chore: retire legacy oil prediction pipeline
Removes everything that was made redundant by the new forecasting stack. Per docs/superpowers/specs/2026-05-01-prediction-rebuild-design.md, this was the cleanup planned at the end of Phase 4. Deleted services and code: - App\Services\Prediction\Signals\* (the old six-signal aggregator — trend, supermarket, day-of-week, brand-behaviour, stickiness, regional-momentum, oil — replaced by RidgeRegressionModel). - App\Services\NationalFuelPredictionService (the post-Phase-4 thin shim; StationSearchService now depends on WeeklyForecastService directly, set up in the previous commit). - App\Services\LlmPrediction\* (AbstractLlmPredictionProvider plus the four provider implementations — Anthropic, OpenAI, Gemini, and the OilPredictionProvider router. Replaced by LlmOverlayService). - App\Services\BrentPricePredictor and App\Services\Ewma. The Ewma helper had no callers left after BrentPricePredictor went. - App\Models\PricePrediction and its factory. - App\Console\Commands\PredictOilPrices (the oil:predict command). - App\Filament\Resources\OilPredictionResource and its Pages. Schema and dashboard: - Drop the price_predictions table via a new migration. - Repoint the Filament StatsOverviewWidget tile from PricePrediction to WeeklyForecast so the dashboard reflects the new pipeline. - Remove the OilPredictionProvider binding from AppServiceProvider. Test cleanup: - Delete tests for every retired service. - Update StatsOverviewWidgetTest to seed weekly_forecasts instead of price_predictions. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Ovidiu U
@@ -1,231 +0,0 @@
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<?php
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namespace App\Services\LlmPrediction;
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use App\Enums\PredictionSource;
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use App\Models\PricePrediction;
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use App\Services\Ewma;
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use Illuminate\Support\Collection;
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use Illuminate\Support\Facades\Http;
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use Illuminate\Support\Facades\Log;
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use Throwable;
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class AnthropicPredictionProvider extends AbstractLlmPredictionProvider
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{
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/**
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* Tries web-search-enriched prediction first, falls back to basic tool use.
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* Overrides the parent flow because Anthropic uses two phases (web search
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* loop + forced tool call) and selects the source dynamically.
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*/
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public function predict(Collection $prices): ?PricePrediction
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{
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if ($this->apiKey() === null) {
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return null;
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}
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$prediction = $this->predictWithWebContext($prices);
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return $prediction ?? $this->predictBasic($prices);
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}
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protected function apiKey(): ?string
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{
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return config('services.anthropic.api_key');
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}
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/** {@inheritDoc} */
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protected function callProvider(string $apiKey, string $priceList): ?array
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{
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return null;
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}
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/**
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* Multi-turn web search phase, then a forced submit_prediction call.
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* Phase 1: let the model search for recent oil/geopolitical news.
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* Phase 2: force submit_prediction with the full conversation context.
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*/
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private function predictWithWebContext(Collection $prices): ?PricePrediction
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{
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$messages = [['role' => 'user', 'content' => $this->contextPrompt($this->buildPriceList($prices))]];
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$url = 'https://api.anthropic.com/v1/messages';
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try {
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for ($i = 0, $response = null; $i < 5; $i++) {
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$response = $this->apiLogger->send('anthropic', 'POST', $url, fn () => Http::timeout(30)
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->withHeaders($this->headers())
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->post($url, [
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'model' => config('services.anthropic.model', 'claude-sonnet-4-6'),
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'max_tokens' => 1024,
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'tools' => [['type' => 'web_search_20250305', 'name' => 'web_search']],
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'messages' => $messages,
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]));
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if (! $response->successful()) {
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Log::error(self::class.': context search request failed', ['status' => $response->status()]);
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return null;
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}
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if ($response->json('stop_reason') !== 'pause_turn') {
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break;
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}
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$messages[] = ['role' => 'assistant', 'content' => $response->json('content')];
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}
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$messages[] = ['role' => 'assistant', 'content' => $response->json('content')];
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$messages[] = ['role' => 'user', 'content' => 'Now submit your prediction using the submit_prediction tool.'];
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$submitResponse = $this->apiLogger->send('anthropic', 'POST', $url, fn () => Http::timeout(15)
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->withHeaders($this->headers())
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->post($url, [
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'model' => config('services.anthropic.model', 'claude-sonnet-4-6'),
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'max_tokens' => 256,
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'tools' => [$this->submitPredictionTool()],
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'tool_choice' => ['type' => 'tool', 'name' => 'submit_prediction'],
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'messages' => $messages,
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]));
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if (! $submitResponse->successful()) {
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Log::error(self::class.': context submit request failed', ['status' => $submitResponse->status()]);
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return null;
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}
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$input = $this->extractToolInput($submitResponse->json('content') ?? []);
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return $input === null
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? null
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: $this->buildPrediction($input, PredictionSource::LlmWithContext);
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} catch (Throwable $e) {
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Log::error(self::class.': predictWithWebContext failed', ['error' => $e->getMessage()]);
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return null;
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}
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}
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/**
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* Single-turn prediction using a forced submit_prediction tool call.
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* Guarantees structured output — no JSON parsing needed.
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*/
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private function predictBasic(Collection $prices): ?PricePrediction
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{
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$chronological = $prices->sortBy('date');
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$ewma3 = Ewma::compute($chronological->take(-3)->pluck('price_usd')->values()->all());
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$ewma7 = Ewma::compute($chronological->take(-7)->pluck('price_usd')->values()->all());
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$ewma14 = Ewma::compute($chronological->pluck('price_usd')->values()->all());
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$url = 'https://api.anthropic.com/v1/messages';
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try {
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$response = $this->apiLogger->send('anthropic', 'POST', $url, fn () => Http::timeout(15)
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->withHeaders($this->headers())
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->post($url, [
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'model' => config('services.anthropic.model', 'claude-haiku-4-5-20251001'),
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'max_tokens' => 256,
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'tools' => [$this->submitPredictionTool()],
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'tool_choice' => ['type' => 'tool', 'name' => 'submit_prediction'],
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'messages' => [[
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'role' => 'user',
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'content' => $this->basicPrompt($this->buildPriceList($prices), $ewma3, $ewma7, $ewma14),
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]],
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]));
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if (! $response->successful()) {
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Log::error(self::class.': basic request failed', ['status' => $response->status()]);
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return null;
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}
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$input = $this->extractToolInput($response->json('content') ?? []);
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return $input === null ? null : $this->buildPrediction($input);
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} catch (Throwable $e) {
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Log::error(self::class.': predictBasic failed', ['error' => $e->getMessage()]);
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return null;
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}
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}
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private function contextPrompt(string $priceList): string
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{
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return <<<PROMPT
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You are analyzing Brent crude oil price data for a UK fuel price alert service.
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Predict the short-term direction over the next 3–5 days.
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First, search for recent news (last 48 hours) about:
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- Brent crude oil price movements
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- OPEC+ production decisions or announcements
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- Major geopolitical events affecting oil supply
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- Global demand signals (China economic data, US inventory reports)
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Recent Brent crude prices (USD/barrel):
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{$priceList}
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After searching, you will be asked to submit your prediction.
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PROMPT;
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}
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private function basicPrompt(string $priceList, float $ewma3, float $ewma7, float $ewma14): string
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{
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return <<<PROMPT
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You are analyzing Brent crude oil price data for a UK fuel price alert service.
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Predict the short-term direction over the next 3–5 days.
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Recent Brent crude prices (USD/barrel):
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{$priceList}
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Pre-computed indicators:
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- 3-day EWMA: \${$ewma3}
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- 7-day EWMA: \${$ewma7}
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- 14-day EWMA: \${$ewma14}
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Use the submit_prediction tool to submit your answer.
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PROMPT;
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}
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/** @return array<string, string> */
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private function headers(): array
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{
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return [
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'x-api-key' => $this->apiKey(),
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'anthropic-version' => '2023-06-01',
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];
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}
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/** @return array{name: string, description: string, input_schema: array<string, mixed>} */
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private function submitPredictionTool(): array
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{
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return [
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'name' => 'submit_prediction',
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'description' => 'Submit the final oil price direction prediction.',
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'input_schema' => [
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'type' => 'object',
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'properties' => [
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'direction' => [
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'type' => 'string',
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'enum' => ['rising', 'falling', 'flat'],
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],
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'confidence' => [
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'type' => 'integer',
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'minimum' => 0,
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'maximum' => self::LLM_MAX_CONFIDENCE,
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],
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'reasoning' => [
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'type' => 'string',
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'description' => 'One sentence explaining the prediction.',
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],
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],
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'required' => ['direction', 'confidence', 'reasoning'],
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],
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];
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}
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/** @param array<int, mixed> $content */
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private function extractToolInput(array $content): ?array
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{
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$block = collect($content)->firstWhere('type', 'tool_use');
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return $block['input'] ?? null;
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}
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}
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