Replace embeddable + panel changes #2
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Heenawter
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| const panelId = await this.replaceEmbeddable( | ||
| previousPanelState.explicitInput.id, | ||
| { | ||
| ...newPanelState.explicitInput, | ||
| id: previousPanelState.explicitInput.id, | ||
| }, | ||
| newPanelState.type, | ||
| generateNewId | ||
| ); |
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Now that replacePanel is basically just a wrapper for replaceEmbeddable, perhaps we should remove it entirely and replace every instance with replaceEmbeddable? 👀
nickpeihl
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Aug 8, 2023
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lgtm!
using this I discovered a flaw in the navigation embeddable by-reference code which I fixed in elastic@ff28220
code review and tested replacing several types of panels as well as controls
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Sep 26, 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" } ```
nickpeihl
pushed a commit
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Dec 5, 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 <[email protected]> Co-authored-by: kibanamachine <[email protected]> Co-authored-by: Aleh Zasypkin <[email protected]>
nickpeihl
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Jan 3, 2024
nickpeihl
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Jan 3, 2024
## Summary The previous PR elastic#161813 was reverted due to the broken webpack config elastic@eef1afc --------- Co-authored-by: Tiago Costa <[email protected]> Co-authored-by: kibanamachine <[email protected]> Co-authored-by: Jon <[email protected]>
nickpeihl
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Jan 30, 2024
…ic#175194) ## Summary This PR fixes the issue causing (mostly) [login journey](https://buildkite.com/elastic/kibana-single-user-performance/builds/12398#018d1149-cc2e-4591-a61c-176768081e2c) stuck for 14 min waiting for Telemetry call response. <img width="964" alt="Screenshot 2024-01-22 at 11 12 24" src="https://github.com/elastic/kibana/assets/10977896/8cadc2ec-ee84-42f6-8a0c-ad949367429c"> I believe the issue was in how we handle the Observables for request events. I added extra comment in the particular code change. I no longer can reproduce it, all the events are reported correctly: <img width="964" alt="image" src="https://github.com/elastic/kibana/assets/10977896/fa2c4b27-dcf2-480b-a07f-aeb23045149a"> Logs cleaning is to log in console only performance metrics event but not all EBT elements. Also not to report some browser errors that not Kibana specific. Testing: run the following script 3-4 times ``` PERFORMANCE_ENABLE_TELEMETRY=1 node scripts/run_performance.js --journey-path x-pack/performance/journeys/login.ts ``` - script is completed without delays (e.g. doesn't hang on after hook in TEST phase) - telemetry requests are logged with correct counter and all finished, e.g. `Waiting for telemetry request #2 to complete` is followed by `Telemetry request #2 complete` - only events started with `Report event "performance_metric"` are in console output
nickpeihl
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May 17, 2024
## Summary Set `security.session.cleanupInterval` to 5h for session concurrency test. ### **Prerequisites** - Task for session cleanup with [default schedule set to 1h](https://github.com/elastic/kibana/blob/main/x-pack/plugins/security/server/config.ts#L222). - Task polling interval is set to [3000ms](https://github.com/elastic/kibana/blob/main/x-pack/plugins/task_manager/server/config.ts#L13). - We override `scheduledAt` once we make a request in [runCleanupTaskSoon](https://github.com/elastic/kibana/blob/main/x-pack/test/security_api_integration/tests/session_concurrent_limit/cleanup.ts#L145). ### **Hypothesis** Taking into consideration that: - `session_cleanup` task is not the only one scheduled during test run. - There is sort of an exponential backoff implemented for task polling if there are too many retries. - Clock jitter. I had a hypothesis that if our whole test run exceeds 1h or polling interval gets adjusted because of retries we might end up executing the scheduled cleanup before we trigger `runCleanupTaskSoon` (this is there we drop 1 session already). ### **FTR runs (x55 each)** - `cleanupInterval` set to 5h: [#1](https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/5986) :green_circle:, [#2](https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/5987) :green_circle: - `cleanupInterval` set to default 1h: [#1](https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/5983) :green_circle:, [#2](https://buildkite.com/elastic/kibana-flaky-test-suite-runner/builds/5982) :red_circle: (has 2 failures out of 55) ### Checklist - [x] [Flaky Test Runner](https://ci-stats.kibana.dev/trigger_flaky_test_runner/1) was used on any tests changed ### For maintainers - [x] 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) __Fixes: https://github.com/elastic/kibana/issues/149091__
nickpeihl
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Sep 10, 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 <[email protected]>
nickpeihl
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Sep 24, 2024
…193441) ## Summary More files to be regenerated with a different shape since the js-yaml update: elastic#190678
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Summary
This PR makes the changes discussed with @ThomThomson and @nickpeihl for making the logic of
replacePanelandreplaceEmbeddableconsistent.