add e2e integration test #2
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rudolf
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Jan 27, 2021
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
rudolf
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Nov 8, 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 <[email protected]> Co-authored-by: Tiago Costa <[email protected]>
rudolf
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May 16, 2022
* Client side execution app level context propagation * context$ + apm rum integration * invert the context parent \ child relationship (cc @mikhail) move more things to top level context * Pass down context to apm on server * types * eslint * parent <> child * docs + eslint + jest * execution context mock * eslint * jest * jest * server jest * check * jest * storybook * jest * report the current space * fix server side context container * Remove spaces for now * docssss * jest * lint * test * docs * revert file * doc * all context params are optional * clear on page change * lint * ts * skipped test again * testing fixes * oops * code review #1 * code review #2 * getAsLabels * maps inherit dashboard context * docs * ts * Give common context to all vis editors * fix test * ts \ es \ tests * labels * missing types * docsy docs * cr elastic#3 * improve jest * Use editor name * Update src/plugins/visualizations/public/visualize_app/components/visualize_editor.tsx Co-authored-by: Marco Liberati <[email protected]> * fix maps context * jest tests for maps * cr * docs * Update execution_context.test.ts * docs * lint Co-authored-by: Marco Liberati <[email protected]>
rudolf
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Jun 23, 2022
* 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 <[email protected]> Co-authored-by: kibanamachine <[email protected]> Co-authored-by: Greg Thompson <[email protected]>
rudolf
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Jun 15, 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)
rudolf
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Sep 25, 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" } ```
rudolf
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Sep 20, 2024
…193441) ## Summary More files to be regenerated with a different shape since the js-yaml update: elastic#190678
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Summary
PR against elastic#78413
Add e2e testing for SO migration v2
7.3sample data set (xpack) currently failing