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Merge branch 'main' into hints-2
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Lines changed: 1496 additions & 283 deletions

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‎src/platform/packages/private/kbn-language-documentation/src/sections/generated/processing_commands.tsx‎

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@@ -497,7 +497,7 @@ ROW a = "12 15.5 15.6 true"
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label: i18n.translate('languageDocumentation.documentationESQL.inline_stats', {
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defaultMessage: 'INLINE STATS',
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}),
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preview: false,
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preview: true,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.inline_stats.markdown',

‎src/platform/packages/private/kbn-language-documentation/src/sections/generated/scalar_functions.tsx‎

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@@ -304,8 +304,8 @@ export const functions = {
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Use \`CHUNK\` to split a text field into smaller chunks.
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\`\`\`esql
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FROM books
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| EVAL chunks = CHUNK(description)
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ROW result = CHUNK("It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief.", '{"strategy": "word", "max_chunk_size": 10, "overlap": 1}')
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| MV_EXPAND result
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\`\`\`
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`,
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description:
@@ -1561,6 +1561,34 @@ export const functions = {
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ROW a="foo;bar;baz"
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| EVAL first_a = MV_FIRST(SPLIT(a, ";"))
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.mv_intersection', {
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defaultMessage: 'MV_INTERSECTION',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.mv_intersection.markdown',
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{
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defaultMessage: `
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### MV INTERSECTION
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Returns the values that appear in both input fields. Returns \`null\` if either field is null or if no values match.
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\`\`\`esql
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ROW a = [1, 2, 3, 4, 5], b = [2, 3, 4, 5, 6]
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| EVAL finalValue = MV_INTERSECTION(a, b)
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| KEEP finalValue
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
@@ -2700,6 +2728,33 @@ export const functions = {
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FROM airports
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| WHERE ST_INTERSECTS(location, TO_GEOSHAPE("POLYGON((42 14, 43 14, 43 15, 42 15, 42 14))"))
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.st_simplify', {
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defaultMessage: 'ST_SIMPLIFY',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.st_simplify.markdown',
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{
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defaultMessage: `
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### ST SIMPLIFY
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Simplifies the input geometry by applying the Douglas-Peucker algorithm with a specified tolerance. Vertices that fall within the tolerance distance from the simplified shape are removed. Note that the resulting geometry may be invalid, even if the original input was valid.
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\`\`\`esql
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ROW wkt = "POLYGON ((7.998 53.827, 9.470 53.068, 15.754 53.801, 16.523 57.160, 11.162 57.868, 8.064 57.445, 6.219 55.317, 7.998 53.827))"
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| EVAL simplified = ST_SIMPLIFY(TO_GEOSHAPE(wkt), 0.7)
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
@@ -3054,8 +3109,8 @@ export const functions = {
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Generates dense vector embeddings from text input using a specified [inference endpoint](docs-content://explore-analyze/elastic-inference/inference-api.md). Use this function to generate query vectors for KNN searches against your vectorized data or others dense vector based operations.
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\`\`\`esql
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ROW input="Who is Victor Hugo?"
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| EVAL embedding = TEXT_EMBEDDING("Who is Victor Hugo?", "test_dense_inference")
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FROM dense_vector_text METADATA _score
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| WHERE KNN(text_embedding_field, TEXT_EMBEDDING("be excellent to each other", "test_dense_inference"))
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\`\`\`
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`,
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description:
@@ -3598,8 +3653,9 @@ export const functions = {
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{
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defaultMessage: `
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### TO LONG
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Converts an input value to a long value. If the input parameter is of a date type,
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its value will be interpreted as milliseconds since the [Unix epoch](https://en.wikipedia.org/wiki/Unix_time), converted to long.
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Converts the input value to a long.
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If the input parameter is of a date type, its value will be interpreted as milliseconds
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since the [Unix epoch](https://en.wikipedia.org/wiki/Unix_time), converted to long.
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Boolean \`true\` will be converted to long \`1\`, \`false\` to \`0\`.
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\`\`\`esql
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\`\`\`esql
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ROW v = TO_VERSION("1.2.3")
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.top_snippets', {
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defaultMessage: 'TOP_SNIPPETS',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.top_snippets.markdown',
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{
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defaultMessage: `
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### TOP SNIPPETS
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Use \`TOP_SNIPPETS\` to extract the best snippets for a given query string from a text field.
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\`\`\`esql
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FROM books
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| EVAL snippets = TOP_SNIPPETS(description, "Tolkien")
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
@@ -3926,6 +4009,147 @@ export const functions = {
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ROW u = "https://example.com/?x=foo bar&y=baz"
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| EVAL u = URL_ENCODE_COMPONENT(u)
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.v_cosine', {
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defaultMessage: 'V_COSINE',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.v_cosine.markdown',
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{
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defaultMessage: `
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### V COSINE
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Calculates the cosine similarity between two dense_vectors.
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\`\`\`esql
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from colors
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| where color != "black"
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| eval similarity = v_cosine(rgb_vector, [0, 255, 255])
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| sort similarity desc, color asc
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.v_dot_product', {
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defaultMessage: 'V_DOT_PRODUCT',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.v_dot_product.markdown',
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{
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defaultMessage: `
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### V DOT PRODUCT
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Calculates the dot product between two dense_vectors.
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\`\`\`esql
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from colors
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| eval similarity = v_dot_product(rgb_vector, [0, 255, 255])
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| sort similarity desc, color asc
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.v_hamming', {
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defaultMessage: 'V_HAMMING',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.v_hamming.markdown',
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{
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defaultMessage: `
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### V HAMMING
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Calculates the Hamming distance between two dense vectors.
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\`\`\`esql
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from colors
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| eval similarity = v_hamming(rgb_byte_vector, [0, 127, 127])
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| sort similarity desc, color asc
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.v_l1_norm', {
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defaultMessage: 'V_L1_NORM',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.v_l1_norm.markdown',
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{
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defaultMessage: `
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### V L1 NORM
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Calculates the l1 norm between two dense_vectors.
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\`\`\`esql
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from colors
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| eval similarity = v_l1_norm(rgb_vector, [0, 255, 255])
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| sort similarity desc, color asc
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',
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ignoreTag: true,
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}
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),
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},
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},
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// Do not edit manually... automatically generated by scripts/generate_esql_docs.ts
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{
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label: i18n.translate('languageDocumentation.documentationESQL.v_l2_norm', {
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defaultMessage: 'V_L2_NORM',
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}),
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preview: true,
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license: undefined,
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description: {
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markdownContent: i18n.translate(
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'languageDocumentation.documentationESQL.v_l2_norm.markdown',
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{
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defaultMessage: `
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### V L2 NORM
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Calculates the l2 norm between two dense_vectors.
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\`\`\`esql
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from colors
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| eval similarity = v_l2_norm(rgb_vector, [0, 255, 255])
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| sort similarity desc, color asc
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\`\`\`
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`,
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description:
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'Text is in markdown. Do not translate function names, special characters, or field names like sum(bytes)',

‎src/platform/packages/private/kbn-language-documentation/src/sections/generated/timeseries_aggregation_functions.tsx‎

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Original file line numberDiff line numberDiff line change
@@ -380,7 +380,7 @@ export const functions = {
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{
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defaultMessage: `
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### PERCENTILE OVER TIME
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Calculates the percentile over time of a numeric field.
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Calculates the percentile over time of a field.
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\`\`\`esql
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TS k8s

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