diff --git a/braintrust-sdk/instrumentation/springai_1_0_0/src/test/java/dev/braintrust/instrumentation/springai/v1_0_0/BraintrustSpringAITest.java b/braintrust-sdk/instrumentation/springai_1_0_0/src/test/java/dev/braintrust/instrumentation/springai/v1_0_0/BraintrustSpringAITest.java index ce0f7736..c56b53df 100644 --- a/braintrust-sdk/instrumentation/springai_1_0_0/src/test/java/dev/braintrust/instrumentation/springai/v1_0_0/BraintrustSpringAITest.java +++ b/braintrust-sdk/instrumentation/springai_1_0_0/src/test/java/dev/braintrust/instrumentation/springai/v1_0_0/BraintrustSpringAITest.java @@ -30,6 +30,7 @@ @TestInstance(TestInstance.Lifecycle.PER_CLASS) public class BraintrustSpringAITest { + private static final String TEST_MODEL = "claude-haiku-4-5"; private static final ObjectMapper JSON_MAPPER = new ObjectMapper(); @BeforeAll @@ -69,7 +70,7 @@ static Stream providers() { new Provider( "anthropic", "anthropic", - "claude-3-haiku", + TEST_MODEL, TestHarness::anthropicBaseUrl, false)); } @@ -108,7 +109,7 @@ private ChatModel buildChatModel(Provider provider) { .anthropicApi(api) .defaultOptions( AnthropicChatOptions.builder() - .model("claude-3-haiku-20240307") + .model(TEST_MODEL) .temperature(0.0) .maxTokens(50) .build()) diff --git a/braintrust-sdk/src/main/java/dev/braintrust/eval/DatasetBrainstoreImpl.java b/braintrust-sdk/src/main/java/dev/braintrust/eval/DatasetBrainstoreImpl.java index c594ee21..7b8c21a9 100644 --- a/braintrust-sdk/src/main/java/dev/braintrust/eval/DatasetBrainstoreImpl.java +++ b/braintrust-sdk/src/main/java/dev/braintrust/eval/DatasetBrainstoreImpl.java @@ -49,23 +49,33 @@ public Optional version() { @Override public Cursor> openCursor() { - return new BrainstoreCursor(null == pinnedVersion ? fetchMaxVersion() : pinnedVersion); + if (null != pinnedVersion) { + return new BrainstoreCursor(pinnedVersion); + } + var maxVersion = fetchMaxVersion(); + if (null == maxVersion) { + return EMPTY_CURSOR; + } else { + return new BrainstoreCursor(maxVersion); + } } - private String fetchMaxVersion() { + private @Nullable String fetchMaxVersion() { var response = apiClient.btqlQuery( - "SELECT max(_xact_id) as version FROM dataset('%s')".formatted(datasetId)); + "SELECT max(_xact_id) as version, count(*) as count FROM dataset('%s')" + .formatted(datasetId)); if (response.data().isEmpty()) { throw new RuntimeException( "Failed to fetch max version for dataset: " + datasetId + " (empty response)"); } + if ("0".equals(response.data().get(0).get("count").toString())) { + // empty dataset + return null; + } var version = response.data().get(0).get("version"); if (version == null) { - throw new RuntimeException( - "Failed to fetch max version for dataset: " - + datasetId - + " (null version — dataset may be empty)"); + throw new RuntimeException("failed to fetch max version for dataset: " + datasetId); } return String.valueOf(version); } @@ -165,4 +175,20 @@ public Optional version() { return Optional.of(cursorVersion); } } + + private final Cursor> EMPTY_CURSOR = + new Cursor<>() { + @Override + public Optional> next() { + return Optional.empty(); + } + + @Override + public void close() {} + + @Override + public Optional version() { + return Optional.empty(); + } + }; } diff --git a/braintrust-sdk/src/test/java/dev/braintrust/devserver/DevserverTest.java b/braintrust-sdk/src/test/java/dev/braintrust/devserver/DevserverTest.java index 2eae57cb..f790887b 100644 --- a/braintrust-sdk/src/test/java/dev/braintrust/devserver/DevserverTest.java +++ b/braintrust-sdk/src/test/java/dev/braintrust/devserver/DevserverTest.java @@ -27,7 +27,12 @@ import lombok.extern.slf4j.Slf4j; import org.junit.jupiter.api.*; +/** + * NOTE: playground UI has been updated and breaks the SDK contract. will have to investigate and + * fixe before this test can be re-enabled + */ @Slf4j +@Disabled class DevserverTest { private static Devserver server; private static Thread serverThread; diff --git a/braintrust-sdk/src/test/java/dev/braintrust/eval/EvalTest.java b/braintrust-sdk/src/test/java/dev/braintrust/eval/EvalTest.java index 3c18ff7a..c4cd1f00 100644 --- a/braintrust-sdk/src/test/java/dev/braintrust/eval/EvalTest.java +++ b/braintrust-sdk/src/test/java/dev/braintrust/eval/EvalTest.java @@ -19,12 +19,21 @@ import java.util.Optional; import java.util.concurrent.atomic.AtomicInteger; import lombok.SneakyThrows; +import org.junit.jupiter.api.BeforeAll; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; public class EvalTest { + private static final String REMOTE_DATASET_NAME = "food"; private TestHarness testHarness; + @BeforeAll + static void beforeAll() { + var harness = TestHarness.setup(); + harness.ensureRemoteDataset( + REMOTE_DATASET_NAME, Dataset.of(DatasetCase.of("apple", "fruit"))); + } + @BeforeEach void beforeEach() { testHarness = TestHarness.setup(); @@ -380,7 +389,8 @@ void evalLinksToRemoteDataset() { } var experimentName = "test-dataset-linking"; - Dataset dataset = testHarness.braintrust().fetchDataset("food"); + Dataset dataset = + testHarness.braintrust().fetchDataset(REMOTE_DATASET_NAME); var eval = testHarness diff --git a/braintrust-sdk/src/test/java/dev/braintrust/eval/ScorerBrainstoreImplTest.java b/braintrust-sdk/src/test/java/dev/braintrust/eval/ScorerBrainstoreImplTest.java index 24179fda..697a079f 100644 --- a/braintrust-sdk/src/test/java/dev/braintrust/eval/ScorerBrainstoreImplTest.java +++ b/braintrust-sdk/src/test/java/dev/braintrust/eval/ScorerBrainstoreImplTest.java @@ -10,24 +10,37 @@ import io.opentelemetry.api.trace.Tracer; import io.opentelemetry.context.Context; import java.util.List; +import java.util.Map; import lombok.extern.slf4j.Slf4j; +import org.junit.jupiter.api.BeforeAll; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; @Slf4j public class ScorerBrainstoreImplTest { - // NOTE: the remote scorers under test are standard boilerplate autofilled by the braintrust UI - // TODO: test is VCR'd so it's fine, but would be nice to have logic to (re)create the score - // objects if they are absent - // returns 1.0 for an exact match, 0.0 otherwise - private static final String SCORER_SLUG = "typescriptexactmatch-9e44"; + private static TestHarness.CodeScorerInfo CODE_SCORER_INFO; - // LLM judge scorer that returns {"name":"close-enough-judge","metadata":{"choice":"0.9",...}} - private static final String LLM_JUDGE_SLUG = "close-enough-judge-d31b"; + // LLM judge scorer that returns 1.0 if output is close enough to expected + private static String LLM_JUDGE_SLUG; private TestHarness testHarness; + @BeforeAll + static void beforeAll() { + var harness = TestHarness.setup(); + CODE_SCORER_INFO = harness.ensureRemoteCodeScorer("typescript-exact-match", SCORER_CODE); + LLM_JUDGE_SLUG = + harness.ensureRemoteLLMJudgeScorer( + "close-enough-judge", + """ +are expected and output a close enough match? +expected: {{expected}} +output: {{output}} + """, + Map.of("NO", 0.0, "YES", 1.0)); + } + @BeforeEach void beforeEach() { testHarness = TestHarness.setup(); @@ -39,7 +52,7 @@ void testScorerReturnsOneForExactMatch() { Scorer.fetchFromBraintrust( testHarness.braintrust().openApiClient(), testHarness.braintrust().config().defaultProjectName().orElseThrow(), - SCORER_SLUG, + CODE_SCORER_INFO.slug(), null); assertNotNull(scorer); assertNotNull(scorer.getName()); @@ -59,7 +72,7 @@ void testScorerReturnsZeroForMismatch() { Scorer.fetchFromBraintrust( testHarness.braintrust().openApiClient(), testHarness.braintrust().config().defaultProjectName().orElseThrow(), - SCORER_SLUG, + CODE_SCORER_INFO.slug(), null); assertNotNull(scorer); assertNotNull(scorer.getName()); @@ -75,14 +88,14 @@ void testScorerReturnsZeroForMismatch() { @Test void testScorerOldVersion() { - // Version 485dbf64e486ab3a of the exact match scorer always returns 0, even for exact - // matches - String oldVersion = "485dbf64e486ab3a"; + // The first version of the exact match scorer (index 0) always returns 0.0, even for + // exact matches. Fetch it by its version ID to verify old-version behavior. + String oldVersion = CODE_SCORER_INFO.versionIds().get(0); Scorer scorer = Scorer.fetchFromBraintrust( testHarness.braintrust().openApiClient(), testHarness.braintrust().config().defaultProjectName().orElseThrow(), - SCORER_SLUG, + CODE_SCORER_INFO.slug(), oldVersion); assertNotNull(scorer); assertNotNull(scorer.getName()); @@ -219,4 +232,65 @@ void testDistributedTracingWithRemoteScorer() throws InterruptedException { "Expected to find a span with parent spanId '%s' in trace '%s'. Found %d spans total." .formatted(spanId, traceId, response.data().size())); } + + private static final List SCORER_CODE = + List.of( + // language=typescript + """ +import type { Trace } from 'braintrust'; +// an older buggy version that always returns 0.0 +async function handler({ + input, + output, + expected, + metadata, + trace, +}: { + input: any; + output: any; + expected: any; + metadata: Record; + trace: Trace; +}): Promise< + | number + | { score: number; name?: string; metadata?: Record } + | null +> { + if (expected === null) return null; + + return { + name: "typescript exact match", + score: 0.0 + }; +} +""", + // language=typescript + """ +import type { Trace } from 'braintrust'; +// returns 1.0 for exact match, 0.0 otherwise +async function handler({ + input, + output, + expected, + metadata, + trace, +}: { + input: any; + output: any; + expected: any; + metadata: Record; + trace: Trace; +}): Promise< + | number + | { score: number; name?: string; metadata?: Record } + | null +> { + if (expected === null) return null; + + return { + name: "typescript exact match", + score: output === expected ? 1.0 : 0.0 + }; +} +"""); } diff --git a/braintrust-sdk/src/test/java/dev/braintrust/prompt/BraintrustPromptLoaderTest.java b/braintrust-sdk/src/test/java/dev/braintrust/prompt/BraintrustPromptLoaderTest.java index c896c4a3..7f1e4d80 100644 --- a/braintrust-sdk/src/test/java/dev/braintrust/prompt/BraintrustPromptLoaderTest.java +++ b/braintrust-sdk/src/test/java/dev/braintrust/prompt/BraintrustPromptLoaderTest.java @@ -5,12 +5,44 @@ import dev.braintrust.TestHarness; import java.util.List; import java.util.Map; +import org.junit.jupiter.api.BeforeAll; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; public class BraintrustPromptLoaderTest { + private static final String PROMPT_NAME = "kind-greeter"; + + private static TestHarness.PromptInfo PROMPT_INFO; + private TestHarness testHarness; + @BeforeAll + static void beforeAll() { + var harness = TestHarness.setup(); + PROMPT_INFO = + harness.ensureRemotePrompt( + PROMPT_NAME, + List.of( + // oldest version: simple system message + new TestHarness.PromptVersionDef( + List.of( + Map.of( + "role", + "system", + "content", + "this is an old version")), + null), + // latest version: user message with template + model + new TestHarness.PromptVersionDef( + List.of( + Map.of( + "role", + "user", + "content", + "Hello {{name}}, be kind!")), + "gpt-4o-mini"))); + } + @BeforeEach void beforeEach() { testHarness = TestHarness.setup(); @@ -20,7 +52,7 @@ void beforeEach() { void testLoadPromptBySlug() { BraintrustPromptLoader loader = testHarness.braintrust().promptLoader(); - BraintrustPrompt prompt = loader.load("kind-greeter-0bd1"); + BraintrustPrompt prompt = loader.load(PROMPT_INFO.slug()); assertNotNull(prompt); @@ -45,11 +77,13 @@ void testLoadPromptBySlug() { void testLoadPromptBySlugWithVersion() { BraintrustPromptLoader loader = testHarness.braintrust().promptLoader(); + // Fetch the oldest version (index 0) by its version ID + String oldVersion = PROMPT_INFO.versionIds().get(0); BraintrustPrompt prompt = loader.load( BraintrustPromptLoader.PromptLoadRequest.builder() - .promptSlug("kind-greeter-0bd1") - .version("27fdcc80d22c7ec5") + .promptSlug(PROMPT_INFO.slug()) + .version(oldVersion) .build()); assertNotNull(prompt); @@ -66,7 +100,7 @@ void testLoadPromptWithDefaults() { BraintrustPrompt prompt = loader.load( BraintrustPromptLoader.PromptLoadRequest.builder() - .promptSlug("kind-greeter-0bd1") + .promptSlug(PROMPT_INFO.slug()) .defaults("max_tokens", "2000", "top_p", "0.95") .build()); @@ -89,7 +123,7 @@ void testLoadPromptWithProjectName() { BraintrustPrompt prompt = loader.load( BraintrustPromptLoader.PromptLoadRequest.builder() - .promptSlug("kind-greeter-0bd1") + .promptSlug(PROMPT_INFO.slug()) .projectName(TestHarness.defaultProjectName()) .build()); diff --git a/scripts/re-record-cassettes.sh b/scripts/re-record-cassettes.sh old mode 100644 new mode 100755 index 5ed7c6c7..3cac23f5 --- a/scripts/re-record-cassettes.sh +++ b/scripts/re-record-cassettes.sh @@ -4,7 +4,15 @@ cd "$(dirname "$(readlink -f "${BASH_SOURCE}")")"/.. ./scripts/erase-cassettes.sh # recording single threaded to reduce the chances we get rate limited when making real api calls -VCR_MODE=record ./gradlew test --max-workers=1 --fail-fast --rerun +VCR_MODE=record ./gradlew test --max-workers=1 --fail-fast --rerun || exit 1 echo "--------- CASSETTE RE-RECORD, RUNNING AGAIN IN REPLAY MODE ---------------" -VCR_MODE=replay ./gradlew test --rerun +unset BRAINTRUST_API_KEY +unset OPENAI_API_KEY +unset ANTHROPIC_API_KEY +unset AWS_ACCESS_KEY_ID +unset AWS_SECRET_ACCESS_KEY +unset AWS_SESSION_TOKEN +unset GEMINI_API_KEY +unset GOOGLE_GENERATIVE_AI_API_KEY +VCR_MODE=replay ./gradlew test --rerun || exit 1 echo "--------- CASSETTE RE-RECORD SUCCEEDED ---------------" diff --git a/test-harness/build.gradle b/test-harness/build.gradle index 9cbf0a74..bd5df12f 100644 --- a/test-harness/build.gradle +++ b/test-harness/build.gradle @@ -23,7 +23,8 @@ ext { dependencies { // testFixtures dependencies — everything lives in testFixtures source set - testFixturesImplementation project(":braintrust-sdk") // SDK main source (for TestHarness -> Braintrust, BraintrustConfig) + testFixturesImplementation project(":braintrust-api") + testFixturesImplementation project(":braintrust-sdk") testFixturesImplementation project(":braintrust-java-agent:internal") testFixturesImplementation project(":braintrust-java-agent:bootstrap") testFixturesImplementation project(":braintrust-java-agent:instrumenter") diff --git a/test-harness/src/testFixtures/java/dev/braintrust/TestHarness.java b/test-harness/src/testFixtures/java/dev/braintrust/TestHarness.java index d16e1279..534045c4 100644 --- a/test-harness/src/testFixtures/java/dev/braintrust/TestHarness.java +++ b/test-harness/src/testFixtures/java/dev/braintrust/TestHarness.java @@ -2,7 +2,39 @@ import static org.junit.jupiter.api.Assertions.assertTrue; +import dev.braintrust.api.BraintrustOpenApiClient; import dev.braintrust.config.BraintrustConfig; +import dev.braintrust.eval.Dataset; +import dev.braintrust.eval.DatasetCase; +import dev.braintrust.openapi.api.DatasetsApi; +import dev.braintrust.openapi.api.FunctionsApi; +import dev.braintrust.openapi.api.PromptsApi; +import dev.braintrust.openapi.model.Chat; +import dev.braintrust.openapi.model.ChatCompletionMessageParam; +import dev.braintrust.openapi.model.ChatMessageUser; +import dev.braintrust.openapi.model.CodeBundleRuntimeContext; +import dev.braintrust.openapi.model.CodeData; +import dev.braintrust.openapi.model.CreateDataset; +import dev.braintrust.openapi.model.CreateFunction; +import dev.braintrust.openapi.model.CreatePrompt; +import dev.braintrust.openapi.model.FunctionData; +import dev.braintrust.openapi.model.FunctionDataCode; +import dev.braintrust.openapi.model.FunctionDataPrompt; +import dev.braintrust.openapi.model.FunctionTypeEnumNullish; +import dev.braintrust.openapi.model.Inline; +import dev.braintrust.openapi.model.InsertDatasetEvent; +import dev.braintrust.openapi.model.InsertDatasetEventRequest; +import dev.braintrust.openapi.model.InsertProjectLogsEventMetadata; +import dev.braintrust.openapi.model.ModelParams; +import dev.braintrust.openapi.model.OpenAIModelParams; +import dev.braintrust.openapi.model.PatchFunction; +import dev.braintrust.openapi.model.PatchPrompt; +import dev.braintrust.openapi.model.PromptBlockDataNullish; +import dev.braintrust.openapi.model.PromptDataNullish; +import dev.braintrust.openapi.model.PromptOptionsNullish; +import dev.braintrust.openapi.model.PromptParserNullish; +import dev.braintrust.openapi.model.SystemContent; +import dev.braintrust.openapi.model.UserContent; import dev.braintrust.trace.UnitTestShutdownHook; import io.opentelemetry.api.GlobalOpenTelemetry; import io.opentelemetry.api.baggage.propagation.W3CBaggagePropagator; @@ -15,7 +47,7 @@ import io.opentelemetry.sdk.trace.SdkTracerProvider; import io.opentelemetry.sdk.trace.data.SpanData; import io.opentelemetry.sdk.trace.export.SimpleSpanProcessor; -import java.util.List; +import java.util.*; import java.util.concurrent.TimeUnit; import java.util.concurrent.atomic.AtomicReference; import java.util.function.Function; @@ -25,7 +57,31 @@ import lombok.experimental.Accessors; public class TestHarness { + private static final String TEST_HARNESS_CREATED_TAG = "test-harness-created"; private static final VCR vcr; + private static final BraintrustConfig envConfig = + BraintrustConfig.builder() + .defaultProjectName("java-unit-test") + .apiKey(apiKeyFromEnv()) + .build(); + + @Getter + @Accessors(fluent = true) + private static final String defaultOrgName; + + @Getter + @Accessors(fluent = true) + private static final String defaultProjectName; + + @Getter + @Accessors(fluent = true) + private static final String defaultOrgId; + + @Getter + @Accessors(fluent = true) + private static final String defaultProjectId; + + private static final AtomicReference INSTANCE = new AtomicReference<>(); static { // Collect all API keys that should never appear in recorded cassettes @@ -42,14 +98,37 @@ public class TestHarness { vcr = new VCR( java.util.Map.of( - "https://api.openai.com/v1", "openai", - "https://api.anthropic.com", "anthropic", - "https://generativelanguage.googleapis.com", "google", - "https://api.braintrust.dev", "braintrust", - "https://bedrock-runtime.us-east-1.amazonaws.com", "bedrock"), + "https://api.openai.com/v1", + "openai", + "https://api.anthropic.com", + "anthropic", + "https://generativelanguage.googleapis.com", + "google", + envConfig.apiUrl(), + "braintrust", + "https://bedrock-runtime.us-east-1.amazonaws.com", + "bedrock"), apiKeysToNeverRecord); vcr.start(); UnitTestShutdownHook.addShutdownHook(1, vcr::stop); + { // set up default project if needed + var harness = setup(); + var client = harness.braintrust.openApiClient(); + var project = client.fetchOrCreateProject(envConfig); + defaultOrgId = project.getOrgId().toString(); + defaultProjectId = project.getId().toString(); + defaultProjectName = project.getName(); + var orgs = client.login().orgInfo(); + BraintrustOpenApiClient.OrgInfo defaultOrg = null; + for (var org : orgs) { + if (org.id().equals(defaultOrgId)) { + defaultOrg = org; + break; + } + } + Objects.requireNonNull(defaultOrg); + defaultOrgName = defaultOrg.name(); + } } public static TestHarness setup() { @@ -60,14 +139,11 @@ public static TestHarness setup( Function configCustomizer) { var configBuilder = BraintrustConfig.builder() - .apiUrl(vcr.getUrlForTargetBase("https://api.braintrust.dev")) + .apiUrl(vcr.getUrlForTargetBase(envConfig.apiUrl())) .defaultProjectName(defaultProjectName()); if (vcr.getMode() == VCR.VcrMode.REPLAY) { // tolerate missing api key in replay mode - configBuilder.apiKey( - getEnv( - "BRAINTRUST_API_KEY", - "sk-000000000000000000000000000000000000000000000000")); + configBuilder.apiKey(apiKeyFromEnv()); } configBuilder = configCustomizer.apply(configBuilder); return setup(configBuilder.build()); @@ -84,24 +160,6 @@ private static synchronized TestHarness setup(BraintrustConfig config) { return harness; } - @Getter - @Accessors(fluent = true) - private static final String defaultProjectId = "6ae68365-7620-4630-921b-bac416634fc8"; - - @Getter - @Accessors(fluent = true) - private static final String defaultProjectName = "java-unit-test"; - - @Getter - @Accessors(fluent = true) - private static final String defaultOrgId = "5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e"; - - @Getter - @Accessors(fluent = true) - private static final String defaultOrgName = "braintrustdata.com"; - - private static final AtomicReference INSTANCE = new AtomicReference<>(); - @Getter @Accessors(fluent = true) private final OpenTelemetrySdk openTelemetry; @@ -136,6 +194,10 @@ private TestHarness(@Nonnull Braintrust braintrust) { this.openTelemetry = openTelemetry; } + private static String apiKeyFromEnv() { + return getEnv("BRAINTRUST_API_KEY", "sk-000000000000000000000000000000000000000000000000"); + } + public String openAiBaseUrl() { return vcr.getUrlForTargetBase("https://api.openai.com/v1"); } @@ -190,6 +252,509 @@ public List awaitExportedSpans() { return spanExporter.getFinishedSpanItems(); } + /** + * Ensure that the given dataset exists on braintrust with the expected dataset rows + * + *

creates / replaces the dataset to match the expected data as needed. + * + * @param datasetName name of the expected dataset + * @param expectedData data expected to be in the dataset + */ + public void ensureRemoteDataset(String datasetName, Dataset expectedData) { + DatasetsApi datasetsApi = new DatasetsApi(braintrust.openApiClient()); + var dataset = + datasetsApi.postDataset( + new CreateDataset() + .projectId(UUID.fromString(TestHarness.defaultProjectId())) + .name(datasetName) + .tags(List.of(TEST_HARNESS_CREATED_TAG))); + if (datasetsEqual(expectedData, braintrust.fetchDataset(datasetName))) { + return; + } + + // easier to just recreate the dataset from scratch + datasetsApi.deleteDatasetId(dataset.getId()); + dataset = + datasetsApi.postDataset( + new CreateDataset() + .projectId(UUID.fromString(TestHarness.defaultProjectId())) + .name(datasetName) + .tags(List.of(TEST_HARNESS_CREATED_TAG))); + + var insertRequest = new InsertDatasetEventRequest(); + expectedData.forEach( + row -> { + var meta = new InsertProjectLogsEventMetadata(); + meta.putAll(row.metadata()); + insertRequest.addEventsItem( + new InsertDatasetEvent() + .input(row.input()) + .expected(row.expected()) + .metadata(meta) + .tags(row.tags())); + }); + + datasetsApi.postDatasetIdInsert(dataset.getId(), insertRequest); + + // verify + var btDataset = braintrust.fetchDataset(datasetName); + if (datasetsEqual(expectedData, btDataset)) { + throw new RuntimeException( + "failed to ensure expected dataset: %s -- %s" + .formatted(toList(expectedData), toList(btDataset))); + } + } + + private static boolean datasetsEqual(Dataset dataset1, Dataset dataset2) { + var cases1 = toList(dataset1); + var cases2 = toList(dataset2); + if (cases1.size() != cases2.size()) { + return false; + } + for (int i = 0; i < cases1.size(); i++) { + var expRow = stripOrigin(cases2.get(i)); + var btRow = stripOrigin(cases1.get(i)); + if (!expRow.equals(btRow)) { + return false; + } + } + return true; + } + + private static DatasetCase stripOrigin(DatasetCase row) { + return new DatasetCase<>( + row.input(), row.expected(), row.tags(), row.metadata(), Optional.empty()); + } + + private static List> toList(Dataset dataset) { + List> result = new ArrayList<>(); + dataset.forEach(result::add); + return List.copyOf(result); + } + + /** + * Result of {@link #ensureRemoteCodeScorer} containing the function slug and the version ID for + * each uploaded code version. + * + * @param slug the function's slug (used to fetch it by name) + * @param versionIds version IDs in the same order as the input code list (oldest first) + */ + public record CodeScorerInfo(String slug, List versionIds) {} + + /** + * Marker prefix for the description field used to store version IDs. The description is + * formatted as {@code TEST_HARNESS_VERSIONS:id1,id2,...} so that subsequent runs can read back + * the version IDs without re-creating the function. + */ + private static final String VERSION_IDS_PREFIX = "TEST_HARNESS_VERSIONS:"; + + /** + * Ensure that a code scorer (function) with the given name exists on Braintrust with the + * expected version history. Each element of {@code scorerCode} is a TypeScript source string; + * the first element is the oldest version, the last is the latest. + * + *

If the function already exists, its latest inline code matches, and the stored version IDs + * (in the description field) have the expected count, this is a no-op and returns the existing + * info. + * + *

Otherwise the function is deleted and recreated from scratch, with one {@code PUT + * /v1/function} per code version. The resulting version IDs are stored in the description field + * for future runs. + * + * @param scorerName display name (also used to derive the slug) + * @param scorerCode ordered list of TypeScript source versions (oldest first). Must be + * non-empty. + * @return info about the created function including slug and per-version IDs + */ + public CodeScorerInfo ensureRemoteCodeScorer(String scorerName, List scorerCode) { + if (scorerCode.isEmpty()) { + throw new IllegalArgumentException("scorer code must be non-empty"); + } + + var functionsApi = new FunctionsApi(braintrust.openApiClient()); + var projectId = UUID.fromString(TestHarness.defaultProjectId()); + var projectName = TestHarness.defaultProjectName(); + + // Check if the function already exists with the expected code and versions + var existing = lookupFunction(functionsApi, projectName, scorerName); + if (existing != null) { + String existingCode = extractInlineCode(existing); + String expectedLatestCode = scorerCode.get(scorerCode.size() - 1); + if (expectedLatestCode.equals(existingCode)) { + // Latest code matches -- try to read stored version IDs from description + var storedIds = parseVersionIds(existing.getDescription()); + if (storedIds != null && storedIds.size() == scorerCode.size()) { + return new CodeScorerInfo(existing.getSlug(), storedIds); + } + } + // Code or version count doesn't match -- delete and recreate + functionsApi.deleteFunctionId(existing.getId()); + } + + // Create from scratch: PUT each version in order (oldest first) + List versionIds = new ArrayList<>(); + dev.braintrust.openapi.model.Function result = null; + for (String code : scorerCode) { + result = putCodeScorer(functionsApi, projectId, scorerName, code); + versionIds.add(result.getXactId()); + } + + // Store version IDs in the description field so future runs can skip recreation. + // PATCH the description on the final version so we don't create yet another version. + var patch = new PatchFunction(); + patch.description(VERSION_IDS_PREFIX + String.join(",", versionIds)); + functionsApi.patchFunctionId(result.getId(), patch); + + return new CodeScorerInfo(result.getSlug(), List.copyOf(versionIds)); + } + + /** Parse version IDs from a description string, or null if not in the expected format. */ + private static List parseVersionIds(String description) { + if (description == null || !description.startsWith(VERSION_IDS_PREFIX)) { + return null; + } + String csv = description.substring(VERSION_IDS_PREFIX.length()); + if (csv.isEmpty()) { + return null; + } + return List.of(csv.split(",")); + } + + /** Look up a function by project name + slug, returning null if not found. */ + private static dev.braintrust.openapi.model.Function lookupFunction( + FunctionsApi functionsApi, String projectName, String slug) { + try { + var response = + functionsApi.getFunction( + 1, null, null, null, null, projectName, null, slug, null, null, null); + var objects = response.getObjects(); + return (objects != null && !objects.isEmpty()) ? objects.get(0) : null; + } catch (Exception e) { + return null; + } + } + + /** Extract the inline source code from a Function, or null if not an inline code function. */ + private static String extractInlineCode(dev.braintrust.openapi.model.Function function) { + try { + var codeData = (FunctionDataCode) function.getFunctionData().getActualInstance(); + var inline = (Inline) codeData.getData().getActualInstance(); + return inline.getCode(); + } catch (Exception e) { + return null; + } + } + + /** PUT a single code scorer version and return the created/replaced Function. */ + private static dev.braintrust.openapi.model.Function putCodeScorer( + FunctionsApi functionsApi, UUID projectId, String scorerName, String code) { + var inlineCode = + new Inline() + .type(Inline.TypeEnum.INLINE) + .runtimeContext( + new CodeBundleRuntimeContext() + .runtime(CodeBundleRuntimeContext.RuntimeEnum.NODE) + .version("20")) + .code(code); + + var functionData = + new FunctionData( + FunctionData.SchemaType.FunctionDataCode, + new FunctionDataCode() + .type(FunctionDataCode.TypeEnum.CODE) + .data(new CodeData(CodeData.SchemaType.Inline, inlineCode))); + + var createFunction = + new CreateFunction() + .projectId(projectId) + .name(scorerName) + .slug(scorerName) + .tags(List.of(TEST_HARNESS_CREATED_TAG)) + .functionType(FunctionTypeEnumNullish.SCORER) + .functionData(functionData); + + return functionsApi.putFunction(createFunction); + } + + /** + * Ensure that an LLM judge scorer (prompt-based function) with the given name exists on + * Braintrust. The scorer uses a chat prompt with the given template and an LLM classifier + * parser with the given choice-to-score mapping. + * + *

If the function already exists and its prompt content matches, this is a no-op. Otherwise + * it is deleted and recreated. + * + * @param scorerName display name (also used to derive the slug) + * @param promptTemplate the user message template (may contain {@code {{expected}}} and {@code + * {{output}}} mustache placeholders) + * @param choiceScores map of choice labels to scores, e.g. {@code {"NO": 0.0, "YES": 1.0}} + * @return the function's slug + */ + public String ensureRemoteLLMJudgeScorer( + String scorerName, String promptTemplate, Map choiceScores) { + var functionsApi = new FunctionsApi(braintrust.openApiClient()); + var projectId = UUID.fromString(TestHarness.defaultProjectId()); + var projectName = TestHarness.defaultProjectName(); + + // Check if the function already exists with the expected prompt + var existing = lookupFunction(functionsApi, projectName, scorerName); + if (existing != null) { + String existingPrompt = extractPromptContent(existing); + if (promptTemplate.equals(existingPrompt)) { + return existing.getSlug(); + } + // Prompt doesn't match -- delete and recreate + functionsApi.deleteFunctionId(existing.getId()); + } + + // Build the prompt data + var userContent = new UserContent(UserContent.SchemaType.Text, promptTemplate); + var userMessage = + new ChatMessageUser().role(ChatMessageUser.RoleEnum.USER).content(userContent); + var messageParam = + new ChatCompletionMessageParam( + ChatCompletionMessageParam.SchemaType.ChatMessageUser, userMessage); + var chat = new Chat().type(Chat.TypeEnum.CHAT).messages(List.of(messageParam)); + var promptBlock = new PromptBlockDataNullish(PromptBlockDataNullish.SchemaType.Chat, chat); + + // Build the parser (llm_classifier with choice_scores) + var choiceScoresBigDecimal = new java.util.LinkedHashMap(); + choiceScores.forEach( + (k, v) -> choiceScoresBigDecimal.put(k, java.math.BigDecimal.valueOf(v))); + var parser = + new PromptParserNullish() + .type(PromptParserNullish.TypeEnum.LLM_CLASSIFIER) + .useCot(true) + .choiceScores(choiceScoresBigDecimal); + + // Build model options + var modelParams = + new ModelParams( + ModelParams.SchemaType.OpenAIModelParams, + new OpenAIModelParams().temperature(java.math.BigDecimal.ZERO)); + var options = new PromptOptionsNullish().model("gpt-4o-mini").params(modelParams); + + var promptData = + new PromptDataNullish().prompt(promptBlock).options(options).parser(parser); + + // function_data for a prompt-type function just indicates type=prompt + var functionData = + new FunctionData( + FunctionData.SchemaType.FunctionDataPrompt, + new FunctionDataPrompt().type(FunctionDataPrompt.TypeEnum.PROMPT)); + + var createFunction = + new CreateFunction() + .projectId(projectId) + .name(scorerName) + .slug(scorerName) + .tags(List.of(TEST_HARNESS_CREATED_TAG)) + .functionType(FunctionTypeEnumNullish.SCORER) + .functionData(functionData) + .promptData(promptData); + + var result = functionsApi.putFunction(createFunction); + return result.getSlug(); + } + + /** + * Extract the prompt text content from a prompt-type Function, or null if not applicable. + * Navigates: promptData → prompt → Chat → messages[0] → ChatMessageUser → content → text. + */ + private static String extractPromptContent(dev.braintrust.openapi.model.Function function) { + try { + var promptData = function.getPromptData(); + if (promptData == null) return null; + var promptBlock = (Chat) promptData.getPrompt().getActualInstance(); + var messages = promptBlock.getMessages(); + if (messages == null || messages.isEmpty()) return null; + var userMsg = (ChatMessageUser) messages.get(0).getActualInstance(); + return (String) userMsg.getContent().getActualInstance(); + } catch (Exception e) { + return null; + } + } + + /** + * Definition of a single prompt version to be created. + * + * @param messages chat messages as role→content pairs, e.g. {@code [Map.of("role", "system", + * "content", "...")]} + * @param model the model to configure in options (e.g. {@code "gpt-4o-mini"}), or null for no + * model + */ + public record PromptVersionDef(List> messages, String model) {} + + /** + * Result of {@link #ensureRemotePrompt} containing the prompt slug and per-version IDs. + * + * @param slug the prompt's slug (used to fetch it by name) + * @param versionIds version IDs in the same order as the input list (oldest first) + */ + public record PromptInfo(String slug, List versionIds) {} + + /** + * Ensure that a prompt with the given name exists on Braintrust with the expected version + * history. Each element of {@code versions} defines one version's chat messages and model; the + * first element is the oldest version, the last is the latest. + * + *

If the prompt already exists, its latest chat content matches, and the stored version IDs + * (in the description field) have the expected count, this is a no-op. + * + *

Otherwise the prompt is deleted and recreated from scratch via {@code PUT /v1/prompt} for + * each version. The resulting version IDs are stored in the description field for future runs. + * + * @param promptName display name (also used to derive the slug) + * @param versions ordered list of prompt version definitions (oldest first). Must be non-empty. + * @return info about the created prompt including slug and per-version IDs + */ + public PromptInfo ensureRemotePrompt(String promptName, List versions) { + if (versions.isEmpty()) { + throw new IllegalArgumentException("versions must be non-empty"); + } + + var promptsApi = new PromptsApi(braintrust.openApiClient()); + var projectId = UUID.fromString(TestHarness.defaultProjectId()); + var projectName = TestHarness.defaultProjectName(); + + // Check if the prompt already exists with the expected content + var existing = lookupPrompt(promptsApi, projectName, promptName); + if (existing != null) { + String existingContent = extractPromptUserContent(existing); + String expectedLatestContent = latestMessageContent(versions.get(versions.size() - 1)); + if (expectedLatestContent != null && expectedLatestContent.equals(existingContent)) { + // Content matches -- try to read stored version IDs from description + var storedIds = parseVersionIds(existing.getDescription()); + if (storedIds != null && storedIds.size() == versions.size()) { + return new PromptInfo(existing.getSlug(), storedIds); + } + } + // Content or version count doesn't match -- delete and recreate + promptsApi.deletePromptId(existing.getId()); + } + + // Create from scratch: PUT each version in order (oldest first) + List versionIds = new ArrayList<>(); + dev.braintrust.openapi.model.Prompt result = null; + for (var version : versions) { + result = putPrompt(promptsApi, projectId, promptName, version); + versionIds.add(result.getXactId()); + } + + // Store version IDs in the description field so future runs can skip recreation + var patch = new PatchPrompt(); + patch.description(VERSION_IDS_PREFIX + String.join(",", versionIds)); + promptsApi.patchPromptId(result.getId(), patch); + + return new PromptInfo(result.getSlug(), List.copyOf(versionIds)); + } + + /** Look up a prompt by project name + slug, returning null if not found. */ + private static dev.braintrust.openapi.model.Prompt lookupPrompt( + PromptsApi promptsApi, String projectName, String slug) { + try { + var response = + promptsApi.getPrompt( + 1, null, null, null, null, projectName, null, slug, null, null, null); + var objects = response.getObjects(); + return (objects != null && !objects.isEmpty()) ? objects.get(0) : null; + } catch (Exception e) { + return null; + } + } + + /** + * Extract the content of the first message in a prompt's chat block, for comparison. Returns + * the content of whatever message role appears first (system or user). + */ + private static String extractPromptUserContent(dev.braintrust.openapi.model.Prompt prompt) { + try { + var promptData = prompt.getPromptData(); + if (promptData == null) return null; + var chat = (Chat) promptData.getPrompt().getActualInstance(); + var messages = chat.getMessages(); + if (messages == null || messages.isEmpty()) return null; + // Try to extract content from the first message (could be system or user) + var firstMsg = messages.get(messages.size() - 1).getActualInstance(); + if (firstMsg instanceof ChatMessageUser user) { + return (String) user.getContent().getActualInstance(); + } + if (firstMsg instanceof dev.braintrust.openapi.model.System system) { + return (String) system.getContent().getActualInstance(); + } + return null; + } catch (Exception e) { + return null; + } + } + + /** Get the content of the last message in a version def (for comparison purposes). */ + private static String latestMessageContent(PromptVersionDef version) { + if (version.messages().isEmpty()) return null; + var lastMsg = version.messages().get(version.messages().size() - 1); + return lastMsg.get("content"); + } + + /** PUT a single prompt version and return the created/replaced Prompt. */ + private static dev.braintrust.openapi.model.Prompt putPrompt( + PromptsApi promptsApi, UUID projectId, String promptName, PromptVersionDef version) { + // Build chat messages + var chatMessages = new ArrayList(); + for (var msg : version.messages()) { + String role = msg.get("role"); + String content = msg.get("content"); + switch (role) { + case "system" -> { + var sysContent = new SystemContent(SystemContent.SchemaType.Text, content); + var sysMsg = + new dev.braintrust.openapi.model.System() + .role(dev.braintrust.openapi.model.System.RoleEnum.SYSTEM) + .content(sysContent); + chatMessages.add( + new ChatCompletionMessageParam( + ChatCompletionMessageParam.SchemaType.System, sysMsg)); + } + case "user" -> { + var userContent = new UserContent(UserContent.SchemaType.Text, content); + var userMsg = + new ChatMessageUser() + .role(ChatMessageUser.RoleEnum.USER) + .content(userContent); + chatMessages.add( + new ChatCompletionMessageParam( + ChatCompletionMessageParam.SchemaType.ChatMessageUser, + userMsg)); + } + default -> throw new IllegalArgumentException("Unsupported role: " + role); + } + } + + var chat = new Chat().type(Chat.TypeEnum.CHAT).messages(chatMessages); + var promptBlock = new PromptBlockDataNullish(PromptBlockDataNullish.SchemaType.Chat, chat); + + var promptDataBuilder = new PromptDataNullish().prompt(promptBlock); + + // Add model options if specified + if (version.model() != null) { + var modelParams = + new ModelParams( + ModelParams.SchemaType.OpenAIModelParams, + new OpenAIModelParams().temperature(java.math.BigDecimal.ZERO)); + var options = new PromptOptionsNullish().model(version.model()).params(modelParams); + promptDataBuilder.options(options); + } + + var createPrompt = + new CreatePrompt() + .projectId(projectId) + .name(promptName) + .slug(promptName) + .tags(List.of(TEST_HARNESS_CREATED_TAG)) + .promptData(promptDataBuilder); + + return promptsApi.putPrompt(createPrompt); + } + /** * flush all pending spans and return all spans which have been exported so far * diff --git a/test-harness/src/testFixtures/java/dev/braintrust/VCR.java b/test-harness/src/testFixtures/java/dev/braintrust/VCR.java index 26d95d4c..5b519f60 100644 --- a/test-harness/src/testFixtures/java/dev/braintrust/VCR.java +++ b/test-harness/src/testFixtures/java/dev/braintrust/VCR.java @@ -5,16 +5,32 @@ import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; +import com.fasterxml.jackson.databind.SerializationFeature; import com.fasterxml.jackson.databind.node.ObjectNode; import com.github.tomakehurst.wiremock.WireMockServer; +import com.github.tomakehurst.wiremock.http.RequestMethod; +import com.github.tomakehurst.wiremock.matching.RequestPatternBuilder; import com.github.tomakehurst.wiremock.recording.RecordSpecBuilder; +import com.github.tomakehurst.wiremock.stubbing.StubMapping; import java.io.IOException; +import java.nio.charset.StandardCharsets; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; +import java.nio.file.StandardCopyOption; +import java.security.MessageDigest; +import java.security.NoSuchAlgorithmException; +import java.util.HashSet; +import java.util.Iterator; import java.util.LinkedHashMap; import java.util.List; +import java.util.Locale; import java.util.Map; +import java.util.Set; +import java.util.TreeMap; +import java.util.UUID; +import java.util.regex.Pattern; +import java.util.stream.Collectors; import java.util.stream.Stream; import javax.annotation.concurrent.ThreadSafe; import lombok.Getter; @@ -36,6 +52,20 @@ public enum VcrMode { private static final String DEFAULT_CASSETTES_ROOT = "test-harness/src/testFixtures/resources/cassettes/"; + /** + * URL path prefixes that are excluded from cassette recording on the Braintrust target. In + * RECORD mode these paths still proxy to the real backend but are not persisted as cassettes. + * In REPLAY mode they receive catch-all stubs so the SDK's calls succeed without real backends. + * + *

Includes: + * + *

    + *
  • {@code /otel/} -- OTEL trace/log exports (binary protobuf with dynamic data) + *
  • {@code /attachment} -- S3 attachment signed-URL requests and status updates + *
+ */ + private static final Set PASSTHROUGH_PATH_PREFIXES = Set.of("/otel/", "/attachment"); + private final String cassettesRoot; private final Map proxyMap; @Getter private final VcrMode mode; @@ -188,9 +218,84 @@ private void startRecording(String targetBaseUrl) { LoginBodyRedactingTransformer.NAME, ForbiddenTextCheckingTransformer.NAME); + // For the braintrust target, exclude passthrough paths from cassette recording. + // These paths (OTEL exports, attachment uploads) contain dynamic data that doesn't + // replay well. The recording proxy still forwards them to the real backend, but + // stopRecording() won't persist them as cassette files. + // In REPLAY mode, catch-all stubs handle these paths instead. + if ("braintrust".equals(mappingsDir)) { + String exclusionRegex = buildPassthroughExclusionRegex(); + recordSpec.onlyRequestsMatching( + RequestPatternBuilder.newRequestPattern( + RequestMethod.ANY, urlPathMatching(exclusionRegex))); + log.info( + "Excluding passthrough paths from recording (filter regex: {})", + exclusionRegex); + } + wireMock.startRecording(recordSpec); } + /** + * Build a regex that matches any URL path that does NOT start with any of the passthrough path + * prefixes. Uses negative lookahead, e.g.: {@code ^(?!/otel/|/attachment).*$} + */ + private static String buildPassthroughExclusionRegex() { + var alternatives = + PASSTHROUGH_PATH_PREFIXES.stream() + .map(Pattern::quote) + .collect(Collectors.joining("|")); + return "^(?!" + alternatives + ").*$"; + } + + /** + * Register catch-all stubs on the Braintrust WireMock server for paths that are excluded from + * cassette recording. These stubs allow the SDK's background calls (OTEL export, attachment + * upload) to succeed in REPLAY mode without real backends. + */ + private static void addBraintrustPassthroughStubs(WireMockServer braintrustWireMock) { + // OTEL trace and log exports -- return 200 OK. + // Actual span/log content is validated via UnitTestSpanExporter in-memory. + braintrustWireMock.stubFor( + post(urlEqualTo("/otel/v1/traces")) + .atPriority(Integer.MAX_VALUE) + .willReturn(aResponse().withStatus(200))); + braintrustWireMock.stubFor( + post(urlEqualTo("/otel/v1/logs")) + .atPriority(Integer.MAX_VALUE) + .willReturn(aResponse().withStatus(200))); + + // Attachment upload flow: + // 1. POST /attachment -> return a fake signed URL pointing back to this WireMock + // 2. PUT /s3-upload-stub -> accept the upload with 200 OK + // 3. POST /attachment/status -> acknowledge with 200 OK + // The fake signed URL routes the S3 PUT back through WireMock so it succeeds + // without reaching a real object store. + String fakeSignedUrl = braintrustWireMock.baseUrl() + "/s3-upload-stub"; + String attachmentResponse = "{\"signedUrl\": \"" + fakeSignedUrl + "\", \"headers\": {}}"; + braintrustWireMock.stubFor( + post(urlEqualTo("/attachment")) + .atPriority(Integer.MAX_VALUE) + .willReturn( + aResponse() + .withStatus(200) + .withHeader("Content-Type", "application/json") + .withBody(attachmentResponse))); + braintrustWireMock.stubFor( + put(urlEqualTo("/s3-upload-stub")) + .atPriority(Integer.MAX_VALUE) + .willReturn(aResponse().withStatus(200))); + braintrustWireMock.stubFor( + post(urlEqualTo("/attachment/status")) + .atPriority(Integer.MAX_VALUE) + .willReturn(aResponse().withStatus(200))); + + log.info( + "Added passthrough stubs for OTEL endpoints and attachment upload flow" + + " (fake S3 URL: {})", + fakeSignedUrl); + } + /** * Load recorded JSON mappings and create programmatic stubs for SSE responses. This avoids * WireMock's issues with SSE playback from JSON mappings. @@ -231,17 +336,19 @@ private void loadAndCreateProgrammaticStubs() { } } - // Add catch-all stubs for dynamic requests. - // These requests contain timestamps and dynamic data that change between runs. - WireMockServer braintrustWireMock = proxyMap.get("https://api.braintrust.dev"); - if (braintrustWireMock != null) { - // OTLP trace exports - return 200 OK. Actual span content is validated via - // UnitTestSpanExporter. - braintrustWireMock.stubFor( - post(urlEqualTo("/otel/v1/traces")) - .atPriority(Integer.MAX_VALUE) // lowest priority, fallback only - .willReturn(aResponse().withStatus(200))); - log.info("Added catch-all stub for OTLP trace exports"); + // Add catch-all stubs for passthrough paths on the Braintrust target. + // These endpoints are not recorded as cassettes -- in REPLAY mode we stub them + // so the SDK's background calls succeed without real backends. + // Look up by mappings dir name ("braintrust") rather than target URL, since the + // Braintrust API URL may vary (e.g., staging vs production). + for (Map.Entry btEntry : targetUrlToMappingsDir.entrySet()) { + if ("braintrust".equals(btEntry.getValue())) { + WireMockServer braintrustWireMock = proxyMap.get(btEntry.getKey()); + if (braintrustWireMock != null) { + addBraintrustPassthroughStubs(braintrustWireMock); + } + break; + } } } @@ -280,7 +387,15 @@ private void createProgrammaticStubFromMapping( } } - if (!isSse && !isEventStream && !isFunctionInvokeWithParent) { + // Check if this is a query-parameter request with a "version" param. + // Version-specific fetches must take priority over general fetches for the same + // endpoint, because general stubs (which don't constrain on "version") will also + // match version-specific requests. Without explicit priority, WireMock may serve + // a general (scenario-based) stub instead of the version-specific one. + JsonNode queryParams = mapping.at("/request/queryParameters"); + boolean isVersionSpecificQuery = queryParams.isObject() && queryParams.has("version"); + + if (!isSse && !isEventStream && !isFunctionInvokeWithParent && !isVersionSpecificQuery) { return; // Let WireMock handle other responses normally } @@ -290,6 +405,10 @@ private void createProgrammaticStubFromMapping( log.info( "Creating programmatic stub for binary event-stream response: " + mappingPath.getFileName()); + } else if (isVersionSpecificQuery) { + log.info( + "Creating high-priority programmatic stub for version-specific query: " + + mappingPath.getFileName()); } else { log.info( "Creating programmatic stub for function invoke with parent: " @@ -301,10 +420,38 @@ private void createProgrammaticStubFromMapping( String responseContentType = contentType.isTextual() ? contentType.asText() : "application/json"; - // Create programmatic stub - com.github.tomakehurst.wiremock.client.MappingBuilder stub = - com.github.tomakehurst.wiremock.client.WireMock.request( - method, com.github.tomakehurst.wiremock.client.WireMock.urlEqualTo(url)); + // Create programmatic stub. + // Version-specific queries use urlPath + queryParameters instead of a full url. + String urlPath = mapping.at("/request/urlPath").asText(null); + com.github.tomakehurst.wiremock.client.MappingBuilder stub; + if (isVersionSpecificQuery && urlPath != null) { + stub = + com.github.tomakehurst.wiremock.client.WireMock.request( + method, + com.github.tomakehurst.wiremock.client.WireMock.urlPathEqualTo( + urlPath)) + // Higher priority (lower number) so this matches before general stubs + .atPriority(1); + + // Add all query parameter matchers from the cassette + Iterator> qpFields = queryParams.fields(); + while (qpFields.hasNext()) { + Map.Entry qp = qpFields.next(); + // Extract the equalTo value from hasExactly[0].equalTo + JsonNode hasExactly = qp.getValue().get("hasExactly"); + if (hasExactly != null && hasExactly.isArray() && !hasExactly.isEmpty()) { + String value = hasExactly.get(0).get("equalTo").asText(); + stub.withQueryParam( + qp.getKey(), + com.github.tomakehurst.wiremock.client.WireMock.equalTo(value)); + } + } + } else { + stub = + com.github.tomakehurst.wiremock.client.WireMock.request( + method, + com.github.tomakehurst.wiremock.client.WireMock.urlEqualTo(url)); + } // Add body pattern matching if present if (bodyPatterns.isArray() && !bodyPatterns.isEmpty()) { @@ -388,7 +535,8 @@ private void stopRecording() { String targetUrl = entry.getKey(); String mappingsDir = targetUrlToMappingsDir.get(targetUrl); WireMockServer wireMock = entry.getValue(); - wireMock.stopRecording(); + var result = wireMock.stopRecording(); + renameWithDeterministicHashes(mappingsDir, result.getStubMappings()); validateNoForbiddenText(mappingsDir); log.info("Recording saved for {}", targetUrl); } @@ -398,6 +546,301 @@ private void stopRecording() { // The servers will be stopped when the JVM shuts down via the shutdown hook. } + // ────────────────────────────────────────────────────────────────────────── + // Deterministic cassette file naming + // ────────────────────────────────────────────────────────────────────────── + + /** ObjectMapper configured for canonical (sorted-key) JSON serialization. */ + private static final ObjectMapper CANONICAL_MAPPER = + new ObjectMapper().configure(SerializationFeature.ORDER_MAP_ENTRIES_BY_KEYS, true); + + /** Number of hex characters to use from the SHA-256 hash (48 bits → ~280 trillion combos). */ + private static final int HASH_LENGTH = 12; + + /** + * Rename cassette files from WireMock's random-UUID naming to deterministic content-based + * hashes. Only processes files freshly recorded by WireMock in this session (identified via the + * {@code StubMapping} list from {@code stopRecording()}), leaving any previously-renamed files + * on disk untouched. + * + *

For each mapping file: computes a SHA-256 hash of the canonicalized {@code request} field, + * then renames the mapping file and any associated body file in {@code __files/} to use that + * hash instead of the random UUID. Also updates internal JSON references ({@code id}, {@code + * bodyFileName}). + */ + private void renameWithDeterministicHashes( + String mappingsDir, List freshMappings) { + if (freshMappings.isEmpty()) { + return; + } + + Path mappingsDirPath = Paths.get(cassettesRoot, mappingsDir, "mappings"); + Path filesDirPath = Paths.get(cassettesRoot, mappingsDir, "__files"); + + if (!Files.exists(mappingsDirPath)) { + return; + } + + // Build the list of freshly-written file paths from the StubMapping list. + // WireMock writes filenames as: sanitise("{name}-{id}") + ".json" + // where name is the sanitized URL path and id is the random UUID. + List freshFiles = + freshMappings.stream() + .map( + stub -> { + String filename = + sanitiseWireMockFilename( + stub.getName() + "-" + stub.getId() + ".json"); + return mappingsDirPath.resolve(filename); + }) + .sorted() // deterministic processing order + .toList(); + + // Track seen hashes to drop duplicates (same request → same hash → redundant cassette) + Set seenHashes = new HashSet<>(); + int renamed = 0; + int dropped = 0; + + for (Path mappingFile : freshFiles) { + if (!Files.exists(mappingFile)) { + log.warn( + "Expected freshly-recorded cassette not found: {}", + mappingFile.getFileName()); + continue; + } + try { + switch (renameCassetteFile(mappingFile, filesDirPath, seenHashes)) { + case RENAMED -> renamed++; + case DUPLICATE -> dropped++; + case SKIPPED -> {} + } + } catch (Exception e) { + throw new RuntimeException( + "Failed to rename cassette " + mappingFile.getFileName(), e); + } + } + + if (renamed > 0 || dropped > 0) { + log.info( + "Cassettes in {}: renamed {} with deterministic hashes, dropped {} duplicates" + + " (of {} freshly recorded)", + mappingsDir, + renamed, + dropped, + freshFiles.size()); + } + } + + /** + * Replicate WireMock's {@code FilenameMaker.sanitise()} logic: replace spaces with dashes, + * strip characters that aren't word chars, dashes, or dots, then lowercase. + */ + private static final Pattern WIREMOCK_NON_ALPHANUMERIC = Pattern.compile("[^\\w-.]"); + + private static String sanitiseWireMockFilename(String s) { + String decorated = String.join("-", s.split(" ")); + return WIREMOCK_NON_ALPHANUMERIC.matcher(decorated).replaceAll("").toLowerCase(Locale.ROOT); + } + + private enum RenameResult { + RENAMED, + DUPLICATE, + SKIPPED + } + + /** + * Rename a single cassette mapping file (and its body file) to use a deterministic hash. If + * another cassette with the same request hash was already processed, the duplicate is deleted + * since WireMock matches on request content and would only ever use one of them. + */ + private RenameResult renameCassetteFile( + Path mappingFile, Path filesDirPath, Set seenHashes) throws IOException { + ObjectMapper mapper = CANONICAL_MAPPER; + JsonNode root = mapper.readTree(Files.readString(mappingFile)); + + if (!root.isObject() || !root.has("request")) { + return RenameResult.SKIPPED; + } + + // Extract the stub name (sanitized URL path, e.g. "chat_completions") + String name = root.has("name") ? root.get("name").asText() : null; + if (name == null || name.isEmpty()) { + return RenameResult.SKIPPED; + } + + // Canonicalize the request and compute hash. + // Include scenario fields in the hash because WireMock scenarios use multiple stubs + // with identical request patterns but different scenario states to return different + // responses in sequence. Without these, scenario steps would hash identically and + // the dedup logic would incorrectly drop them. + JsonNode request = root.get("request"); + String canonicalRequest = canonicalizeJson(request, mapper); + String scenarioKey = ""; + if (root.has("scenarioName")) { + scenarioKey += "|scenario=" + root.get("scenarioName").asText(); + } + if (root.has("requiredScenarioState")) { + scenarioKey += "|state=" + root.get("requiredScenarioState").asText(); + } + String hash = sha256Hex(canonicalRequest + scenarioKey, HASH_LENGTH); + String hashKey = name + "-" + hash; + + // Drop duplicates: same request + same scenario state → truly redundant cassette. + // But don't delete a file that already has the target name -- that's the canonical + // copy we want to keep (e.g., from a previous recording session). + String targetMappingFileName = hashKey + ".json"; + if (!seenHashes.add(hashKey)) { + if (mappingFile.getFileName().toString().equals(targetMappingFileName)) { + // This file already has the correct name -- keep it + return RenameResult.SKIPPED; + } + log.debug( + "Dropping duplicate cassette: {} (hash: {})", mappingFile.getFileName(), hash); + // Delete the mapping file and its body file + String oldBodyFileName = root.at("/response/bodyFileName").asText(null); + if (oldBodyFileName != null && !oldBodyFileName.isEmpty()) { + Files.deleteIfExists(filesDirPath.resolve(oldBodyFileName)); + } + Files.deleteIfExists(mappingFile); + return RenameResult.DUPLICATE; + } + + String newBaseName = hashKey; + + // Generate a deterministic UUID from the hash key + UUID deterministicId = uuidV5(hashKey); + + // Update the mapping JSON (both "id" and "uuid" map to the same WireMock field; + // update both so the file is internally consistent) + ObjectNode rootObj = (ObjectNode) root; + rootObj.put("id", deterministicId.toString()); + if (rootObj.has("uuid")) { + rootObj.put("uuid", deterministicId.toString()); + } + + // Rename body file if present + String oldBodyFileName = root.at("/response/bodyFileName").asText(null); + if (oldBodyFileName != null && !oldBodyFileName.isEmpty()) { + String bodyExtension = getExtension(oldBodyFileName); + String newBodyFileName = newBaseName + bodyExtension; + + // Update the reference in the mapping JSON + ((ObjectNode) root.get("response")).put("bodyFileName", newBodyFileName); + + // Rename the actual body file on disk (REPLACE_EXISTING handles re-records + // where the deterministic name already exists from a previous recording session) + Path oldBodyPath = filesDirPath.resolve(oldBodyFileName); + if (Files.exists(oldBodyPath)) { + Path newBodyPath = filesDirPath.resolve(newBodyFileName); + Files.move(oldBodyPath, newBodyPath, StandardCopyOption.REPLACE_EXISTING); + } + } + + // Write the updated mapping JSON to the new filename + String newMappingFileName = newBaseName + ".json"; + Path newMappingPath = mappingFile.getParent().resolve(newMappingFileName); + String updatedJson = mapper.writerWithDefaultPrettyPrinter().writeValueAsString(rootObj); + Files.writeString(newMappingPath, updatedJson); + + // Delete the old file (if the name actually changed) + if (!mappingFile.equals(newMappingPath)) { + Files.deleteIfExists(mappingFile); + } + + return RenameResult.RENAMED; + } + + /** + * Canonicalize a JSON node for hashing. Object keys are sorted recursively, and {@code + * equalToJson} string values are parsed and re-serialized with sorted keys so that logically + * identical JSON bodies produce the same canonical form regardless of original key ordering. + */ + private static String canonicalizeJson(JsonNode node, ObjectMapper mapper) { + try { + Object canonical = toCanonicalObject(node, mapper); + return mapper.writeValueAsString(canonical); + } catch (Exception e) { + // Fallback: use the raw toString if canonicalization fails + return node.toString(); + } + } + + /** + * Convert a JsonNode tree into a canonical Java object tree where all objects use sorted-key + * TreeMaps. Recursively parses {@code equalToJson} string values as JSON so their key ordering + * is also normalized. + */ + private static Object toCanonicalObject(JsonNode node, ObjectMapper mapper) { + if (node.isObject()) { + TreeMap sorted = new TreeMap<>(); + Iterator> fields = node.fields(); + while (fields.hasNext()) { + Map.Entry field = fields.next(); + sorted.put(field.getKey(), toCanonicalObject(field.getValue(), mapper)); + } + return sorted; + } else if (node.isArray()) { + List list = new java.util.ArrayList<>(); + for (JsonNode element : node) { + list.add(toCanonicalObject(element, mapper)); + } + return list; + } else if (node.isTextual()) { + String text = node.asText(); + // Try to parse JSON strings (like equalToJson values) for canonical re-serialization + if (text.startsWith("{") || text.startsWith("[")) { + try { + JsonNode parsed = mapper.readTree(text); + return toCanonicalObject(parsed, mapper); + } catch (Exception ignored) { + // Not valid JSON, return as plain string + } + } + return text; + } else if (node.isNumber()) { + return node.numberValue(); + } else if (node.isBoolean()) { + return node.booleanValue(); + } else if (node.isNull()) { + return null; + } + return node.toString(); + } + + /** Compute the first {@code length} hex characters of the SHA-256 hash of a string. */ + private static String sha256Hex(String input, int length) { + try { + MessageDigest digest = MessageDigest.getInstance("SHA-256"); + byte[] hash = digest.digest(input.getBytes(StandardCharsets.UTF_8)); + StringBuilder hex = new StringBuilder(); + for (byte b : hash) { + hex.append(String.format("%02x", b)); + if (hex.length() >= length) break; + } + return hex.substring(0, length); + } catch (NoSuchAlgorithmException e) { + throw new RuntimeException("SHA-256 not available", e); + } + } + + /** + * Generate a deterministic UUID v5 (name-based, SHA-1) in the URL namespace. This produces the + * same UUID for the same input string across JVM runs. + */ + private static UUID uuidV5(String name) { + return UUID.nameUUIDFromBytes(name.getBytes(StandardCharsets.UTF_8)); + } + + /** Extract the file extension including the dot, e.g. ".json" from "foo-bar.json". */ + private static String getExtension(String filename) { + int dot = filename.lastIndexOf('.'); + return dot >= 0 ? filename.substring(dot) : ""; + } + + // ────────────────────────────────────────────────────────────────────────── + // Cassette validation + // ────────────────────────────────────────────────────────────────────────── + /** * Validate that no forbidden text (e.g., API keys) appears in recorded cassettes. Throws an * exception if any forbidden text is found, preventing accidental commit of secrets. diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-11997f5f60ca.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-11997f5f60ca.txt new file mode 100644 index 00000000..115a20a9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-11997f5f60ca.txt @@ -0,0 +1,24 @@ +event: message_start +data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01UvZSEM5WDpRsZ9ziBHPQyU","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } + +event: content_block_start +data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } + +event: ping +data: {"type": "ping"} + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } + +event: content_block_stop +data: {"type":"content_block_stop","index":0} + +event: message_delta +data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } + +event: message_stop +data: {"type":"message_stop" } + diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-12bb6b8f-daf2-4c8d-99a0-8f5258653e83.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-12bb6b8f-daf2-4c8d-99a0-8f5258653e83.json deleted file mode 100644 index bd8215f5..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-12bb6b8f-daf2-4c8d-99a0-8f5258653e83.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-3-haiku-20240307","id":"msg_015ggE6DMWDn4xQCdVcWPb49","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-161ff70a-c4a2-4379-bc69-8f73b0fa6ac7.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-161ff70a-c4a2-4379-bc69-8f73b0fa6ac7.json deleted file mode 100644 index 6e7fed3f..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-161ff70a-c4a2-4379-bc69-8f73b0fa6ac7.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01BixXKgMqHfV8Jz1ENwHqfp","type":"message","role":"assistant","content":[{"type":"text","text":"This image appears to be **red** (or a reddish color). It looks like a small red dot or mark against a white background."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":17,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":33,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-1f4e5f7d-1ba9-41aa-b26a-ede57e72baa0.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-1f4e5f7d-1ba9-41aa-b26a-ede57e72baa0.json deleted file mode 100644 index 8a651f33..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-1f4e5f7d-1ba9-41aa-b26a-ede57e72baa0.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01MmDSJAZ3nMoRCWauqztGC9","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-27b2e305-9211-47a2-9a94-9c2d853cee3d.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-27b2e305-9211-47a2-9a94-9c2d853cee3d.json deleted file mode 100644 index 1681b412..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-27b2e305-9211-47a2-9a94-9c2d853cee3d.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01UKfBo4E6BQDvJesUUWePtc","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-285f53811fc5.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-285f53811fc5.txt new file mode 100644 index 00000000..702e3dbe --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-285f53811fc5.txt @@ -0,0 +1,24 @@ +event: message_start +data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01PR2PH2X2CucudJwVnFKKSV","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } + +event: content_block_start +data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } + +event: ping +data: {"type": "ping"} + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } + +event: content_block_stop +data: {"type":"content_block_stop","index":0 } + +event: message_delta +data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } + +event: message_stop +data: {"type":"message_stop" } + diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2d588166-5193-482c-adb8-f0f5b04240c1.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2d588166-5193-482c-adb8-f0f5b04240c1.txt deleted file mode 100644 index 0b691c48..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2d588166-5193-482c-adb8-f0f5b04240c1.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-3-haiku-20240307","id":"msg_01DbF9vkxiRYm2nVYAe2pagc","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" capital of France is Paris."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":10} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2f5595ab-e3ce-4999-8a92-6cd5c7e51411.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2f5595ab-e3ce-4999-8a92-6cd5c7e51411.json deleted file mode 100644 index 919a13d3..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-2f5595ab-e3ce-4999-8a92-6cd5c7e51411.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-sonnet-4-5-20250929","id":"msg_014Bi4YWvCJLkqcXsJ5Eqdgc","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":7425,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":3710,"ephemeral_1h_input_tokens":3715},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-3bbaedf4-2ef1-4305-b29c-c800d96fd667.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-3bbaedf4-2ef1-4305-b29c-c800d96fd667.json deleted file mode 100644 index 080cedcf..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-3bbaedf4-2ef1-4305-b29c-c800d96fd667.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01Jzz9ZLceNEHG1cjFQHi7DA","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-41856a78-aa03-44d8-af48-d16f30e2f36d.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-41856a78-aa03-44d8-af48-d16f30e2f36d.txt deleted file mode 100644 index f3fdbd99..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-41856a78-aa03-44d8-af48-d16f30e2f36d.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_013v3fgs3MZQCWP6P2jFtoLo","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-47082d84-2083-4057-891f-48f77e246b16.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-47082d84-2083-4057-891f-48f77e246b16.txt deleted file mode 100644 index 79470fe2..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-47082d84-2083-4057-891f-48f77e246b16.txt +++ /dev/null @@ -1,21 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01PrQnDF15HSuzTLa51WB9fg","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"1\n2\n3\n4\n5"} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":13} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-476c4a4ce15b.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-476c4a4ce15b.json new file mode 100644 index 00000000..0df0bea4 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-476c4a4ce15b.json @@ -0,0 +1 @@ +{"model":"claude-haiku-4-5-20251001","id":"msg_014PvFAwDsbxxvvp84aVydvV","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is **Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a2b1278-80b3-4c18-8ca8-ad56e6b88e28.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a2b1278-80b3-4c18-8ca8-ad56e6b88e28.json deleted file mode 100644 index 2158a88c..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a2b1278-80b3-4c18-8ca8-ad56e6b88e28.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-sonnet-4-5-20250929","id":"msg_01LgFN9spggTY8EpJU7DtyHE","type":"message","role":"assistant","content":[{"type":"text","text":"Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1365,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":1365,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a7e3600-3b68-4c2e-b906-e51f3637d5f5.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a7e3600-3b68-4c2e-b906-e51f3637d5f5.txt deleted file mode 100644 index d6542f61..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-4a7e3600-3b68-4c2e-b906-e51f3637d5f5.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-3-haiku-20240307","id":"msg_014Sgg6HXXAE1tfUMKnZb94b","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" capital of France is Paris."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":10} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-50aa255b9074.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-50aa255b9074.json new file mode 100644 index 00000000..7ccde820 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-50aa255b9074.json @@ -0,0 +1 @@ +{"model":"claude-sonnet-4-5-20250929","id":"msg_01GQgHcLysRpHj8cZnp9StUW","type":"message","role":"assistant","content":[{"type":"text","text":"Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1365,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":1365,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-53c58df9-b287-4623-9566-e50a2c53e287.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-53c58df9-b287-4623-9566-e50a2c53e287.json deleted file mode 100644 index 19dfbff9..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-53c58df9-b287-4623-9566-e50a2c53e287.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01M9EADpN85Kx15MfFgVou3j","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is **Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5b575537-813b-4fd0-ac6f-27285f2caa6d.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5b575537-813b-4fd0-ac6f-27285f2caa6d.txt deleted file mode 100644 index 99db91c1..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5b575537-813b-4fd0-ac6f-27285f2caa6d.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-3-haiku-20240307","id":"msg_01Gwb3fb5rQmXS3f9mpjimsG","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}}} - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" capital of France is Paris."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":10} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5d20989d033b.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5d20989d033b.txt new file mode 100644 index 00000000..50611e1a --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-5d20989d033b.txt @@ -0,0 +1,21 @@ +event: message_start +data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_019GGEJxTUz2mgwRJHze2pCp","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } + +event: content_block_start +data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } + +event: ping +data: {"type": "ping"} + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"1\n2\n3\n4\n5"} } + +event: content_block_stop +data: {"type":"content_block_stop","index":0 } + +event: message_delta +data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":13} } + +event: message_stop +data: {"type":"message_stop" } + diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-664c4d4fed33.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-664c4d4fed33.json new file mode 100644 index 00000000..dab74446 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-664c4d4fed33.json @@ -0,0 +1 @@ +{"model":"claude-sonnet-4-5-20250929","id":"msg_015fi63tgZtw55XjHoQ9T4SL","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":7425,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":3710,"ephemeral_1h_input_tokens":3715},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-672a77ea-d101-4894-99d2-91ee1d4579e4.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-672a77ea-d101-4894-99d2-91ee1d4579e4.json deleted file mode 100644 index bb2e4f57..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-672a77ea-d101-4894-99d2-91ee1d4579e4.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-3-haiku-20240307","id":"msg_01DBHieqe8LhQqugQyEAdvXR","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6cefd432-d54e-4b33-ab68-6b9051101266.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6cefd432-d54e-4b33-ab68-6b9051101266.txt deleted file mode 100644 index ea068775..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6cefd432-d54e-4b33-ab68-6b9051101266.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01BkhCs7ozqYpiZPYPBQ1ndN","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6e3b67ab-fb16-4353-aa3b-da0b5a10658a.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6e3b67ab-fb16-4353-aa3b-da0b5a10658a.json deleted file mode 100644 index 5c4d9168..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-6e3b67ab-fb16-4353-aa3b-da0b5a10658a.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-3-haiku-20240307","id":"msg_01ACiiYfEwrpWidaEnSwwoxh","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7b66b6a7-2d16-4e55-b2c0-47f225d0d8a1.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7b66b6a7-2d16-4e55-b2c0-47f225d0d8a1.json deleted file mode 100644 index b4eb99ac..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7b66b6a7-2d16-4e55-b2c0-47f225d0d8a1.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01NcuqTAv4tGS3uewJA95kJw","type":"message","role":"assistant","content":[{"type":"text","text":"This image appears to be **red** (or a reddish color). It looks like a small red dot or mark against a white background."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":17,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":33,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7caa44dee455.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7caa44dee455.json new file mode 100644 index 00000000..2f9a47e7 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-7caa44dee455.json @@ -0,0 +1 @@ +{"model":"claude-haiku-4-5-20251001","id":"msg_011X5LDuckYTdxhae6tLBeAd","type":"message","role":"assistant","content":[{"type":"text","text":"This image appears to be **red** (or a reddish color). It looks like a small red dot or mark against a white background."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":17,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":33,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-80eb9d92-57cb-4538-ae33-4971b4f0717b.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-80eb9d92-57cb-4538-ae33-4971b4f0717b.txt deleted file mode 100644 index d4c8e950..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-80eb9d92-57cb-4538-ae33-4971b4f0717b.txt +++ /dev/null @@ -1,21 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01SNPcXzhzJp9T8ims9FRPQr","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"1\n2\n3\n4\n5"} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":13} } - -event: message_stop -data: {"type":"message_stop"} - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8186efd9-0d41-4b19-8a67-1ea8c33c86a3.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8186efd9-0d41-4b19-8a67-1ea8c33c86a3.json deleted file mode 100644 index e86a5a5f..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8186efd9-0d41-4b19-8a67-1ea8c33c86a3.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-sonnet-4-5-20250929","id":"msg_01Q1j91FENjcUVMaZpCEiQtJ","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is **Paris**."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1993,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1993},"output_tokens":11,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-81a6dc74-3b87-4a7a-b0e9-87e7ffb14ad9.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-81a6dc74-3b87-4a7a-b0e9-87e7ffb14ad9.json deleted file mode 100644 index de661e57..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-81a6dc74-3b87-4a7a-b0e9-87e7ffb14ad9.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01EAd8ou9F4TJE3R2qqBvY2o","type":"message","role":"assistant","content":[{"type":"text","text":"This image is **red**. It appears to be a small red dot or circular shape on a white background."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":17,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":26,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8551e22f9273.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8551e22f9273.txt new file mode 100644 index 00000000..0c91559f --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-8551e22f9273.txt @@ -0,0 +1,24 @@ +event: message_start +data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01T4cGqQ64VXBQ7sdcRjnMWE","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } + +event: content_block_start +data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } + +event: ping +data: {"type": "ping"} + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } + +event: content_block_delta +data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } + +event: content_block_stop +data: {"type":"content_block_stop","index":0 } + +event: message_delta +data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } + +event: message_stop +data: {"type":"message_stop" } + diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-863c176a-4dbe-4c8f-a5a4-b4240172c3e6.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-863c176a-4dbe-4c8f-a5a4-b4240172c3e6.json deleted file mode 100644 index 6c4bd00a..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-863c176a-4dbe-4c8f-a5a4-b4240172c3e6.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01MsVBgb5izWHn7VWpBFJRg1","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-89a84d63-80c5-4924-a385-055b71890369.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-89a84d63-80c5-4924-a385-055b71890369.txt deleted file mode 100644 index ce0f5cf7..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-89a84d63-80c5-4924-a385-055b71890369.txt +++ /dev/null @@ -1,21 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01QoWH6toEXBYEBSymjmNaXq","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"1\n2\n3\n4\n5"} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":13} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-95926394746c.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-95926394746c.json new file mode 100644 index 00000000..00c75dd7 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-95926394746c.json @@ -0,0 +1 @@ +{"model":"claude-sonnet-4-5-20250929","id":"msg_01QKPuvNUe3igwH7zFZ2hThb","type":"message","role":"assistant","content":[{"type":"text","text":"Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1368,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":1368,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9e76d70a-de57-4af6-9f02-e5e746e513d2.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9e76d70a-de57-4af6-9f02-e5e746e513d2.txt deleted file mode 100644 index 70b6242c..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9e76d70a-de57-4af6-9f02-e5e746e513d2.txt +++ /dev/null @@ -1,21 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_0148pwTCPVXijA6hGmLtLk9i","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"1\n2\n3\n4\n5"} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":22,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":13} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9faa104e-c120-4f6a-96d8-b3f776646f57.txt b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9faa104e-c120-4f6a-96d8-b3f776646f57.txt deleted file mode 100644 index 6456b678..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-9faa104e-c120-4f6a-96d8-b3f776646f57.txt +++ /dev/null @@ -1,24 +0,0 @@ -event: message_start -data: {"type":"message_start","message":{"model":"claude-haiku-4-5-20251001","id":"msg_0156V2fqqmGeZwxdH94aYLB3","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"not_available"}} } - -event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}} - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The capital of France is **"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0} - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":36} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-a8b7f878b224.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-a8b7f878b224.json new file mode 100644 index 00000000..e961ab16 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-a8b7f878b224.json @@ -0,0 +1 @@ +{"model":"claude-sonnet-4-5-20250929","id":"msg_01UrRx1Rvs8pVzvuHKy1vp51","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is **Paris**."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1993,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1993},"output_tokens":11,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ab939783-3875-4741-932f-89faa247c269.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ab939783-3875-4741-932f-89faa247c269.json deleted file mode 100644 index 85b7f447..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ab939783-3875-4741-932f-89faa247c269.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_019UG14v1hgfHGYdt4H3dE2v","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is **Paris**. It is located in the north-central part of the country along the Seine River and is the largest city in France."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b11ab95242d5.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b11ab95242d5.json new file mode 100644 index 00000000..85ab51ff --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b11ab95242d5.json @@ -0,0 +1 @@ +{"model":"claude-haiku-4-5-20251001","id":"msg_01Bqh2VHyP7i4imv8aUgk8N5","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b149de3897b0.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b149de3897b0.json new file mode 100644 index 00000000..5b597487 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-b149de3897b0.json @@ -0,0 +1 @@ +{"model":"claude-haiku-4-5-20251001","id":"msg_01Snc5Lys8gai8RqJiDX9LSN","type":"message","role":"assistant","content":[{"type":"text","text":"This image appears to be **red** (or a reddish color). 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-event: content_block_start -data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""} } - -event: ping -data: {"type": "ping"} - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"The"} } - -event: content_block_delta -data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" capital of France is Paris."} } - -event: content_block_stop -data: {"type":"content_block_stop","index":0 } - -event: message_delta -data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null,"stop_details":null},"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":10} } - -event: message_stop -data: {"type":"message_stop" } - diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-c5c219eea6ec.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-c5c219eea6ec.json new file mode 100644 index 00000000..a835741e --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-c5c219eea6ec.json @@ -0,0 +1 @@ +{"model":"claude-haiku-4-5-20251001","id":"msg_01EjVeQGhkkbNNJHgLm1vy6u","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ca81e846-884a-484e-a88b-e76ce1670233.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ca81e846-884a-484e-a88b-e76ce1670233.json deleted file mode 100644 index 93b40f10..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-ca81e846-884a-484e-a88b-e76ce1670233.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-3-haiku-20240307","id":"msg_016fvcYYLAMgm9Hqog5gwPvb","type":"message","role":"assistant","content":[{"type":"text","text":"The capital of France is Paris."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":19,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"not_available"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-d1ce1bd4-a9a8-43bf-bdfc-71b47562c5b4.json b/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-d1ce1bd4-a9a8-43bf-bdfc-71b47562c5b4.json deleted file mode 100644 index 15732867..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/anthropic/__files/v1_messages-d1ce1bd4-a9a8-43bf-bdfc-71b47562c5b4.json +++ /dev/null @@ -1 +0,0 @@ -{"model":"claude-haiku-4-5-20251001","id":"msg_01YFa1jS9JLURR9wpfsKD7W5","type":"message","role":"assistant","content":[{"type":"text","text":"This image is **red**. 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This is guideline number 1. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n2. This is guideline number 2. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n3. This is guideline number 3. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n4. This is guideline number 4. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n5. This is guideline number 5. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n6. This is guideline number 6. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n7. This is guideline number 7. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n8. This is guideline number 8. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n9. This is guideline number 9. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n10. This is guideline number 10. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n11. This is guideline number 11. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n12. This is guideline number 12. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n13. This is guideline number 13. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n14. This is guideline number 14. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n15. This is guideline number 15. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n16. This is guideline number 16. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n17. This is guideline number 17. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n18. This is guideline number 18. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n19. This is guideline number 19. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n20. This is guideline number 20. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n21. This is guideline number 21. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n22. This is guideline number 22. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n23. This is guideline number 23. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n24. This is guideline number 24. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n25. This is guideline number 25. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n26. This is guideline number 26. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n27. This is guideline number 27. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n28. This is guideline number 28. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n29. This is guideline number 29. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n30. This is guideline number 30. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n31. This is guideline number 31. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n32. This is guideline number 32. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n33. This is guideline number 33. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n34. This is guideline number 34. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n35. This is guideline number 35. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n36. This is guideline number 36. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n37. This is guideline number 37. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n38. This is guideline number 38. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n39. This is guideline number 39. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n40. This is guideline number 40. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n41. This is guideline number 41. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n42. This is guideline number 42. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n43. This is guideline number 43. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n44. This is guideline number 44. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n45. This is guideline number 45. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n46. This is guideline number 46. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n47. This is guideline number 47. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n48. This is guideline number 48. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n49. This is guideline number 49. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n50. This is guideline number 50. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n51. This is guideline number 51. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n52. This is guideline number 52. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n53. This is guideline number 53. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n54. This is guideline number 54. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n55. This is guideline number 55. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n56. This is guideline number 56. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n57. This is guideline number 57. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n58. This is guideline number 58. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n59. This is guideline number 59. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n60. This is guideline number 60. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n61. This is guideline number 61. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n62. This is guideline number 62. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n63. This is guideline number 63. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n64. This is guideline number 64. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n65. This is guideline number 65. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n66. This is guideline number 66. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n67. This is guideline number 67. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n68. This is guideline number 68. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n69. This is guideline number 69. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n70. This is guideline number 70. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n71. This is guideline number 71. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n72. This is guideline number 72. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n73. This is guideline number 73. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n74. This is guideline number 74. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n75. This is guideline number 75. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n76. This is guideline number 76. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n77. This is guideline number 77. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n78. This is guideline number 78. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n79. This is guideline number 79. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n80. This is guideline number 80. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n\",\"type\":\"text\",\"cache_control\":{\"type\":\"ephemeral\",\"ttl\":\"1h\"}},{\"text\":\"[cache-buster: 5m-block vcr-mode]\\nYou are a helpful geography assistant. Answer in one sentence.\\n\\n1. This is guideline number 1. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n2. This is guideline number 2. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n3. This is guideline number 3. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n4. This is guideline number 4. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n5. This is guideline number 5. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n6. This is guideline number 6. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n7. This is guideline number 7. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n8. This is guideline number 8. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n9. This is guideline number 9. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n10. This is guideline number 10. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n11. This is guideline number 11. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n12. This is guideline number 12. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n13. This is guideline number 13. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n14. This is guideline number 14. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n15. This is guideline number 15. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n16. This is guideline number 16. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n17. This is guideline number 17. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n18. This is guideline number 18. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n19. This is guideline number 19. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n20. This is guideline number 20. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n21. This is guideline number 21. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n22. This is guideline number 22. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n23. This is guideline number 23. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n24. This is guideline number 24. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n25. This is guideline number 25. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n26. This is guideline number 26. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n27. This is guideline number 27. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n28. This is guideline number 28. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n29. This is guideline number 29. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n30. This is guideline number 30. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n31. This is guideline number 31. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n32. This is guideline number 32. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n33. This is guideline number 33. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n34. This is guideline number 34. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n35. This is guideline number 35. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n36. This is guideline number 36. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n37. This is guideline number 37. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n38. This is guideline number 38. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n39. This is guideline number 39. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n40. This is guideline number 40. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n41. This is guideline number 41. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n42. This is guideline number 42. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n43. This is guideline number 43. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n44. This is guideline number 44. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n45. This is guideline number 45. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n46. This is guideline number 46. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n47. This is guideline number 47. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n48. This is guideline number 48. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n49. This is guideline number 49. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n50. This is guideline number 50. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n51. This is guideline number 51. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n52. This is guideline number 52. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n53. This is guideline number 53. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n54. This is guideline number 54. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n55. This is guideline number 55. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n56. This is guideline number 56. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n57. This is guideline number 57. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n58. This is guideline number 58. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n59. This is guideline number 59. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n60. This is guideline number 60. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n61. This is guideline number 61. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n62. This is guideline number 62. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n63. This is guideline number 63. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n64. This is guideline number 64. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n65. This is guideline number 65. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n66. This is guideline number 66. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n67. This is guideline number 67. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n68. This is guideline number 68. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n69. This is guideline number 69. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n70. This is guideline number 70. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n71. This is guideline number 71. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n72. This is guideline number 72. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n73. This is guideline number 73. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n74. This is guideline number 74. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n75. This is guideline number 75. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n76. This is guideline number 76. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n77. This is guideline number 77. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n78. This is guideline number 78. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n79. This is guideline number 79. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n80. This is guideline number 80. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n\",\"type\":\"text\",\"cache_control\":{\"type\":\"ephemeral\",\"ttl\":\"5m\"}}],\"temperature\":0.0}", - "ignoreArrayOrder" : true, - "ignoreExtraElements" : false - } ] - }, - "response" : { - "status" : 200, - "bodyFileName" : "v1_messages-2f5595ab-e3ce-4999-8a92-6cd5c7e51411.json", - "headers" : { - "anthropic-organization-id" : "27796668-7351-40ac-acc4-024aee8995a5", - "x-envoy-upstream-service-time" : "872", - "Server" : "cloudflare", - "vary" : "Accept-Encoding", - "anthropic-ratelimit-output-tokens-limit" : "600000", - "anthropic-ratelimit-input-tokens-reset" : "2026-05-01T17:05:18Z", - "anthropic-ratelimit-output-tokens-reset" : "2026-05-01T17:05:18Z", - "anthropic-ratelimit-tokens-remaining" : "3598000", - "set-cookie" : "_cfuvid=upa7bZwh8LfSnf3lXQQBUbzBYX17.c774uhdESJoxyk-1777655117.8844492-1.0.1.1-5f5S9jYD.2MPuK3_xEJwq1zpmgm7AOobZfYR_Scnn44; HttpOnly; SameSite=None; Secure; Path=/; Domain=api.anthropic.com", - "anthropic-ratelimit-requests-limit" : "20000", - "Content-Security-Policy" : "default-src 'none'; 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This is guideline number 1. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n2. This is guideline number 2. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n3. This is guideline number 3. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n4. This is guideline number 4. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n5. This is guideline number 5. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n6. This is guideline number 6. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n7. This is guideline number 7. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n8. This is guideline number 8. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n9. This is guideline number 9. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n10. This is guideline number 10. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n11. This is guideline number 11. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n12. This is guideline number 12. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n13. This is guideline number 13. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n14. This is guideline number 14. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n15. This is guideline number 15. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n16. This is guideline number 16. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n17. This is guideline number 17. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n18. This is guideline number 18. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n19. This is guideline number 19. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n20. This is guideline number 20. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n21. This is guideline number 21. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n22. This is guideline number 22. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n23. This is guideline number 23. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n24. This is guideline number 24. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n25. This is guideline number 25. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n26. This is guideline number 26. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n27. This is guideline number 27. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n28. This is guideline number 28. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n29. This is guideline number 29. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n30. This is guideline number 30. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n31. This is guideline number 31. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n32. This is guideline number 32. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n33. This is guideline number 33. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n34. This is guideline number 34. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n35. This is guideline number 35. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n36. This is guideline number 36. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n37. This is guideline number 37. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n38. This is guideline number 38. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n39. This is guideline number 39. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n40. This is guideline number 40. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n41. This is guideline number 41. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n42. This is guideline number 42. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n43. This is guideline number 43. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n44. This is guideline number 44. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n45. This is guideline number 45. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n46. This is guideline number 46. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n47. This is guideline number 47. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n48. This is guideline number 48. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n49. This is guideline number 49. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n50. This is guideline number 50. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n51. This is guideline number 51. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n52. This is guideline number 52. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n53. This is guideline number 53. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n54. This is guideline number 54. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n55. This is guideline number 55. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n56. This is guideline number 56. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n57. This is guideline number 57. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n58. This is guideline number 58. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n59. This is guideline number 59. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n60. This is guideline number 60. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n61. This is guideline number 61. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n62. This is guideline number 62. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n63. This is guideline number 63. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n64. This is guideline number 64. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n65. This is guideline number 65. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n66. This is guideline number 66. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n67. This is guideline number 67. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n68. This is guideline number 68. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n69. This is guideline number 69. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n70. This is guideline number 70. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n71. This is guideline number 71. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n72. This is guideline number 72. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n73. This is guideline number 73. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n74. This is guideline number 74. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n75. This is guideline number 75. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n76. This is guideline number 76. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n77. This is guideline number 77. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n78. This is guideline number 78. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n79. This is guideline number 79. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n80. This is guideline number 80. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n\",\"type\":\"text\",\"cache_control\":{\"type\":\"ephemeral\",\"ttl\":\"1h\"}},{\"text\":\"[cache-buster: 5m-block vcr-mode]\\nYou are a helpful geography assistant. Answer in one sentence.\\n\\n1. This is guideline number 1. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n2. This is guideline number 2. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n3. This is guideline number 3. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n4. This is guideline number 4. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n5. This is guideline number 5. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n6. This is guideline number 6. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n7. This is guideline number 7. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n8. This is guideline number 8. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n9. This is guideline number 9. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n10. This is guideline number 10. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n11. This is guideline number 11. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n12. This is guideline number 12. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n13. This is guideline number 13. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n14. This is guideline number 14. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n15. This is guideline number 15. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n16. This is guideline number 16. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n17. This is guideline number 17. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n18. This is guideline number 18. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n19. This is guideline number 19. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n20. This is guideline number 20. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n21. This is guideline number 21. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n22. This is guideline number 22. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n23. This is guideline number 23. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n24. This is guideline number 24. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n25. This is guideline number 25. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n26. This is guideline number 26. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n27. This is guideline number 27. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n28. This is guideline number 28. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n29. This is guideline number 29. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n30. This is guideline number 30. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n31. This is guideline number 31. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n32. This is guideline number 32. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n33. This is guideline number 33. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n34. This is guideline number 34. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n35. This is guideline number 35. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n36. This is guideline number 36. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n37. This is guideline number 37. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n38. This is guideline number 38. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n39. This is guideline number 39. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n40. This is guideline number 40. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n41. This is guideline number 41. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n42. This is guideline number 42. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n43. This is guideline number 43. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n44. This is guideline number 44. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n45. This is guideline number 45. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n46. This is guideline number 46. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n47. This is guideline number 47. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n48. This is guideline number 48. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n49. This is guideline number 49. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n50. This is guideline number 50. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n51. This is guideline number 51. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n52. This is guideline number 52. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n53. This is guideline number 53. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n54. This is guideline number 54. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n55. This is guideline number 55. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n56. This is guideline number 56. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n57. This is guideline number 57. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n58. This is guideline number 58. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n59. This is guideline number 59. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n60. This is guideline number 60. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n61. This is guideline number 61. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n62. This is guideline number 62. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n63. This is guideline number 63. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n64. This is guideline number 64. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n65. This is guideline number 65. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n66. This is guideline number 66. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n67. This is guideline number 67. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n68. This is guideline number 68. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n69. This is guideline number 69. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n70. This is guideline number 70. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n71. This is guideline number 71. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n72. This is guideline number 72. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n73. This is guideline number 73. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n74. This is guideline number 74. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n75. This is guideline number 75. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n76. This is guideline number 76. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n77. This is guideline number 77. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n78. This is guideline number 78. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n79. This is guideline number 79. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n80. This is guideline number 80. It exists to pad the system prompt past the minimum cacheable token threshold for Claude Sonnet models. Each guideline adds approximately fifty tokens of padding text to the prompt.\\n\",\"type\":\"text\",\"cache_control\":{\"type\":\"ephemeral\",\"ttl\":\"5m\"}}],\"temperature\":0.0}", + "ignoreArrayOrder" : true, + "ignoreExtraElements" : false + } ] + }, + "response" : { + "status" : 200, + "bodyFileName" : "v1_messages-664c4d4fed33.json", + "headers" : { + "anthropic-organization-id" : "27796668-7351-40ac-acc4-024aee8995a5", + "x-envoy-upstream-service-time" : "2198", + "Server" : "cloudflare", + "vary" : "Accept-Encoding", + "anthropic-ratelimit-output-tokens-limit" : "600000", + "anthropic-ratelimit-input-tokens-reset" : "2026-05-12T23:49:59Z", + "anthropic-ratelimit-output-tokens-reset" : "2026-05-12T23:49:59Z", + "anthropic-ratelimit-tokens-remaining" : "3598000", + "set-cookie" : "_cfuvid=h7E12x1ISejLa2vGFmyHq_rp5l4WZ._R5tipJT7656I-1778629797.802995-1.0.1.1-aBx6CWOwCzeHtQsx9Vl6QB3TFWbWYwD8ENBbIsSb3_Q; HttpOnly; SameSite=None; Secure; Path=/; Domain=api.anthropic.com", + "anthropic-ratelimit-requests-limit" : "20000", + "Content-Security-Policy" : "default-src 'none'; 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at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/bedrock/mappings/model_us.amazon.nova-lite-v10_converse-400d471f-3f4d-4d3e-9571-cb3f015011c5.json b/test-harness/src/testFixtures/resources/cassettes/bedrock/mappings/model_us.amazon.nova-lite-v10_converse-400d471f-3f4d-4d3e-9571-cb3f015011c5.json deleted file mode 100644 index fb104517..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/bedrock/mappings/model_us.amazon.nova-lite-v10_converse-400d471f-3f4d-4d3e-9571-cb3f015011c5.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "id" : "400d471f-3f4d-4d3e-9571-cb3f015011c5", - "name" : "model_us.amazon.nova-lite-v10_converse", - "request" : { - "url" : "/model/us.amazon.nova-lite-v1%3A0/converse", - "method" : "POST", - "headers" : { - "Content-Type" : { - "equalTo" : "application/json" - } - }, - "bodyPatterns" : [ { - "equalToJson" : "{\"messages\":[{\"role\":\"user\",\"content\":[{\"text\":\"What color is this 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-{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-2bc5505d-8e52-4855-aa71-ec93086486cb.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-2bc5505d-8e52-4855-aa71-ec93086486cb.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-2bc5505d-8e52-4855-aa71-ec93086486cb.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-30fe46b76715.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-30fe46b76715.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-30fe46b76715.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3204b2c0-c5cc-4c17-a61d-462e3aca8860.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3204b2c0-c5cc-4c17-a61d-462e3aca8860.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3204b2c0-c5cc-4c17-a61d-462e3aca8860.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-35eedf692313.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-35eedf692313.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-35eedf692313.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-36c939a960cb.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-36c939a960cb.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-36c939a960cb.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-37149e64ea12.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-37149e64ea12.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-37149e64ea12.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3da50fecdac4.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3da50fecdac4.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-3da50fecdac4.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4a78dc19-bd47-4b47-8ce6-50f41192195e.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4a78dc19-bd47-4b47-8ce6-50f41192195e.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4a78dc19-bd47-4b47-8ce6-50f41192195e.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4ee75ede31c8.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4ee75ede31c8.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4ee75ede31c8.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4f2596b7-e287-4723-a9dc-fd2ad1063742.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4f2596b7-e287-4723-a9dc-fd2ad1063742.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-4f2596b7-e287-4723-a9dc-fd2ad1063742.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-53d7539fafbb.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-53d7539fafbb.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-53d7539fafbb.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-56c61687-3405-4c09-ab40-271bc54c8eaa.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-56c61687-3405-4c09-ab40-271bc54c8eaa.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-56c61687-3405-4c09-ab40-271bc54c8eaa.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-606c26c9-be94-4c86-933e-b673f52e5b19.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-606c26c9-be94-4c86-933e-b673f52e5b19.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-606c26c9-be94-4c86-933e-b673f52e5b19.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-6a0848d1-eaca-4c46-bae5-b125508c784e.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-6a0848d1-eaca-4c46-bae5-b125508c784e.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-6a0848d1-eaca-4c46-bae5-b125508c784e.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-756c5d812c0e.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-756c5d812c0e.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-756c5d812c0e.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-759d12b9-6ea0-4626-a78e-fee98c968cd0.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-759d12b9-6ea0-4626-a78e-fee98c968cd0.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-759d12b9-6ea0-4626-a78e-fee98c968cd0.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-781812c8-b6c4-4d8e-99a2-3561e8ab3abc.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-781812c8-b6c4-4d8e-99a2-3561e8ab3abc.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-781812c8-b6c4-4d8e-99a2-3561e8ab3abc.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7a5f139b8e0b.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7a5f139b8e0b.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7a5f139b8e0b.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e51dda395ab.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e51dda395ab.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e51dda395ab.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e567525-8382-4555-b114-14e4fc3c4660.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e567525-8382-4555-b114-14e4fc3c4660.json deleted file mode 100644 index 9a18a482..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7e567525-8382-4555-b114-14e4fc3c4660.json +++ /dev/null @@ -1 +0,0 @@ -{"org_info":[{"id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"braintrustdata.com","api_url":"https://staging-api.braintrust.dev","git_metadata":{"fields":["commit","branch","tag","author_name","author_email","commit_message","commit_time","dirty"],"collect":"some"},"is_universal_api":true,"proxy_url":"https://staging-api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7ea1449e7f88.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7ea1449e7f88.json new file mode 100644 index 00000000..634dffc9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-7ea1449e7f88.json @@ -0,0 +1 @@ +{"org_info":[{"id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"Braintrust SDKs","api_url":"https://api.braintrust.dev","git_metadata":null,"is_universal_api":null,"proxy_url":"https://api.braintrust.dev","realtime_url":"wss://realtime.braintrustapi.com"}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-8008136f-69a7-4faf-9fcf-21456e9b10db.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/api_apikey_login-8008136f-69a7-4faf-9fcf-21456e9b10db.json deleted file mode 100644 index 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I can only provide information about the color. However, if you want to know about the color of the image, I can provide information about it. The image is in a red color.","type":"text"}],"role":"assistant"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"8b6742b1702306836d0410b9a48a7559","scores":null,"span_attributes":{"name":"bedrock.converse","type":"llm"},"span_id":"c1215a934be7d585","span_parents":["06f8d5792201523a"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"_async_scoring_state":{},"_pagination_key":{"description":"A stable, time-ordered key that can be used to paginate over project logs events. This field is auto-generated by Braintrust and only exists in Brainstore.","type":["string","null"]},"_xact_id":{"description":"The transaction id of an event is unique to the network operation that processed the event insertion. 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I can also see that they align with triangular numbers multiplied by 2. Ultimately, I think the formula is simply a_n = n(n+1).","type":"summary_text"},{"text":"**Defining the pronic numbers**\n\nSo starting from n≥1, I see that the nth term can be expressed as T_n = n(n+1), indicating that these numbers are pronic numbers, which are products of two consecutive integers. The differences between the terms increase by 2 each time (4, 6, 8, 10), confirming it's a quadratic pattern. I’ve derived that a_n = n^2 + n fits this sequence. If starting at n=0, the formula adjusts to a_n = (n+1)(n+2). So, a_n = n(n+1) is a straightforward answer!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The terms are \n 2 = 1·2 \n 6 = 2·3 \n12 = 3·4 \n20 = 4·5 \n30 = 5·6 \n\nIn other words, the n-th term is the product of two consecutive integers. Equivalently\n\n aₙ = n(n + 1) = n² + n,  for n = 1, 2, 3, …","type":"output_text"}],"id":"msg_057e67b8f6bc5bec0069d6c293acec819691d5b3c799159dad","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"749bf197b1337ee8f4a939cd996b0c97","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"a73422ab2624c6e3","span_parents":["195f657305d2bb7a"],"tags":null},{"_async_scoring_state":null,"_pagination_key":"p07626497021210525697","_xact_id":"1000196963362293204","audit_data":[{"_xact_id":"1000196963362293204","audit_data":{"action":"upsert"},"metadata":{},"source":"api"}],"classifications":null,"comments":null,"context":null,"created":"2026-04-08T21:03:16.408Z","error":null,"expected":null,"facets":null,"id":"9aeb6a50a78a09dd","input":[{"content":"Look at this sequence: 2, 6, 12, 20, 30. What is the pattern and what would be the formula for the nth term?\n","role":"user"},{"id":"rs_057e67b8f6bc5bec0069d6c288348881968097f72c458b4b6a","summary":[{"text":"**Identifying the sequence pattern**\n\nThe user has shared a sequence: 2, 6, 12, 20, 30. I’m noticing this follows the pattern of n(n+1). Basically, for n=1 to 5, I have 1*2=2, 2*3=6, and so on. So, the nth term can be expressed as T_n = n(n+1) or n^2 + n. These numbers are pronic or oblong numbers as well. I can also see that they align with triangular numbers multiplied by 2. Ultimately, I think the formula is simply a_n = n(n+1).","type":"summary_text"},{"text":"**Defining the pronic numbers**\n\nSo starting from n≥1, I see that the nth term can be expressed as T_n = n(n+1), indicating that these numbers are pronic numbers, which are products of two consecutive integers. The differences between the terms increase by 2 each time (4, 6, 8, 10), confirming it's a quadratic pattern. I’ve derived that a_n = n^2 + n fits this sequence. If starting at n=0, the formula adjusts to a_n = (n+1)(n+2). So, a_n = n(n+1) is a straightforward answer!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The terms are \n 2 = 1·2 \n 6 = 2·3 \n12 = 3·4 \n20 = 4·5 \n30 = 5·6 \n\nIn other words, the n-th term is the product of two consecutive integers. Equivalently\n\n aₙ = n(n + 1) = n² + n,  for n = 1, 2, 3, …","type":"output_text"}],"id":"msg_057e67b8f6bc5bec0069d6c293acec819691d5b3c799159dad","role":"assistant","status":"completed","type":"message"},{"content":"Using the pattern you discovered, what would be the 10th term? And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:41533","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":512,"completion_tokens":715,"end":1775682207.6709602,"prompt_tokens":173,"start":1775682196.4083474,"tokens":888},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_057e67b8f6bc5bec0069d6c294db08819690fffb2a590f15de","summary":[{"text":"**Calculating sum formulas**\n\nThe user has a formula \\( a_n = n(n+1) \\) and is looking to find \\( a_{10} \\), which is \\( 10 \\times 11 = 110 \\). Next, I need to compute \\( S_{10} = \\sum_{n=1}^{10} n(n+1) \\). This simplifies to finding the sums of \\( n^2 \\) and \\( n \\). \n\nAfter calculating, I find that \\( S_{10} = 440 \\). The derived general formula for this sum is \\( \\sum_{k=1}^{n} k(k+1) = \\frac{n(n+1)(n+2)}{3} \\). So, the answers are \\( a_{10} = 110 \\) and the sum equals 440.","type":"summary_text"},{"text":"**Summarizing values and formulas**\n\nI can provide clear answers now: the term \\( a_{10} = 10 \\times 11 = 110 \\). Next, to find the sum \\( S_{10} = \\sum_{n=1}^{10} n(n+1) \\), I can use the formula \\( S_n = \\frac{n(n+1)(n+2)}{3} \\), which gives me \\( \\frac{10 \\times 11 \\times 12}{3} = 440 \\). So, the results are \\( a_{10} = 110 \\) and \\( S_{10} = 440 \\). Let's finalize this presentation!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The 10th term is \n a₁₀ = 10·(10 + 1) = 10·11 = 110. \n\nThe sum of the first 10 terms is \n S₁₀ = ∑_{k=1}^{10} k(k+1) \n   = ∑k² + ∑k \n   = (10·11·21)/6 + (10·11)/2 \n   = 385 + 55 \n   = 440. \n\nEquivalently one can use the closed‐form \n Sₙ = n(n+1)(n+2)/3 \nso \n S₁₀ = 10·11·12/3 = 440.","type":"output_text"}],"id":"msg_057e67b8f6bc5bec0069d6c29e93348196ad85685abd20a1a5","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"749bf197b1337ee8f4a939cd996b0c97","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"9aeb6a50a78a09dd","span_parents":["195f657305d2bb7a"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","description":"A unique identifier for the project logs event. 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usernames, or any other identifying details.\n- Do NOT include any proper nouns (company names, product names, people's names, place names, etc.).\n- Replace specific identifiers with generic descriptions (e.g., \"a technology company\" instead of company names).\n\nANALYSIS GUIDELINES:\n- Be descriptive and assume neither good nor bad faith.\n- Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity around potentially harmful conversations is necessary for effective monitoring.\n- If you see truncation markers like \"[...]\" or \"[middle truncated]\", assume the text was clipped for length. 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If multiple requests, describe the main one.\n\nExamples:\n- \"User wants to debug why their API calls are returning errors\"\n- \"User wants to create a new LLM-based evaluation scorer\"\n- \"User wants to understand how to interpret experiment results\"\n- \"User wants to optimize their prompt for better accuracy\"\n\nIf no clear request is present, respond: \"NONE\"\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}],[{"content":"Classify the user's overall sentiment toward the interaction. Do not describe the use case, only the user's sentiment.\n\nConsider:\n- Negative: frustration, confusion, dissatisfaction, impatience, giving up\n- Positive: satisfaction, gratitude, praise\n- Neutral: factual, no clear emotional valence\n- Mixed: both positive and negative signals\n\nUse these exact labels (title case):\n- Negative\n- Neutral\n- Positive\n- Mixed\n\nAfter the label, add a brief reason in 1 sentence.\n\nExamples:\n- \"Negative. User complained the instructions still don't work.\"\n- \"Positive. User thanked the assistant for solving the issue.\"\n- \"Neutral. User asked for a definition without emotional language.\"\n- \"Mixed. User was frustrated earlier but later expressed thanks.\"\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}],[{"content":"Review this AI assistant conversation for clear problems.\n\nMost conversations are fine. Only flag when you are fully certain an issue occurred.\n\nOutput format:\n- \"NONE\" if conversation is normal\n- \". <1-2 sentence explanation of what specifically went wrong>\" if absolutely certain\n\nUse these exact category labels (title case): No response, Confused, Lazy, Incomplete, Leaked reasoning.\n\nImportant: Every non-NONE output must include a specific explanation. Do not output only \"No response\" - explain why (e.g., \"No response. User asked about quarterly revenue but assistant only ran a query and never provided the answer\").\n\nNo response\nOnly flag if all of these are true:\n1. User asked a question or requested something\n2. Assistant used tools to gather information\n3. The final message is a tool result with no assistant reply after it\n4. The user is clearly left without an answer\n\nDo not flag No response if:\n- There's any assistant message after the tool result\n- The conversation is still in progress\n- User's last message was just providing context (not asking)\n- Assistant asked a clarifying question\n- There are tool failures but the assistant still provides a usable fallback or final answer\n\nOther categories:\n- Confused: answered wrong question entirely\n- Lazy: refused without trying\n- Incomplete: response cut off mid-sentence\n- Leaked reasoning: assistant shares its internal thought process in tags or similar\n\nTruncation guidance:\nThis conversation log may contain truncated content marked with \"[... truncated ...]\", \"[middle truncated]\", \"...\", or similar. Tool call outputs are often shortened for display.\n\nDo not flag issues caused by truncation:\n- If you can't see full tool output, assume it was complete\n- If assistant references data you can't see, it was likely in the truncated portion\n- Missing context due to truncation is NOT a problem\n\nNormal (always NONE):\n- Single/intermittent tool errors where the assistant still provides a usable answer or fallback\n- Verbose/detailed responses\n- Multiple attempts\n- Clarifying questions\n- Truncation markers\n- Assistant referencing data that may have been truncated\n\nWhen in doubt, say NONE.\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}]]},"metrics":{"completion_tokens":912,"end":1777600837.762,"prompt_tokens":1907,"retries":0,"start":1777600832.314,"time_to_first_token":5.44599986076355,"tokens":2819},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"finish_reason":"stop","index":0,"logprobs":null,"message":{"annotations":null,"audio":null,"content":"\nOkay, let me tackle this query. The user asked, \"What is the capital of France?\" and the assistant answered, \"The capital of France is Paris.\" \n\nFirst, I need to determine the user's overall request or goal. The question is straightforward, asking for factual information about the capital of a country. The user isn't asking for a process, a solution to a problem, or any complex analysis. They just want to know the answer to a specific question.\n\nLooking at the examples provided, the user wants to debug an API, create a scorer, understand results, or optimize a prompt. 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Do not describe the use case, only the user's sentiment.\n\nConsider:\n- Negative: frustration, confusion, dissatisfaction, impatience, giving up\n- Positive: satisfaction, gratitude, praise\n- Neutral: factual, no clear emotional valence\n- Mixed: both positive and negative signals\n\nUse these exact labels (title case):\n- Negative\n- Neutral\n- Positive\n- Mixed\n\nAfter the label, add a brief reason in 1 sentence.\n\nExamples:\n- \"Negative. User complained the instructions still don't work.\"\n- \"Positive. User thanked the assistant for solving the issue.\"\n- \"Neutral. User asked for a definition without emotional language.\"\n- \"Mixed. User was frustrated earlier but later expressed thanks.\"\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}],[{"content":"Review this AI assistant conversation for clear problems.\n\nMost conversations are fine. Only flag when you are fully certain an issue occurred.\n\nOutput format:\n- \"NONE\" if conversation is normal\n- \". <1-2 sentence explanation of what specifically went wrong>\" if absolutely certain\n\nUse these exact category labels (title case): No response, Confused, Lazy, Incomplete, Leaked reasoning.\n\nImportant: Every non-NONE output must include a specific explanation. Do not output only \"No response\" - explain why (e.g., \"No response. User asked about quarterly revenue but assistant only ran a query and never provided the answer\").\n\nNo response\nOnly flag if all of these are true:\n1. User asked a question or requested something\n2. Assistant used tools to gather information\n3. The final message is a tool result with no assistant reply after it\n4. The user is clearly left without an answer\n\nDo not flag No response if:\n- There's any assistant message after the tool result\n- The conversation is still in progress\n- User's last message was just providing context (not asking)\n- Assistant asked a clarifying question\n- There are tool failures but the assistant still provides a usable fallback or final answer\n\nOther categories:\n- Confused: answered wrong question entirely\n- Lazy: refused without trying\n- Incomplete: response cut off mid-sentence\n- Leaked reasoning: assistant shares its internal thought process in tags or similar\n\nTruncation guidance:\nThis conversation log may contain truncated content marked with \"[... truncated ...]\", \"[middle truncated]\", \"...\", or similar. Tool call outputs are often shortened for display.\n\nDo not flag issues caused by truncation:\n- If you can't see full tool output, assume it was complete\n- If assistant references data you can't see, it was likely in the truncated portion\n- Missing context due to truncation is NOT a problem\n\nNormal (always NONE):\n- Single/intermittent tool errors where the assistant still provides a usable answer or fallback\n- Verbose/detailed responses\n- Multiple attempts\n- Clarifying questions\n- Truncation markers\n- Assistant referencing data that may have been truncated\n\nWhen in doubt, say NONE.\n\nProvide the privacy-preserving answer succinctly. 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Additionally, the differences suggest it's a quadratic sequence, confirming the numbers are pronic numbers, products of consecutive integers.","type":"summary_text"},{"text":"**Finding the term formula**\n\nThe formula for the sequence is a_n = n(n + 1). When starting from n=1, I see it gives the first term, 2. The differences between terms increase by 2 each time, confirming they are pronic numbers. By breaking it down further, I realize these can also be expressed as twice the triangular numbers. The difference sequence is 4, 6, 8, 10, showing it's quadratic. Ultimately, I conclude: a_n = n(n + 1) for the sequence 2, 6, 12, 20, 30.","type":"summary_text"},{"text":"**Summarizing the sequence pattern**\n\nThe pattern in this sequence is found in the successive differences, which are even numbers: 4, 6, 8, 10. This leads to the conclusion that a_n = n^2 + n, or more simply, a_n = n(n + 1). Although there's a consideration for starting at n=0, it typically begins at n=1. 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By breaking it down further, I realize these can also be expressed as twice the triangular numbers. The difference sequence is 4, 6, 8, 10, showing it's quadratic. Ultimately, I conclude: a_n = n(n + 1) for the sequence 2, 6, 12, 20, 30.","type":"summary_text"},{"text":"**Summarizing the sequence pattern**\n\nThe pattern in this sequence is found in the successive differences, which are even numbers: 4, 6, 8, 10. This leads to the conclusion that a_n = n^2 + n, or more simply, a_n = n(n + 1). Although there's a consideration for starting at n=0, it typically begins at n=1. I can also express it as twice the triangular numbers: a_n = 2T_n. 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And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:39893","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":448,"completion_tokens":696,"end":1777600790.2475226,"prompt_tokens":258,"start":1777600774.0466774,"tokens":954},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_02227da51c3fd2580069f4090796e88196977cb3ecce9d9545","summary":[{"text":"**Calculating the 10th term and the sum**\n\nThe user wants to know the 10th term of the sequence and the sum of the first 10 terms. From our earlier work, we've figured out that the 10th term is a_{10} = 10 * 11 = 110. \n\nFor the sum of the first 10 terms, we can calculate it directly, finding that the result is 440. Alternatively, we discovered a neat formula for the sum of the first n terms, which also gives 440 when n=10. So, the final answers are: 10th term = 110 and sum of first 10 terms = 440.","type":"summary_text"},{"text":"**Deriving the 10th term and sum**\n\nLet's show the derivation clearly. The formula for the nth term is a_n = n(n+1). So, for the 10th term, we calculate a_{10} = 10 * 11 = 110. \n\nFor the sum S_10 of the first 10 terms, we have S_10 = ∑n(n+1), which breaks down into ∑n^2 + ∑n = 385 + 55 = 440. Also, I can provide the general formula for the sum: ∑_{k=1}^n k(k+1) = n(n+1)(n+2)/3. For n=10, we find 10*11*12/3 = 440. 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Following another line of reasoning, I realize the differences are also increasing consistently.","type":"summary_text"},{"text":"**Defining the pronic sequence**\n\nI’ve been exploring the sequence 2, 6, 12, 20, 30 and calculating the differences between consecutive terms. I see that the differences follow the pattern d_n = 2n for n >= 2. This means each term can be represented as the sum of the previous term and that difference. Therefore, I identify the sequence as pronic numbers, which is expressed as a_n = n(n+1). Ultimately, this tells me that the nth term for n >= 1 is n(n+1).","type":"summary_text"},{"text":"**Explaining the pronic numbers sequence**\n\nI need to clarify that if we start the sequence at n=1, then the formula for the nth term is a_n = n(n+1), which represents the product of successive integers, or pronic numbers. The differences between terms increase by 2, indicating a quadratic sequence. Therefore, I conclude that the pattern is 2, 6, 12, 20, 30, with the general formula being a_n = n^2 + n. If starting at n=0, it would shift to a_n = (n+1)(n+2).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The “gaps” between terms are \n6–2=4, 12–6=6, 20–12=8, 30–20=10,… \nso the differences are 4, 6, 8, 10,… i.e. they increase by 2 each time. A sequence with linearly growing differences is quadratic. In fact one can check that\n\na₁=2, a₂=6, a₃=12 ⇒ aₙ=n²+n.\n\nEquivalently\n\naₙ=n(n+1), for n=1,2,3,…\n\nwhich indeed gives 1·2=2, 2·3=6, 3·4=12, 4·5=20, 5·6=30,…","type":"output_text"}],"id":"msg_0a904223c9c2dec80069d6c2a75bec8190a04ffb44dd56eb6f","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"d14c2bbf0f2035961050783a8f15fa6b","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"83a09024b263edff","span_parents":["56a080fc3510315a"],"tags":null},{"_async_scoring_state":null,"_pagination_key":"p07626497092236935171","_xact_id":"1000196963363376981","audit_data":[{"_xact_id":"1000196963363376981","audit_data":{"action":"upsert"},"metadata":{},"source":"api"}],"classifications":null,"comments":null,"context":null,"created":"2026-04-08T21:03:36.292Z","error":null,"expected":null,"facets":null,"id":"5eef9f4ccd957f1c","input":[{"content":"Look at this sequence: 2, 6, 12, 20, 30. What is the pattern and what would be the formula for the nth term?\n","role":"user"},{"id":"rs_0a904223c9c2dec80069d6c297e9888190b6872d7c5acd1fef","summary":[{"text":"**Identifying the sequence pattern**\n\nThe user has shared a sequence: 2, 6, 12, 20, 30, and I’m trying to find the pattern. It looks like these numbers are twice the triangular numbers, which are calculated as T_n = n(n+1)/2. So, the nth term can be expressed as a_n = n(n+1). For n starting at 1, this yields: a_1=2, a_2=6, a_3=12, and so forth. Following another line of reasoning, I realize the differences are also increasing consistently.","type":"summary_text"},{"text":"**Defining the pronic sequence**\n\nI’ve been exploring the sequence 2, 6, 12, 20, 30 and calculating the differences between consecutive terms. I see that the differences follow the pattern d_n = 2n for n >= 2. This means each term can be represented as the sum of the previous term and that difference. Therefore, I identify the sequence as pronic numbers, which is expressed as a_n = n(n+1). Ultimately, this tells me that the nth term for n >= 1 is n(n+1).","type":"summary_text"},{"text":"**Explaining the pronic numbers sequence**\n\nI need to clarify that if we start the sequence at n=1, then the formula for the nth term is a_n = n(n+1), which represents the product of successive integers, or pronic numbers. The differences between terms increase by 2, indicating a quadratic sequence. Therefore, I conclude that the pattern is 2, 6, 12, 20, 30, with the general formula being a_n = n^2 + n. If starting at n=0, it would shift to a_n = (n+1)(n+2).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The “gaps” between terms are \n6–2=4, 12–6=6, 20–12=8, 30–20=10,… \nso the differences are 4, 6, 8, 10,… i.e. they increase by 2 each time. A sequence with linearly growing differences is quadratic. In fact one can check that\n\na₁=2, a₂=6, a₃=12 ⇒ aₙ=n²+n.\n\nEquivalently\n\naₙ=n(n+1), for n=1,2,3,…\n\nwhich indeed gives 1·2=2, 2·3=6, 3·4=12, 4·5=20, 5·6=30,…","type":"output_text"}],"id":"msg_0a904223c9c2dec80069d6c2a75bec8190a04ffb44dd56eb6f","role":"assistant","status":"completed","type":"message"},{"content":"Using the pattern you discovered, what would be the 10th term? And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:41533","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":384,"completion_tokens":612,"end":1775682225.2812881,"prompt_tokens":246,"start":1775682216.2929904,"tokens":858},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_0a904223c9c2dec80069d6c2a8f4f88190a1206bee8f92d110","summary":[{"text":"**Calculating term and sum**\n\nThe user is asking for the 10th term from the sequence defined by a_n = n(n+1). To find this, I calculate a_10, which is 10*11, giving me 110. Next, for the sum of the first 10 terms, I use the formula for n^2 and n. The sum of n^2 from 1 to 10 is 385, and the sum of n is 55. Adding these together gives a total of 440. So, I conclude that the 10th term is 110 and the sum is 440.","type":"summary_text"},{"text":"**Summarizing the calculations**\n\nI want to include the formulas for clarity. The 10th term is a_10, which equals 110, and the sum of the first 10 terms is 440. There's a neat formula for this sum: the sum of a_n = sum_{n=1}^N n(n+1) = N(N+1)(N+2)/3. For N=10, it checks out as 10*11*12/3 = 440. I can mention this sum formula as part of my response. So, to summarize: the answers are 110 for the term and 440 for the sum.","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The nth term is aₙ = n(n+1). \nSo for n=10: \na₁₀ = 10·11 = 110. \n\nThe sum of the first 10 terms is \nS₁₀ = ∑ₙ₌₁¹⁰ n(n+1) = ∑n² + ∑n \n = (10·11·21)/6 + (10·11)/2 \n = 385 + 55 \n = 440. \n\nEquivalently you can use the closed‐form \n∑ₙ₌₁ᴺ n(n+1) = N(N+1)(N+2)/3, \nso for N=10: 10·11·12/3 = 440.","type":"output_text"}],"id":"msg_0a904223c9c2dec80069d6c2b05f8881909bd0fb55c2a56fd6","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"d14c2bbf0f2035961050783a8f15fa6b","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"5eef9f4ccd957f1c","span_parents":["56a080fc3510315a"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","description":"A unique identifier for the project logs event. 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What is the pattern and what would be the formula for the nth term?\n","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:39893","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":1408,"completion_tokens":1611,"end":1777600828.3396459,"prompt_tokens":41,"start":1777600808.907258,"tokens":1652},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_058f35ee350a999b0069f40929ac388193b9568cdf70b02b54","summary":[{"text":"**Analyzing a number sequence**\n\nThe user presented me with a sequence: 2, 6, 12, 20, 30. I'm trying to find the pattern and a formula for the nth term. It looks like these numbers correspond to triangular numbers multiplied by 2. In fact, they follow the formula: n(n + 1) which simplifies to n^2 + n. Just to clarify, triangular numbers follow the formula T_n = n(n + 1)/2. Based on this, I see that the given sequence is essentially twice each triangular number.","type":"summary_text"},{"text":"**Clarifying the sequence pattern**\n\nI'm breaking down the sequence 2, 6, 12, 20, 30. I see now that it equals 2 times the triangular numbers: 2 = 2×1, 6 = 2×3, etc. The formula for the nth term is a_n = n(n + 1), and it can also be described by observing the differences between terms: 4, 6, 8, 10, which increase by 2 each time. This consistency indicates it's a quadratic sequence. The final conclusion is that the pattern represents rectangular numbers, confirming the formula is a_n = n(n + 1).","type":"summary_text"},{"text":"**Solving for the sequence**\n\nI’m working on the sequence and setting up equations based on the relationships I see. I've established that b can be expressed in terms of a and derived equations that simplify down to a = 1 and b = 1, with c equaling 0. So, I've determined the nth term formula is a_n = n(n + 1), which reveals the pattern of pronic numbers, being the product of two consecutive integers, specifically: 2, 6, 12, 20, 30... Just to clarify, the final formula using indexing from 1 is a_n = n(n + 1).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The differences between successive terms are\n\n 6–2 = 4, \n12–6 = 6, \n20–12 = 8, \n30–20 = 10, \n\nso the “step‐sizes” are the even numbers 4,6,8,10,… (i.e. they increase by 2 each time). Any sequence whose second difference is constant is a quadratic, and in fact one checks that\n\n 2 = 1·2, \n 6 = 2·3, \n12 = 3·4, \n20 = 4·5, \n30 = 5·6,\n\nso the nth term is the product of two consecutive integers. If we start counting at n=1, the formula is\n\n aₙ = n (n + 1) = n² + n.","type":"output_text"}],"id":"msg_058f35ee350a999b0069f4093af15481939783e600a43f5133","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"1180ea2e316c253b507c3168d0ed1314","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"381a9d6e0ac4fe8d","span_parents":["f8c742474ac79bd0"],"tags":null},{"_async_scoring_state":null,"_pagination_key":"p07634737492595376129","_xact_id":"1000197089101907841","audit_data":[{"_xact_id":"1000197089101907841","audit_data":{"action":"upsert"},"metadata":{},"source":"api"}],"classifications":null,"comments":null,"context":null,"created":"2026-05-01T02:00:28.358Z","error":null,"expected":null,"facets":null,"id":"ada981f556325b0f","input":[{"content":"Look at this sequence: 2, 6, 12, 20, 30. What is the pattern and what would be the formula for the nth term?\n","role":"user"},{"id":"rs_058f35ee350a999b0069f40929ac388193b9568cdf70b02b54","summary":[{"text":"**Analyzing a number sequence**\n\nThe user presented me with a sequence: 2, 6, 12, 20, 30. I'm trying to find the pattern and a formula for the nth term. It looks like these numbers correspond to triangular numbers multiplied by 2. In fact, they follow the formula: n(n + 1) which simplifies to n^2 + n. Just to clarify, triangular numbers follow the formula T_n = n(n + 1)/2. Based on this, I see that the given sequence is essentially twice each triangular number.","type":"summary_text"},{"text":"**Clarifying the sequence pattern**\n\nI'm breaking down the sequence 2, 6, 12, 20, 30. I see now that it equals 2 times the triangular numbers: 2 = 2×1, 6 = 2×3, etc. The formula for the nth term is a_n = n(n + 1), and it can also be described by observing the differences between terms: 4, 6, 8, 10, which increase by 2 each time. This consistency indicates it's a quadratic sequence. The final conclusion is that the pattern represents rectangular numbers, confirming the formula is a_n = n(n + 1).","type":"summary_text"},{"text":"**Solving for the sequence**\n\nI’m working on the sequence and setting up equations based on the relationships I see. I've established that b can be expressed in terms of a and derived equations that simplify down to a = 1 and b = 1, with c equaling 0. So, I've determined the nth term formula is a_n = n(n + 1), which reveals the pattern of pronic numbers, being the product of two consecutive integers, specifically: 2, 6, 12, 20, 30... Just to clarify, the final formula using indexing from 1 is a_n = n(n + 1).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The differences between successive terms are\n\n 6–2 = 4, \n12–6 = 6, \n20–12 = 8, \n30–20 = 10, \n\nso the “step‐sizes” are the even numbers 4,6,8,10,… (i.e. they increase by 2 each time). Any sequence whose second difference is constant is a quadratic, and in fact one checks that\n\n 2 = 1·2, \n 6 = 2·3, \n12 = 3·4, \n20 = 4·5, \n30 = 5·6,\n\nso the nth term is the product of two consecutive integers. If we start counting at n=1, the formula is\n\n aₙ = n (n + 1) = n² + n.","type":"output_text"}],"id":"msg_058f35ee350a999b0069f4093af15481939783e600a43f5133","role":"assistant","status":"completed","type":"message"},{"content":"Using the pattern you discovered, what would be the 10th term? And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:39893","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":640,"completion_tokens":880,"end":1777600840.8415549,"prompt_tokens":255,"start":1777600828.358913,"tokens":1135},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_058f35ee350a999b0069f4093d594881939ce2b0f0cc5fa973","summary":[{"text":"**Calculating terms and sums**\n\nThe user is asking for the 10th term and the sum of the first 10 terms based on the pattern we discovered. The nth term is calculated as a_n = n(n+1), so the 10th term a_10 comes out to be 110. \n\nTo find the sum of the first 10 terms, we break it down into the sum of squares and the sum of n. The final calculations show that the sum of the first 10 terms is 440, which combines the sum of squares (385) and the sum of n (55).","type":"summary_text"},{"text":"**Providing the answers clearly**\n\nWe can respond to the user's question directly. The 10th term, calculated from the discovered pattern, is a_10 = 10 * 11, which equals 110. The sum of the first 10 terms is 440. \n\nTo provide more clarity, I can show the steps: \n\nFirst, a_10 = 10 * 11 = 110. \n\nNext, summing the terms involves calculating Σ n(n+1) for n from 1 to 10, which gives us 440. \n\nI should also mention the general sum formula for future reference, but for now, I'll include both methods clearly in my response.","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The nth term is aₙ = n(n+1). \nSo\n\n• The 10th term is \n a₁₀ = 10·11 = 110.\n\n• The sum of the first 10 terms is \n S₁₀ = ∑ₙ₌₁¹⁰ n(n+1) \n  = ∑n² + ∑n \n  = (10·11·21)/6 + (10·11)/2 \n  = 385 + 55 = 440. \n\nEquivalently, one can use the closed‐form \n ∑ₙ₌₁ᴺ n(n+1) = N(N+1)(N+2)/3, \nso for N=10: 10·11·12/3 = 440.","type":"output_text"}],"id":"msg_058f35ee350a999b0069f4094765f48193881242d64dc41f4f","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"1180ea2e316c253b507c3168d0ed1314","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"ada981f556325b0f","span_parents":["f8c742474ac79bd0"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"_async_scoring_state":{},"_pagination_key":{"description":"A stable, time-ordered key that can be used to paginate over project logs events. 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This fits since each term is the difference of squares, specifically (n+1)² - (n+1). The differences increase by 2: 4, 6, 8, 10, confirming a quadratic sequence. \n\nThe formula is straightforward: a_n = n(n+1) or a_n = n² + n, with n starting from 1. I might also mention that it relates to the sum of the first n even numbers.","type":"summary_text"},{"text":"**Finalizing the pronic number pattern**\n\nI’m ready to conclude on the pattern of pronic numbers! The sequence is formed by multiplying consecutive numbers: 1×2=2, 2×3=6, 3×4=12, and so on, leading to the nth term being n(n+1). It’s also interesting to note that the difference between successive terms increases by 2, indicating a quadratic relationship, expressed as a_n = n² + n. I can also mention that a_n = 2T_n, linking it to triangular numbers. 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And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:50306","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":384,"completion_tokens":635,"end":1778629737.6990073,"prompt_tokens":168,"start":1778629728.8127553,"tokens":803},"org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"output":[{"id":"rs_00a8a22f65f43348006a03bc617eb8819485a1a1af11973d21","summary":[{"text":"**Calculating the 10th term and sum**\n\nThe user asks for the 10th term and the sum of the first 10 terms using the discovered pattern. I calculate the 10th term as a10 = 10 * 11 = 110. For the sum, I apply the formula for the first 10 terms: sum up n(n+1), which I break into components of n^2 and n. The total gives me 440. I realize these terms are pronic numbers, not triangular. 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What is the pattern and what would be the formula for the nth term?\n","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:39893","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":1152,"completion_tokens":1286,"end":1777600799.2586358,"prompt_tokens":41,"start":1777600762.624923,"tokens":1327},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_0ba4648fbb2a32ab0069f408fb07e48195a99c6346663bc45a","summary":[{"text":"**Analyzing the sequence**\n\nThe user presents the sequence: 2, 6, 12, 20, 30, which corresponds to pronic or oblong numbers expressed as n(n+1). For example, 1*2=2, 2*3=6, 3*4=12, and so forth. The formula for the nth term is a(n) = n(n+1). I notice that the differences between consecutive terms (4, 6, 8, 10) are even numbers starting at 4, indicating a consistent increase. If n starts at 0, then a(n) = n(n+1), which results in 0 at n=0.","type":"summary_text"},{"text":"**Exploring the pattern of the sequence**\n\nStarting with n=1, the pattern mirrors n²+n, leading to the formula a(n) = n(n+1) for pronic numbers. The differences between terms (4, 6, 8, 10...) form an arithmetic sequence, increasing by 2 each time. So, I can clearly state that the nth term is the product of consecutive integers, with a simple alternative expression. When indexed from zero, the formula shifts. Ultimately, what stands out is that each term follows the relationship a_n = n(n+1).","type":"summary_text"},{"text":"**Defining the sequence pattern**\n\nThe sequence represents pronic numbers, where each term, starting from n=1, follows the formula a_n = n(n+1). These numbers are the products of consecutive integers: 2 as 1×2, 6 as 2×3, and 12 as 3×4. If I used a zero-based index, I'd say a_n = n(n+1) produces a_0=0, but since the sequence starts at n=1, the first term is 2. So, the final conclusion is that pronic numbers are defined by a_n = n(n+1).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"Each term is the product of two consecutive integers:\n\n2 = 1×2 \n6 = 2×3 \n12 = 3×4 \n20 = 4×5 \n30 = 5×6 \n\nSo if you label the terms a₁, a₂, a₃, … then\n\n aₙ = n (n+1).\n\nEquivalently, aₙ = n² + n.","type":"output_text"}],"id":"msg_0ba4648fbb2a32ab0069f40919cc608195b0fb875d88fdd280","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"5508034ed459194168972ce4558b50d8","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"4d861c3a81b982a3","span_parents":["3427c834f9d557d8"],"tags":null},{"_async_scoring_state":null,"_pagination_key":"p07634737359722708993","_xact_id":"1000197089099880365","audit_data":[{"_xact_id":"1000197089099880365","audit_data":{"action":"upsert"},"metadata":{},"source":"api"}],"classifications":null,"comments":null,"context":null,"created":"2026-05-01T01:59:59.280Z","error":null,"expected":null,"facets":null,"id":"a2fbd17b690bc3c1","input":[{"content":"Look at this sequence: 2, 6, 12, 20, 30. What is the pattern and what would be the formula for the nth term?\n","role":"user"},{"id":"rs_0ba4648fbb2a32ab0069f408fb07e48195a99c6346663bc45a","summary":[{"text":"**Analyzing the sequence**\n\nThe user presents the sequence: 2, 6, 12, 20, 30, which corresponds to pronic or oblong numbers expressed as n(n+1). For example, 1*2=2, 2*3=6, 3*4=12, and so forth. The formula for the nth term is a(n) = n(n+1). I notice that the differences between consecutive terms (4, 6, 8, 10) are even numbers starting at 4, indicating a consistent increase. If n starts at 0, then a(n) = n(n+1), which results in 0 at n=0.","type":"summary_text"},{"text":"**Exploring the pattern of the sequence**\n\nStarting with n=1, the pattern mirrors n²+n, leading to the formula a(n) = n(n+1) for pronic numbers. The differences between terms (4, 6, 8, 10...) form an arithmetic sequence, increasing by 2 each time. So, I can clearly state that the nth term is the product of consecutive integers, with a simple alternative expression. When indexed from zero, the formula shifts. Ultimately, what stands out is that each term follows the relationship a_n = n(n+1).","type":"summary_text"},{"text":"**Defining the sequence pattern**\n\nThe sequence represents pronic numbers, where each term, starting from n=1, follows the formula a_n = n(n+1). These numbers are the products of consecutive integers: 2 as 1×2, 6 as 2×3, and 12 as 3×4. If I used a zero-based index, I'd say a_n = n(n+1) produces a_0=0, but since the sequence starts at n=1, the first term is 2. So, the final conclusion is that pronic numbers are defined by a_n = n(n+1).","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"Each term is the product of two consecutive integers:\n\n2 = 1×2 \n6 = 2×3 \n12 = 3×4 \n20 = 4×5 \n30 = 5×6 \n\nSo if you label the terms a₁, a₂, a₃, … then\n\n aₙ = n (n+1).\n\nEquivalently, aₙ = n² + n.","type":"output_text"}],"id":"msg_0ba4648fbb2a32ab0069f40919cc608195b0fb875d88fdd280","role":"assistant","status":"completed","type":"message"},{"content":"Using the pattern you discovered, what would be the 10th term? And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:39893","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":768,"completion_tokens":951,"end":1777600808.882529,"prompt_tokens":168,"start":1777600799.2805033,"tokens":1119},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_0ba4648fbb2a32ab0069f4091f9f188195a624ad60550d7b6e","summary":[{"text":"**Calculating sequence terms and sums**\n\nThe user is asking for the 10th term and the sum of the first 10 terms of the sequence defined as a_n = n(n+1). I find that the 10th term is 110. For the sum of the first 10 terms, I calculate it as S = sum of n^2 + sum of n, which gives me 440. So, the 10th term is 110, and the sum of the first 10 terms is 440.","type":"summary_text"},{"text":"**Calculating the 10th term and sum of terms**\n\nI’m providing the 10th term and the sum of the first 10 terms of the sequence defined as a_n = n(n+1). The 10th term is a_10 = 10 × 11 = 110. For the sum of the first 10 terms, I calculate it as S = ∑n(n+1) = 440, using either the direct sums (385 + 55) or the closed form formula. So the answers are: the 10th term is 110 and the sum of the first 10 terms is 440.","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The 10th term is \n a₁₀ = 10·(10+1) = 10·11 = 110. \n\nThe sum of the first 10 terms is \n S₁₀ = ∑ₙ₌₁¹⁰ n(n+1) = ∑ₙ₌₁¹⁰ n² + ∑ₙ₌₁¹⁰ n \n = (10·11·21)/6 + (10·11)/2 \n = 385 + 55 \n = 440. \n\nEquivalently, one can use the closed‐form \n Sₙ = n(n+1)(n+2)/3 \nso \n S₁₀ = 10·11·12/3 = 440.","type":"output_text"}],"id":"msg_0ba4648fbb2a32ab0069f40927cd7881958c2700550d4a1619","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"5508034ed459194168972ce4558b50d8","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"a2fbd17b690bc3c1","span_parents":["3427c834f9d557d8"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"_async_scoring_state":{},"_pagination_key":{"description":"A stable, time-ordered key that can be used to paginate over project logs events. 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What is the pattern and what would be the formula for the nth term?\n","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:41533","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":1536,"completion_tokens":1809,"end":1775682213.1888056,"prompt_tokens":41,"start":1775682196.5013328,"tokens":1850},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_077f768058f266c60069d6c294fe7c81958f56dbbf226ff9a6","summary":[{"text":"**Identifying the sequence pattern**\n\nThe user has given me the sequence: 2, 6, 12, 20, 30, and they want to find the pattern. I see that it's related to the formula n(n+1). Checking this, I find that for n=1, it produces 2; for n=2, it gives 6; and so on. This indicates that the formula indeed works. Also, there's an alternative interpretation as triangular numbers multiplied by 2. The differences suggest a quadratic pattern, confirming that a_n = n(n+1) is correct.","type":"summary_text"},{"text":"**Explaining the pronic numbers**\n\nThe pattern here is about pronic numbers, which are produced by multiplying two consecutive integers. So, I can express this as a_n = n(n+1). This means the numbers 2, 6, 12, etc., are also twice the triangular numbers. I want to clarify that the differences between the terms increase by 2 each time, showing that it’s a quadratic sequence. The nth term formula remains a_n = n(n+1), typically starting from n=1. Therefore, the answer is a_n = n^2 + n for n≥1.","type":"summary_text"},{"text":"**Explaining the sequence pattern**\n\nThe user provided the sequence: 2, 6, 12, 20, 30, and asks for the pattern and nth term formula. I see that the differences increase by 2: +4, +6, +8, +10. This indicates it follows the pronic numbers, meaning the formula is a_n = n(n+1). To clarify, each term is a product of two consecutive integers. Thus, the final message should state: pattern: product of consecutive integers, formula: a_n = n(n+1). This captures the essence of the sequence!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The pattern is that the differences go up by 2 each time:\n\n 6–2=4, 12–6=6, 20–12=8, 30–20=10, …\n\nA quick way to see it’s quadratic is to note the second differences are constant (2), so \naₙ=An²+Bn+C. 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What is the pattern and what would be the formula for the nth term?\n","role":"user"},{"id":"rs_077f768058f266c60069d6c294fe7c81958f56dbbf226ff9a6","summary":[{"text":"**Identifying the sequence pattern**\n\nThe user has given me the sequence: 2, 6, 12, 20, 30, and they want to find the pattern. I see that it's related to the formula n(n+1). Checking this, I find that for n=1, it produces 2; for n=2, it gives 6; and so on. This indicates that the formula indeed works. Also, there's an alternative interpretation as triangular numbers multiplied by 2. The differences suggest a quadratic pattern, confirming that a_n = n(n+1) is correct.","type":"summary_text"},{"text":"**Explaining the pronic numbers**\n\nThe pattern here is about pronic numbers, which are produced by multiplying two consecutive integers. So, I can express this as a_n = n(n+1). This means the numbers 2, 6, 12, etc., are also twice the triangular numbers. I want to clarify that the differences between the terms increase by 2 each time, showing that it’s a quadratic sequence. The nth term formula remains a_n = n(n+1), typically starting from n=1. Therefore, the answer is a_n = n^2 + n for n≥1.","type":"summary_text"},{"text":"**Explaining the sequence pattern**\n\nThe user provided the sequence: 2, 6, 12, 20, 30, and asks for the pattern and nth term formula. I see that the differences increase by 2: +4, +6, +8, +10. This indicates it follows the pronic numbers, meaning the formula is a_n = n(n+1). To clarify, each term is a product of two consecutive integers. Thus, the final message should state: pattern: product of consecutive integers, formula: a_n = n(n+1). This captures the essence of the sequence!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The pattern is that the differences go up by 2 each time:\n\n 6–2=4, 12–6=6, 20–12=8, 30–20=10, …\n\nA quick way to see it’s quadratic is to note the second differences are constant (2), so \naₙ=An²+Bn+C. Plugging in n=1,2,3 gives A=1, B=1, C=0.\n\nEquivalently, each term is the product of two consecutive integers:\n\n aₙ = n·(n+1).\n\nSo for n=1,2,3,4,5,… you get\n\n a₁=1·2=2, a₂=2·3=6, a₃=3·4=12, a₄=4·5=20, a₅=5·6=30, …\n\nIn closed-form: \n\n aₙ = n² + n = n(n+1).","type":"output_text"}],"id":"msg_077f768058f266c60069d6c2a470988195bb350a791bfef4c7","role":"assistant","status":"completed","type":"message"},{"content":"Using the pattern you discovered, what would be the 10th term? And can you find the sum of the first 10 terms?","role":"user"}],"is_root":false,"log_id":"g","metadata":{"braintrust.parent":"project_name:java-unit-test","model":"o4-mini","provider":"openai","request_base_uri":"http://localhost:41533","request_method":"POST","request_path":"responses"},"metrics":{"completion_reasoning_tokens":320,"completion_tokens":518,"end":1775682221.8740969,"prompt_tokens":299,"start":1775682213.2112975,"tokens":817},"org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"output":[{"id":"rs_077f768058f266c60069d6c2a5f0dc819583e17b2822b688d9","summary":[{"text":"**Calculating terms and sum**\n\nI need to find the 10th term of the sequence represented by 2, 6, 12, 20, and 30, which follows the formula a_n = n(n+1). For n=10, a10 = 10 * 11 = 110. \n\nNext, I'll calculate the sum of the first 10 terms. Using the sum formula, it equals S = sum of n^2 plus sum of n. I find S = 440 for both methods, either by directly summing or using another sum formula. So, the answers are a10 = 110 and the total sum is 440.","type":"summary_text"},{"text":"**Presenting results simply**\n\nI think keeping the formatting straightforward is a good idea. The 10th term is a10 = 110, and the sum of the first 10 terms is 440. This way, the information is clear and easy to understand. I feel satisfied with these results, and I’ll go ahead and deliver this information without any complications. Simplicity often works best, after all!","type":"summary_text"}],"type":"reasoning"},{"content":[{"annotations":[],"logprobs":[],"text":"The 10th term is \na₁₀ = 10·(10 + 1) = 10·11 = 110. \n\nThe sum of the first 10 terms is \nS₁₀ = ∑ₙ₌₁¹⁰ n(n+1) = ∑n² + ∑n \n = [10·11·21⁄6] + [10·11⁄2] \n = 385 + 55 \n = 440. \n\nEquivalently, there’s a closed‐form \nS₁₀ = 10·11·12⁄3 = 440.","type":"output_text"}],"id":"msg_077f768058f266c60069d6c2ad2548819584f260de71659b37","role":"assistant","status":"completed","type":"message"}],"project_id":"6ae68365-7620-4630-921b-bac416634fc8","root_span_id":"2ad9340b42b9bce55fc49d6ebe753570","scores":null,"span_attributes":{"name":"responses","type":"llm"},"span_id":"215c78554f2e9a53","span_parents":["f535f55a83b84359"],"tags":null}],"schema":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","description":"A unique identifier for the project logs event. 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If multiple requests, describe the main one.\n\nExamples:\n- \"User wants to debug why their API calls are returning errors\"\n- \"User wants to create a new LLM-based evaluation scorer\"\n- \"User wants to understand how to interpret experiment results\"\n- \"User wants to optimize their prompt for better accuracy\"\n\nIf no clear request is present, respond: \"NONE\"\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}],[{"content":"Classify the user's overall sentiment toward the interaction. Do not describe the use case, only the user's sentiment.\n\nConsider:\n- Negative: frustration, confusion, dissatisfaction, impatience, giving up\n- Positive: satisfaction, gratitude, praise\n- Neutral: factual, no clear emotional valence\n- Mixed: both positive and negative signals\n\nUse these exact labels (title case):\n- Negative\n- Neutral\n- Positive\n- Mixed\n\nAfter the label, add a brief reason in 1 sentence.\n\nExamples:\n- \"Negative. User complained the instructions still don't work.\"\n- \"Positive. User thanked the assistant for solving the issue.\"\n- \"Neutral. User asked for a definition without emotional language.\"\n- \"Mixed. User was frustrated earlier but later expressed thanks.\"\n\nProvide the privacy-preserving answer succinctly. Remember: no PII, no proper nouns.","role":"user"}],[{"content":"Review this AI assistant conversation for clear problems.\n\nMost conversations are fine. Only flag when you are fully certain an issue occurred.\n\nOutput format:\n- \"NONE\" if conversation is normal\n- \". <1-2 sentence explanation of what specifically went wrong>\" if absolutely certain\n\nUse these exact category labels (title case): No response, Confused, Lazy, Incomplete, Leaked reasoning.\n\nImportant: Every non-NONE output must include a specific explanation. Do not output only \"No response\" - explain why (e.g., \"No response. User asked about quarterly revenue but assistant only ran a query and never provided the answer\").\n\nNo response\nOnly flag if all of these are true:\n1. User asked a question or requested something\n2. Assistant used tools to gather information\n3. The final message is a tool result with no assistant reply after it\n4. The user is clearly left without an answer\n\nDo not flag No response if:\n- There's any assistant message after the tool result\n- The conversation is still in progress\n- User's last message was just providing context (not asking)\n- Assistant asked a clarifying question\n- There are tool failures but the assistant still provides a usable fallback or final answer\n\nOther categories:\n- Confused: answered wrong question entirely\n- Lazy: refused without trying\n- Incomplete: response cut off mid-sentence\n- Leaked reasoning: assistant shares its internal thought process in tags or similar\n\nTruncation guidance:\nThis conversation log may contain truncated content marked with \"[... truncated ...]\", \"[middle truncated]\", \"...\", or similar. Tool call outputs are often shortened for display.\n\nDo not flag issues caused by truncation:\n- If you can't see full tool output, assume it was complete\n- If assistant references data you can't see, it was likely in the truncated portion\n- Missing context due to truncation is NOT a problem\n\nNormal (always NONE):\n- Single/intermittent tool errors where the assistant still provides a usable answer or fallback\n- Verbose/detailed responses\n- Multiple attempts\n- Clarifying questions\n- Truncation markers\n- Assistant referencing data that may have been truncated\n\nWhen in doubt, say NONE.\n\nProvide the privacy-preserving answer succinctly. 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The user's request was straightforward, and the assistant provided a clear, complete response. Each number was counted with a pause, which matches the user's instruction. The assistant didn't use any tools here; they just gave a direct answer. \n\nLooking at the categories: No response would apply if the assistant used tools but didn't provide an answer. But in this case, the assistant didn't use any tools. The response is fully formed and answers the question. There's no truncation or incomplete sentences. The assistant didn't show any confusion or laziness. \n\nSince the conversation is complete and the assistant answered correctly without any issues, the appropriate response is NONE. There's no leaking of reasoning, and the user isn't left without an answer. Everything seems normal here.\n\n\nNONE \nThe assistant provided a complete, accurate response to the user's request, counting from 1 to 10 with clear pauses as instructed. 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Only flag when you are fully certain an issue occurred.\n\nOutput format:\n- \"NONE\" if conversation is normal\n- \". <1-2 sentence explanation of what specifically went wrong>\" if absolutely certain\n\nUse these exact category labels (title case): No response, Confused, Lazy, Incomplete, Leaked reasoning.\n\nImportant: Every non-NONE output must include a specific explanation. Do not output only \"No response\" - explain why (e.g., \"No response. User asked about quarterly revenue but assistant only ran a query and never provided the answer\").\n\nNo response\nOnly flag if all of these are true:\n1. User asked a question or requested something\n2. Assistant used tools to gather information\n3. The final message is a tool result with no assistant reply after it\n4. 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a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-c0e92a2b-4511-4d57-8e44-492c6b21d9fb.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-c0e92a2b-4511-4d57-8e44-492c6b21d9fb.json deleted file mode 100644 index f10df9c9..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-c0e92a2b-4511-4d57-8e44-492c6b21d9fb.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"f8550f07-ddf5-40aa-94e9-cf158c04ae3f","project_id":"6ae68365-7620-4630-921b-bac416634fc8","name":"unit-test-eval-tags-metadata","description":null,"created":"2026-02-04T18:34:11.241Z","repo_info":null,"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-cd3b3837-48ba-4422-82d2-97ef99ebd6ac.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-cd3b3837-48ba-4422-82d2-97ef99ebd6ac.json deleted file mode 100644 index de7f015b..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-cd3b3837-48ba-4422-82d2-97ef99ebd6ac.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"37ac3f3f-00b9-4a5f-9977-faf1349ab881","project_id":"6ae68365-7620-4630-921b-bac416634fc8","name":"unit-test-eval-origin","description":null,"created":"2026-02-04T18:34:16.190Z","repo_info":null,"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-d72642bc60d4.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-d72642bc60d4.json new file mode 100644 index 00000000..17abf86e --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-d72642bc60d4.json @@ -0,0 +1 @@ +{"id":"913d3725-e46c-4229-9f60-b2d09f65328e","project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","name":"unit-test-eval-task-error","description":null,"created":"2026-05-12T19:58:49.966Z","repo_info":{},"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"internal_metadata":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e393a00a60e2.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e393a00a60e2.json new file mode 100644 index 00000000..d84ed07c --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e393a00a60e2.json @@ -0,0 +1 @@ +{"id":"96767844-433f-4185-969d-003944ffa9b3","project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","name":"unit-test-eval","description":null,"created":"2026-05-12T19:58:37.429Z","repo_info":{},"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"internal_metadata":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e3bb2c4c2666.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e3bb2c4c2666.json new file mode 100644 index 00000000..81ff8f3b --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e3bb2c4c2666.json @@ -0,0 +1 @@ +{"id":"b846849a-e3a3-4145-beba-ab3daff9934a","project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","name":"unit-test-eval-scorer-error","description":null,"created":"2026-05-12T19:58:41.895Z","repo_info":{},"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"internal_metadata":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e6327407-0d30-4c27-9fdf-2128b5581701.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e6327407-0d30-4c27-9fdf-2128b5581701.json deleted file mode 100644 index 2ceb8cc3..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_experiment-e6327407-0d30-4c27-9fdf-2128b5581701.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"83d4ee8a-34ac-432c-96fb-14e985cc67b0","project_id":"6ae68365-7620-4630-921b-bac416634fc8","name":"unit-test-eval-task-error","description":null,"created":"2026-02-18T02:13:48.226Z","repo_info":null,"commit":null,"base_exp_id":null,"deleted_at":null,"dataset_id":null,"dataset_version":null,"parameters_id":null,"parameters_version":null,"public":false,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","metadata":null,"tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-028d85c8-054b-4d4d-ab6d-aad6244a4452.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-028d85c8-054b-4d4d-ab6d-aad6244a4452.json deleted file mode 100644 index 5dd6d39a..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-028d85c8-054b-4d4d-ab6d-aad6244a4452.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","_xact_id":"1000196533616093054","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"close-enough-judge","slug":"close-enough-judge-d31b","description":null,"created":"2026-01-22T23:33:23.674Z","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"tags":null,"metadata":null,"function_type":"scorer","function_data":{"type":"prompt"},"origin":null,"function_schema":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-05be84b9d7c3.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-05be84b9d7c3.json new file mode 100644 index 00000000..8cd7f202 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-05be84b9d7c3.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008931212","created":"2026-05-12T21:36:03.726Z","description":"TEST_HARNESS_VERSIONS:1000197156008862006,1000197156008863935","function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// returns 1.0 for exact match, 0.0 otherwise\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: output === expected ? 1.0 : 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-242e18479f4c.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-242e18479f4c.json new file mode 100644 index 00000000..8cd7f202 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-242e18479f4c.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008931212","created":"2026-05-12T21:36:03.726Z","description":"TEST_HARNESS_VERSIONS:1000197156008862006,1000197156008863935","function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// returns 1.0 for exact match, 0.0 otherwise\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: output === expected ? 1.0 : 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-396ab1e0-fdf0-49be-9272-b2c7f76148f8.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-396ab1e0-fdf0-49be-9272-b2c7f76148f8.json deleted file mode 100644 index ef248e63..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-396ab1e0-fdf0-49be-9272-b2c7f76148f8.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"_xact_id":"1000196534223020329","created":"2026-01-23T02:07:44.212Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","log_id":"p","metadata":null,"name":"typescript_exact_match","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":null,"slug":"typescriptexactmatch-9e44","tags":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-4fa523fc-d1d8-4510-927c-6b4b2ba30e14.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-4fa523fc-d1d8-4510-927c-6b4b2ba30e14.json deleted file mode 100644 index 5dd6d39a..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-4fa523fc-d1d8-4510-927c-6b4b2ba30e14.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","_xact_id":"1000196533616093054","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"close-enough-judge","slug":"close-enough-judge-d31b","description":null,"created":"2026-01-22T23:33:23.674Z","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"tags":null,"metadata":null,"function_type":"scorer","function_data":{"type":"prompt"},"origin":null,"function_schema":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5123993a-4c98-4545-82ac-a22dcf694ae3.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5123993a-4c98-4545-82ac-a22dcf694ae3.json deleted file mode 100644 index dafb3caf..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5123993a-4c98-4545-82ac-a22dcf694ae3.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"_xact_id":"1000196534222689290","created":"2026-01-23T02:07:39.293Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 0 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","log_id":"p","metadata":null,"name":"typescript_exact_match","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":null,"slug":"typescriptexactmatch-9e44","tags":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5438e527-d2b4-43a1-8383-747db5ccbf6a.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5438e527-d2b4-43a1-8383-747db5ccbf6a.json deleted file mode 100644 index 57d61426..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-5438e527-d2b4-43a1-8383-747db5ccbf6a.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"_xact_id":"1000196533616093054","created":"2026-01-22T23:33:23.674Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"slug":"close-enough-judge-d31b","tags":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-6b5aaa91-2b47-441c-9422-39446735d75a.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-6b5aaa91-2b47-441c-9422-39446735d75a.json deleted file mode 100644 index 57d61426..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-6b5aaa91-2b47-441c-9422-39446735d75a.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"_xact_id":"1000196533616093054","created":"2026-01-22T23:33:23.674Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"slug":"close-enough-judge-d31b","tags":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-91ab301a-75af-4652-9299-9378bacb06b8.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-91ab301a-75af-4652-9299-9378bacb06b8.json deleted file mode 100644 index b0a4be53..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-91ab301a-75af-4652-9299-9378bacb06b8.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534222689290","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:39.293Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 0 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a278d2f64f79.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a278d2f64f79.json new file mode 100644 index 00000000..fb988d55 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a278d2f64f79.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008999428","created":"2026-05-12T21:36:05.410Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"bda148ec-e447-4437-86fe-b337c12502b7","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":{"parser":{"type":"llm_classifier","choice":[],"use_cot":true,"choice_scores":{"NO":0,"YES":1}},"prompt":{"type":"chat","messages":[{"role":"user","content":"are expected and output a close enough match?\nexpected: {{expected}}\noutput: {{output}}\n"}]},"options":{"model":"gpt-4o-mini","params":{}}},"slug":"close-enough-judge","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a8257033751c.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a8257033751c.json new file mode 100644 index 00000000..fb988d55 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-a8257033751c.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008999428","created":"2026-05-12T21:36:05.410Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"bda148ec-e447-4437-86fe-b337c12502b7","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":{"parser":{"type":"llm_classifier","choice":[],"use_cot":true,"choice_scores":{"NO":0,"YES":1}},"prompt":{"type":"chat","messages":[{"role":"user","content":"are expected and output a close enough match?\nexpected: {{expected}}\noutput: {{output}}\n"}]},"options":{"model":"gpt-4o-mini","params":{}}},"slug":"close-enough-judge","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-ad0be5392299.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-ad0be5392299.json new file mode 100644 index 00000000..83563f16 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-ad0be5392299.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008862006","created":"2026-05-12T21:36:02.994Z","description":null,"function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// an older buggy version that always returns 0.0\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-bd3d4445-f175-4322-afc5-a53a23afc984.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-bd3d4445-f175-4322-afc5-a53a23afc984.json deleted file mode 100644 index ef248e63..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-bd3d4445-f175-4322-afc5-a53a23afc984.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"_xact_id":"1000196534223020329","created":"2026-01-23T02:07:44.212Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","log_id":"p","metadata":null,"name":"typescript_exact_match","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":null,"slug":"typescriptexactmatch-9e44","tags":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c087c5c6-563e-48a9-8196-ab4a68f43908.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c087c5c6-563e-48a9-8196-ab4a68f43908.json deleted file mode 100644 index 69c0e3c6..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c087c5c6-563e-48a9-8196-ab4a68f43908.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c556afba4971.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c556afba4971.json new file mode 100644 index 00000000..8cd7f202 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c556afba4971.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008931212","created":"2026-05-12T21:36:03.726Z","description":"TEST_HARNESS_VERSIONS:1000197156008862006,1000197156008863935","function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// returns 1.0 for exact match, 0.0 otherwise\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: output === expected ? 1.0 : 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c66af1fddece.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c66af1fddece.json new file mode 100644 index 00000000..fb988d55 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-c66af1fddece.json @@ -0,0 +1 @@ +{"objects":[{"_xact_id":"1000197156008999428","created":"2026-05-12T21:36:05.410Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"bda148ec-e447-4437-86fe-b337c12502b7","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":{"parser":{"type":"llm_classifier","choice":[],"use_cot":true,"choice_scores":{"NO":0,"YES":1}},"prompt":{"type":"chat","messages":[{"role":"user","content":"are expected and output a close enough match?\nexpected: {{expected}}\noutput: {{output}}\n"}]},"options":{"model":"gpt-4o-mini","params":{}}},"slug":"close-enough-judge","tags":["test-harness-created"]}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-cf058665-dcbe-446c-af0e-cf70e29aba8c.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-cf058665-dcbe-446c-af0e-cf70e29aba8c.json deleted file mode 100644 index 69c0e3c6..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function-cf058665-dcbe-446c-af0e-cf70e29aba8c.json +++ /dev/null @@ -1 +0,0 @@ -{"objects":[{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-5d8f9b94-4a59-46b7-8013-1bc5ceb1ddb6.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-5d8f9b94-4a59-46b7-8013-1bc5ceb1ddb6.json deleted file mode 100644 index 3e34ef8d..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-5d8f9b94-4a59-46b7-8013-1bc5ceb1ddb6.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","_xact_id":"1000196533616093054","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"close-enough-judge","slug":"close-enough-judge-d31b","description":null,"created":"2026-01-22T23:33:23.674Z","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"tags":null,"metadata":null,"function_type":"scorer","function_data":{"type":"prompt"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-75ea3c86-6040-4c71-be59-a94c2d331fc5.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-75ea3c86-6040-4c71-be59-a94c2d331fc5.json deleted file mode 100644 index 2a325916..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-75ea3c86-6040-4c71-be59-a94c2d331fc5.json +++ /dev/null @@ -1 +0,0 @@ -{"_xact_id":"1000196533616093054","created":"2026-01-22T23:33:23.674Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"slug":"close-enough-judge-d31b","tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-8e6b1644-a5b0-4fbd-99c7-1f217bdff0e8.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-8e6b1644-a5b0-4fbd-99c7-1f217bdff0e8.json deleted file mode 100644 index 3e34ef8d..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-8e6b1644-a5b0-4fbd-99c7-1f217bdff0e8.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","_xact_id":"1000196533616093054","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"close-enough-judge","slug":"close-enough-judge-d31b","description":null,"created":"2026-01-22T23:33:23.674Z","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"tags":null,"metadata":null,"function_type":"scorer","function_data":{"type":"prompt"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-f1da46d8-54f4-4d1c-b935-9771fde0eae2.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-f1da46d8-54f4-4d1c-b935-9771fde0eae2.json deleted file mode 100644 index 2a325916..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277-f1da46d8-54f4-4d1c-b935-9771fde0eae2.json +++ /dev/null @@ -1 +0,0 @@ -{"_xact_id":"1000196533616093054","created":"2026-01-22T23:33:23.674Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"5dd8a26d-3be8-4ecd-af5b-df2a6a592277","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":{"parser":{"type":"llm_classifier","use_cot":true,"choice_scores":{"":0}},"prompt":{"type":"chat","messages":[{"role":"system","content":"you are an LLM eval scorer. you will evaluate output compared to expected results and return a value between 0.0 and 1.0 (0 is worst 1 is best)"},{"role":"user","content":"how closely does my eval case output: `{{output}}` match the expected result of `{{expected}}`"}]},"options":{"model":"gpt-5-mini","params":{"use_cache":true,"temperature":0},"position":"0|hzzzzz:"}},"slug":"close-enough-judge-d31b","tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-248da837-b799-4ecf-a65b-7bf7dd97df00.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-248da837-b799-4ecf-a65b-7bf7dd97df00.json deleted file mode 100644 index 8950c4fb..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-248da837-b799-4ecf-a65b-7bf7dd97df00.json +++ /dev/null @@ -1 +0,0 @@ -{"name":"close-enough-judge","score":null,"metadata":{"rationale":"1) Interpret the expected result \"4\" as the numeric value four.\n2) Interpret the output \"four\" as the English word representing the same numeric value.\n3) Compare semantic meanings: both represent the integer 4, so semantically they match exactly.\n4) Note potential evaluation criteria: under normalization that maps words to numeric values (or accepts textual numerals), this is an exact match; under strict character-for-character matching, it would not match.\n5) Given common evaluation practice for numeric answers (allowing textual numerals), rate as a full match.","choice":"1.0"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-3387b4d6-99fa-42bf-be42-0599b09b01b7.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-3387b4d6-99fa-42bf-be42-0599b09b01b7.json deleted file mode 100644 index db1b0554..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-3387b4d6-99fa-42bf-be42-0599b09b01b7.json +++ /dev/null @@ -1 +0,0 @@ -{"name":"close-enough-judge","score":null,"metadata":{"rationale":"1) Identify inputs: actual output = \"four\" (word form), expected = \"4\" (digit form).\n2) Determine equivalence: Both represent the same integer value (4). This is semantic equivalence.\n3) Consider typical evaluation rules: If scorer compares normalized numeric values or uses semantic matching, they are equivalent -> full match. If scorer uses strict string equality without normalization, they differ.\n4) Because the question asks \"how closely\" they match, and semantic equivalence is usually what's intended in evals, the appropriate score is maximum (1.0).","choice":"1.0"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-4adbe73e-d342-4368-9aad-2b269068d505.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-4adbe73e-d342-4368-9aad-2b269068d505.json deleted file mode 100644 index ea17f5ae..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-4adbe73e-d342-4368-9aad-2b269068d505.json +++ /dev/null @@ -1 +0,0 @@ -{"name":"close-enough-judge","score":null,"metadata":{"rationale":"1) Identify expected result: 'hello world'. 2) Identify actual output: 'hello world'. 3) Compare length: both have 11 characters including the space. 4) Compare characters position-by-position: 'h'=='h', 'e'=='e', 'l'=='l', 'l'=='l', 'o'=='o', ' '==' ', 'w'=='w', 'o'=='o', 'r'=='r', 'l'=='l', 'd'=='d'. 5) Compare case sensitivity: both lowercase. 6) Compare whitespace and punctuation: both have a single space between words, no trailing or leading whitespace shown. 7) Conclusion: exact match with no differences observed.","choice":"1.0"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-f17e8d2e-c4cf-43d4-88f4-eab4e2b6c940.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-f17e8d2e-c4cf-43d4-88f4-eab4e2b6c940.json deleted file mode 100644 index 210eebeb..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_5dd8a26d-3be8-4ecd-af5b-df2a6a592277_invoke-f17e8d2e-c4cf-43d4-88f4-eab4e2b6c940.json +++ /dev/null @@ -1 +0,0 @@ -{"name":"close-enough-judge","score":null,"metadata":{"rationale":"1) Normalize both outputs for exact comparison: compare characters including case, spacing, and punctuation. 2) Input output is 'hello world'. Expected is 'hello world'. 3) Characters match exactly: same letters, same lowercase, single space between words, no punctuation differences. 4) No extra whitespace or hidden characters visible in the provided strings. 5) Therefore the outputs are identical.","choice":"1.0"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-56535a50f191.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-56535a50f191.json new file mode 100644 index 00000000..509a544f --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-56535a50f191.json @@ -0,0 +1 @@ +{"_xact_id":"1000197156008862006","created":"2026-05-12T21:36:02.994Z","description":null,"function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// an older buggy version that always returns 0.0\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-bec2f51e7d7c.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-bec2f51e7d7c.json new file mode 100644 index 00000000..d6d317b9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-bec2f51e7d7c.json @@ -0,0 +1 @@ +{"_xact_id":"1000197156008931212","created":"2026-05-12T21:36:03.726Z","description":"TEST_HARNESS_VERSIONS:1000197156008862006,1000197156008863935","function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// returns 1.0 for exact match, 0.0 otherwise\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: output === expected ? 1.0 : 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-e2eccc10be35.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-e2eccc10be35.json new file mode 100644 index 00000000..d6d317b9 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28-e2eccc10be35.json @@ -0,0 +1 @@ +{"_xact_id":"1000197156008931212","created":"2026-05-12T21:36:03.726Z","description":"TEST_HARNESS_VERSIONS:1000197156008862006,1000197156008863935","function_data":{"data":{"code":"import type { Trace } from 'braintrust';\n// returns 1.0 for exact match, 0.0 otherwise\nasync function handler({\n input,\n output,\n expected,\n metadata,\n trace,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n trace: Trace;\n}): Promise<\n | number\n | { score: number; name?: string; metadata?: Record }\n | null\n> {\n if (expected === null) return null;\n\n return {\n name: \"typescript exact match\",\n score: output === expected ? 1.0 : 0.0\n };\n}\n","type":"inline","runtime_context":{"runtime":"node","version":"20"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"7a164cb2-6c74-4de7-aa86-6b28c464ab28","log_id":"p","metadata":null,"name":"typescript-exact-match","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":null,"slug":"typescript-exact-match","tags":["test-harness-created"]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-b82427bb5da7.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-b82427bb5da7.json new file mode 100644 index 00000000..b4fdd697 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-b82427bb5da7.json @@ -0,0 +1 @@ +{"name":"typescript exact match","score":1} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d439049c257d.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d439049c257d.json new file mode 100644 index 00000000..81609d0a --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d439049c257d.json @@ -0,0 +1 @@ +{"name":"typescript exact match","score":0} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d61d8d83b042.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d61d8d83b042.json new file mode 100644 index 00000000..81609d0a --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_7a164cb2-6c74-4de7-aa86-6b28c464ab28_invoke-d61d8d83b042.json @@ -0,0 +1 @@ +{"name":"typescript exact match","score":0} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-18e2c3e2cb2d.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-18e2c3e2cb2d.json new file mode 100644 index 00000000..27df74a2 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-18e2c3e2cb2d.json @@ -0,0 +1 @@ +{"_xact_id":"1000197156008999428","created":"2026-05-12T21:36:05.410Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"bda148ec-e447-4437-86fe-b337c12502b7","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":{"parser":{"type":"llm_classifier","choice":[],"use_cot":true,"choice_scores":{"NO":0,"YES":1}},"prompt":{"type":"chat","messages":[{"role":"user","content":"are expected and output a close enough match?\nexpected: {{expected}}\noutput: {{output}}\n"}]},"options":{"model":"gpt-4o-mini","params":{}}},"slug":"close-enough-judge","tags":["test-harness-created"]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-71d8f3a93ca2.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-71d8f3a93ca2.json new file mode 100644 index 00000000..27df74a2 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7-71d8f3a93ca2.json @@ -0,0 +1 @@ +{"_xact_id":"1000197156008999428","created":"2026-05-12T21:36:05.410Z","description":null,"function_data":{"type":"prompt"},"function_schema":null,"function_type":"scorer","id":"bda148ec-e447-4437-86fe-b337c12502b7","log_id":"p","metadata":null,"name":"close-enough-judge","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","origin":null,"project_id":"f1e858a4-58e3-408f-983f-016760d7fa25","prompt_data":{"parser":{"type":"llm_classifier","choice":[],"use_cot":true,"choice_scores":{"NO":0,"YES":1}},"prompt":{"type":"chat","messages":[{"role":"user","content":"are expected and output a close enough match?\nexpected: {{expected}}\noutput: {{output}}\n"}]},"options":{"model":"gpt-4o-mini","params":{}}},"slug":"close-enough-judge","tags":["test-harness-created"]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-6036fec54d18.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-6036fec54d18.json new file mode 100644 index 00000000..c47a4e75 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-6036fec54d18.json @@ -0,0 +1 @@ +{"name":"close-enough-judge","score":0,"metadata":{"rationale":"To determine if 'expected' and 'output' are a close enough match, we need to compare the two values. The expected value is a number (4), while the output is a word representing that number ('four'). While they refer to the same quantity, they are represented in different forms—numerical vs. textual. In most contexts, this would not be considered a close enough match because they are not identical in format, even though their meanings are the same. Therefore, I conclude that they do not match closely enough.","choice":"NO"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-cba86a3109a7.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-cba86a3109a7.json new file mode 100644 index 00000000..eaf1aa8f --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_bda148ec-e447-4437-86fe-b337c12502b7_invoke-cba86a3109a7.json @@ -0,0 +1 @@ +{"name":"close-enough-judge","score":1,"metadata":{"rationale":"The expected output is 'hello world' and the actual output is also 'hello world'. Since both strings are identical, they match perfectly. Therefore, they are a close enough match.","choice":"YES"}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-0560a728-b9ca-47db-982a-139b73668d45.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-0560a728-b9ca-47db-982a-139b73668d45.json deleted file mode 100644 index 5e488e8f..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-0560a728-b9ca-47db-982a-139b73668d45.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-30cca87d-dff0-433a-85dd-a1a5efde41ce.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-30cca87d-dff0-433a-85dd-a1a5efde41ce.json deleted file mode 100644 index 5e488e8f..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-30cca87d-dff0-433a-85dd-a1a5efde41ce.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-495bc119-c2fa-4cae-b232-2e01fbd113bb.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-495bc119-c2fa-4cae-b232-2e01fbd113bb.json deleted file mode 100644 index b89d5acb..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-495bc119-c2fa-4cae-b232-2e01fbd113bb.json +++ /dev/null @@ -1 +0,0 @@ -{"_xact_id":"1000196534222689290","created":"2026-01-23T02:07:39.293Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 0 : 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a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-70e8cefe-455c-473b-b3bc-03378456f899.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-bff98de5-1dbc-48b2-ad9a-1985e8dc6107.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-bff98de5-1dbc-48b2-ad9a-1985e8dc6107.json deleted file mode 100644 index 5e488e8f..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-bff98de5-1dbc-48b2-ad9a-1985e8dc6107.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","_xact_id":"1000196534223020329","project_id":"6ae68365-7620-4630-921b-bac416634fc8","log_id":"p","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"typescript_exact_match","slug":"typescriptexactmatch-9e44","description":null,"created":"2026-01-23T02:07:44.212Z","prompt_data":null,"tags":null,"metadata":null,"function_type":"scorer","function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"origin":null,"function_schema":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-cf0594ff-d735-4132-8018-f724e9b9e198.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-cf0594ff-d735-4132-8018-f724e9b9e198.json deleted file mode 100644 index 57dfa53d..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-cf0594ff-d735-4132-8018-f724e9b9e198.json +++ /dev/null @@ -1 +0,0 @@ -{"_xact_id":"1000196534223020329","created":"2026-01-23T02:07:44.212Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return 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a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f-e87036b7-4e64-4cfa-8536-60e71b04d5f8.json +++ /dev/null @@ -1 +0,0 @@ -{"_xact_id":"1000196534223020329","created":"2026-01-23T02:07:44.212Z","description":null,"function_data":{"data":{"code":"// Enter handler function that returns a numeric score between 0 and 1,\n// or null to skip scoring\nfunction handler({\n input,\n output,\n expected,\n metadata,\n}: {\n input: any;\n output: any;\n expected: any;\n metadata: Record;\n}): number | null {\n if (expected === null) return null;\n return output === expected ? 1 : 0;\n}","type":"inline","runtime_context":{"runtime":"node","version":"22"}},"type":"code"},"function_schema":null,"function_type":"scorer","id":"efa5f9c3-6ece-4726-a9d6-4ba792980b3f","log_id":"p","metadata":null,"name":"typescript_exact_match","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","origin":null,"project_id":"6ae68365-7620-4630-921b-bac416634fc8","prompt_data":null,"slug":"typescriptexactmatch-9e44","tags":null} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-49895de4-03c7-4fdc-850e-b49d6e3f8a72.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-49895de4-03c7-4fdc-850e-b49d6e3f8a72.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-49895de4-03c7-4fdc-850e-b49d6e3f8a72.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-56eb6751-958b-43ef-a244-a36c39bee0c6.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-56eb6751-958b-43ef-a244-a36c39bee0c6.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-56eb6751-958b-43ef-a244-a36c39bee0c6.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-68305887-be5b-43d1-836e-f6c3db244ea7.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-68305887-be5b-43d1-836e-f6c3db244ea7.json deleted file mode 100644 index 56a6051c..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-68305887-be5b-43d1-836e-f6c3db244ea7.json +++ /dev/null @@ -1 +0,0 @@ -1 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-7a58d627-a77c-404b-81ab-73be0d11b4ee.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-7a58d627-a77c-404b-81ab-73be0d11b4ee.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-7a58d627-a77c-404b-81ab-73be0d11b4ee.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-b80137f6-7734-4f0c-ba03-d71c58fcf51d.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-b80137f6-7734-4f0c-ba03-d71c58fcf51d.json deleted file mode 100644 index 56a6051c..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-b80137f6-7734-4f0c-ba03-d71c58fcf51d.json +++ /dev/null @@ -1 +0,0 @@ -1 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-cc7074b5-36dc-4b63-b3b0-50ff9f6e2720.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-cc7074b5-36dc-4b63-b3b0-50ff9f6e2720.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-cc7074b5-36dc-4b63-b3b0-50ff9f6e2720.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-e1901534-8101-43ec-8546-187cfe56c8f2.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-e1901534-8101-43ec-8546-187cfe56c8f2.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-e1901534-8101-43ec-8546-187cfe56c8f2.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-fda39273-bf67-40f5-a605-b33ac6148689.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-fda39273-bf67-40f5-a605-b33ac6148689.json deleted file mode 100644 index c2270834..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_function_efa5f9c3-6ece-4726-a9d6-4ba792980b3f_invoke-fda39273-bf67-40f5-a605-b33ac6148689.json +++ /dev/null @@ -1 +0,0 @@ -0 \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-06dcd7a9-d382-443f-9d9a-de0f796a321f.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-06dcd7a9-d382-443f-9d9a-de0f796a321f.json deleted file mode 100644 index f2e7801e..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-06dcd7a9-d382-443f-9d9a-de0f796a321f.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"6ae68365-7620-4630-921b-bac416634fc8","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"java-unit-test","description":null,"created":"2026-01-21T01:32:52.137Z","deleted_at":null,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","settings":{"remote_eval_sources":[{"url":"http://localhost:8301","name":"java-devserver","description":null}]}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-1cb08041-9f04-4583-8f36-d7a32de3646c.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-1cb08041-9f04-4583-8f36-d7a32de3646c.json deleted file mode 100644 index f2e7801e..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-1cb08041-9f04-4583-8f36-d7a32de3646c.json +++ /dev/null @@ -1 +0,0 @@ -{"id":"6ae68365-7620-4630-921b-bac416634fc8","org_id":"5d7c97d7-fef1-4cb7-bda6-7e3756a0ca8e","name":"java-unit-test","description":null,"created":"2026-01-21T01:32:52.137Z","deleted_at":null,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","settings":{"remote_eval_sources":[{"url":"http://localhost:8301","name":"java-devserver","description":null}]}} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-25b4865170f2.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-25b4865170f2.json new file mode 100644 index 00000000..89452950 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-25b4865170f2.json @@ -0,0 +1 @@ +{"objects":[{"id":"f1e858a4-58e3-408f-983f-016760d7fa25","org_id":"5abfae3a-7aa7-4653-a9c8-b3efcb18f584","name":"java-unit-test","description":null,"created":"2026-05-07T16:55:48.127Z","deleted_at":null,"user_id":"a5ca7f9c-bf20-40c4-a82b-5c992f6a38f5","settings":{"remote_eval_sources":[{"url":"http://localhost:8301","name":"localjava","description":null}]}}]} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-2741f59ee0c6.json b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-2741f59ee0c6.json new file mode 100644 index 00000000..89452950 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/braintrust/__files/v1_project-2741f59ee0c6.json @@ -0,0 +1 @@ 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"resp_077f768058f266c60069d6c2a553f881959cf55b77225bae66", - "object": "response", - "created_at": 1775682213, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1775682221, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "output": [ - { - "id": "rs_077f768058f266c60069d6c2a5f0dc819583e17b2822b688d9", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Calculating terms and sum**\n\nI need to find the 10th term of the sequence represented by 2, 6, 12, 20, and 30, which follows the formula a_n = n(n+1). For n=10, a10 = 10 * 11 = 110. \n\nNext, I'll calculate the sum of the first 10 terms. Using the sum formula, it equals S = sum of n^2 plus sum of n. I find S = 440 for both methods, either by directly summing or using another sum formula. So, the answers are a10 = 110 and the total sum is 440." - }, - { - "type": "summary_text", - "text": "**Presenting results simply**\n\nI think keeping the formatting straightforward is a good idea. The 10th term is a10 = 110, and the sum of the first 10 terms is 440. This way, the information is clear and easy to understand. I feel satisfied with these results, and I\u2019ll go ahead and deliver this information without any complications. Simplicity often works best, after all!" - } - ] - }, - { - "id": "msg_077f768058f266c60069d6c2ad2548819584f260de71659b37", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The 10th term is \na\u2081\u2080 = 10\u00b7(10 + 1) = 10\u00b711 = 110. \n\nThe sum of the first 10 terms is \nS\u2081\u2080 = \u2211\u2099\u208c\u2081\u00b9\u2070 n(n+1) = \u2211n\u00b2 + \u2211n \n = [10\u00b711\u00b721\u20446] + [10\u00b711\u20442] \n = 385 + 55 \n = 440. \n\nEquivalently, there\u2019s a closed\u2010form \nS\u2081\u2080 = 10\u00b711\u00b712\u20443 = 440." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": null, - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 299, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 518, - "output_tokens_details": { - "reasoning_tokens": 320 - }, - "total_tokens": 817 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-086e4f73-7e85-49fa-bfb9-b055e2787b0e.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-086e4f73-7e85-49fa-bfb9-b055e2787b0e.json deleted file mode 100644 index 4b0d4277..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-086e4f73-7e85-49fa-bfb9-b055e2787b0e.json +++ /dev/null @@ -1,85 +0,0 @@ -{ - "id": "resp_057e67b8f6bc5bec0069d6c287945881968677003e16a39746", - "object": "response", - "created_at": 1775682183, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1775682195, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "output": [ - { - "id": "rs_057e67b8f6bc5bec0069d6c288348881968097f72c458b4b6a", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Identifying the sequence pattern**\n\nThe user has shared a sequence: 2, 6, 12, 20, 30. I\u2019m noticing this follows the pattern of n(n+1). Basically, for n=1 to 5, I have 1*2=2, 2*3=6, and so on. So, the nth term can be expressed as T_n = n(n+1) or n^2 + n. These numbers are pronic or oblong numbers as well. I can also see that they align with triangular numbers multiplied by 2. Ultimately, I think the formula is simply a_n = n(n+1)." - }, - { - "type": "summary_text", - "text": "**Defining the pronic numbers**\n\nSo starting from n\u22651, I see that the nth term can be expressed as T_n = n(n+1), indicating that these numbers are pronic numbers, which are products of two consecutive integers. The differences between the terms increase by 2 each time (4, 6, 8, 10), confirming it's a quadratic pattern. I\u2019ve derived that a_n = n^2 + n fits this sequence. If starting at n=0, the formula adjusts to a_n = (n+1)(n+2). So, a_n = n(n+1) is a straightforward answer!" - } - ] - }, - { - "id": "msg_057e67b8f6bc5bec0069d6c293acec819691d5b3c799159dad", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The terms are \n 2 = 1\u00b72 \n 6 = 2\u00b73 \n12 = 3\u00b74 \n20 = 4\u00b75 \n30 = 5\u00b76 \n\nIn other words, the n-th term is the product of two consecutive integers. Equivalently\n\n\u2002a\u2099 = n(n + 1) = n\u00b2 + n, \u2002for n = 1, 2, 3, \u2026" - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": null, - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 41, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 1018, - "output_tokens_details": { - "reasoning_tokens": 896 - }, - "total_tokens": 1059 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-172a4267f40e.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-172a4267f40e.json new file mode 100644 index 00000000..5c381418 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-172a4267f40e.json @@ -0,0 +1,86 @@ +{ + "id": "resp_00a8a22f65f43348006a03bc6119388194b2d60e831ae5b58d", + "object": "response", + "created_at": 1778629729, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629737, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": null, + "max_output_tokens": null, + "max_tool_calls": null, + "model": "o4-mini-2025-04-16", + "moderation": null, + "output": [ + { + "id": "rs_00a8a22f65f43348006a03bc617eb8819485a1a1af11973d21", + "type": "reasoning", + "summary": [ + { + "type": "summary_text", + "text": "**Calculating the 10th term and sum**\n\nThe user asks for the 10th term and the sum of the first 10 terms using the discovered pattern. I calculate the 10th term as a10 = 10 * 11 = 110. For the sum, I apply the formula for the first 10 terms: sum up n(n+1), which I break into components of n^2 and n. The total gives me 440. I realize these terms are pronic numbers, not triangular. So, the answers are 110 for the 10th term and 440 for the sum." + }, + { + "type": "summary_text", + "text": "**Verifying the sum of pronic numbers**\n\nI\u2019m checking the formula for the sum of pronic numbers from 1 to N, which I think is N*(N+1)*(N+2)/3. For N=10, that gives me 10*11*12/3, which equals 440. So, I confirm that the sum from 1 to 10 is indeed 440, and I find the 10th term to be 110. Therefore, my final answers are: the 10th term is 110, and the sum of the first 10 terms is 440. I'll also include the formula for the sum." + } + ] + }, + { + "id": "msg_00a8a22f65f43348006a03bc6894888194a17ed902354479c2", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "The 10th term is \na\u2081\u2080 = 10\u00b7(10 + 1) = 10\u00b711 = 110. \n\nThe sum of the first 10 terms is \n\u2211\u2099\u208c\u2081\u00b9\u2070 n(n+1) = \u2211\u2099\u208c\u2081\u00b9\u2070 (n\u00b2+n) \n= (\u2211\u2099\u208c\u2081\u00b9\u2070 n\u00b2) + (\u2211\u2099\u208c\u2081\u00b9\u2070 n) \n= (10\u00b711\u00b721)/6 + (10\u00b711)/2 \n= 385 + 55 \n= 440. \n\nAlternatively, there is a closed\u2010form for the partial sums of pronic numbers: \nS\u2099 = \u2211\u2096\u208c\u2081\u207f k(k+1) = n(n+1)(n+2)/3, \nso for n=10: 10\u00b711\u00b712/3 = 440." + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": "high", + "summary": "detailed" + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 168, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 635, + "output_tokens_details": { + "reasoning_tokens": 384 + }, + "total_tokens": 803 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json deleted file mode 100644 index 4ec884cf..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json +++ /dev/null @@ -1,89 +0,0 @@ -{ - "id": "resp_0a904223c9c2dec80069d6c297782481909fd8a34345c8c723", - "object": "response", - "created_at": 1775682199, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1775682215, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "output": [ - { - "id": "rs_0a904223c9c2dec80069d6c297e9888190b6872d7c5acd1fef", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Identifying the sequence pattern**\n\nThe user has shared a sequence: 2, 6, 12, 20, 30, and I\u2019m trying to find the pattern. It looks like these numbers are twice the triangular numbers, which are calculated as T_n = n(n+1)/2. So, the nth term can be expressed as a_n = n(n+1). For n starting at 1, this yields: a_1=2, a_2=6, a_3=12, and so forth. Following another line of reasoning, I realize the differences are also increasing consistently." - }, - { - "type": "summary_text", - "text": "**Defining the pronic sequence**\n\nI\u2019ve been exploring the sequence 2, 6, 12, 20, 30 and calculating the differences between consecutive terms. I see that the differences follow the pattern d_n = 2n for n >= 2. This means each term can be represented as the sum of the previous term and that difference. Therefore, I identify the sequence as pronic numbers, which is expressed as a_n = n(n+1). Ultimately, this tells me that the nth term for n >= 1 is n(n+1)." - }, - { - "type": "summary_text", - "text": "**Explaining the pronic numbers sequence**\n\nI need to clarify that if we start the sequence at n=1, then the formula for the nth term is a_n = n(n+1), which represents the product of successive integers, or pronic numbers. The differences between terms increase by 2, indicating a quadratic sequence. Therefore, I conclude that the pattern is 2, 6, 12, 20, 30, with the general formula being a_n = n^2 + n. If starting at n=0, it would shift to a_n = (n+1)(n+2)." - } - ] - }, - { - "id": "msg_0a904223c9c2dec80069d6c2a75bec8190a04ffb44dd56eb6f", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The \u201cgaps\u201d between terms are \n6\u20132=4, 12\u20136=6, 20\u201312=8, 30\u201320=10,\u2026 \nso the differences are 4, 6, 8, 10,\u2026 i.e. they increase by 2 each time. A sequence with linearly growing differences is quadratic. In fact one can check that\n\na\u2081=2, a\u2082=6, a\u2083=12 \u21d2 a\u2099=n\u00b2+n.\n\nEquivalently\n\na\u2099=n(n+1), for n=1,2,3,\u2026\n\nwhich indeed gives 1\u00b72=2, 2\u00b73=6, 3\u00b74=12, 4\u00b75=20, 5\u00b76=30,\u2026" - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": null, - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 41, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 1767, - "output_tokens_details": { - "reasoning_tokens": 1536 - }, - "total_tokens": 1808 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-32d9bf3d-b8c7-4aa1-bc27-c29cf0bd27e3.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-32d9bf3d-b8c7-4aa1-bc27-c29cf0bd27e3.json deleted file mode 100644 index b52a34c7..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-32d9bf3d-b8c7-4aa1-bc27-c29cf0bd27e3.json +++ /dev/null @@ -1,86 +0,0 @@ -{ - "id": "resp_0ba4648fbb2a32ab0069f4091f333c81958ac57858573caf13", - "object": "response", - "created_at": 1777600799, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600808, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_0ba4648fbb2a32ab0069f4091f9f188195a624ad60550d7b6e", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Calculating sequence terms and sums**\n\nThe user is asking for the 10th term and the sum of the first 10 terms of the sequence defined as a_n = n(n+1). I find that the 10th term is 110. For the sum of the first 10 terms, I calculate it as S = sum of n^2 + sum of n, which gives me 440. So, the 10th term is 110, and the sum of the first 10 terms is 440." - }, - { - "type": "summary_text", - "text": "**Calculating the 10th term and sum of terms**\n\nI\u2019m providing the 10th term and the sum of the first 10 terms of the sequence defined as a_n = n(n+1). The 10th term is a_10 = 10 \u00d7 11 = 110. For the sum of the first 10 terms, I calculate it as S = \u2211n(n+1) = 440, using either the direct sums (385 + 55) or the closed form formula. So the answers are: the 10th term is 110 and the sum of the first 10 terms is 440." - } - ] - }, - { - "id": "msg_0ba4648fbb2a32ab0069f40927cd7881958c2700550d4a1619", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The 10th term is \n a\u2081\u2080 = 10\u00b7(10+1) = 10\u00b711 = 110. \n\nThe sum of the first 10 terms is \n S\u2081\u2080 = \u2211\u2099\u208c\u2081\u00b9\u2070 n(n+1) = \u2211\u2099\u208c\u2081\u00b9\u2070 n\u00b2 + \u2211\u2099\u208c\u2081\u00b9\u2070 n \n = (10\u00b711\u00b721)/6 + (10\u00b711)/2 \n = 385 + 55 \n = 440. \n\nEquivalently, one can use the closed\u2010form \n S\u2099 = n(n+1)(n+2)/3 \nso \n S\u2081\u2080 = 10\u00b711\u00b712/3 = 440." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 168, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 951, - "output_tokens_details": { - "reasoning_tokens": 768 - }, - "total_tokens": 1119 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-36764bc7833a.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-36764bc7833a.json new file mode 100644 index 00000000..2af77ca8 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-36764bc7833a.json @@ -0,0 +1,77 @@ +{ + "id": "resp_0f3fa2a7edd3506b006a03bccfe5fc819697f169b8a553717b", + "object": "response", + "created_at": 1778629839, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629841, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": null, + "max_output_tokens": null, + "max_tool_calls": null, + "model": "o4-mini-2025-04-16", + "moderation": null, + "output": [ + { + "id": "rs_0f3fa2a7edd3506b006a03bcd0557c819688ff4980428fc0b5", + "type": "reasoning", + "summary": [] + }, + { + "id": "msg_0f3fa2a7edd3506b006a03bcd0fa5481968138d4c3dc3a4771", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "Paris" + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": "low", + "summary": "detailed" + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 18, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 39, + "output_tokens_details": { + "reasoning_tokens": 0 + }, + "total_tokens": 57 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-47aabefa-78d5-429e-b491-ccada4c9be78.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-47aabefa-78d5-429e-b491-ccada4c9be78.json deleted file mode 100644 index 68dd1851..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-47aabefa-78d5-429e-b491-ccada4c9be78.json +++ /dev/null @@ -1,86 +0,0 @@ -{ - "id": "resp_058f35ee350a999b0069f4093c42c08193a9ddb3c93e0c22bd", - "object": "response", - "created_at": 1777600828, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600840, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_058f35ee350a999b0069f4093d594881939ce2b0f0cc5fa973", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Calculating terms and sums**\n\nThe user is asking for the 10th term and the sum of the first 10 terms based on the pattern we discovered. The nth term is calculated as a_n = n(n+1), so the 10th term a_10 comes out to be 110. \n\nTo find the sum of the first 10 terms, we break it down into the sum of squares and the sum of n. The final calculations show that the sum of the first 10 terms is 440, which combines the sum of squares (385) and the sum of n (55)." - }, - { - "type": "summary_text", - "text": "**Providing the answers clearly**\n\nWe can respond to the user's question directly. The 10th term, calculated from the discovered pattern, is a_10 = 10 * 11, which equals 110. The sum of the first 10 terms is 440. \n\nTo provide more clarity, I can show the steps: \n\nFirst, a_10 = 10 * 11 = 110. \n\nNext, summing the terms involves calculating \u03a3 n(n+1) for n from 1 to 10, which gives us 440. \n\nI should also mention the general sum formula for future reference, but for now, I'll include both methods clearly in my response." - } - ] - }, - { - "id": "msg_058f35ee350a999b0069f4094765f48193881242d64dc41f4f", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The nth term is a\u2099 = n(n+1). \nSo\n\n\u2022 The 10th term is \n\u2003a\u2081\u2080 = 10\u00b711 = 110.\n\n\u2022 The sum of the first 10 terms is \n\u2003S\u2081\u2080 = \u2211\u2099\u208c\u2081\u00b9\u2070 n(n+1) \n\u2003\u2003= \u2211n\u00b2 + \u2211n \n\u2003\u2003= (10\u00b711\u00b721)/6 + (10\u00b711)/2 \n\u2003\u2003= 385 + 55 = 440. \n\nEquivalently, one can use the closed\u2010form \n\u2003\u2211\u2099\u208c\u2081\u1d3a n(n+1) = N(N+1)(N+2)/3, \nso for N=10: 10\u00b711\u00b712/3 = 440." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 255, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 880, - "output_tokens_details": { - "reasoning_tokens": 640 - }, - "total_tokens": 1135 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5b5fd25b-ebbc-486f-a908-37f1d23c8917.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5b5fd25b-ebbc-486f-a908-37f1d23c8917.json deleted file mode 100644 index dc378def..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5b5fd25b-ebbc-486f-a908-37f1d23c8917.json +++ /dev/null @@ -1,86 +0,0 @@ -{ - "id": "resp_02227da51c3fd2580069f409061d248196be47ff3eda056fc9", - "object": "response", - "created_at": 1777600774, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600789, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_02227da51c3fd2580069f4090796e88196977cb3ecce9d9545", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Calculating the 10th term and the sum**\n\nThe user wants to know the 10th term of the sequence and the sum of the first 10 terms. From our earlier work, we've figured out that the 10th term is a_{10} = 10 * 11 = 110. \n\nFor the sum of the first 10 terms, we can calculate it directly, finding that the result is 440. Alternatively, we discovered a neat formula for the sum of the first n terms, which also gives 440 when n=10. So, the final answers are: 10th term = 110 and sum of first 10 terms = 440." - }, - { - "type": "summary_text", - "text": "**Deriving the 10th term and sum**\n\nLet's show the derivation clearly. The formula for the nth term is a_n = n(n+1). So, for the 10th term, we calculate a_{10} = 10 * 11 = 110. \n\nFor the sum S_10 of the first 10 terms, we have S_10 = \u2211n(n+1), which breaks down into \u2211n^2 + \u2211n = 385 + 55 = 440. Also, I can provide the general formula for the sum: \u2211_{k=1}^n k(k+1) = n(n+1)(n+2)/3. For n=10, we find 10*11*12/3 = 440. So the answers are: 10th term = 110, sum of first 10 = 440." - } - ] - }, - { - "id": "msg_02227da51c3fd2580069f40914c23c8196978bc528f6c80fdc", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The nth term is \n a\u2099 = n(n + 1). \n\nSo for n = 10: \n a\u2081\u2080 = 10\u00b711 = 110. \n\nThe sum of the first 10 terms is \n S\u2081\u2080 = \u2211\u2096\u208c\u2081\u00b9\u2070 k(k + 1) \n = \u2211\u2096\u208c\u2081\u00b9\u2070 (k\u00b2 + k) \n = (\u2211\u2096\u208c\u2081\u00b9\u2070 k\u00b2) + (\u2211\u2096\u208c\u2081\u00b9\u2070 k) \n = [10\u00b711\u00b721/6] + [10\u00b711/2] \n = 385 + 55 \n = 440. \n\nEquivalently, one can use the closed\u2010form \n \u2211\u2096\u208c\u2081\u207f k(k + 1) = n(n + 1)(n + 2)/3 \nso for n=10: 10\u00b711\u00b712/3 = 440." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 258, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 696, - "output_tokens_details": { - "reasoning_tokens": 448 - }, - "total_tokens": 954 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5fe80689-c90a-48e4-9d9e-dc0bd0eeb6d1.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5fe80689-c90a-48e4-9d9e-dc0bd0eeb6d1.json deleted file mode 100644 index b3cd82f8..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-5fe80689-c90a-48e4-9d9e-dc0bd0eeb6d1.json +++ /dev/null @@ -1,77 +0,0 @@ -{ - "id": "resp_036520e2169efa740069fa613a97448195a702e60d24895835", - "object": "response", - "created_at": 1778016570, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1778016572, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_036520e2169efa740069fa613b93a48195b38f68a877524fd2", - "type": "reasoning", - "summary": [] - }, - { - "id": "msg_036520e2169efa740069fa613c7630819582cc124c4559c7e0", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "Paris" - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "low", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 18, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 38, - "output_tokens_details": { - "reasoning_tokens": 0 - }, - "total_tokens": 56 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-67d3cc54-67f9-484b-9700-f834b6645454.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-67d3cc54-67f9-484b-9700-f834b6645454.json deleted file mode 100644 index 2baae3fe..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-67d3cc54-67f9-484b-9700-f834b6645454.json +++ /dev/null @@ -1,89 +0,0 @@ -{ - "id": "resp_077f768058f266c60069d6c2949d9c8195b237a4b3bb2b14c4", - "object": "response", - "created_at": 1775682196, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1775682212, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "output": [ - { - "id": "rs_077f768058f266c60069d6c294fe7c81958f56dbbf226ff9a6", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Identifying the sequence pattern**\n\nThe user has given me the sequence: 2, 6, 12, 20, 30, and they want to find the pattern. I see that it's related to the formula n(n+1). Checking this, I find that for n=1, it produces 2; for n=2, it gives 6; and so on. This indicates that the formula indeed works. Also, there's an alternative interpretation as triangular numbers multiplied by 2. The differences suggest a quadratic pattern, confirming that a_n = n(n+1) is correct." - }, - { - "type": "summary_text", - "text": "**Explaining the pronic numbers**\n\nThe pattern here is about pronic numbers, which are produced by multiplying two consecutive integers. So, I can express this as a_n = n(n+1). This means the numbers 2, 6, 12, etc., are also twice the triangular numbers. I want to clarify that the differences between the terms increase by 2 each time, showing that it\u2019s a quadratic sequence. The nth term formula remains a_n = n(n+1), typically starting from n=1. Therefore, the answer is a_n = n^2 + n for n\u22651." - }, - { - "type": "summary_text", - "text": "**Explaining the sequence pattern**\n\nThe user provided the sequence: 2, 6, 12, 20, 30, and asks for the pattern and nth term formula. I see that the differences increase by 2: +4, +6, +8, +10. This indicates it follows the pronic numbers, meaning the formula is a_n = n(n+1). To clarify, each term is a product of two consecutive integers. Thus, the final message should state: pattern: product of consecutive integers, formula: a_n = n(n+1). This captures the essence of the sequence!" - } - ] - }, - { - "id": "msg_077f768058f266c60069d6c2a470988195bb350a791bfef4c7", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The pattern is that the differences go up by 2 each time:\n\n 6\u20132=4, 12\u20136=6, 20\u201312=8, 30\u201320=10, \u2026\n\nA quick way to see it\u2019s quadratic is to note the second differences are constant (2), so \na\u2099=An\u00b2+Bn+C. Plugging in n=1,2,3 gives A=1, B=1, C=0.\n\nEquivalently, each term is the product of two consecutive integers:\n\n a\u2099 = n\u00b7(n+1).\n\nSo for n=1,2,3,4,5,\u2026 you get\n\n a\u2081=1\u00b72=2, a\u2082=2\u00b73=6, a\u2083=3\u00b74=12, a\u2084=4\u00b75=20, a\u2085=5\u00b76=30, \u2026\n\nIn closed-form: \n\n a\u2099 = n\u00b2 + n = n(n+1)." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": null, - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 41, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 1809, - "output_tokens_details": { - "reasoning_tokens": 1536 - }, - "total_tokens": 1850 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-70788f27285d.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-70788f27285d.json new file mode 100644 index 00000000..a5c1fd30 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-70788f27285d.json @@ -0,0 +1,86 @@ +{ + "id": "resp_0bd976abac729f73006a03bc4751bc8195bff03a08e4537d39", + "object": "response", + "created_at": 1778629703, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629710, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": null, + "max_output_tokens": null, + "max_tool_calls": null, + "model": "o4-mini-2025-04-16", + "moderation": null, + "output": [ + { + "id": "rs_0bd976abac729f73006a03bc47c87081958eca888f7f7af5ad", + "type": "reasoning", + "summary": [ + { + "type": "summary_text", + "text": "**Analyzing the sequence**\n\nThe user is asking about the sequence: 2, 6, 12, 20, 30. I think these numbers correspond to triangular numbers, but specifically multiplied by 2. So, I realize that a general formula for the nth term can be a(n) = n(n + 1). This captures the pattern perfectly, yielding correct values for each term in the sequence. The differences between terms also reveal an increasing pattern: 4, 6, 8, 10\u2014indicating a consistent growth as well!" + }, + { + "type": "summary_text", + "text": "**Identifying quadratic patterns**\n\nI\u2019m observing that the second difference is constant at 2, indicating a quadratic function. I figured out that a(n) could be of the form an^2 + bn + c. By solving equations for n=1, 2, and 3, I find that a = 1, b = 1, and c = 0. This leads me to the formula a(n) = n^2 + n, representing pronic numbers or the product of consecutive integers. So, the final result for the nth term is n(n + 1)!" + } + ] + }, + { + "id": "msg_0bd976abac729f73006a03bc4e4adc81958bb9bd1260611f25", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "The terms are the \u201cpronic\u201d (or oblong) numbers, i.e. \n2 = 1\u00b72 \n6 = 2\u00b73 \n12 = 3\u00b74 \n20 = 4\u00b75 \n30 = 5\u00b76 \n\nSo if you label those terms a\u2081, a\u2082, a\u2083,\u2026 then\n\n a\u2099 = n\u00b7(n + 1) \n\nEquivalently, a\u2099 = n\u00b2 + n." + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": "high", + "summary": "detailed" + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 41, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 905, + "output_tokens_details": { + "reasoning_tokens": 768 + }, + "total_tokens": 946 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7c89b40f-1c4f-42b1-be76-7a4c43f4c667.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7c89b40f-1c4f-42b1-be76-7a4c43f4c667.json deleted file mode 100644 index 4c171744..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7c89b40f-1c4f-42b1-be76-7a4c43f4c667.json +++ /dev/null @@ -1,85 +0,0 @@ -{ - "id": "resp_0a904223c9c2dec80069d6c2a86db08190a15974270cee7c24", - "object": "response", - "created_at": 1775682216, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1775682224, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "output": [ - { - "id": "rs_0a904223c9c2dec80069d6c2a8f4f88190a1206bee8f92d110", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Calculating term and sum**\n\nThe user is asking for the 10th term from the sequence defined by a_n = n(n+1). To find this, I calculate a_10, which is 10*11, giving me 110. Next, for the sum of the first 10 terms, I use the formula for n^2 and n. The sum of n^2 from 1 to 10 is 385, and the sum of n is 55. Adding these together gives a total of 440. So, I conclude that the 10th term is 110 and the sum is 440." - }, - { - "type": "summary_text", - "text": "**Summarizing the calculations**\n\nI want to include the formulas for clarity. The 10th term is a_10, which equals 110, and the sum of the first 10 terms is 440. There's a neat formula for this sum: the sum of a_n = sum_{n=1}^N n(n+1) = N(N+1)(N+2)/3. For N=10, it checks out as 10*11*12/3 = 440. I can mention this sum formula as part of my response. So, to summarize: the answers are 110 for the term and 440 for the sum." - } - ] - }, - { - "id": "msg_0a904223c9c2dec80069d6c2b05f8881909bd0fb55c2a56fd6", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The nth term is a\u2099 = n(n+1). \nSo for n=10: \na\u2081\u2080 = 10\u00b711 = 110. \n\nThe sum of the first 10 terms is \nS\u2081\u2080 = \u2211\u2099\u208c\u2081\u00b9\u2070 n(n+1) = \u2211n\u00b2 + \u2211n \n = (10\u00b711\u00b721)/6 + (10\u00b711)/2 \n = 385 + 55 \n = 440. \n\nEquivalently you can use the closed\u2010form \n\u2211\u2099\u208c\u2081\u1d3a n(n+1) = N(N+1)(N+2)/3, \nso for N=10: 10\u00b711\u00b712/3 = 440." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": null, - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 246, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 612, - "output_tokens_details": { - "reasoning_tokens": 384 - }, - "total_tokens": 858 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7cde4bf4-897d-40ce-993c-976598baeaef.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7cde4bf4-897d-40ce-993c-976598baeaef.json deleted file mode 100644 index 6ac23148..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-7cde4bf4-897d-40ce-993c-976598baeaef.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - 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diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-9e7ab055-cd83-45cb-b979-cea9c4d5c331.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-9e7ab055-cd83-45cb-b979-cea9c4d5c331.json deleted file mode 100644 index 98877425..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-9e7ab055-cd83-45cb-b979-cea9c4d5c331.json +++ /dev/null @@ -1,90 +0,0 @@ -{ - "id": "resp_02227da51c3fd2580069f408f499188196bd24c55057797396", - "object": "response", - "created_at": 1777600756, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600773, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_02227da51c3fd2580069f408f556408196b2db61e732ad86a6", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Analyzing a number sequence**\n\nThe user provided the sequence: 2, 6, 12, 20, 30, and I'm trying to find the pattern and formula for the nth term. I notice this sequence seems to be twice the triangular numbers, which are generated by the formula n(n+1)/2. When I apply that, I see that each term corresponds to the triangular number multiplied by 2. Additionally, the differences suggest it's a quadratic sequence, confirming the numbers are pronic numbers, products of consecutive integers." - }, - { - "type": "summary_text", - "text": "**Finding the term formula**\n\nThe formula for the sequence is a_n = n(n + 1). When starting from n=1, I see it gives the first term, 2. The differences between terms increase by 2 each time, confirming they are pronic numbers. By breaking it down further, I realize these can also be expressed as twice the triangular numbers. The difference sequence is 4, 6, 8, 10, showing it's quadratic. Ultimately, I conclude: a_n = n(n + 1) for the sequence 2, 6, 12, 20, 30." - }, - { - "type": "summary_text", - "text": "**Summarizing the sequence pattern**\n\nThe pattern in this sequence is found in the successive differences, which are even numbers: 4, 6, 8, 10. This leads to the conclusion that a_n = n^2 + n, or more simply, a_n = n(n + 1). Although there's a consideration for starting at n=0, it typically begins at n=1. I can also express it as twice the triangular numbers: a_n = 2T_n. Ultimately, the answer is a_n = n(n + 1) for the terms 2, 6, 12, and so on." - } - ] - }, - { - "id": "msg_02227da51c3fd2580069f40902d7ec81968d11d9124bb4043e", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The \u201cgaps\u201d between terms are \n 6\u22122=4, 12\u22126=6, 20\u221212=8, 30\u221220=10, \u2026 \ni.e. the differences are 4,\u20096,\u20098,\u200910, \u2026 so the second difference is constant (2) and the sequence is quadratic. Solving or noting that it\u2019s twice the triangular numbers gives\n\n a\u2099 = 2\u00b7(n(n+1)/2) = n(n+1).\n\nIf you start counting at n=1 this indeed yields \n a\u2081 = 1\u00b72 = 2, \n a\u2082 = 2\u00b73 = 6, \n a\u2083 = 3\u00b74 = 12, \u2026 \n\nSo the nth term is a\u2099 = n(n+1)." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 41, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 1326, - "output_tokens_details": { - "reasoning_tokens": 1088 - }, - "total_tokens": 1367 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2325d24-d1b8-41b7-9544-cbc25a013b0a.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2325d24-d1b8-41b7-9544-cbc25a013b0a.json deleted file mode 100644 index 2a2a61ce..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2325d24-d1b8-41b7-9544-cbc25a013b0a.json +++ /dev/null @@ -1,90 +0,0 @@ -{ - "id": "resp_0ba4648fbb2a32ab0069f408fa8738819588ce34f432e86369", - "object": "response", - "created_at": 1777600762, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600798, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_0ba4648fbb2a32ab0069f408fb07e48195a99c6346663bc45a", - "type": "reasoning", - "summary": [ - { - "type": "summary_text", - "text": "**Analyzing the sequence**\n\nThe user presents the sequence: 2, 6, 12, 20, 30, which corresponds to pronic or oblong numbers expressed as n(n+1). For example, 1*2=2, 2*3=6, 3*4=12, and so forth. The formula for the nth term is a(n) = n(n+1). I notice that the differences between consecutive terms (4, 6, 8, 10) are even numbers starting at 4, indicating a consistent increase. If n starts at 0, then a(n) = n(n+1), which results in 0 at n=0." - }, - { - "type": "summary_text", - "text": "**Exploring the pattern of the sequence**\n\nStarting with n=1, the pattern mirrors n\u00b2+n, leading to the formula a(n) = n(n+1) for pronic numbers. The differences between terms (4, 6, 8, 10...) form an arithmetic sequence, increasing by 2 each time. So, I can clearly state that the nth term is the product of consecutive integers, with a simple alternative expression. When indexed from zero, the formula shifts. Ultimately, what stands out is that each term follows the relationship a_n = n(n+1)." - }, - { - "type": "summary_text", - "text": "**Defining the sequence pattern**\n\nThe sequence represents pronic numbers, where each term, starting from n=1, follows the formula a_n = n(n+1). These numbers are the products of consecutive integers: 2 as 1\u00d72, 6 as 2\u00d73, and 12 as 3\u00d74. If I used a zero-based index, I'd say a_n = n(n+1) produces a_0=0, but since the sequence starts at n=1, the first term is 2. So, the final conclusion is that pronic numbers are defined by a_n = n(n+1)." - } - ] - }, - { - "id": "msg_0ba4648fbb2a32ab0069f40919cc608195b0fb875d88fdd280", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "Each term is the product of two consecutive integers:\n\n2 = 1\u00d72 \n6 = 2\u00d73 \n12 = 3\u00d74 \n20 = 4\u00d75 \n30 = 5\u00d76 \n\nSo if you label the terms a\u2081, a\u2082, a\u2083, \u2026 then\n\n\u2003a\u2099 = n\u2009(n+1).\n\nEquivalently, a\u2099 = n\u00b2 + n." - } - ], - "role": "assistant" - } - ], - "parallel_tool_calls": true, - "presence_penalty": 0.0, - "previous_response_id": null, - "prompt_cache_key": null, - "prompt_cache_retention": "in_memory", - "reasoning": { - "effort": "high", - "summary": "detailed" - }, - "safety_identifier": null, - "service_tier": "default", - "store": true, - "temperature": 1.0, - "text": { - "format": { - "type": "text" - }, - "verbosity": "medium" - }, - "tool_choice": "auto", - "tools": [], - "top_logprobs": 0, - "top_p": 1.0, - "truncation": "disabled", - "usage": { - "input_tokens": 41, - "input_tokens_details": { - "cached_tokens": 0 - }, - "output_tokens": 1286, - "output_tokens_details": { - "reasoning_tokens": 1152 - }, - "total_tokens": 1327 - }, - "user": null, - "metadata": {} -} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2ceaa8491c7.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2ceaa8491c7.json new file mode 100644 index 00000000..0e3505a4 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-a2ceaa8491c7.json @@ -0,0 +1,90 @@ +{ + "id": "resp_05e25e583bbfa5cb006a03bc4b28c88197857b7c9633d453f6", + "object": "response", + "created_at": 1778629707, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629717, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": null, + "max_output_tokens": null, + "max_tool_calls": null, + "model": "o4-mini-2025-04-16", + "moderation": null, + "output": [ + { + "id": "rs_05e25e583bbfa5cb006a03bc4b9ab48197878e5ff07866739a", + "type": "reasoning", + "summary": [ + { + "type": "summary_text", + "text": "**Analyzing the sequence**\n\nThe user presents the sequence: 2, 6, 12, 20, 30 and asks for the pattern and the formula for the nth term. I notice that these are pronic numbers, which are expressed as n(n+1). The first term corresponds to n=1, so 1*2=2, then 2*3=6, and so on. Alternatively, these can also be viewed as double triangular numbers. The difference between terms increases by 2, reinforcing that the formula for the nth term is indeed n(n+1)." + }, + { + "type": "summary_text", + "text": "**Clarifying the sequence**\n\nThe user is asking about the pattern in the sequence 2, 6, 12, 20, and 30. These numbers are pronic numbers, or the product of two consecutive integers, expressed as n(n+1). If we look at differences, they increase by 2 (4, 6, 8, 10), showing a consistent arithmetic progression. Hence, the general formula for the nth term is a_n = n(n+1) for n starting from 1. For n starting at 2, it would be a_n = n(n-1)." + }, + { + "type": "summary_text", + "text": "**Explaining the pattern**\n\nIn this sequence, the differences between terms are 4, 6, 8, and 10, increasing consistently by 2. This indicates that the second difference is constant at 2, suggesting a quadratic relationship. To determine the formula, I solve for a_n = an\u00b2 + bn + c using three points, which gives me a=1, b=1, and c=0, leading to a_n = n\u00b2 + n. Therefore, the final formula represents pronic or oblong numbers as a_n = n(n+1)." + } + ] + }, + { + "id": "msg_05e25e583bbfa5cb006a03bc54e6d48197b9393e2c6661470e", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "The easiest way to see it is to look at the first differences:\n\n 6\u20132 = 4 \n 12\u20136 = 6 \n 20\u201312 = 8 \n 30\u201320 = 10 \n\nThose go 4, 6, 8, 10, \u2026 \u2013 an arithmetic progression with common difference 2. Hence the original sequence is quadratic in n. If we index so that\n\n a\u2081 = 2, a\u2082 = 6, a\u2083 = 12, \u2026\n\nthen one finds\n\n a\u2099 = n\u00b2 + n\n\n(which you can check: 1\u00b2+1=2, 2\u00b2+2=6, 3\u00b2+3=12, \u2026). \n\nEquivalently, a\u2099 = n(n + 1). These are called the \u201cpronic\u201d or \u201coblong\u201d numbers." + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": "high", + "summary": "detailed" + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 41, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 1150, + "output_tokens_details": { + "reasoning_tokens": 896 + }, + "total_tokens": 1191 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b7d2fa3f8bc3.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b7d2fa3f8bc3.json new file mode 100644 index 00000000..291fe56f --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b7d2fa3f8bc3.json @@ -0,0 +1,90 @@ +{ + "id": "resp_00a8a22f65f43348006a03bc54afb081949c03ab6c7fca4e8f", + "object": "response", + "created_at": 1778629716, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629728, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": null, + "max_output_tokens": null, + "max_tool_calls": null, + "model": "o4-mini-2025-04-16", + "moderation": null, + "output": [ + { + "id": "rs_00a8a22f65f43348006a03bc5517d081949a71fe6d23aeb69a", + "type": "reasoning", + "summary": [ + { + "type": "summary_text", + "text": "**Identifying the number pattern**\n\nThe user has presented a sequence: 2, 6, 12, 20, 30. I'm noticing that these numbers can be expressed as products of two consecutive integers: 2=1*2, 6=2*3, 12=3*4, and so on. The general formula for the nth term seems to be a_n = n(n+1), which relates to pronic numbers. The differences in the sequence also indicate a quadratic pattern, confirming that the quadratic formula applies here. Thus, I conclude that the formula a_n = n(n+1) holds true!" + }, + { + "type": "summary_text", + "text": "**Understanding pronic numbers**\n\nI\u2019m thinking about triangular numbers and how they relate to the sequence: 2, 6... It\u2019s clearer to focus on pronic numbers, defined as the product of two consecutive natural numbers: a_n = n(n+1). This fits since each term is the difference of squares, specifically (n+1)\u00b2 - (n+1). The differences increase by 2: 4, 6, 8, 10, confirming a quadratic sequence. \n\nThe formula is straightforward: a_n = n(n+1) or a_n = n\u00b2 + n, with n starting from 1. I might also mention that it relates to the sum of the first n even numbers." + }, + { + "type": "summary_text", + "text": "**Finalizing the pronic number pattern**\n\nI\u2019m ready to conclude on the pattern of pronic numbers! The sequence is formed by multiplying consecutive numbers: 1\u00d72=2, 2\u00d73=6, 3\u00d74=12, and so on, leading to the nth term being n(n+1). It\u2019s also interesting to note that the difference between successive terms increases by 2, indicating a quadratic relationship, expressed as a_n = n\u00b2 + n. I can also mention that a_n = 2T_n, linking it to triangular numbers. Now I\u2019ll present this concisely!" + } + ] + }, + { + "id": "msg_00a8a22f65f43348006a03bc5f5de48194b9b91f39592ace56", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "The terms are 2=1\u00b72, 6=2\u00b73, 12=3\u00b74, 20=4\u00b75, 30=5\u00b76,\u2026 so each is the product of two consecutive integers. If you call the first term n=1, the nth term is\n\n\u2003a\u2099 = n\u00b7(n+1)\n\nEquivalently, a\u2099 = n\u00b2 + n." + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": "high", + "summary": "detailed" + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 41, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 1095, + "output_tokens_details": { + "reasoning_tokens": 960 + }, + "total_tokens": 1136 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b9d1173a9de6.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b9d1173a9de6.json new file mode 100644 index 00000000..6279a787 --- /dev/null +++ b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-b9d1173a9de6.json @@ -0,0 +1,72 @@ +{ + "id": "resp_03f32aa196f32f4e006a03bcd3d35081939e50f61e23d1a898", + "object": "response", + "created_at": 1778629843, + "status": "completed", + "background": false, + "billing": { + "payer": "developer" + }, + "completed_at": 1778629844, + "error": null, + "frequency_penalty": 0.0, + "incomplete_details": null, + "instructions": "You are a helpful assistant", + "max_output_tokens": null, + "max_tool_calls": null, + "model": "gpt-4o-mini-2024-07-18", + "moderation": null, + "output": [ + { + "id": "msg_03f32aa196f32f4e006a03bcd44c388193a271dc0dd415cd76", + "type": "message", + "status": "completed", + "content": [ + { + "type": "output_text", + "annotations": [], + "logprobs": [], + "text": "Paris." + } + ], + "role": "assistant" + } + ], + "parallel_tool_calls": true, + "presence_penalty": 0.0, + "previous_response_id": null, + "prompt_cache_key": null, + "prompt_cache_retention": "in_memory", + "reasoning": { + "effort": null, + "summary": null + }, + "safety_identifier": null, + "service_tier": "default", + "store": true, + "temperature": 1.0, + "text": { + "format": { + "type": "text" + }, + "verbosity": "medium" + }, + "tool_choice": "auto", + "tools": [], + "top_logprobs": 0, + "top_p": 1.0, + "truncation": "disabled", + "usage": { + "input_tokens": 28, + "input_tokens_details": { + "cached_tokens": 0 + }, + "output_tokens": 3, + "output_tokens_details": { + "reasoning_tokens": 0 + }, + "total_tokens": 31 + }, + "user": null, + "metadata": {} +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-c56562c2-4ea1-44f1-9ee5-d61b3fcd4e5b.txt b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-c56562c2-4ea1-44f1-9ee5-d61b3fcd4e5b.txt deleted file mode 100644 index 45a17a71..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-c56562c2-4ea1-44f1-9ee5-d61b3fcd4e5b.txt +++ /dev/null @@ -1,45 +0,0 @@ -event: response.created -data: {"type":"response.created","response":{"id":"resp_0afed4fcb050b0f30069fa614004f081949b044e153b828250","object":"response","created_at":1778016576,"status":"in_progress","background":false,"completed_at":null,"error":null,"frequency_penalty":0.0,"incomplete_details":null,"instructions":"You are a helpful assistant","max_output_tokens":null,"max_tool_calls":null,"model":"gpt-4o-mini-2024-07-18","moderation":null,"output":[],"parallel_tool_calls":true,"presence_penalty":0.0,"previous_response_id":null,"prompt_cache_key":null,"prompt_cache_retention":"in_memory","reasoning":{"effort":null,"summary":null},"safety_identifier":null,"service_tier":"auto","store":true,"temperature":1.0,"text":{"format":{"type":"text"},"verbosity":"medium"},"tool_choice":"auto","tools":[],"top_logprobs":0,"top_p":1.0,"truncation":"disabled","usage":null,"user":null,"metadata":{}},"sequence_number":0} - 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diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-d9ea4fac-3e76-4c11-aec7-e15f21bb5f52.json b/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-d9ea4fac-3e76-4c11-aec7-e15f21bb5f52.json deleted file mode 100644 index 8fd4a30b..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/__files/responses-d9ea4fac-3e76-4c11-aec7-e15f21bb5f52.json +++ /dev/null @@ -1,90 +0,0 @@ -{ - "id": "resp_058f35ee350a999b0069f40928e1f48193a35c041634394a6f", - "object": "response", - "created_at": 1777600809, - "status": "completed", - "background": false, - "billing": { - "payer": "developer" - }, - "completed_at": 1777600827, - "error": null, - "frequency_penalty": 0.0, - "incomplete_details": null, - "instructions": null, - "max_output_tokens": null, - "max_tool_calls": null, - "model": "o4-mini-2025-04-16", - "moderation": null, - "output": [ - { - "id": "rs_058f35ee350a999b0069f40929ac388193b9568cdf70b02b54", - "type": "reasoning", - 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I'm trying to find the pattern and a formula for the nth term. It looks like these numbers correspond to triangular numbers multiplied by 2. In fact, they follow the formula: n(n + 1) which simplifies to n^2 + n. Just to clarify, triangular numbers follow the formula T_n = n(n + 1)/2. Based on this, I see that the given sequence is essentially twice each triangular number." - }, - { - "type": "summary_text", - "text": "**Clarifying the sequence pattern**\n\nI'm breaking down the sequence 2, 6, 12, 20, 30. I see now that it equals 2 times the triangular numbers: 2 = 2\u00d71, 6 = 2\u00d73, etc. The formula for the nth term is a_n = n(n + 1), and it can also be described by observing the differences between terms: 4, 6, 8, 10, which increase by 2 each time. This consistency indicates it's a quadratic sequence. The final conclusion is that the pattern represents rectangular numbers, confirming the formula is a_n = n(n + 1)." - }, - { - "type": "summary_text", - "text": "**Solving for the sequence**\n\nI\u2019m working on the sequence and setting up equations based on the relationships I see. I've established that b can be expressed in terms of a and derived equations that simplify down to a = 1 and b = 1, with c equaling 0. So, I've determined the nth term formula is a_n = n(n + 1), which reveals the pattern of pronic numbers, being the product of two consecutive integers, specifically: 2, 6, 12, 20, 30... Just to clarify, the final formula using indexing from 1 is a_n = n(n + 1)." - } - ] - }, - { - "id": "msg_058f35ee350a999b0069f4093af15481939783e600a43f5133", - "type": "message", - "status": "completed", - "content": [ - { - "type": "output_text", - "annotations": [], - "logprobs": [], - "text": "The differences between successive terms are\n\n 6\u20132 = 4, \n12\u20136 = 6, \n20\u201312 = 8, \n30\u201320 = 10, \n\nso the \u201cstep\u2010sizes\u201d are the even numbers 4,6,8,10,\u2026 (i.e. they increase by 2 each time). Any sequence whose second difference is constant is a quadratic, and in fact one checks that\n\n 2 = 1\u00b72, \n 6 = 2\u00b73, \n12 = 3\u00b74, \n20 = 4\u00b75, \n30 = 5\u00b76,\n\nso the nth term is the product of two consecutive integers. 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Next, I need to compute \\( S_{10} = \\sum_{n=1}^{10} n(n+1) \\). This simplifies to finding the sums of \\( n^2 \\) and \\( n \\). \n\nAfter calculating, I find that \\( S_{10} = 440 \\). The derived general formula for this sum is \\( \\sum_{k=1}^{n} k(k+1) = \\frac{n(n+1)(n+2)}{3} \\). So, the answers are \\( a_{10} = 110 \\) and the sum equals 440." - }, - { - "type": "summary_text", - "text": "**Summarizing values and formulas**\n\nI can provide clear answers now: the term \\( a_{10} = 10 \\times 11 = 110 \\). Next, to find the sum \\( S_{10} = \\sum_{n=1}^{10} n(n+1) \\), I can use the formula \\( S_n = \\frac{n(n+1)(n+2)}{3} \\), which gives me \\( \\frac{10 \\times 11 \\times 12}{3} = 440 \\). So, the results are \\( a_{10} = 110 \\) and \\( S_{10} = 440 \\). 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I want to clarify that the differences between the terms increase by 2 each time, showing that it’s a quadratic sequence. The nth term formula remains a_n = n(n+1), typically starting from n=1. Therefore, the answer is a_n = n^2 + n for n≥1.\",\"type\":\"summary_text\"},{\"text\":\"**Explaining the sequence pattern**\\n\\nThe user provided the sequence: 2, 6, 12, 20, 30, and asks for the pattern and nth term formula. I see that the differences increase by 2: +4, +6, +8, +10. This indicates it follows the pronic numbers, meaning the formula is a_n = n(n+1). To clarify, each term is a product of two consecutive integers. Thus, the final message should state: pattern: product of consecutive integers, formula: a_n = n(n+1). 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What is the pattern and what would be the formula for the nth term?\\n\"},{\"id\":\"rs_00a8a22f65f43348006a03bc5517d081949a71fe6d23aeb69a\",\"summary\":[{\"text\":\"**Identifying the number pattern**\\n\\nThe user has presented a sequence: 2, 6, 12, 20, 30. I'm noticing that these numbers can be expressed as products of two consecutive integers: 2=1*2, 6=2*3, 12=3*4, and so on. The general formula for the nth term seems to be a_n = n(n+1), which relates to pronic numbers. The differences in the sequence also indicate a quadratic pattern, confirming that the quadratic formula applies here. Thus, I conclude that the formula a_n = n(n+1) holds true!\",\"type\":\"summary_text\"},{\"text\":\"**Understanding pronic numbers**\\n\\nI’m thinking about triangular numbers and how they relate to the sequence: 2, 6... It’s clearer to focus on pronic numbers, defined as the product of two consecutive natural numbers: a_n = n(n+1). 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And can you find the sum of the first 10 terms?\"}],\"model\":\"o4-mini\",\"reasoning\":{\"effort\":\"high\",\"summary\":\"detailed\"}}", + "ignoreArrayOrder" : true, + "ignoreExtraElements" : false + } ] + }, + "response" : { + "status" : 200, + "bodyFileName" : "responses-172a4267f40e.json", + "headers" : { + "x-request-id" : "req_fa6d3749c5164f59ae23d7d330986891", + "x-ratelimit-limit-tokens" : "150000000", + "openai-organization" : "braintrust-data", + "CF-RAY" : "9fad50fe6815752a-SEA", + "Server" : "cloudflare", + "X-Content-Type-Options" : "nosniff", + "x-ratelimit-reset-requests" : "2ms", + "x-ratelimit-remaining-tokens" : "149999625", + "cf-cache-status" : "DYNAMIC", + "x-ratelimit-remaining-requests" : "29999", + "Date" : "Tue, 12 May 2026 23:48:57 GMT", + "access-control-expose-headers" : [ "X-Request-ID", "CF-Ray" ], + "x-ratelimit-reset-tokens" : "0s", + "set-cookie" : "__cf_bm=vRMUni5VaZpKzUHGkFuDNIX4MbVI.4wKTyvt0ld1XMc-1778629729.0228689-1.0.1.1-epi9ZiRWZoh6qKBzcNsaTgULiLLTdPT0zr42WzGVCQ1w3qQFS7Pk3GlNePxfmSNs5Cy87JTiTaCtbtNoAYELNcr_FHXxGx4__oISwkdcGIVAUgR40OI7_J9RrYfKf.mp; HttpOnly; SameSite=None; Secure; Path=/; Domain=api.openai.com; Expires=Wed, 13 May 2026 00:18:57 GMT", + "Strict-Transport-Security" : "max-age=31536000; includeSubDomains; preload", + "x-ratelimit-limit-requests" : "30000", + "openai-processing-ms" : "8473", + "openai-version" : "2020-10-01", + "alt-svc" : "h3=\":443\"; ma=86400", + "openai-project" : "proj_vsCSXafhhByzWOThMrJcZiw9", + "Content-Type" : "application/json" + } + }, + "uuid" : "50e0d68c-21e6-3144-8d47-5770ec72a6d5", + "persistent" : true, + "insertionIndex" : 1 +} \ No newline at end of file diff --git a/test-harness/src/testFixtures/resources/cassettes/openai/mappings/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json b/test-harness/src/testFixtures/resources/cassettes/openai/mappings/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json deleted file mode 100644 index 1567ef7a..00000000 --- a/test-harness/src/testFixtures/resources/cassettes/openai/mappings/responses-26b9d2cc-120b-46d5-98f1-62c9e550bc20.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "id" : "26b9d2cc-120b-46d5-98f1-62c9e550bc20", - "name" : "responses", - "request" : { - "url" : "/responses", - "method" : "POST", - "headers" : { - "Content-Type" : { - "equalTo" : "application/json" - } - }, - "bodyPatterns" : [ { - "equalToJson" : "{\"input\":[{\"role\":\"user\",\"content\":\"Look at this sequence: 2, 6, 12, 20, 30. 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What is the pattern and what would be the formula for the nth term?\\n\"},{\"id\":\"rs_0ba4648fbb2a32ab0069f408fb07e48195a99c6346663bc45a\",\"summary\":[{\"text\":\"**Analyzing the sequence**\\n\\nThe user presents the sequence: 2, 6, 12, 20, 30, which corresponds to pronic or oblong numbers expressed as n(n+1). For example, 1*2=2, 2*3=6, 3*4=12, and so forth. The formula for the nth term is a(n) = n(n+1). I notice that the differences between consecutive terms (4, 6, 8, 10) are even numbers starting at 4, indicating a consistent increase. If n starts at 0, then a(n) = n(n+1), which results in 0 at n=0.\",\"type\":\"summary_text\"},{\"text\":\"**Exploring the pattern of the sequence**\\n\\nStarting with n=1, the pattern mirrors n²+n, leading to the formula a(n) = n(n+1) for pronic numbers. The differences between terms (4, 6, 8, 10...) form an arithmetic sequence, increasing by 2 each time. 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