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extract distinctUntilChangedWithInitialValue operator
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* Initial App Search in Kibana plugin work - Initializes a new platform plugin that ships out of the box w/ x-pack - Contains a very basic front-end that shows AS engines, error states, or a Setup Guide - Contains a very basic server that remotely calls the AS internal engines API and returns results * Update URL casing to match Kibana best practices - URL casing appears to be snake_casing, but kibana.json casing appears to be camelCase * Register App Search plugin in Home Feature Catalogue * Add custom App Search in Kibana logo - I haven't had much success in surfacing a SVG file via a server-side endpoint/URL, but then I realized EuiIcon supports passing in a ReactElement directly. Woo! * Fix appSearch.host config setting to be optional - instead of crashing folks on load * Rename plugin to Enterprise Search - per product decision, URL should be enterprise_search/app_search and Workplace Search should also eventually live here - reorganize folder structure in anticipation for another workplace_search plugin/codebase living alongside app_search - rename app.tsx/main.tsx to a standard top-level index.tsx (which will contain top-level routes/state) - rename AS->ES files/vars where applicable - TODO: React Router * Set up React Router URL structure * Convert showSetupGuide action/flag to a React Router link - remove showSetupGuide flag - add a new shared helper component for combining EuiButton/EuiLink with React Router behavior (https://github.com/elastic/eui/blob/master/wiki/react-router.md#react-router-51) * Implement Kibana Chrome breadcrumbs - create shared helper (WS will presumably also want this) for generating EUI breadcrumb objects with React Router links+click behavior - create React component that calls chrome.setBreadcrumbs on page mount - clean up type definitions - move app-wide props to IAppSearchProps and update most pages/views to simply import it instead of calling their own definitions * Added server unit tests (#2) * Added unit test for server * PR Feedback * Refactor top-level Kibana props to a global context state - rather them passing them around verbosely as props, the components that need them should be able to call the useContext hook + Remove IAppSearchProps in favor of IKibanaContext + Also rename `appSearchUrl` to `enterpriseSearchUrl`, since this context will contained shared/Kibana-wide values/actions useful to both AS and WS * Added unit tests for public (elastic#4) * application.test.ts * Added Unit Test for EngineOverviewHeader * Added Unit Test for generate_breadcrumbs * Added Unit Test for set_breadcrumb.tsx * Added a unit test for link_events - Also changed link_events.tsx to link_events.ts since it's just TS, no React - Modified letBrowserHandleEvent so it will still return a false boolean when target is blank * Betterize these tests Co-Authored-By: Constance <constancecchen@users.noreply.github.com> Co-authored-by: Constance <constancecchen@users.noreply.github.com> * Add UI telemetry tracking to AS in Kibana (elastic#5) * Set up Telemetry usageCollection, savedObjects, route, & shared helper - The Kibana UsageCollection plugin handles collecting our telemetry UI data (views, clicks, errors, etc.) and pushing it to elastic's telemetry servers - That data is stored in incremented in Kibana's savedObjects lib/plugin (as well as mapped) - When an end-user hits a certain view or action, the shared helper will ping the app search telemetry route which increments the savedObject store * Update client-side views/links to new shared telemetry helper * Write tests for new telemetry files * Implement remaining unit tests (elastic#7) * Write tests for React Router+EUI helper components * Update generate_breadcrumbs test - add test suite for generateBreadcrumb() itself (in order to cover a missing branch) - minor lint fixes - remove unnecessary import from set_breadcrumbs test * Write test for get_username util + update test to return a more consistent falsey value (null) * Add test for SetupGuide * [Refactor] Pull out various Kibana context mocks into separate files - I'm creating a reusable useContext mock for shallow()ed enzyme components + add more documentation comments + examples * Write tests for empty state components + test new usecontext shallow mock * Empty state components: Add extra getUserName branch test * Write test for app search index/routes * Write tests for engine overview table + fix bonus bug * Write Engine Overview tests + Update EngineOverview logic to account for issues found during tests :) - Move http to async/await syntax instead of promise syntax (works better with existing HttpServiceMock jest.fn()s) - hasValidData wasn't strict enough in type checking/object nest checking and was causing the app itself to crash (no bueno) * Refactor EngineOverviewHeader test to use shallow + to full coverage - missed adding this test during telemetry work - switching to shallow and beforeAll reduces the test time from 5s to 4s! * [Refactor] Pull out React Router history mocks into a test util helper + minor refactors/updates * Add small tests to increase branch coverage - mostly testing fallbacks or removing fallbacks in favor of strict type interface - these are slightly obsessive so I'd also be fine ditching them if they aren't terribly valuable * Address larger tech debt/TODOs (elastic#8) * Fix optional chaining TODO - turns out my local Prettier wasn't up to date, completely my bad * Fix constants TODO - adds a common folder/architecture for others to use in the future * Remove TODO for eslint-disable-line and specify lint rule being skipped - hopefully that's OK for review, I can't think of any other way to sanely do this without re-architecting the entire file or DDoSing our API * Add server-side logging to route dependencies + add basic example of error catching/logging to Telemetry route + [extra] refactor mockResponseFactory name to something slightly easier to read * Move more Engines Overview API logic/logging to server-side - handle data validation in the server-side - wrap server-side API in a try/catch to account for fetch issues - more correctly return 2xx/4xx statuses and more correctly deal with those responses in the front-end - Add server info/error/debug logs (addresses TODO) - Update tests + minor refactors/cleanup - remove expectResponseToBe200With helper (since we're now returning multiple response types) and instead make mockResponse var name more readable - one-line header auth - update tests with example error logs - update schema validation for `type` to be an enum of `indexed`/`meta` (more accurately reflecting API) * Per telemetry team feedback, rename usageCollection telemetry mapping name to simpler 'app_search' - since their mapping already nests under 'kibana.plugins' - note: I left the savedObjects name with the '_telemetry' suffix, as there very well may be a use case for top-level generic 'app_search' saved objects * Update Setup Guide installation instructions (elastic#9) Co-authored-by: Chris Cressman <chris@chriscressman.com> * [Refactor] DRY out route test helper * [Refactor] Rename public/test_utils to public/__mocks__ - to better follow/use jest setups and for .mock.ts suffixes * Add platinum licensing check to Meta Engines table/call (elastic#11) * Licensing plugin setup * Add LicensingContext setup * Update EngineOverview to not hit meta engines API on platinum license * Add Jest test helpers for future shallow/context use * Update plugin to use new Kibana nav + URL update (elastic#12) * Update new nav categories to add Enterprise Search + update plugin to use new category - per @johnbarrierwilson and Matt Riley, Enterprise Search should be under Kibana and above Observability - Run `node scripts/check_published_api_changes.js --accept` since this new category affects public API * [URL UPDATE] Change '/app/enterprise_search/app_search' to '/app/app_search' - This needs to be done because App Search and Workplace search *have* to be registered as separate plugins to have 2 distinct nav links - Currently Kibana doesn't support nested app names (see: elastic#59190) but potentially will in the future - To support this change, we need to update applications/index.tsx to NOT handle '/app/enterprise_search' level routing, but instead accept an async imported app component (e.g. AppSearch, WorkplaceSearch). - AppSearch should now treat its router as root '/' instead of '/app_search' - (Addl) Per Josh Dover's recommendation, switch to `<Router history={params.history}>` from `<BrowserRouter basename={params.appBasePath}>` since they're deprecating appBasePath * Update breadcrumbs helper to account for new URLs - Remove path for Enterprise Search breadcrumb, since '/app/enterprise_search' will not link anywhere meaningful for the foreseeable future, so the Enterprise Search root should not go anywhere - Update App Search helper to go to root path, per new React Router setup Test changes: - Mock custom basepath for App Search tests - Swap enterpriseSearchBreadcrumbs and appSearchBreadcrumbs test order (since the latter overrides the default mock) * Add create_first_engine_button telemetry tracking to EmptyState * Switch plugin URLs back to /app/enterprise_search/app_search Now that elastic#66455 has been merged in 🎉 * Add i18n formatted messages / translations (elastic#13) * Add i18n provider and formatted/i18n translated messages * Update tests to account for new I18nProvider context + FormattedMessage components - Add new mountWithContext helper that provides all contexts+providers used in top-level app - Add new shallowWithIntl helper for shallow() components that dive into FormattedMessage * Format i18n dates and numbers + update some mock tests to not throw react-intl invalid date messages * Update EngineOverviewHeader to disable button on prop * Address review feedback (elastic#14) * Fix Prettier linting issues * Escape App Search API endpoint URLs - per PR feedback - querystring should automatically encodeURIComponent / escape query param strings * Update server plugin.ts to use getStartServices() rather than storing local references from start() - Per feedback: https://github.com/elastic/kibana/blob/master/src/core/CONVENTIONS.md#applications - Note: savedObjects.registerType needs to be outside of getStartServices, or an error is thrown - Side update to registerTelemetryUsageCollector to simplify args - Update/fix tests to account for changes * E2E testing (elastic#6) * Wired up basics for E2E testing * Added version with App Search * Updated naming * Switched configuration around * Added concept of 'fixtures' * Figured out how to log in as the enterprise_search user * Refactored to use an App Search service * Added some real tests * Added a README * Cleanup * More cleanup * Error handling + README updatre * Removed unnecessary files * Apply suggestions from code review Co-authored-by: Constance <constancecchen@users.noreply.github.com> * Update x-pack/plugins/enterprise_search/public/applications/app_search/components/engine_overview/engine_table.tsx Co-authored-by: Constance <constancecchen@users.noreply.github.com> * PR feedback - updated README * Additional lint fixes Co-authored-by: Constance <constancecchen@users.noreply.github.com> * Add README and CODEOWNERS (elastic#15) * Add plugin README and CODEOWNERS * Fix Typescript errors (elastic#16) * Fix public mocks * Fix empty states types * Fix engine table component errors * Fix engine overview component errors * Fix setup guide component errors - SetBreadcrumbs will be fixed in a separate commit * Fix App Search index errors * Fix engine overview header component errors * Fix applications context index errors * Fix kibana breadcrumb helper errors * Fix license helper errors * ❗ Refactor React Router EUI link/button helpers - in order to fix typescript errors - this changes the component logic significantly to a react render prop, so that the Link and Button components can have different types - however, end behavior should still remain the same * Fix telemetry helper errors * Minor unused var cleanup in plugin files * Fix telemetry collector/savedobjects errors * Fix MockRouter type errors and add IRouteDependencies export - routes will use IRouteDependencies in the next few commits * Fix engines route errors * Fix telemetry route errors * Remove any type from source code - thanks to Scotty for the inspiration * Add eslint rules for Enterprise Search plugin - Add checks for type any, but only on non-test files - Disable react-hooks/exhaustive-deps, since we're already disabling it in a few files and other plugins also have it turned off * Cover uncovered lines in engines_table and telemetry tests * Fixed TS warnings in E2E tests (elastic#17) * Feedback: Convert static CSS values to EUI variables where possible * Feedback: Flatten nested CSS where possible - Prefer setting CSS class overrides on individual EUI components, not on a top-level page + Change CSS class casing from kebab-case to camelCase to better match EUI/Kibana + Remove unnecessary .euiPageContentHeader margin-bottom override by changing the panelPaddingSize of euiPageContent + Decrease engine overview table padding on mobile * Refactor out components shared with Workplace Search (elastic#18) * Move getUserName helper to shared - in preparation for Workplace Search plugin also using this helper * Move Setup Guide layout to a shared component * Setup Guide: add extra props for standard/native auth links Note: It's possible this commit may be unnecessary if we can publish shared Enterprise Search security mode docs * Update copy per feedback from copy team * Address various telemetry issues - saved objects: removing indexing per elastic#43673 - add schema and generate json per elastic#64942 - move definitions over to collectors since saved objects is mostly empty at this point, and schema throws an error when it imports an obj instead of being defined inline - istanbul ignore saved_objects file since it doesn't have anything meaningful to test but was affecting code coverage * Disable plugin access if a normal user does not have access to App Search (elastic#19) * Set up new server security dependency and configs * Set up access capabilities * Set up checkAccess helper/caller * Remove NoUserState component from the public UI - Since this is now being handled by checkAccess / normal users should never see the plugin at all if they don't have an account/access, the component is no longer needed * Update server routes to account for new changes - Remove login redirect catch from routes, since the access helper should now handle that for most users by disabling the plugin (superusers will see a generic cannot connect/error screen) - Refactor out new config values to a shared mock * Refactor Enterprise Search http call to hit/return new internal API endpoint + pull out the http call to a separate library for upcoming public URL work (so that other files can call it directly as well) * [Discussion] Increase timeout but add another warning timeout for slow servers - per recommendation/convo with Brandon * Register feature control * Remove no_as_account from UI telemetry - since we're no longer tracking that in the UI * Address PR feedback - isSuperUser check * Public URL support for Elastic Cloud (elastic#21) * Add server-side public URL route - Per feedback from Kibana platform team, it's not possible to pass info from server/ to public/ without a HTTP call :[ * Update MockRouter for routes without any payload/params * Add client-side helper for calling the new public URL API + API seems to return a URL a trailing slash, which we need to omit * Update public/plugin.ts to check and set a public URL - relies on this.hasCheckedPublicUrl to only make the call once per page load instead of on every page nav * Fix failing feature control tests - Split up scenario cases as needed - Add plugin as an exception alongside ML & Monitoring * Address PR feedback - version: kibana - copy edits - Sass vars - code cleanup * Casing feedback: change all plugin registration IDs from snake_case to camelCase - note: current remainng snake_case exceptions are telemetry keys - file names and api endpoints are snake_case per conventions * Misc security feedback - remove set - remove unnecessary capabilities registration - telemetry namespace agnostic * Security feedback: add warn logging to telemetry collector see elastic#66922 (comment) - add if statement - pass log dependency around (this is kinda medium, should maybe refactor) - update tests - move test file comment to the right file (was meant for telemetry route file) * Address feedback from Pierre - Remove unnecessary ServerConfigType - Remove unnecessary uiCapabilities - Move registerTelemetryRoute / SavedObjectsServiceStart workaround - Remove unnecessary license optional chaining * PR feedback Address type/typos * Fix telemetry API call returning 415 on Chrome - I can't even?? I swear charset=utf-8 fixed the same error a few weeks ago * Fix failing tests * Update Enterprise Search functional tests (without host) to run on CI - Fix incorrect navigateToApp slug (hadn't realized this was a URL, not an ID) - Update without_host_configured tests to run without API key - Update README * Address PR feedback from Pierre - remove unnecessary authz? - remove unnecessary content-type json headers - add loggingSystemMock.collect(mockLogger).error assertion - reconstrcut new MockRouter on beforeEach for better sandboxing - fix incorrect describe()s -should be it() - pull out reusable mockDependencies helper (renamed/extended from mockConfig) for tests that don't particularly use config/log but still want to pass type definitions - Fix comment copy Co-authored-by: Jason Stoltzfus <jastoltz24@gmail.com> Co-authored-by: Chris Cressman <chris@chriscressman.com> Co-authored-by: scottybollinger <scotty.bollinger@elastic.co> Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
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kibanamachine
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Feb 1, 2021
…astic#85778) * Migrations V2 on by default * esArchiver delete migrations v2 indices * Fix saved_objects_management api_integration tests * Try to fix v2 migrations for pre-release builds * esArchiver delete auto-created v2 migration indices like .kibana_8.0.0 * Try to fix v2 migrations for pre-release builds * Use require_alias to prevent auto-created saved objects index * Wrap SO routes until core logs all internal errors * Fix api_integration tests requiring an empty kibana index * Delete corrupt saved object from lens archives * Update docs * Fix ui_settings tests * Fix core jest tests * Fix type errors * Fix accessibility tests * Fix plugin functional tests * Fix api_integration tests after merging in master * Fix plugin functional tests #2 * EsArchiver: Don't reset ui settings after the .kibana index was deleted * Fix functional management/visualize tests * Fix oss security functional tests * EsArchiver clean task manager indices to fix alerting api integration tests * migrationsv2 correctly handle unknown saved object type mappings * Revert "Try to fix v2 migrations for pre-release builds" This reverts commit a1a1567. * Revert "Try to fix v2 migrations for pre-release builds" This reverts commit a9a9355. * Re-enable v2 migrations in tests after merging in master * Try to fix async dashboard functional test * Restore UiSettings defaults after emptyKibanaIndex() * Review feedback: rename test to match behaviour
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Aug 9, 2021
* Updated spaces management page * Fixed test failures * updated snapshot * Added suggestions form code review * Fixed unit test * Review suggestion #2 * WIP * Fix build errors * fix type * remove test for popup that doesnt exist anymore * fix test * fix a11y issues * fix a11y issue * Removed unused css * Fix functional test * Added suggestions from code review * Fix typescript errors * Added suggestions from code review Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com>
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…_transpilation when setting up node env (elastic#114940) * fix(NA): adds no_transpilation_dist to avoid preserve_symlinks on dist * chore(NA): setup node env correctly on functional tests * chore(NA): try to fix tests * chore(NA): correctly separate split * chore(NA): check ensure preserve symlinks need * chore(NA): investigate path resolve result * chore(NA): investigate path resolve result #2 * chore(NA): comment out preserve symlinks * chore(NA): apply fs.realpathSync into the calculated REPO_ROOT paths on babel_register_for_test_plugins * chore(NA): removes debug code * chore(NA): move array definition * chore(NA): correctly import fs Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com>
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…_transpilation when setting up node env (elastic#115246) * fix(NA): adds no_transpilation_dist to avoid preserve_symlinks on dist * chore(NA): setup node env correctly on functional tests * chore(NA): try to fix tests * chore(NA): correctly separate split * chore(NA): check ensure preserve symlinks need * chore(NA): investigate path resolve result * chore(NA): investigate path resolve result #2 * chore(NA): comment out preserve symlinks * chore(NA): apply fs.realpathSync into the calculated REPO_ROOT paths on babel_register_for_test_plugins * chore(NA): removes debug code * chore(NA): move array definition * chore(NA): correctly import fs * chore(NA): add debug code * chore(NA): some more debug statements * chore(NA): remove ensure symlinks * chore(NA): trying to solve double symlinking * chore(NA): test mappings * chore(NA): process path * chore(NA): test a second map * chore(NA): using a different mappings * chore(NA): more debug cases * chore(NA): more debug logic * chore(NA): more debug cases * chore(NA): more debug cases * chore(NA): more debug cases * chore(NA): try to add realpathSync into require * chore(NA): try to add realpathSync into require * fix(NA): jenkins and buildkite run * chore(NA): add debug logs * chore(NA): correct path * chore(NA): correct path * chore(NA): add more test maps * chore(NA): add more test maps * chore(NA): add some more test maps experiments * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): include all correct transpilations for each jenkins path * chore(NA): include all correct transpilations for each used asset path * chore(NA): include all correct transpilations for each used asset path * chore(NA): remove jenkins support Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com>
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Oct 27, 2021
…_transpilation when setting up node env (elastic#115246) (elastic#115993) * fix(NA): adds no_transpilation_dist to avoid preserve_symlinks on dist * chore(NA): setup node env correctly on functional tests * chore(NA): try to fix tests * chore(NA): correctly separate split * chore(NA): check ensure preserve symlinks need * chore(NA): investigate path resolve result * chore(NA): investigate path resolve result #2 * chore(NA): comment out preserve symlinks * chore(NA): apply fs.realpathSync into the calculated REPO_ROOT paths on babel_register_for_test_plugins * chore(NA): removes debug code * chore(NA): move array definition * chore(NA): correctly import fs * chore(NA): add debug code * chore(NA): some more debug statements * chore(NA): remove ensure symlinks * chore(NA): trying to solve double symlinking * chore(NA): test mappings * chore(NA): process path * chore(NA): test a second map * chore(NA): using a different mappings * chore(NA): more debug cases * chore(NA): more debug logic * chore(NA): more debug cases * chore(NA): more debug cases * chore(NA): more debug cases * chore(NA): try to add realpathSync into require * chore(NA): try to add realpathSync into require * fix(NA): jenkins and buildkite run * chore(NA): add debug logs * chore(NA): correct path * chore(NA): correct path * chore(NA): add more test maps * chore(NA): add more test maps * chore(NA): add some more test maps experiments * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): try to remove another test map dep * chore(NA): include all correct transpilations for each jenkins path * chore(NA): include all correct transpilations for each used asset path * chore(NA): include all correct transpilations for each used asset path * chore(NA): remove jenkins support Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com> Co-authored-by: Tiago Costa <tiagoffcc@hotmail.com>
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* Initial commit for EUI 57.0.0 upgrade * Handle i18n changes * Resolved type errors in DatePicker and Markdown Editor * Resolve test failures in Jest Test Suite #1. Updated multiple snapshots for euiLink and euiTitle as they have been converted to Emotion * Resolved failing tests for Jest Suite #2. Updated snapshots for euiHealth, euiAvatar, euiSpacer, euiTitle, and euiLink as they have recently been converted to Emotion * Resolved failing tests for Jest Suite 3. Updated failing snapshots as EuiSpacer, EuiText, EuiCallout, EuiHorizontalRule, EuiTitle, and EuiLink have been converted to Emotion. Updated the i18n translation snapshots * Upgrade EUI verion to 58.0.0 * Resolved tests failures from Jest Test Suite 4. Updated snapshots as EuiLink, EuiTitle, EuiHorizontalRule, EuiSpace, and EuiCallout have been converted to Emotion * Resolved failing test cases for Jest Test Suite 5. Updated snapshots as EuiLoader has been converted to Emotion * Resolved failing tests in Jest Test Suite 6. Updated snapshots as EuiSpacer, EuiHorizontalRule, Eui Callout, and EuiLink have been converted to Emotion * Resolved type errors for EuiDatePicker component * Resolved type error within EuiContextMenu by removing the watchedItemProps prop. It was recently deprecated in EUI PR# 5880 (elastic/eui#5880) as is no longer needed * Resolved type error within EuiContextMenu by removing the watchedItemProps prop. It was recently deprecated in EUI PR# 5880 (elastic/eui#5880) as is no longer needed * Resolved type error within EuiContextMenu by removing the watchedItemProps prop. It was recently deprecated in EUI PR# 5880 (elastic/eui#5880) as is no longer needed * Resolved type errors by updating the popoverPlacement prop for the EuiDatePicket component with new / valid values. A list of values were deprecated and new values were added in EUI PR elastic#5868 (elastic/eui#5868) * Resolved type error within EuiTabs by removing instances of display: condensed as it is no longer a part of the Amsterdam theme via EUI PR elastic#5868(elastic/eui#5868) * Remove deprecated `display` prop from EuiTabs * Deprecate `.eui-textOverflowWrap` * Deprecate EuiSuggestItem `labelDisplay` prop * [EuiStepsHorizontal] Replace deprecated `isComplete`/`isSelected` with `status` * Update last EuiStepsHorizontal `status` migration - this one was more complex than the previous commit due to existing `status` usage and conditional steps. Some amount of logic was simplified via `completedStep` * Resolved type error within EuiTabs by removing instances of display: condensed as it is no longer a part of the Amsterdam theme via EUI PR elastic#5868(elastic/eui#5868) * Resolved failing test cases in Jest Test Suite 5. Updated snapshots as EuiTitle has been converted to Emotion * Resovlved failing test cases in Jest Test Suite 4. Updated snapshots as EuiTitle and EuiSpacer have been converted to Emotion. Resolved failing tests for EuiLink click simlulations by esuring the test is referencing the correct element. * Resolved failing test cases in Jest Test Suite 3. Updated snapshots as EuiLink, EuiSpacr, and EuiTItle have been converted to Emotion. Updated various test cases to ensure that the references to EuiLink are correct * [CI] Auto-commit changed files from 'node scripts/eslint --no-cache --fix' * Resolved failing test cases in Jest Test Suite 1. Updated snapshots as EUI text utilities have been converted to Emotion. Updated referenes to EuiLink to ensure test are simulating clicks on the correct elements * Resolved failing test for Jet Test Suite 2. Updated required snapshots. Updated references to EuiLink to ensure that tests are simulating clicks on the correct elements * [CI] Auto-commit changed files from 'node scripts/eslint --no-cache --fix' * Resolved failing test cases for Jest Test Suite 5. Updated references to EuiLink to ensure tests are targeting the correct element * [CI] Auto-commit changed files from 'node scripts/eslint --no-cache --fix' * Resolved failing test cases across the Jest Test Suites. Updated required snaphots for components recently converted to Emotion. Updated test cases to ensure that tests targeting EuiLink are using the correct element. * Resolved failing tests from multiple Jest test suites. Updated snapshots for components that have recently been converted to Emotion. Updated tests that reference the EuiLink component to ensure the correct element is being targeted * Updated the getEuiStepsHorizontal function. Previously, this function used the .euiStepHorizontal-isSelected class (now deprecated) to determine which step was current. The function has been updated to use the status prop. * Updated Jest integration test snapshots to account for the recent conversion of EuiLoader to Emotion * Resolved failing tests in Jest suites 2 and 4. Updated required snapshots and references for tests using EuiLink * Removed a console statement. Extracted a nested turnary operation into its own function. * Rollback new turnary function and replace it with a simple if/else * Rollback new turnary function and replace it with a simple if/else * Rollback new turnary function and replace it with a simple if/else * Rollback new turnary function and replace it with a simple if/else * [CI] Auto-commit changed files from 'node scripts/precommit_hook.js --ref HEAD~1..HEAD --fix' * Resolved failing test cases in Jest Suites 3 and 5 by updating required snapshots * revert doc_viewer_source test and snapshot changes * Take care of merge conflict in license_checker: * Reverted .render() change for analytics_no_data_page.component.test.tsx. Restored snapshot * Reverted .render() change for analytics_no_data_page.component.test.tsx. Restored snapshot Co-authored-by: Constance Chen <constance.chen@elastic.co> Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com> Co-authored-by: Greg Thompson <thompson.glowe@gmail.com>
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* Misc enhancements following PR comments * Adding functional tests * Fixing types * Fixing tests * Removing unnecessary Promise.all * Cleanup * Misc fixes and simplifications * Add missing tsconfig.json * [CI] Auto-commit changed files from 'node scripts/build_plugin_list_docs' * Add dependency to Actions plugin in tsconfig.json * Separate setup logic from start logic * Fix bulkEnqueueExecution params structure * Update README * Add UTs * Check license type >platinum for email notifications * Fix incorrect UTs * Import types when possible * Misc enhancements and code cleanup * Transform factory => provider, update start contract * Code cleanup, update README * Fix TS error * Fix CI types error * Address PR remarks * Address PR remarks #2 Co-authored-by: Ying Mao <ying.mao@elastic.co> Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com>
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Jun 12, 2023
…lastic#159352) ## Summary Skip `Security Solution Tests #2 / rule snoozing Rule editing page / actions tab adds an action to a snoozed rule` [This test failed on `main` as soon as it was merged.](https://buildkite.com/elastic/kibana-on-merge-unsupported-ftrs/builds/2952) ### For maintainers - [ ] This was checked for breaking API changes and was [labeled appropriately](https://www.elastic.co/guide/en/kibana/master/contributing.html#kibana-release-notes-process)
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Oct 10, 2023
… integration for ES|QL query generation via ELSER (elastic#167097) ## [Security Solution] [Elastic AI Assistant] LangChain Agents and Tools integration for ES|QL query generation via ELSER This PR integrates [LangChain](https://www.langchain.com/) [Agents](https://js.langchain.com/docs/modules/agents/) and [Tools](https://js.langchain.com/docs/modules/agents/tools/) with the [Elastic AI Assistant](https://www.elastic.co/blog/introducing-elastic-ai-assistant). These abstractions enable the LLM to dynamically choose whether or not to query, via [ELSER](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html), an [ES|QL](https://www.elastic.co/blog/elasticsearch-query-language-esql) knowledge base. Context from the knowledge base is used to generate `ES|QL` queries, or answer questions about `ES|QL`. Registration of the tool occurs in `x-pack/plugins/elastic_assistant/server/lib/langchain/execute_custom_llm_chain/index.ts`: ```typescript const tools: Tool[] = [ new ChainTool({ name: 'esql-language-knowledge-base', description: 'Call this for knowledge on how to build an ESQL query, or answer questions about the ES|QL query language.', chain, }), ]; ``` The `tools` array above may be updated in future PRs to include, for example, an `ES|QL` query validator endpoint. ### Details The `callAgentExecutor` function in `x-pack/plugins/elastic_assistant/server/lib/langchain/execute_custom_llm_chain/index.ts`: 1. Creates a `RetrievalQAChain` from an `ELSER` backed `ElasticsearchStore`, which serves as a knowledge base for `ES|QL`: ```typescript // ELSER backed ElasticsearchStore for Knowledge Base const esStore = new ElasticsearchStore(esClient, KNOWLEDGE_BASE_INDEX_PATTERN, logger); const chain = RetrievalQAChain.fromLLM(llm, esStore.asRetriever()); ``` 2. Registers the chain as a tool, which may be invoked by the LLM based on its description: ```typescript const tools: Tool[] = [ new ChainTool({ name: 'esql-language-knowledge-base', description: 'Call this for knowledge on how to build an ESQL query, or answer questions about the ES|QL query language.', chain, }), ]; ``` 3. Creates an Agent executor that combines the `tools` above, the `ActionsClientLlm` (an abstraction that calls `actionsClient.execute`), and memory of the previous messages in the conversation: ```typescript const executor = await initializeAgentExecutorWithOptions(tools, llm, { agentType: 'chat-conversational-react-description', memory, verbose: false, }); ``` Note: Set `verbose` above to `true` to for detailed debugging output from LangChain. 4. Calls the `executor`, kicking it off with `latestMessage`: ```typescript await executor.call({ input: latestMessage[0].content }); ``` ### Changes to `x-pack/packages/kbn-elastic-assistant` A client side change was required to the assistant, because the response returned from the agent executor is JSON. This response is parsed on the client in `x-pack/packages/kbn-elastic-assistant/impl/assistant/api.tsx`: ```typescript return assistantLangChain ? getFormattedMessageContent(result) : result; ``` Client-side parsing of the response only happens when then `assistantLangChain` feature flag is `true`. ## Desk testing Set ```typescript assistantLangChain={true} ``` in `x-pack/plugins/security_solution/public/assistant/provider.tsx` to enable this experimental feature in development environments. Also (optionally) set `verbose` to `true` in the following code in ``x-pack/plugins/elastic_assistant/server/lib/langchain/execute_custom_llm_chain/index.ts``: ```typescript const executor = await initializeAgentExecutorWithOptions(tools, llm, { agentType: 'chat-conversational-react-description', memory, verbose: true, }); ``` After setting the feature flag and optionally enabling verbose debugging output, you may ask the assistant to generate an `ES|QL` query, per the example in the next section. ### Example output When the Elastic AI Assistant is asked: ``` From employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. "September 2019". Only show the query ``` it replies: ``` Here is the query to get the employee number and the formatted hire date for the 5 earliest employees by hire_date: FROM employees | KEEP emp_no, hire_date | EVAL month_year = DATE_FORMAT(hire_date, "MMMM YYYY") | SORT hire_date | LIMIT 5 ``` Per the screenshot below:  The `verbose: true` output from LangChain logged to the console reveals that the prompt sent to the LLM includes text like the following: ``` Assistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\\n\\nesql-language-knowledge-base: Call this for knowledge on how to build an ESQL query, or answer questions about the ES|QL query language. ``` along with instructions for "calling" the tool like a function. The debugging output also reveals the agent selecting the tool, and returning results from ESLR: ``` [agent/action] [1:chain:AgentExecutor] Agent selected action: { "tool": "esql-language-knowledge-base", "toolInput": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.", "log": "```json\n{\n \"action\": \"esql-language-knowledge-base\",\n \"action_input\": \"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\"\n}\n```" } [tool/start] [1:chain:AgentExecutor > 4:tool:ChainTool] Entering Tool run with input: "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." [chain/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain] Entering Chain run with input: { "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." } [retriever/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 6:retriever:VectorStoreRetriever] Entering Retriever run with input: { "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." } [retriever/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 6:retriever:VectorStoreRetriever] [115ms] Exiting Retriever run with output: { "documents": [ { "pageContent": "[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n", ``` The documents containing `ES|QL` examples, retrieved from ELSER, are sent back to the LLM to answer the original question, per the abridged output below: ``` [llm/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain > 8:chain:LLMChain > 9:llm:ActionsClientLlm] Entering LLM run with input: { "prompts": [ "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n\n\n[[esql-date_trunc]]\n=== `DATE_TRUNC`\nRounds down a date to the closest interval. Intervals can be expressed using the\n<<esql-timespan-literals,timespan literal syntax>>.\n\n[source,esql]\n----\nFROM employees\n| EVAL year_hired = DATE_TRUNC(1 year, hire_date)\n| STATS count(emp_no) BY year_hired\n| SORT year_hired\n----\n\n\n[[esql-from]]\n=== `FROM`\n\nThe `FROM` source command returns a table with up to 10,000 documents from a\ndata stream, index, ``` ### Complete (verbose) LangChain output from the example The following `verbose: true` output from LangChain below was produced via the example in the previous section: ``` [chain/start] [1:chain:AgentExecutor] Entering Chain run with input: { "input": "\n\n\n\nFrom employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. \"September 2019\". Only show the query", "chat_history": [] } [chain/start] [1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with input: { "input": "\n\n\n\nFrom employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. \"September 2019\". Only show the query", "chat_history": [], "agent_scratchpad": [], "stop": [ "Observation:" ] } [llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ActionsClientLlm] Entering LLM run with input: { "prompts": [ "[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"SystemMessage\"],\"kwargs\":{\"content\":\"Assistant is a large language model trained by OpenAI.\\n\\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\\n\\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\\n\\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist. However, above all else, all responses must adhere to the format of RESPONSE FORMAT INSTRUCTIONS.\",\"additional_kwargs\":{}}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"HumanMessage\"],\"kwargs\":{\"content\":\"TOOLS\\n------\\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\\n\\nesql-language-knowledge-base: Call this for knowledge on how to build an ESQL query, or answer questions about the ES|QL query language.\\n\\nRESPONSE FORMAT INSTRUCTIONS\\n----------------------------\\n\\nOutput a JSON markdown code snippet containing a valid JSON object in one of two formats:\\n\\n**Option 1:**\\nUse this if you want the human to use a tool.\\nMarkdown code snippet formatted in the following schema:\\n\\n```json\\n{\\n \\\"action\\\": string, // The action to take. Must be one of [esql-language-knowledge-base]\\n \\\"action_input\\\": string // The input to the action. May be a stringified object.\\n}\\n```\\n\\n**Option #2:**\\nUse this if you want to respond directly and conversationally to the human. Markdown code snippet formatted in the following schema:\\n\\n```json\\n{\\n \\\"action\\\": \\\"Final Answer\\\",\\n \\\"action_input\\\": string // You should put what you want to return to use here and make sure to use valid json newline characters.\\n}\\n```\\n\\nFor both options, remember to always include the surrounding markdown code snippet delimiters (begin with \\\"```json\\\" and end with \\\"```\\\")!\\n\\n\\nUSER'S INPUT\\n--------------------\\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\\n\\n\\n\\n\\n\\nFrom employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. \\\"September 2019\\\". Only show the query\",\"additional_kwargs\":{}}}]" ] } [llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ActionsClientLlm] [3.08s] Exiting LLM run with output: { "generations": [ [ { "text": "```json\n{\n \"action\": \"esql-language-knowledge-base\",\n \"action_input\": \"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\"\n}\n```" } ] ] } [chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [3.09s] Exiting Chain run with output: { "text": "```json\n{\n \"action\": \"esql-language-knowledge-base\",\n \"action_input\": \"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\"\n}\n```" } [agent/action] [1:chain:AgentExecutor] Agent selected action: { "tool": "esql-language-knowledge-base", "toolInput": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.", "log": "```json\n{\n \"action\": \"esql-language-knowledge-base\",\n \"action_input\": \"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\"\n}\n```" } [tool/start] [1:chain:AgentExecutor > 4:tool:ChainTool] Entering Tool run with input: "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." [chain/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain] Entering Chain run with input: { "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." } [retriever/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 6:retriever:VectorStoreRetriever] Entering Retriever run with input: { "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." } [retriever/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 6:retriever:VectorStoreRetriever] [115ms] Exiting Retriever run with output: { "documents": [ { "pageContent": "[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/functions/date_format.asciidoc" } }, { "pageContent": "[[esql-date_trunc]]\n=== `DATE_TRUNC`\nRounds down a date to the closest interval. Intervals can be expressed using the\n<<esql-timespan-literals,timespan literal syntax>>.\n\n[source,esql]\n----\nFROM employees\n| EVAL year_hired = DATE_TRUNC(1 year, hire_date)\n| STATS count(emp_no) BY year_hired\n| SORT year_hired\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/functions/date_trunc.asciidoc" } }, { "pageContent": "[[esql-from]]\n=== `FROM`\n\nThe `FROM` source command returns a table with up to 10,000 documents from a\ndata stream, index, or alias. Each row in the resulting table represents a\ndocument. Each column corresponds to a field, and can be accessed by the name\nof that field.\n\n[source,esql]\n----\nFROM employees\n----\n\nYou can use <<api-date-math-index-names,date math>> to refer to indices, aliases\nand data streams. This can be useful for time series data, for example to access\ntoday's index:\n\n[source,esql]\n----\nFROM <logs-{now/d}>\n----\n\nUse comma-separated lists or wildcards to query multiple data streams, indices,\nor aliases:\n\n[source,esql]\n----\nFROM employees-00001,employees-*\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/source_commands/from.asciidoc" } }, { "pageContent": "[[esql-where]]\n=== `WHERE`\n\nUse `WHERE` to produce a table that contains all the rows from the input table\nfor which the provided condition evaluates to `true`:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=where]\n----\n\nWhich, if `still_hired` is a boolean field, can be simplified to:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereBoolean]\n----\n\n[discrete]\n==== Operators\n\nRefer to <<esql-operators>> for an overview of the supported operators.\n\n[discrete]\n==== Functions\n`WHERE` supports various functions for calculating values. Refer to\n<<esql-functions,Functions>> for more information.\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereFunction]\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/processing_commands/where.asciidoc" } } ] } [chain/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain] Entering Chain run with input: { "question": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.", "input_documents": [ { "pageContent": "[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/functions/date_format.asciidoc" } }, { "pageContent": "[[esql-date_trunc]]\n=== `DATE_TRUNC`\nRounds down a date to the closest interval. Intervals can be expressed using the\n<<esql-timespan-literals,timespan literal syntax>>.\n\n[source,esql]\n----\nFROM employees\n| EVAL year_hired = DATE_TRUNC(1 year, hire_date)\n| STATS count(emp_no) BY year_hired\n| SORT year_hired\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/functions/date_trunc.asciidoc" } }, { "pageContent": "[[esql-from]]\n=== `FROM`\n\nThe `FROM` source command returns a table with up to 10,000 documents from a\ndata stream, index, or alias. Each row in the resulting table represents a\ndocument. Each column corresponds to a field, and can be accessed by the name\nof that field.\n\n[source,esql]\n----\nFROM employees\n----\n\nYou can use <<api-date-math-index-names,date math>> to refer to indices, aliases\nand data streams. This can be useful for time series data, for example to access\ntoday's index:\n\n[source,esql]\n----\nFROM <logs-{now/d}>\n----\n\nUse comma-separated lists or wildcards to query multiple data streams, indices,\nor aliases:\n\n[source,esql]\n----\nFROM employees-00001,employees-*\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/source_commands/from.asciidoc" } }, { "pageContent": "[[esql-where]]\n=== `WHERE`\n\nUse `WHERE` to produce a table that contains all the rows from the input table\nfor which the provided condition evaluates to `true`:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=where]\n----\n\nWhich, if `still_hired` is a boolean field, can be simplified to:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereBoolean]\n----\n\n[discrete]\n==== Operators\n\nRefer to <<esql-operators>> for an overview of the supported operators.\n\n[discrete]\n==== Functions\n`WHERE` supports various functions for calculating values. Refer to\n<<esql-functions,Functions>> for more information.\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereFunction]\n----\n", "metadata": { "source": "/Users/andrew.goldstein/Projects/forks/spong/kibana/x-pack/plugins/elastic_assistant/server/knowledge_base/esql/docs/processing_commands/where.asciidoc" } } ], "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'." } [chain/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain > 8:chain:LLMChain] Entering Chain run with input: { "question": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.", "query": "Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.", "context": "[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n\n\n[[esql-date_trunc]]\n=== `DATE_TRUNC`\nRounds down a date to the closest interval. Intervals can be expressed using the\n<<esql-timespan-literals,timespan literal syntax>>.\n\n[source,esql]\n----\nFROM employees\n| EVAL year_hired = DATE_TRUNC(1 year, hire_date)\n| STATS count(emp_no) BY year_hired\n| SORT year_hired\n----\n\n\n[[esql-from]]\n=== `FROM`\n\nThe `FROM` source command returns a table with up to 10,000 documents from a\ndata stream, index, or alias. Each row in the resulting table represents a\ndocument. Each column corresponds to a field, and can be accessed by the name\nof that field.\n\n[source,esql]\n----\nFROM employees\n----\n\nYou can use <<api-date-math-index-names,date math>> to refer to indices, aliases\nand data streams. This can be useful for time series data, for example to access\ntoday's index:\n\n[source,esql]\n----\nFROM <logs-{now/d}>\n----\n\nUse comma-separated lists or wildcards to query multiple data streams, indices,\nor aliases:\n\n[source,esql]\n----\nFROM employees-00001,employees-*\n----\n\n\n[[esql-where]]\n=== `WHERE`\n\nUse `WHERE` to produce a table that contains all the rows from the input table\nfor which the provided condition evaluates to `true`:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=where]\n----\n\nWhich, if `still_hired` is a boolean field, can be simplified to:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereBoolean]\n----\n\n[discrete]\n==== Operators\n\nRefer to <<esql-operators>> for an overview of the supported operators.\n\n[discrete]\n==== Functions\n`WHERE` supports various functions for calculating values. Refer to\n<<esql-functions,Functions>> for more information.\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereFunction]\n----\n" } [llm/start] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain > 8:chain:LLMChain > 9:llm:ActionsClientLlm] Entering LLM run with input: { "prompts": [ "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n[[esql-date_format]]\n=== `DATE_FORMAT`\nReturns a string representation of a date in the provided format. If no format\nis specified, the `yyyy-MM-dd'T'HH:mm:ss.SSSZ` format is used.\n\n[source,esql]\n----\nFROM employees\n| KEEP first_name, last_name, hire_date\n| EVAL hired = DATE_FORMAT(hire_date, \"YYYY-MM-dd\")\n----\n\n\n[[esql-date_trunc]]\n=== `DATE_TRUNC`\nRounds down a date to the closest interval. Intervals can be expressed using the\n<<esql-timespan-literals,timespan literal syntax>>.\n\n[source,esql]\n----\nFROM employees\n| EVAL year_hired = DATE_TRUNC(1 year, hire_date)\n| STATS count(emp_no) BY year_hired\n| SORT year_hired\n----\n\n\n[[esql-from]]\n=== `FROM`\n\nThe `FROM` source command returns a table with up to 10,000 documents from a\ndata stream, index, or alias. Each row in the resulting table represents a\ndocument. Each column corresponds to a field, and can be accessed by the name\nof that field.\n\n[source,esql]\n----\nFROM employees\n----\n\nYou can use <<api-date-math-index-names,date math>> to refer to indices, aliases\nand data streams. This can be useful for time series data, for example to access\ntoday's index:\n\n[source,esql]\n----\nFROM <logs-{now/d}>\n----\n\nUse comma-separated lists or wildcards to query multiple data streams, indices,\nor aliases:\n\n[source,esql]\n----\nFROM employees-00001,employees-*\n----\n\n\n[[esql-where]]\n=== `WHERE`\n\nUse `WHERE` to produce a table that contains all the rows from the input table\nfor which the provided condition evaluates to `true`:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=where]\n----\n\nWhich, if `still_hired` is a boolean field, can be simplified to:\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereBoolean]\n----\n\n[discrete]\n==== Operators\n\nRefer to <<esql-operators>> for an overview of the supported operators.\n\n[discrete]\n==== Functions\n`WHERE` supports various functions for calculating values. Refer to\n<<esql-functions,Functions>> for more information.\n\n[source,esql]\n----\ninclude::{esql-specs}/docs.csv-spec[tag=whereFunction]\n----\n\n\nQuestion: Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\nHelpful Answer:" ] } [llm/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain > 8:chain:LLMChain > 9:llm:ActionsClientLlm] [2.23s] Exiting LLM run with output: { "generations": [ [ { "text": "FROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5" } ] ] } [chain/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain > 8:chain:LLMChain] [2.23s] Exiting Chain run with output: { "text": "FROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5" } [chain/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain > 7:chain:StuffDocumentsChain] [2.23s] Exiting Chain run with output: { "text": "FROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5" } [chain/end] [1:chain:AgentExecutor > 4:tool:ChainTool > 5:chain:RetrievalQAChain] [2.35s] Exiting Chain run with output: { "text": "FROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5" } [tool/end] [1:chain:AgentExecutor > 4:tool:ChainTool] [2.35s] Exiting Tool run with output: "FROM employees | KEEP emp_no, hire_date | EVAL month_year = DATE_FORMAT(hire_date, "MMMM YYYY") | SORT hire_date | LIMIT 5" [chain/start] [1:chain:AgentExecutor > 10:chain:LLMChain] Entering Chain run with input: { "input": "\n\n\n\nFrom employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. \"September 2019\". Only show the query", "chat_history": [], "agent_scratchpad": [ { "lc": 1, "type": "constructor", "id": [ "langchain", "schema", "AIMessage" ], "kwargs": { "content": "```json\n{\n \"action\": \"esql-language-knowledge-base\",\n \"action_input\": \"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\"\n}\n```", "additional_kwargs": {} } }, { "lc": 1, "type": "constructor", "id": [ "langchain", "schema", "HumanMessage" ], "kwargs": { "content": "TOOL RESPONSE:\n---------------------\nFROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5\n\nUSER'S INPUT\n--------------------\n\nOkay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else.", "additional_kwargs": {} } } ], "stop": [ "Observation:" ] } [llm/start] [1:chain:AgentExecutor > 10:chain:LLMChain > 11:llm:ActionsClientLlm] Entering LLM run with input: { "prompts": [ "[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"SystemMessage\"],\"kwargs\":{\"content\":\"Assistant is a large language model trained by OpenAI.\\n\\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\\n\\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\\n\\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist. However, above all else, all responses must adhere to the format of RESPONSE FORMAT INSTRUCTIONS.\",\"additional_kwargs\":{}}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"HumanMessage\"],\"kwargs\":{\"content\":\"TOOLS\\n------\\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\\n\\nesql-language-knowledge-base: Call this for knowledge on how to build an ESQL query, or answer questions about the ES|QL query language.\\n\\nRESPONSE FORMAT INSTRUCTIONS\\n----------------------------\\n\\nOutput a JSON markdown code snippet containing a valid JSON object in one of two formats:\\n\\n**Option 1:**\\nUse this if you want the human to use a tool.\\nMarkdown code snippet formatted in the following schema:\\n\\n```json\\n{\\n \\\"action\\\": string, // The action to take. Must be one of [esql-language-knowledge-base]\\n \\\"action_input\\\": string // The input to the action. May be a stringified object.\\n}\\n```\\n\\n**Option #2:**\\nUse this if you want to respond directly and conversationally to the human. Markdown code snippet formatted in the following schema:\\n\\n```json\\n{\\n \\\"action\\\": \\\"Final Answer\\\",\\n \\\"action_input\\\": string // You should put what you want to return to use here and make sure to use valid json newline characters.\\n}\\n```\\n\\nFor both options, remember to always include the surrounding markdown code snippet delimiters (begin with \\\"```json\\\" and end with \\\"```\\\")!\\n\\n\\nUSER'S INPUT\\n--------------------\\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\\n\\n\\n\\n\\n\\nFrom employees, I want to see the 5 earliest employees (hire_date), I want to display only the month and the year that they were hired in and their employee number (emp_no). Format the date as e.g. \\\"September 2019\\\". Only show the query\",\"additional_kwargs\":{}}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"AIMessage\"],\"kwargs\":{\"content\":\"```json\\n{\\n \\\"action\\\": \\\"esql-language-knowledge-base\\\",\\n \\\"action_input\\\": \\\"Display the 'emp_no', month and year of the 5 earliest employees by 'hire_date'. Format the date as 'Month Year'.\\\"\\n}\\n```\",\"additional_kwargs\":{}}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"HumanMessage\"],\"kwargs\":{\"content\":\"TOOL RESPONSE:\\n---------------------\\nFROM employees\\n| KEEP emp_no, hire_date\\n| EVAL month_year = DATE_FORMAT(hire_date, \\\"MMMM YYYY\\\")\\n| SORT hire_date\\n| LIMIT 5\\n\\nUSER'S INPUT\\n--------------------\\n\\nOkay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else.\",\"additional_kwargs\":{}}}]" ] } [llm/end] [1:chain:AgentExecutor > 10:chain:LLMChain > 11:llm:ActionsClientLlm] [6.47s] Exiting LLM run with output: { "generations": [ [ { "text": "```json\n{\n \"action\": \"Final Answer\",\n \"action_input\": \"Here is the query to get the employee number and the formatted hire date for the 5 earliest employees by hire_date:\\n\\nFROM employees\\n| KEEP emp_no, hire_date\\n| EVAL month_year = DATE_FORMAT(hire_date, \\\"MMMM YYYY\\\")\\n| SORT hire_date\\n| LIMIT 5\"\n}\n```" } ] ] } [chain/end] [1:chain:AgentExecutor > 10:chain:LLMChain] [6.47s] Exiting Chain run with output: { "text": "```json\n{\n \"action\": \"Final Answer\",\n \"action_input\": \"Here is the query to get the employee number and the formatted hire date for the 5 earliest employees by hire_date:\\n\\nFROM employees\\n| KEEP emp_no, hire_date\\n| EVAL month_year = DATE_FORMAT(hire_date, \\\"MMMM YYYY\\\")\\n| SORT hire_date\\n| LIMIT 5\"\n}\n```" } [chain/end] [1:chain:AgentExecutor] [11.91s] Exiting Chain run with output: { "output": "Here is the query to get the employee number and the formatted hire date for the 5 earliest employees by hire_date:\n\nFROM employees\n| KEEP emp_no, hire_date\n| EVAL month_year = DATE_FORMAT(hire_date, \"MMMM YYYY\")\n| SORT hire_date\n| LIMIT 5" } ```
lukeelmers
pushed a commit
that referenced
this pull request
Dec 8, 2023
## Summary ### This PR enables user roles testing in FTR We use SAML authentication to get session cookie for user with the specific role. The cookie is cached on FTR service side so we only make SAML auth one time per user within FTR config run. For Kibana CI service relies on changes coming in elastic#170852 In order to run FTR tests locally against existing MKI project: - add `.ftr/role_users.json` in Kibana root dir ``` { "viewer": { "email": "...", "password": "..." }, "developer": { "email": "...", "password": "..." } } ``` - set Cloud hostname (!not project hostname!) with TEST_CLOUD_HOST_NAME, e.g. `export TEST_CLOUD_HOST_NAME=console.qa.cld.elstc.co` ### How to use: - functional tests: ``` const svlCommonPage = getPageObject('svlCommonPage'); before(async () => { // login with Viewer role await svlCommonPage.loginWithRole('viewer'); // you are logged in in browser and on project home page, start the test }); it('has project header', async () => { await svlCommonPage.assertProjectHeaderExists(); }); ``` - API integration tests: ``` const svlUserManager = getService('svlUserManager'); const supertestWithoutAuth = getService('supertestWithoutAuth'); let credentials: { Cookie: string }; before(async () => { // get auth header for Viewer role credentials = await svlUserManager.getApiCredentialsForRole('viewer'); }); it('returns full status payload for authenticated request', async () => { const { body } = await supertestWithoutAuth .get('/api/status') .set(credentials) .set('kbn-xsrf', 'kibana'); expect(body.name).to.be.a('string'); expect(body.uuid).to.be.a('string'); expect(body.version.number).to.be.a('string'); }); ``` Flaky-test-runner: #1 https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/4081 #2 https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/4114 --------- Co-authored-by: Robert Oskamp <traeluki@gmail.com> Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com> Co-authored-by: Aleh Zasypkin <aleh.zasypkin@gmail.com>
lukeelmers
pushed a commit
that referenced
this pull request
Sep 5, 2024
## Summary Resolves elastic#143905. This PR adds support for integration-level outputs. This means that different integrations within the same agent policy can now be configured to send data to different locations. This feature is gated behind `enterprise` level subscription. For each input, the agent policy will configure sending data to the following outputs in decreasing order of priority: 1. Output set specifically on the integration policy 2. Output set specifically on the integration's parent agent policy (including the case where an integration policy belongs to multiple agent policies) 3. Global default data output set via Fleet Settings Integration-level outputs will respect the same rules as agent policy-level outputs: - Certain integrations are disallowed from using certain output types, attempting to add them to each other via creation, updating, or "defaulting", will fail - `fleet-server`, `synthetics`, and `apm` can only use same-cluster Elasticsearch output - When an output is deleted, any integrations that were specifically using it will "clear" their output configuration and revert back to either `#2` or `#3` in the above list - When an output is edited, all agent policies across all spaces that use it will be bumped to a new revision, this includes: - Agent policies that have that output specifically set in their settings (existing behavior) - Agent policies that contain integrations which specifically has that output set (new behavior) - When a proxy is edited, the same new revision bump above will apply for any outputs using that proxy The final agent policy YAML that is generated will have: - `outputs` block that includes: - Data and monitoring outputs set at the agent policy level (existing behavior) - Any additional outputs set at the integration level, if they differ from the above - `outputs_permissions` block that includes permissions for each Elasticsearch output depending on which integrations and/or agent monitoring are assigned to it Integration policies table now includes `Output` column. If the output is defaulting to agent policy-level output, or global setting output, a tooltip is shown: <img width="1392" alt="image" src="https://github.com/user-attachments/assets/5534716b-49b5-402a-aa4a-4ba6533e0ca8"> Configuring an integration-level output is done under Advanced options in the policy editor. Setting to the blank value will "clear" the output configuration. The list of available outputs is filtered by what outputs are available for that integration (see above): <img width="799" alt="image" src="https://github.com/user-attachments/assets/617af6f4-e8f8-40b1-b476-848f8ac96e76"> An example of failure: ES output cannot be changed to Kafka while there is an integration <img width="1289" alt="image" src="https://github.com/user-attachments/assets/11847eb5-fd5d-4271-8464-983d7ab39218"> ## TODO - [x] Adjust side effects of editing/deleting output when policies use it across different spaces - [x] Add API integration tests - [x] Update OpenAPI spec - [x] Create doc issue ### Checklist Delete any items that are not applicable to this PR. - [x] Any text added follows [EUI's writing guidelines](https://elastic.github.io/eui/#/guidelines/writing), uses sentence case text and includes [i18n support](https://github.com/elastic/kibana/blob/main/packages/kbn-i18n/README.md) - [ ] [Documentation](https://www.elastic.co/guide/en/kibana/master/development-documentation.html) was added for features that require explanation or tutorials - [x] [Unit or functional tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html) were updated or added to match the most common scenarios --------- Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com>
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Summary
Proposed by @joshdover in elastic#51692 (comment)_
Removes
from/toStorageMapper.