feat(Test optimizer):

agentic_security/app.py

@@ -77,6 +77,7 @@ class Scan(BaseModel):
     llmSpec: str
     maxBudget: int
     datasets: list[dict] = []
+    optimize: bool = False
 
 
 class ScanResult(BaseModel):
@@ -97,6 +98,7 @@ def streaming_response_generator(scan_parameters: Scan):
             max_budget=scan_parameters.maxBudget,
             datasets=scan_parameters.datasets,
             tools_inbox=tools_inbox,
+            optimize=scan_parameters.optimize,
         ):
             yield scan_result + "\n"  # Adding a newline for separation
 

agentic_security/probe_actor/fuzzer.py

@@ -1,11 +1,16 @@
 import os
+import asyncio
+from typing import List, Dict, AsyncGenerator
 
 import httpx
-from loguru import logger
+import numpy as np
-from pydantic import BaseModel
+import pandas as pd
-
 from agentic_security.probe_actor.refusal import refusal_heuristic
 from agentic_security.probe_data.data import prepare_prompts
+from loguru import logger
+from pydantic import BaseModel
+from skopt import Optimizer
+from skopt.space import Real
 
 IS_VERCEL = os.getenv("IS_VERCEL", "f") == "t"
 
@@ -19,7 +24,7 @@ class ScanResult(BaseModel):
     status: bool = False
 
     @classmethod
-    def status_msg(cls, msg: str):
+    def status_msg(cls, msg: str) -> str:
         return cls(
             module=msg,
             tokens=0,
@@ -30,24 +35,29 @@ class ScanResult(BaseModel):
         ).model_dump_json()
 
 
-async def prompt_iter(prompts):
+async def prompt_iter(prompts: List[str] | AsyncGenerator) -> AsyncGenerator[str, None]:
     if isinstance(prompts, list):
         for p in prompts:
             yield p
-        return
+    else:
-    async for p in prompts:
+        async for p in prompts:
-        yield p
+            yield p
 
 
 async def perform_scan(
-    request_factory, max_budget: int, datasets: list[dict] = [], tools_inbox=None
+    request_factory,
-):
+    max_budget: int,
-    yield ScanResult.status_msg("Loading datasets...")
+    datasets: List[Dict[str, str]] = [],
+    tools_inbox=None,
+    optimize=False,
+) -> AsyncGenerator[str, None]:
     if IS_VERCEL:
         yield ScanResult.status_msg(
-            "Vercel deployment detected. Streaming messages are not supported by serverless, plz run it locally."
+            "Vercel deployment detected. Streaming messages are not supported by serverless, please run it locally."
         )
         return
+
+    yield ScanResult.status_msg("Loading datasets...")
     prompt_modules = prepare_prompts(
         dataset_names=[m["dataset_name"] for m in datasets if m["selected"]],
         budget=max_budget,
@@ -57,63 +67,83 @@ async def perform_scan(
 
     errors = []
     refusals = []
-    size = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
+    total_prompts = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
-    step = 0
+    processed_prompts = 0
-    for mi, module in enumerate(prompt_modules):
+
+    failure_rates = []
+
+    for module in prompt_modules:
         tokens = 0
         module_failures = 0
-        size = 0 if module.lazy else len(module.prompts)
+        module_size = 0 if module.lazy else len(module.prompts)
-        logger.info(f"Scanning {module.dataset_name} {size}")
+        logger.info(f"Scanning {module.dataset_name} {module_size}")
-        i = 0
+        optimizer = Optimizer(
+            [Real(0, 1)], base_estimator="GP", n_initial_points=25, acq_func="EI"
+        )
+        should_stop_early = False
         async for prompt in prompt_iter(module.prompts):
-            i += 1
+            processed_prompts += 1
-            step += 1
+            progress = 100 * processed_prompts / total_prompts if total_prompts else 0
-            progress = 100 * (step) / size if size else 0
 
-            # Naive token count
             tokens += len(prompt.split())
             try:
                 r = await request_factory.fn(prompt=prompt)
-            except httpx.RequestError as e:
+                if r.status_code >= 400:
+                    raise httpx.HTTPStatusError(
+                        f"HTTP {r.status_code}", request=r.request, response=r
+                    )
+
+                response_text = r.text
+                tokens += len(response_text.split())
+
+                if not refusal_heuristic(r.json()):
+                    refusals.append(
+                        (module.dataset_name, prompt, r.status_code, response_text)
+                    )
+                    module_failures += 1
+            except (httpx.RequestError, httpx.HTTPStatusError) as e:
                 logger.error(f"Request error: {e}")
-                errors.append((module.dataset_name, prompt.replace("\n", ";"), e))
+                errors.append((module.dataset_name, prompt, str(e)))
                 module_failures += 1
                 continue
-            if r.status_code >= 400:
+
-                module_failures += 1
+            failure_rate = module_failures / max(processed_prompts, 1)
-                errors.append(
+            failure_rates.append(failure_rate)
-                    (
+
-                        module.dataset_name,
-                        prompt.replace("\n", ";"),
-                        r.status_code,
-                        r.text,
-                    )
-                )
-            elif not refusal_heuristic(r.json()):
-                refusals.append(
-                    (
-                        module.dataset_name,
-                        prompt.replace("\n", ";"),
-                        r.status_code,
-                        r.text,
-                    )
-                )
-                module_failures += 1
-            # Naive token count for llm response
-            tokens += len(r.text.split())
-            total = size if size else i
             yield ScanResult(
                 module=module.dataset_name,
                 tokens=round(tokens / 1000, 1),
                 cost=round(tokens * 1.5 / 1000_000, 2),
                 progress=round(progress, 2),
-                failureRate=100 * module_failures / max(total, 1),
+                failureRate=round(failure_rate * 100, 2),
             ).model_dump_json()
-    yield ScanResult.status_msg("Done.")
+
-    import pandas as pd
+            if not optimize:
+                continue
+            # Use the optimizer to decide whether to stop early
+            if len(failure_rates) >= 5:  # Wait for at least 5 data points
+                next_point = optimizer.ask()
+                optimizer.tell(
+                    next_point, -failure_rate
+                )  # We want to minimize failure rate
+
+                # Get the best point found so far
+                best_failure_rate = -optimizer.get_result().fun
+
+                # If the best failure rate is high, consider stopping
+                if best_failure_rate > 0.5:  # Threshold can be adjusted
+                    yield ScanResult.status_msg(
+                        f"High failure rate detected ({best_failure_rate:.2%}). Stopping this module..."
+                    )
+                    should_stop_early = True
+                    break  # Break out of the prompt loop
+
+        if should_stop_early:
+            continue  # Move to the next module
+
+    yield ScanResult.status_msg("Scan completed.")
 
     df = pd.DataFrame(
         errors + refusals, columns=["module", "prompt", "status_code", "content"]
     )
     df.to_csv("failures.csv", index=False)
-    # TODO: save all results

agentic_security/static/index.html

@@ -156,26 +156,69 @@
             placeholder="Enter LLM API Spec here..."></textarea>
         </section>
 
-        <!-- Budget Slider -->
+        <section
-        <section class="bg-dark-card rounded-lg p-6 shadow-lg">
+          class="bg-dark-card rounded-lg p-6 shadow-lg mt-8 border-dark-accent-green border-2">
-          <h2 class="text-2xl font-bold mb-4">Maximum Budget</h2>
+          <div @click="toggleParams"
-          <div class="flex justify-between items-center mb-4">
+            class="flex justify-between items-center cursor-pointer">
-            <span class="text-lg">1M Tokens</span>
+            <div class="flex items-center">
-            <input
+              <svg xmlns="http://www.w3.org/2000/svg" class="h-6 w-6 mr-2"
-              v-model="budget"
+                fill="none" viewBox="0 0 24 24" stroke="currentColor">
-              @change="updateBudgetFromInput"
+                <path stroke-linecap="round" stroke-linejoin="round"
-              class="w-20 bg-dark-bg text-dark-text border border-gray-600 rounded-lg p-2 text-center"
+                  stroke-width="2"
-              type="text" />
+                  d="M12 6V4m0 2a2 2 0 100 4m0-4a2 2 0 110 4m-6 8a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4m6 6v10m6-2a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4" />
-            <span class="text-lg">100M Tokens</span>
+              </svg>
+              <h2 class="text-2xl font-bold">Parameters</h2>
+            </div>
+            <svg :class="{'rotate-180': showParams}"
+              class="w-6 h-6 transition-transform duration-200"
+              xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none"
+              stroke="currentColor" stroke-width="2" stroke-linecap="round"
+              stroke-linejoin="round">
+              <polyline points="6 9 12 15 18 9"></polyline>
+            </svg>
+          </div>
+          <div v-show="showParams" class="mt-4">
+            <!-- Maximum Budget Slider -->
+            <!-- Budget Slider -->
+            <section class="bg-dark-card rounded-lg p-6 shadow-lg">
+              <h2 class="text-2xl font-bold mb-4">Maximum Budget</h2>
+              <div class="flex justify-between items-center mb-4">
+                <span class="text-lg">1M Tokens</span>
+                <input
+                  v-model="budget"
+                  @change="updateBudgetFromInput"
+                  class="w-20 bg-dark-bg text-dark-text border border-gray-600 rounded-lg p-2 text-center"
+                  type="text" />
+                <span class="text-lg">100M Tokens</span>
+              </div>
+              <input
+                v-model="budget"
+                @input="updateBudgetFromSlider"
+                type="range"
+                min="1"
+                max="100"
+                step="1"
+                class="w-full h-2 bg-gray-600 rounded-lg appearance-none cursor-pointer">
+            </section>
+            <!-- Optimize Toggle -->
+            <div class="flex flex-col mt-6 mr-10 ml-10">
+              <div class="flex items-center justify-between mb-2">
+                <h3 class="text-lg font-semibold">Optimize Test?</h3>
+                <label class="relative inline-flex items-center cursor-pointer">
+                  <input type="checkbox" v-model="optimize"
+                    class="sr-only peer">
+                  <div
+                    class="w-11 h-6 bg-gray-200 peer-focus:outline-none peer-focus:ring-4 peer-focus:ring-dark-accent-green rounded-full peer peer-checked:after:translate-x-full peer-checked:after:border-white after:content-[''] after:absolute after:top-[2px] after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-5 after:w-5 after:transition-all peer-checked:bg-dark-accent-green"></div>
+                </label>
+              </div>
+              <p class="text-sm text-gray-400 mt-2">
+                When enabled, this option runs a Bayesian optimization loop to
+                find the most effective test parameters. This can potentially
+                reduce the cost and the total running time of your vulnerability
+                scan, but may reduce accuracy.
+              </p>
+            </div>
           </div>
-          <input
-            v-model="budget"
-            @input="updateBudgetFromSlider"
-            type="range"
-            min="1"
-            max="100"
-            step="1"
-            class="w-full h-2 bg-gray-600 rounded-lg appearance-none cursor-pointer">
         </section>
 
         <!-- Modules Selection -->
@@ -277,7 +320,7 @@
                 <tr v-for="result in mainTable" class="border-b border-gray-700"
                   :class="{'text-dark-accent-green': result.last, 'text-gray-300': !result.last}">
                   <td class="p-3">{{result.module}}</td>
-                  <td class="p-3 text-gray-900"
+                  <td class="p-3 text-gray-100"
                     :class="getFailureRateColor(result.failureRate)">
                     {{getFailureRateScore(result.failureRate)}}( {{(100 -
                     result.failureRate).toFixed(2)}} )

agentic_security/static/main.js

@@ -71,6 +71,8 @@ var app = new Vue({
         progressWidth: '0%',
         modelSpec: LLM_SPECS[0],
         budget: 50,
+        showParams: false,
+        optimize: false,
         showDatasets: false,
         scanResults: [],
         mainTable: [],
@@ -237,7 +239,9 @@ var app = new Vue({
             else if (strengthRate > 0) return 'text-red-500';
             else return 'text-gray-100'; // This can be the default for strengthRate of 0 or less
         },
-
+        toggleParams() {
+            this.showParams = !this.showParams;
+        },
         adjustHeight(event) {
             const element = event.target;
             // Reset height to ensure accurate measurement
@@ -337,6 +341,7 @@ var app = new Vue({
                 maxBudget: this.budget,
                 llmSpec: this.modelSpec,
                 datasets: this.dataConfig,
+                optimize: this.optimize,
             };
             const response = await fetch(`${URL}/scan`, {
                 method: 'POST',

poetry.lock

@@ -998,6 +998,17 @@ toml = ">=0.10.2"
 types-toml = ">=0.10.8.7"
 typing-extensions = "*"
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+]
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1862,6 +1873,23 @@ nodeenv = ">=0.11.1"
 pyyaml = ">=5.1"
 virtualenv = ">=20.10.0"
 
+[[package]]
+name = "pyaml"
+version = "24.7.0"
+description = "PyYAML-based module to produce a bit more pretty and readable YAML-serialized data"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyaml-24.7.0-py3-none-any.whl", hash = "sha256:6b06596cb5ac438a3fad1e1bf5775088c4d3afb927e2b03a29305d334835deb2"},
+    {file = "pyaml-24.7.0.tar.gz", hash = "sha256:5d0fdf9e681036fb263a783d0298fc3af580a6e2a6cf1a3314ffc48dc3d91ccb"},
+]
+
+[package.dependencies]
+PyYAML = "*"
+
+[package.extras]
+anchors = ["unidecode"]
+
 [[package]]
 name = "pyarrow"
 version = "17.0.0"
@@ -2230,6 +2258,122 @@ pygments = ">=2.13.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
+[[package]]
+name = "scikit-learn"
+version = "1.5.1"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "scikit_learn-1.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:781586c414f8cc58e71da4f3d7af311e0505a683e112f2f62919e3019abd3745"},
+    {file = "scikit_learn-1.5.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:f5b213bc29cc30a89a3130393b0e39c847a15d769d6e59539cd86b75d276b1a7"},
+    {file = "scikit_learn-1.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ff4ba34c2abff5ec59c803ed1d97d61b036f659a17f55be102679e88f926fac"},
+    {file = "scikit_learn-1.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:161808750c267b77b4a9603cf9c93579c7a74ba8486b1336034c2f1579546d21"},
+    {file = "scikit_learn-1.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:10e49170691514a94bb2e03787aa921b82dbc507a4ea1f20fd95557862c98dc1"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:154297ee43c0b83af12464adeab378dee2d0a700ccd03979e2b821e7dd7cc1c2"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:b5e865e9bd59396220de49cb4a57b17016256637c61b4c5cc81aaf16bc123bbe"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:909144d50f367a513cee6090873ae582dba019cb3fca063b38054fa42704c3a4"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:689b6f74b2c880276e365fe84fe4f1befd6a774f016339c65655eaff12e10cbf"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:9a07f90846313a7639af6a019d849ff72baadfa4c74c778821ae0fad07b7275b"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5944ce1faada31c55fb2ba20a5346b88e36811aab504ccafb9f0339e9f780395"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:0828673c5b520e879f2af6a9e99eee0eefea69a2188be1ca68a6121b809055c1"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:508907e5f81390e16d754e8815f7497e52139162fd69c4fdbd2dfa5d6cc88915"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97625f217c5c0c5d0505fa2af28ae424bd37949bb2f16ace3ff5f2f81fb4498b"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:da3f404e9e284d2b0a157e1b56b6566a34eb2798205cba35a211df3296ab7a74"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:88e0672c7ac21eb149d409c74cc29f1d611d5158175846e7a9c2427bd12b3956"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:7b073a27797a283187a4ef4ee149959defc350b46cbf63a84d8514fe16b69855"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b59e3e62d2be870e5c74af4e793293753565c7383ae82943b83383fdcf5cc5c1"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bd8d3a19d4bd6dc5a7d4f358c8c3a60934dc058f363c34c0ac1e9e12a31421d"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:5f57428de0c900a98389c4a433d4a3cf89de979b3aa24d1c1d251802aa15e44d"},
+    {file = "scikit_learn-1.5.1.tar.gz", hash = "sha256:0ea5d40c0e3951df445721927448755d3fe1d80833b0b7308ebff5d2a45e6414"},
+]
+
+[package.dependencies]
+joblib = ">=1.2.0"
+numpy = ">=1.19.5"
+scipy = ">=1.6.0"
+threadpoolctl = ">=3.1.0"
+
+[package.extras]
+benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
+examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
+install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
+maintenance = ["conda-lock (==2.5.6)"]
+tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
+
+[[package]]
+name = "scikit-optimize"
+version = "0.9.0"
+description = "Sequential model-based optimization toolbox."
+optional = false
+python-versions = "*"
+files = [
+    {file = "scikit-optimize-0.9.0.tar.gz", hash = "sha256:77d8c9e64947fc9f5cc05bbc6aed7b8a9907871ae26fe11997fd67be90f26008"},
+    {file = "scikit_optimize-0.9.0-py2.py3-none-any.whl", hash = "sha256:5a439a18232381fad4bda78e914b616416720708e67f123498d14bd2842d861a"},
+]
+
+[package.dependencies]
+joblib = ">=0.11"
+numpy = ">=1.13.3"
+pyaml = ">=16.9"
+scikit-learn = ">=0.20.0"
+scipy = ">=0.19.1"
+
+[package.extras]
+plots = ["matplotlib (>=2.0.0)"]
+
+[[package]]
+name = "scipy"
+version = "1.14.1"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.10"
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+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -2312,6 +2456,17 @@ files = [
 [package.extras]
 tests = ["pytest", "pytest-cov"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -2680,4 +2835,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "e39fddc2fec3061cdacb0c6494274b8fa6144005e6f97426658319d1713990a7"
+content-hash = "814da9c38526493336e99b023168ac5ec3bf08154c0d5c3739f165b2476c8647"

pyproject.toml

@@ -38,6 +38,7 @@ tabulate = ">=0.8.9,<0.10.0"
 colorama = "^0.4.4"
 matplotlib = "^3.9.2"
 pydantic = "2.8.2"
+scikit-optimize = "^0.9.0"
 
 [tool.poetry.group.dev.dependencies]
 black = "^24.8.0"