + optimizable: The object to optimize (Cache or Router) + test_dict: List of test cases + eval_fn: Function to evaluate performance + opt_fn: Function to perform optimization + """ + self.test_data = _validate_test_dict(test_dict) + self.optimizable = optimizable + self.eval_metric = EvalMetric(eval_metric) + self.opt_fn = opt_fn + + @abstractmethod + def optimize(self, **kwargs: Any): + """Optimize thresholds using the provided optimization function.""" + pass diff --git a/redisvl/utils/optimize/cache.py b/redisvl/utils/optimize/cache.py new file mode 100644 index 00000000..e787bc66 --- /dev/null +++ b/redisvl/utils/optimize/cache.py @@ -0,0 +1,85 @@ +from typing import Any, Callable, Dict, List + +import numpy as np +from ranx import Qrels, Run, evaluate + +from redisvl.extensions.llmcache.semantic import SemanticCache +from redisvl.query import RangeQuery +from redisvl.utils.optimize.base import BaseThresholdOptimizer, EvalMetric +from redisvl.utils.optimize.schema import TestData +from redisvl.utils.optimize.utils import NULL_RESPONSE_KEY, _format_qrels + + +def _generate_run_cache(test_data: List[TestData], threshold: float) -> Run: + """Format observed data for evaluation with ranx""" + run_dict: Dict[str, Dict[str, int]] = {} + + for td in test_data: + run_dict[td.id] = {} + for res in td.response: + if float(res["vector_distance"]) < threshold: + # value of 1 is irrelevant checks only on match for f1 + run_dict[td.id][res["id"]] = 1 + + if not run_dict[td.id]: + # ranx is a little odd in that if there are no matches it errors + # if however there are no keys that match you get the correct score + run_dict[td.id][NULL_RESPONSE_KEY] = 1 + + return Run(run_dict) + + +def _eval_cache( + test_data: List[TestData], threshold: float, qrels: Qrels, metric: str +) -> float: + """Formats run data and evaluates supported metric""" + run = _generate_run_cache(test_data, threshold) + return evaluate(qrels, run, metric, make_comparable=True) + + +def _get_best_threshold(metrics: dict) -> float: + """ + Returns the threshold with the highest F1 score. + If multiple thresholds have the same F1 score, returns the lowest threshold. + """ + return max(metrics.items(), key=lambda x: (x[1]["score"], -x[0]))[0] + + +def _grid_search_opt_cache( + cache: SemanticCache, test_data: List[TestData], eval_metric: EvalMetric +): + """Evaluates all thresholds in linspace for cache to determine optimal""" + thresholds = np.linspace(0.01, 0.8, 60) + metrics = {} + + for td in test_data: + vec = cache._vectorizer.embed(td.query) + query = RangeQuery( + vec, vector_field_name="prompt_vector", distance_threshold=1.0 + ) + res = cache.index.query(query) + td.response = res + + qrels = _format_qrels(test_data) + + for threshold in thresholds: + score = _eval_cache(test_data, threshold, qrels, eval_metric.value) + metrics[threshold] = {"score": score} + + best_threshold = _get_best_threshold(metrics) + cache.set_threshold(best_threshold) + + +class CacheThresholdOptimizer(BaseThresholdOptimizer): + def __init__( + self, + cache: SemanticCache, + test_dict: List[Dict], + opt_fn: Callable = _grid_search_opt_cache, + eval_metric: str = "f1", + ): + super().__init__(cache, test_dict, opt_fn, eval_metric) + + def optimize(self, **kwargs: Any): + """Optimize thresholds using the provided optimization function for cache case.""" + self.opt_fn(self.optimizable, self.test_data, self.eval_metric, **kwargs) diff --git a/redisvl/utils/optimize/router.py b/redisvl/utils/optimize/router.py new file mode 100644 index 00000000..30e70004 --- /dev/null +++ b/redisvl/utils/optimize/router.py @@ -0,0 +1,102 @@ +import random +from typing import Any, Callable, Dict, List + +import numpy as np +from ranx import Qrels, Run, evaluate + +from redisvl.extensions.router.semantic import SemanticRouter +from redisvl.utils.optimize.base import BaseThresholdOptimizer, EvalMetric +from redisvl.utils.optimize.schema import TestData +from redisvl.utils.optimize.utils import NULL_RESPONSE_KEY, _format_qrels + + +def _generate_run_router(test_data: List[TestData], router: SemanticRouter) -> Run: + """Format router results into format for ranx Run""" + run_dict: Dict[Any, Any] = {} + + for td in test_data: + run_dict[td.id] = {} + route_match = router(td.query) + if route_match and route_match.name == td.query_match: + run_dict[td.id][td.query_match] = 1 + else: + run_dict[td.id][NULL_RESPONSE_KEY] = 1 + + return Run(run_dict) + + +def _eval_router( + router: SemanticRouter, test_data: List[TestData], qrels: Qrels, eval_metric: str +) -> float: + """Evaluate acceptable metric given run and qrels data""" + run = _generate_run_router(test_data, router) + return evaluate(qrels, run, eval_metric, make_comparable=True) + + +def _router_random_search( + route_names: List[str], route_thresholds: dict, search_step=0.10 +): + """Performances random search for many threshold to many route context""" + score_threshold_values = [] + for route in route_names: + score_threshold_values.append( + np.linspace( + start=max(route_thresholds[route] - search_step, 0), + stop=route_thresholds[route] + search_step, + num=100, + ) + ) + + return { + route: float(random.choice(score_threshold_values[i])) + for i, route in enumerate(route_names) + } + + +def _random_search_opt_router( + router: SemanticRouter, + test_data: List[TestData], + qrels: Qrels, + eval_metric: EvalMetric, + **kwargs: Any, +): + """Performs complete optimization for router cases provide acceptable metric""" + + start_score = _eval_router(router, test_data, qrels, eval_metric.value) + best_score = start_score + best_thresholds = router.route_thresholds + + max_iterations = kwargs.get("max_iterations", 20) + + for _ in range(max_iterations): + route_names = router.route_names + route_thresholds = router.route_thresholds + thresholds = _router_random_search( + route_names=route_names, route_thresholds=route_thresholds + ) + router.update_route_thresholds(thresholds) + score = _eval_router(router, test_data, qrels, eval_metric.value) + if score > best_score: + best_score = score + best_thresholds = thresholds + + print( + f"Eval metric {eval_metric.value.upper()}: start {round(start_score, 3)}, end {round(best_score, 3)} \nEnding thresholds: {router.route_thresholds}" + ) + router.update_route_thresholds(best_thresholds) + + +class RouterThresholdOptimizer(BaseThresholdOptimizer): + def __init__( + self, + router: SemanticRouter, + test_dict: List[Dict], + opt_fn: Callable = _random_search_opt_router, + eval_metric: str = "f1", + ): + super().__init__(router, test_dict, opt_fn, eval_metric) + + def optimize(self, **kwargs: Any): + """Optimize thresholds using the provided optimization function for router case.""" + qrels = _format_qrels(self.test_data) + self.opt_fn(self.optimizable, self.test_data, qrels, self.eval_metric, **kwargs) diff --git a/redisvl/utils/optimize/schema.py b/redisvl/utils/optimize/schema.py new file mode 100644 index 00000000..dfade3cc --- /dev/null +++ b/redisvl/utils/optimize/schema.py @@ -0,0 +1,11 @@ +from typing import List, Optional + +from pydantic import BaseModel, Field +from ulid import ULID + + +class TestData(BaseModel): + id: str = Field(default_factory=lambda: str(ULID())) + query: str + query_match: Optional[str] + response: List[dict] = [] diff --git a/redisvl/utils/optimize/utils.py b/redisvl/utils/optimize/utils.py new file mode 100644 index 00000000..2a56504c --- /dev/null +++ b/redisvl/utils/optimize/utils.py @@ -0,0 +1,26 @@ +from typing import List + +from ranx import Qrels + +from redisvl.utils.optimize.schema import TestData + +NULL_RESPONSE_KEY = "no_match" + + +def _format_qrels(test_data: List[TestData]) -> Qrels: + """Utility function for creating qrels for evaluation with ranx""" + qrels_dict = {} + + for td in test_data: + if td.query_match: + qrels_dict[td.id] = {td.query_match: 1} + else: + # This is for capturing true negatives from test set + qrels_dict[td.id] = {NULL_RESPONSE_KEY: 1} + + return Qrels(qrels_dict) + + +def _validate_test_dict(test_dict: List[dict]) -> List[TestData]: + """Convert/validate test_dict for use in optimizer""" + return [TestData(**d) for d in test_dict] diff --git a/schemas/semantic_router.yaml b/schemas/semantic_router.yaml index 0f5bab19..d3e24f85 100644 --- a/schemas/semantic_router.yaml +++ b/schemas/semantic_router.yaml @@ -13,7 +13,7 @@ routes: - goodbye metadata: type: farewell - distance_threshold: 0.3 + distance_threshold: 0.2 vectorizer: type: hf model: sentence-transformers/all-mpnet-base-v2 diff --git a/scripts.py b/scripts.py index 77020aad..31e9a390 100644 --- a/scripts.py +++ b/scripts.py @@ -39,7 +39,16 @@ def test(): def test_verbose(): - test_cmd = ["python", "-m", "pytest", "-n", "auto", "-vv", "-s", "--log-level=CRITICAL"] + test_cmd = [ + "python", + "-m", + "pytest", + "-n", + "auto", + "-vv", + "-s", + "--log-level=CRITICAL", + ] # Get any extra arguments passed to the script extra_args = sys.argv[1:] if extra_args: @@ -48,7 +57,22 @@ def test_verbose(): def test_notebooks(): - subprocess.run("cd docs/ && python -m pytest --nbval-lax ./user_guide -vv", shell=True, check=True) + test_cmd = [ + "python", + "-m", + "pytest", + "--nbval-lax", + "./docs/user_guide", + "-vvv", + ] + extra_args = sys.argv[1:] + if extra_args: + test_cmd.extend(extra_args) + + subprocess.run( + test_cmd, + check=True, + ) def build_docs(): diff --git a/tests/integration/test_semantic_router.py b/tests/integration/test_semantic_router.py index ed8f2f0e..07750b42 100644 --- a/tests/integration/test_semantic_router.py +++ b/tests/integration/test_semantic_router.py @@ -33,7 +33,7 @@ def routes(): name="farewell", references=["bye", "goodbye"], metadata={"type": "farewell"}, - distance_threshold=0.3, + distance_threshold=0.2, ), ] @@ -43,7 +43,7 @@ def semantic_router(client, routes): router = SemanticRouter( name="test-router", routes=routes, - routing_config=RoutingConfig(distance_threshold=0.3, max_k=2), + routing_config=RoutingConfig(max_k=2), redis_client=client, overwrite=False, ) @@ -71,7 +71,7 @@ def test_router_properties(semantic_router): thresholds = semantic_router.route_thresholds assert thresholds["greeting"] == 0.3 - assert thresholds["farewell"] == 0.3 + assert thresholds["farewell"] == 0.2 def test_get_route(semantic_router): diff --git a/tests/integration/test_threshold_optimizer.py b/tests/integration/test_threshold_optimizer.py new file mode 100644 index 00000000..09a93dd3 --- /dev/null +++ b/tests/integration/test_threshold_optimizer.py @@ -0,0 +1,299 @@ +import sys + +import pytest + +if sys.version_info.major == 3 and sys.version_info.minor < 10: + pytest.skip("Test requires Python 3.10 or higher", allow_module_level=True) + +from redisvl.extensions.llmcache import SemanticCache +from redisvl.extensions.router import Route, SemanticRouter +from redisvl.extensions.router.schema import RoutingConfig +from redisvl.redis.connection import compare_versions +from redisvl.utils.optimize import ( + CacheThresholdOptimizer, + EvalMetric, + RouterThresholdOptimizer, +) + + +@pytest.fixture +def routes(): + return [ + Route( + name="greeting", + references=["hello", "hi"], + metadata={"type": "greeting"}, + distance_threshold=0.3, + ), + Route( + name="farewell", + references=["bye", "goodbye"], + metadata={"type": "farewell"}, + distance_threshold=0.2, + ), + ] + + +@pytest.fixture +def semantic_router(client, routes): + router = SemanticRouter( + name="test-router", + routes=routes, + routing_config=RoutingConfig(max_k=2), + redis_client=client, + overwrite=False, + ) + yield router + router.delete() + + +@pytest.fixture +def test_data_optimization(): + return [ + # Greetings + {"query": "hello", "query_match": "greeting"}, # English + {"query": "hola", "query_match": "greeting"}, # Spanish + {"query": "bonjour", "query_match": "greeting"}, # French + {"query": "ciao", "query_match": "greeting"}, # Italian + {"query": "hallo", "query_match": "greeting"}, # German + {"query": "こんにちは", "query_match": "greeting"}, # Japanese + {"query": "안녕하세요", "query_match": "greeting"}, # Korean + {"query": "你好", "query_match": "greeting"}, # Chinese + {"query": "مرحبا", "query_match": "greeting"}, # Arabic + {"query": "привет", "query_match": "greeting"}, # Russian + {"query": "γεια σας", "query_match": "greeting"}, # Greek + {"query": "namaste", "query_match": "greeting"}, # Hindi + {"query": "olá", "query_match": "greeting"}, # Portuguese + {"query": "salut", "query_match": "greeting"}, # French informal + {"query": "cześć", "query_match": "greeting"}, # Polish + # Farewells + {"query": "goodbye", "query_match": "farewell"}, # English + {"query": "adiós", "query_match": "farewell"}, # Spanish + {"query": "au revoir", "query_match": "farewell"}, # French + {"query": "arrivederci", "query_match": "farewell"}, # Italian + {"query": "auf wiedersehen", "query_match": "farewell"}, # German + {"query": "さようなら", "query_match": "farewell"}, # Japanese + {"query": "안녕히 가세요", "query_match": "farewell"}, # Korean + {"query": "再见", "query_match": "farewell"}, # Chinese + {"query": "مع السلامة", "query_match": "farewell"}, # Arabic + {"query": "до свидания", "query_match": "farewell"}, # Russian + {"query": "αντίο", "query_match": "farewell"}, # Greek + {"query": "अलविदा", "query_match": "farewell"}, # Hindi + {"query": "adeus", "query_match": "farewell"}, # Portuguese + {"query": "tchau", "query_match": "farewell"}, # Portuguese informal + {"query": "do widzenia", "query_match": "farewell"}, # Polish + ] + + +def test_routes_different_distance_thresholds_optimizer_default( + semantic_router, routes, redis_url, test_data_optimization +): + redis_version = semantic_router._index.client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + zero_threshold = 0.0 + + # Test that it updates the thresholds + routes[0].distance_threshold = zero_threshold + routes[1].distance_threshold = zero_threshold + + router = SemanticRouter( + name="test_routes_different_distance_optimizer", + routes=routes, + redis_url=redis_url, + overwrite=True, + ) + + # szia is hello in hungarian and not in our test data + matches = router.route_many("Szia", max_k=2) + assert len(matches) == 0 + + # now run optimizer + router_optimizer = RouterThresholdOptimizer(router, test_data_optimization) + router_optimizer.optimize(max_iterations=10) + + # test that it updated thresholds beyond the null case + for route in routes: + assert route.distance_threshold > zero_threshold + + +def test_routes_different_distance_thresholds_optimizer_precision( + semantic_router, routes, redis_url, test_data_optimization +): + + redis_version = semantic_router._index.client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + zero_threshold = 0.0 + + # Test that it updates the thresholds + routes[0].distance_threshold = zero_threshold + routes[1].distance_threshold = zero_threshold + + router = SemanticRouter( + name="test_routes_different_distance_optimizer", + routes=routes, + redis_url=redis_url, + overwrite=True, + ) + + # szia is hello in hungarian and not in our test data + matches = router.route_many("Szia", max_k=2) + assert len(matches) == 0 + + # now run optimizer + router_optimizer = RouterThresholdOptimizer( + router, test_data_optimization, eval_metric="precision" + ) + router_optimizer.optimize(max_iterations=10) + + # test that it updated thresholds beyond the null case + for route in routes: + assert route.distance_threshold > zero_threshold + + +def test_routes_different_distance_thresholds_optimizer_recall( + semantic_router, routes, redis_url, test_data_optimization +): + redis_version = semantic_router._index.client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + zero_threshold = 0.0 + + # Test that it updates the thresholds + routes[0].distance_threshold = zero_threshold + routes[1].distance_threshold = zero_threshold + + router = SemanticRouter( + name="test_routes_different_distance_optimizer", + routes=routes, + redis_url=redis_url, + overwrite=True, + ) + + # szia is hello in hungarian and not in our test data + matches = router.route_many("Szia", max_k=2) + assert len(matches) == 0 + + # now run optimizer + router_optimizer = RouterThresholdOptimizer( + router, test_data_optimization, eval_metric="recall" + ) + router_optimizer.optimize(max_iterations=10) + + # test that it updated thresholds beyond the null case + for route in routes: + assert route.distance_threshold > zero_threshold + + +def test_optimize_threshold_cache_default(redis_url): + null_threshold = 0.0 + cache = SemanticCache( + name="test_optimize_threshold_cache", + redis_url=redis_url, + distance_threshold=null_threshold, + ) + + redis_version = cache._index.client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + paris_key = cache.store(prompt="what is the capital of france?", response="paris") + rabat_key = cache.store(prompt="what is the capital of morocco?", response="rabat") + + test_dict = [ + {"query": "what actually is the capital of france?", "query_match": paris_key}, + {"query": "what actually is the capital of morocco?", "query_match": rabat_key}, + {"query": "What is the state bird of virginia?", "query_match": ""}, + ] + + cache_optimizer = CacheThresholdOptimizer(cache, test_dict) + + cache_optimizer.optimize() + + assert cache.distance_threshold > null_threshold + + +def test_optimize_threshold_cache_precision(client, redis_url): + redis_version = client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + null_threshold = 0.0 + cache = SemanticCache( + name="test_optimize_threshold_cache", + redis_url=redis_url, + distance_threshold=null_threshold, + ) + + paris_key = cache.store(prompt="what is the capital of france?", response="paris") + rabat_key = cache.store(prompt="what is the capital of morocco?", response="rabat") + + test_dict = [ + {"query": "what actually is the capital of france?", "query_match": paris_key}, + {"query": "what actually is the capital of morocco?", "query_match": rabat_key}, + {"query": "What is the state bird of virginia?", "query_match": ""}, + ] + + cache_optimizer = CacheThresholdOptimizer(cache, test_dict, eval_metric="precision") + + cache_optimizer.optimize() + + assert cache.distance_threshold > null_threshold + + +def test_optimize_threshold_cache_recall(client, redis_url): + redis_version = client.info()["redis_version"] + if not compare_versions(redis_version, "7.0.0"): + pytest.skip("Not using a late enough version of Redis") + + null_threshold = 0.0 + cache = SemanticCache( + name="test_optimize_threshold_cache", + redis_url=redis_url, + distance_threshold=null_threshold, + ) + + paris_key = cache.store(prompt="what is the capital of france?", response="paris") + rabat_key = cache.store(prompt="what is the capital of morocco?", response="rabat") + + test_dict = [ + {"query": "what actually is the capital of france?", "query_match": paris_key}, + {"query": "what actually is the capital of morocco?", "query_match": rabat_key}, + {"query": "What is the state bird of virginia?", "query_match": ""}, + ] + + cache_optimizer = CacheThresholdOptimizer(cache, test_dict, eval_metric="recall") + + cache_optimizer.optimize() + + assert cache.distance_threshold > null_threshold + + +def test_eval_metric_from_string(): + """Test that EvalMetric.from_string works for valid metrics.""" + assert EvalMetric("f1") == EvalMetric.F1 + assert EvalMetric("precision") == EvalMetric.PRECISION + assert EvalMetric("recall") == EvalMetric.RECALL + + +def test_eval_metric_invalid(): + """Test that EvalMetric.from_string raises ValueError for invalid metrics.""" + with pytest.raises(ValueError): + EvalMetric("invalid_metric") + + +def test_optimizer_with_invalid_metric(redis_url): + """Test that optimizers raise ValueError when initialized with invalid metric.""" + cache = SemanticCache( + name="test_invalid_metric", + redis_url=redis_url, + ) + + test_dict = [{"query": "test", "query_match": ""}] + + with pytest.raises(ValueError): + CacheThresholdOptimizer(cache, test_dict, eval_metric="invalid_metric") diff --git a/tests/unit/test_threshold_optimizer_utility.py b/tests/unit/test_threshold_optimizer_utility.py new file mode 100644 index 00000000..9dc844c9 --- /dev/null +++ b/tests/unit/test_threshold_optimizer_utility.py @@ -0,0 +1,77 @@ +import sys + +import pytest + +if sys.version_info.major == 3 and sys.version_info.minor < 10: + pytest.skip("Test requires Python 3.10 or higher", allow_module_level=True) + +from ranx import evaluate + +from redisvl.utils.optimize import TestData +from redisvl.utils.optimize.cache import _generate_run_cache +from redisvl.utils.optimize.utils import _format_qrels + +# Note: these tests are not intended to test ranx but to test that our data formatting for the package is correct + + +def test_known_precision_case(): + """ + Test case with known precision value. + + Setup: + - 2 queries + - Query 1 expects doc1, gets doc1 and doc2 (precision 0.5) + - Query 2 expects doc3, gets doc3 (precision 1.0) + Expected overall precision: 0.75 + """ + # Setup test data + test_data = [ + TestData( + query="test query 1", + query_match="doc1", + response=[ + {"id": "doc1", "vector_distance": 0.2}, + {"id": "doc2", "vector_distance": 0.3}, + ], + ), + TestData( + query="test query 2", + query_match="doc3", + response=[ + {"id": "doc3", "vector_distance": 0.2}, + {"id": "doc4", "vector_distance": 0.8}, + ], + ), + ] + + # Create qrels (ground truth) + qrels = _format_qrels(test_data) + + threshold = 0.4 + run = _generate_run_cache(test_data, threshold) + + # Calculate precision using ranx + precision = evaluate(qrels, run, "precision") + assert precision == 0.75 # (0.5 + 1.0) / 2 + + +def test_known_precision_with_no_matches(): + """Test case where some queries have no matches.""" + test_data = [ + TestData( + query="test query 2", + query_match="", # Expecting no match + response=[], + ), + ] + + # Create qrels + qrels = _format_qrels(test_data) + + # Generate run with threshold that excludes all docs for first query + threshold = 0.3 + run = _generate_run_cache(test_data, threshold) + + # Calculate precision + precision = evaluate(qrels, run, "precision") + assert precision == 1.0 # (0.0 + 1.0) / 2