cbf7417c0f
- Add DevContainer configuration for Codespaces - Add GitHub Actions workflows for automation - Add Ollama support to Memory plugin - Add comprehensive documentation
1317 lines
52 KiB
JavaScript
1317 lines
52 KiB
JavaScript
"use strict";
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// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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var desc = Object.getOwnPropertyDescriptor(m, k);
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if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
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desc = { enumerable: true, get: function() { return m[k]; } };
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}
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Object.defineProperty(o, k2, desc);
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}) : (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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o[k2] = m[k];
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}));
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var __exportStar = (this && this.__exportStar) || function(m, exports) {
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for (var p in m) if (p !== "default" && !Object.prototype.hasOwnProperty.call(exports, p)) __createBinding(exports, m, p);
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.MakeArrowTableOptions = exports.VectorColumnOptions = void 0;
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exports.isMultiVector = isMultiVector;
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exports.isIntoVector = isIntoVector;
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exports.isArrowTable = isArrowTable;
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exports.isNull = isNull;
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exports.isInt = isInt;
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exports.isFloat = isFloat;
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exports.isBinary = isBinary;
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exports.isLargeBinary = isLargeBinary;
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exports.isUtf8 = isUtf8;
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exports.isLargeUtf8 = isLargeUtf8;
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exports.isBool = isBool;
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exports.isDecimal = isDecimal;
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exports.isDate = isDate;
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exports.isTime = isTime;
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exports.isTimestamp = isTimestamp;
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exports.isInterval = isInterval;
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exports.isDuration = isDuration;
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exports.isList = isList;
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exports.isStruct = isStruct;
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exports.isUnion = isUnion;
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exports.isFixedSizeBinary = isFixedSizeBinary;
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exports.isFixedSizeList = isFixedSizeList;
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exports.makeArrowTable = makeArrowTable;
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exports.makeEmptyTable = makeEmptyTable;
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exports.convertToTable = convertToTable;
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exports.newVectorType = newVectorType;
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exports.fromRecordsToBuffer = fromRecordsToBuffer;
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exports.fromRecordsToStreamBuffer = fromRecordsToStreamBuffer;
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exports.fromTableToBuffer = fromTableToBuffer;
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exports.fromDataToBuffer = fromDataToBuffer;
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exports.fromBufferToRecordBatch = fromBufferToRecordBatch;
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exports.fromRecordBatchToBuffer = fromRecordBatchToBuffer;
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exports.fromTableToStreamBuffer = fromTableToStreamBuffer;
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exports.createEmptyTable = createEmptyTable;
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exports.ensureNestedFieldsExist = ensureNestedFieldsExist;
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exports.dataTypeToJson = dataTypeToJson;
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const apache_arrow_1 = require("apache-arrow");
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const registry_1 = require("./embedding/registry");
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const sanitize_1 = require("./sanitize");
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/**
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* Check if a field name indicates a vector column.
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*/
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function nameSuggestsVectorColumn(fieldName) {
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const nameLower = fieldName.toLowerCase();
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return nameLower.includes("vector") || nameLower.includes("embedding");
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}
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__exportStar(require("apache-arrow"), exports);
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function isMultiVector(value) {
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return Array.isArray(value) && isIntoVector(value[0]);
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}
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function isIntoVector(value) {
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return (value instanceof Float32Array ||
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value instanceof Float64Array ||
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(Array.isArray(value) && !Array.isArray(value[0])));
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}
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function isArrowTable(value) {
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if (value instanceof apache_arrow_1.Table)
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return true;
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return "schema" in value && "batches" in value;
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}
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function isNull(value) {
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return value instanceof apache_arrow_1.Null || apache_arrow_1.DataType.isNull(value);
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}
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function isInt(value) {
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return value instanceof apache_arrow_1.Int || apache_arrow_1.DataType.isInt(value);
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}
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function isFloat(value) {
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return value instanceof apache_arrow_1.Float || apache_arrow_1.DataType.isFloat(value);
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}
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function isBinary(value) {
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return value instanceof apache_arrow_1.Binary || apache_arrow_1.DataType.isBinary(value);
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}
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function isLargeBinary(value) {
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return value instanceof apache_arrow_1.LargeBinary || apache_arrow_1.DataType.isLargeBinary(value);
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}
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function isUtf8(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isUtf8(value);
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}
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function isLargeUtf8(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isLargeUtf8(value);
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}
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function isBool(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isBool(value);
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}
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function isDecimal(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isDecimal(value);
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}
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function isDate(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isDate(value);
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}
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function isTime(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isTime(value);
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}
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function isTimestamp(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isTimestamp(value);
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}
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function isInterval(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isInterval(value);
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}
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function isDuration(value) {
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return value instanceof apache_arrow_1.Utf8 || apache_arrow_1.DataType.isDuration(value);
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}
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function isList(value) {
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return value instanceof apache_arrow_1.List || apache_arrow_1.DataType.isList(value);
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}
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function isStruct(value) {
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return value instanceof apache_arrow_1.Struct || apache_arrow_1.DataType.isStruct(value);
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}
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function isUnion(value) {
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return value instanceof apache_arrow_1.Struct || apache_arrow_1.DataType.isUnion(value);
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}
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function isFixedSizeBinary(value) {
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return value instanceof apache_arrow_1.FixedSizeBinary || apache_arrow_1.DataType.isFixedSizeBinary(value);
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}
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function isFixedSizeList(value) {
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return value instanceof apache_arrow_1.FixedSizeList || apache_arrow_1.DataType.isFixedSizeList(value);
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}
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/*
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* Options to control how a column should be converted to a vector array
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*/
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class VectorColumnOptions {
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/** Vector column type. */
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type = new apache_arrow_1.Float32();
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constructor(values) {
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Object.assign(this, values);
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}
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}
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exports.VectorColumnOptions = VectorColumnOptions;
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// biome-ignore lint/suspicious/noExplicitAny: skip
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function vectorFromArray(data, type) {
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// Workaround for: https://github.com/apache/arrow/issues/45862
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// If FSL type with float
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if (apache_arrow_1.DataType.isFixedSizeList(type) && apache_arrow_1.DataType.isFloat(type.valueType)) {
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const extendedData = [...data, new Array(type.listSize).fill(0.0)];
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const array = (0, apache_arrow_1.vectorFromArray)(extendedData, type);
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return array.slice(0, data.length);
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}
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else if (type === undefined) {
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return (0, apache_arrow_1.vectorFromArray)(data);
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}
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else {
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return (0, apache_arrow_1.vectorFromArray)(data, type);
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}
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}
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/** Options to control the makeArrowTable call. */
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class MakeArrowTableOptions {
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/*
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* Schema of the data.
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*
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* If this is not provided then the data type will be inferred from the
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* JS type. Integer numbers will become int64, floating point numbers
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* will become float64 and arrays will become variable sized lists with
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* the data type inferred from the first element in the array.
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*
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* The schema must be specified if there are no records (e.g. to make
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* an empty table)
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*/
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schema;
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/*
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* Mapping from vector column name to expected type
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*
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* Lance expects vector columns to be fixed size list arrays (i.e. tensors)
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* However, `makeArrowTable` will not infer this by default (it creates
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* variable size list arrays). This field can be used to indicate that a column
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* should be treated as a vector column and converted to a fixed size list.
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*
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* The keys should be the names of the vector columns. The value specifies the
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* expected data type of the vector columns.
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*
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* If `schema` is provided then this field is ignored.
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*
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* By default, the column named "vector" will be assumed to be a float32
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* vector column.
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*/
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vectorColumns = {
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vector: new VectorColumnOptions(),
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};
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embeddings;
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embeddingFunction;
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/**
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* If true then string columns will be encoded with dictionary encoding
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*
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* Set this to true if your string columns tend to repeat the same values
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* often. For more precise control use the `schema` property to specify the
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* data type for individual columns.
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*
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* If `schema` is provided then this property is ignored.
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*/
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dictionaryEncodeStrings = false;
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constructor(values) {
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Object.assign(this, values);
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}
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}
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exports.MakeArrowTableOptions = MakeArrowTableOptions;
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/**
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* An enhanced version of the apache-arrow makeTable function from Apache Arrow
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* that supports nested fields and embeddings columns.
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*
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* (typically you do not need to call this function. It will be called automatically
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* when creating a table or adding data to it)
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*
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* This function converts an array of Record<String, any> (row-major JS objects)
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* to an Arrow Table (a columnar structure)
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*
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* If a schema is provided then it will be used to determine the resulting array
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* types. Fields will also be reordered to fit the order defined by the schema.
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*
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* If a schema is not provided then the types will be inferred and the field order
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* will be controlled by the order of properties in the first record. If a type
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* is inferred it will always be nullable.
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*
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* If not all fields are found in the data, then a subset of the schema will be
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* returned.
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*
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* If the input is empty then a schema must be provided to create an empty table.
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*
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* When a schema is not specified then data types will be inferred. The inference
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* rules are as follows:
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*
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* - boolean => Bool
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* - number => Float64
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* - bigint => Int64
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* - String => Utf8
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* - Buffer => Binary
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* - Record<String, any> => Struct
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* - Array<any> => List
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* @example
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* ```ts
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* import { fromTableToBuffer, makeArrowTable } from "../arrow";
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* import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";
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*
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* const schema = new Schema([
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* new Field("a", new Int32()),
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* new Field("b", new Float32()),
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* new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
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* ]);
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* const table = makeArrowTable([
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* { a: 1, b: 2, c: [1, 2, 3] },
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* { a: 4, b: 5, c: [4, 5, 6] },
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* { a: 7, b: 8, c: [7, 8, 9] },
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* ], { schema });
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* ```
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*
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* By default it assumes that the column named `vector` is a vector column
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* and it will be converted into a fixed size list array of type float32.
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* The `vectorColumns` option can be used to support other vector column
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* names and data types.
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*
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* ```ts
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* const schema = new Schema([
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* new Field("a", new Float64()),
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* new Field("b", new Float64()),
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* new Field(
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* "vector",
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* new FixedSizeList(3, new Field("item", new Float32()))
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* ),
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* ]);
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* const table = makeArrowTable([
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* { a: 1, b: 2, vector: [1, 2, 3] },
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* { a: 4, b: 5, vector: [4, 5, 6] },
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* { a: 7, b: 8, vector: [7, 8, 9] },
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* ]);
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* assert.deepEqual(table.schema, schema);
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* ```
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*
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* You can specify the vector column types and names using the options as well
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*
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* ```ts
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* const schema = new Schema([
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* new Field('a', new Float64()),
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* new Field('b', new Float64()),
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* new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
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* new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
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* ]);
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* const table = makeArrowTable([
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* { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
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* { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
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* { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
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* ], {
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* vectorColumns: {
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* vec1: { type: new Float16() },
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* vec2: { type: new Float16() }
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* }
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* }
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* assert.deepEqual(table.schema, schema)
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* ```
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*/
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function makeArrowTable(data, options, metadata) {
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const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
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let schema = undefined;
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if (opt.schema !== undefined && opt.schema !== null) {
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schema = (0, sanitize_1.sanitizeSchema)(opt.schema);
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schema = validateSchemaEmbeddings(schema, data, options?.embeddingFunction);
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}
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let schemaMetadata = schema?.metadata || new Map();
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if (metadata !== undefined) {
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schemaMetadata = new Map([...schemaMetadata, ...metadata]);
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}
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if (data.length === 0 &&
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(options?.schema === undefined || options?.schema === null)) {
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throw new Error("At least one record or a schema needs to be provided");
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}
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else if (data.length === 0) {
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if (schema === undefined) {
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throw new Error("A schema must be provided if data is empty");
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}
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else {
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schema = new apache_arrow_1.Schema(schema.fields, schemaMetadata);
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return new apache_arrow_1.Table(schema);
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}
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}
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let inferredSchema = inferSchema(data, schema, opt);
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inferredSchema = new apache_arrow_1.Schema(inferredSchema.fields, schemaMetadata);
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const finalColumns = {};
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for (const field of inferredSchema.fields) {
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finalColumns[field.name] = transposeData(data, field);
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}
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return new apache_arrow_1.Table(inferredSchema, finalColumns);
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}
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function inferSchema(data, schema, opts) {
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// We will collect all fields we see in the data.
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const pathTree = new PathTree();
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for (const [rowI, row] of data.entries()) {
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for (const [path, value] of rowPathsAndValues(row)) {
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if (!pathTree.has(path)) {
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// First time seeing this field.
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if (schema !== undefined) {
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const field = getFieldForPath(schema, path);
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if (field === undefined) {
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throw new Error(`Found field not in schema: ${path.join(".")} at row ${rowI}`);
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}
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else {
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pathTree.set(path, field.type);
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}
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}
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else {
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const inferredType = inferType(value, path, opts);
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if (inferredType === undefined) {
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throw new Error(`Failed to infer data type for field ${path.join(".")} at row ${rowI}. \
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Consider providing an explicit schema.`);
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}
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pathTree.set(path, inferredType);
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}
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}
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else if (schema === undefined) {
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const currentType = pathTree.get(path);
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const newType = inferType(value, path, opts);
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if (currentType !== newType) {
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new Error(`Failed to infer schema for data. Previously inferred type \
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${currentType} but found ${newType} at row ${rowI}. Consider \
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providing an explicit schema.`);
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}
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}
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}
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}
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if (schema === undefined) {
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function fieldsFromPathTree(pathTree) {
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const fields = [];
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for (const [name, value] of pathTree.map.entries()) {
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if (value instanceof PathTree) {
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const children = fieldsFromPathTree(value);
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fields.push(new apache_arrow_1.Field(name, new apache_arrow_1.Struct(children), true));
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}
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else {
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fields.push(new apache_arrow_1.Field(name, value, true));
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}
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}
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return fields;
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}
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const fields = fieldsFromPathTree(pathTree);
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return new apache_arrow_1.Schema(fields);
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}
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else {
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function takeMatchingFields(fields, pathTree) {
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const outFields = [];
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for (const field of fields) {
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if (pathTree.map.has(field.name)) {
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const value = pathTree.get([field.name]);
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if (value instanceof PathTree) {
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const struct = field.type;
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const children = takeMatchingFields(struct.children, value);
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outFields.push(new apache_arrow_1.Field(field.name, new apache_arrow_1.Struct(children), field.nullable));
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}
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else {
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outFields.push(new apache_arrow_1.Field(field.name, value, field.nullable));
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}
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|
}
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}
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return outFields;
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|
}
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const fields = takeMatchingFields(schema.fields, pathTree);
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return new apache_arrow_1.Schema(fields);
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}
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|
}
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|
function* rowPathsAndValues(row, basePath = []) {
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|
for (const [key, value] of Object.entries(row)) {
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|
if (isObject(value)) {
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yield* rowPathsAndValues(value, [...basePath, key]);
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|
}
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|
else {
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|
// Skip undefined values - they should be treated the same as missing fields
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|
// for embedding function purposes
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|
if (value !== undefined) {
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yield [[...basePath, key], value];
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|
}
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|
}
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|
}
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|
}
|
|
function isObject(value) {
|
|
return (typeof value === "object" &&
|
|
value !== null &&
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|
!Array.isArray(value) &&
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|
!(value instanceof RegExp) &&
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|
!(value instanceof Date) &&
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|
!(value instanceof Set) &&
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|
!(value instanceof Map) &&
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|
!(value instanceof Buffer));
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|
}
|
|
function getFieldForPath(schema, path) {
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|
let current = schema;
|
|
for (const key of path) {
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|
if (current instanceof apache_arrow_1.Schema) {
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|
const field = current.fields.find((f) => f.name === key);
|
|
if (field === undefined) {
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|
return undefined;
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|
}
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|
current = field;
|
|
}
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|
else if (current instanceof apache_arrow_1.Field && apache_arrow_1.DataType.isStruct(current.type)) {
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|
const struct = current.type;
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|
const field = struct.children.find((f) => f.name === key);
|
|
if (field === undefined) {
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|
return undefined;
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|
}
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current = field;
|
|
}
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|
else {
|
|
return undefined;
|
|
}
|
|
}
|
|
if (current instanceof apache_arrow_1.Field) {
|
|
return current;
|
|
}
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|
else {
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|
return undefined;
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|
}
|
|
}
|
|
/**
|
|
* Try to infer which Arrow type to use for a given value.
|
|
*
|
|
* May return undefined if the type cannot be inferred.
|
|
*/
|
|
function inferType(value, path, opts) {
|
|
if (typeof value === "bigint") {
|
|
return new apache_arrow_1.Int64();
|
|
}
|
|
else if (typeof value === "number") {
|
|
// Even if it's an integer, it's safer to assume Float64. Users can
|
|
// always provide an explicit schema or use BigInt if they mean integer.
|
|
return new apache_arrow_1.Float64();
|
|
}
|
|
else if (typeof value === "string") {
|
|
if (opts.dictionaryEncodeStrings) {
|
|
return new apache_arrow_1.Dictionary(new apache_arrow_1.Utf8(), new apache_arrow_1.Int32());
|
|
}
|
|
else {
|
|
return new apache_arrow_1.Utf8();
|
|
}
|
|
}
|
|
else if (typeof value === "boolean") {
|
|
return new apache_arrow_1.Bool();
|
|
}
|
|
else if (value instanceof Buffer) {
|
|
return new apache_arrow_1.Binary();
|
|
}
|
|
else if (Array.isArray(value)) {
|
|
if (value.length === 0) {
|
|
return undefined; // Without any values we can't infer the type
|
|
}
|
|
if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
|
|
const floatType = (0, sanitize_1.sanitizeType)(opts.vectorColumns[path[0]].type);
|
|
return new apache_arrow_1.FixedSizeList(value.length, new apache_arrow_1.Field("item", floatType, true));
|
|
}
|
|
const valueType = inferType(value[0], path, opts);
|
|
if (valueType === undefined) {
|
|
return undefined;
|
|
}
|
|
// Try to automatically detect embedding columns.
|
|
if (nameSuggestsVectorColumn(path[path.length - 1])) {
|
|
// Check if value is a Uint8Array for integer vector type determination
|
|
if (value instanceof Uint8Array) {
|
|
// For integer vectors, we default to Uint8 (matching Python implementation)
|
|
const child = new apache_arrow_1.Field("item", new apache_arrow_1.Uint8(), true);
|
|
return new apache_arrow_1.FixedSizeList(value.length, child);
|
|
}
|
|
else {
|
|
// For float vectors, we default to Float32
|
|
const child = new apache_arrow_1.Field("item", new apache_arrow_1.Float32(), true);
|
|
return new apache_arrow_1.FixedSizeList(value.length, child);
|
|
}
|
|
}
|
|
else {
|
|
const child = new apache_arrow_1.Field("item", valueType, true);
|
|
return new apache_arrow_1.List(child);
|
|
}
|
|
}
|
|
else {
|
|
// TODO: timestamp
|
|
return undefined;
|
|
}
|
|
}
|
|
class PathTree {
|
|
map;
|
|
constructor(entries) {
|
|
this.map = new Map();
|
|
if (entries !== undefined) {
|
|
for (const [path, value] of entries) {
|
|
this.set(path, value);
|
|
}
|
|
}
|
|
}
|
|
has(path) {
|
|
let ref = this;
|
|
for (const part of path) {
|
|
if (!(ref instanceof PathTree) || !ref.map.has(part)) {
|
|
return false;
|
|
}
|
|
ref = ref.map.get(part);
|
|
}
|
|
return true;
|
|
}
|
|
get(path) {
|
|
let ref = this;
|
|
for (const part of path) {
|
|
if (!(ref instanceof PathTree) || !ref.map.has(part)) {
|
|
return undefined;
|
|
}
|
|
ref = ref.map.get(part);
|
|
}
|
|
return ref;
|
|
}
|
|
set(path, value) {
|
|
let ref = this;
|
|
for (const part of path.slice(0, path.length - 1)) {
|
|
if (!ref.map.has(part)) {
|
|
ref.map.set(part, new PathTree());
|
|
}
|
|
ref = ref.map.get(part);
|
|
}
|
|
ref.map.set(path[path.length - 1], value);
|
|
}
|
|
}
|
|
function transposeData(data, field, path = []) {
|
|
if (field.type instanceof apache_arrow_1.Struct) {
|
|
const childFields = field.type.children;
|
|
const fullPath = [...path, field.name];
|
|
const childVectors = childFields.map((child) => {
|
|
return transposeData(data, child, fullPath);
|
|
});
|
|
const structData = (0, apache_arrow_1.makeData)({
|
|
type: field.type,
|
|
children: childVectors,
|
|
});
|
|
return (0, apache_arrow_1.makeVector)(structData);
|
|
}
|
|
else {
|
|
const valuesPath = [...path, field.name];
|
|
const values = data.map((datum) => {
|
|
let current = datum;
|
|
for (const key of valuesPath) {
|
|
if (current == null) {
|
|
return null;
|
|
}
|
|
if (isObject(current) &&
|
|
(Object.hasOwn(current, key) || key in current)) {
|
|
current = current[key];
|
|
}
|
|
else {
|
|
return null;
|
|
}
|
|
}
|
|
return current;
|
|
});
|
|
return makeVector(values, field.type, undefined, field.nullable);
|
|
}
|
|
}
|
|
/**
|
|
* Create an empty Arrow table with the provided schema
|
|
*/
|
|
function makeEmptyTable(schema, metadata) {
|
|
return makeArrowTable([], { schema }, metadata);
|
|
}
|
|
/**
|
|
* Helper function to convert Array<Array<any>> to a variable sized list array
|
|
*/
|
|
// @ts-expect-error (Vector<unknown> is not assignable to Vector<any>)
|
|
function makeListVector(lists) {
|
|
if (lists.length === 0 || lists[0].length === 0) {
|
|
throw Error("Cannot infer list vector from empty array or empty list");
|
|
}
|
|
const sampleList = lists[0];
|
|
// biome-ignore lint/suspicious/noExplicitAny: skip
|
|
let inferredType;
|
|
try {
|
|
const sampleVector = makeVector(sampleList);
|
|
inferredType = sampleVector.type;
|
|
}
|
|
catch (error) {
|
|
// eslint-disable-next-line @typescript-eslint/restrict-template-expressions
|
|
throw Error(`Cannot infer list vector. Cannot infer inner type: ${error}`);
|
|
}
|
|
const listBuilder = (0, apache_arrow_1.makeBuilder)({
|
|
type: new apache_arrow_1.List(new apache_arrow_1.Field("item", inferredType, true)),
|
|
});
|
|
for (const list of lists) {
|
|
listBuilder.append(list);
|
|
}
|
|
return listBuilder.finish().toVector();
|
|
}
|
|
/** Helper function to convert an Array of JS values to an Arrow Vector */
|
|
function makeVector(values, type, stringAsDictionary, nullable) {
|
|
if (type !== undefined) {
|
|
// Convert undefined values to null for nullable fields
|
|
if (nullable) {
|
|
values = values.map((v) => (v === undefined ? null : v));
|
|
}
|
|
// workaround for: https://github.com/apache/arrow-js/issues/68
|
|
if (apache_arrow_1.DataType.isBool(type)) {
|
|
const hasNonNullValue = values.some((v) => v !== null && v !== undefined);
|
|
if (!hasNonNullValue) {
|
|
const nullBitmap = new Uint8Array(Math.ceil(values.length / 8));
|
|
const data = (0, apache_arrow_1.makeData)({
|
|
type: type,
|
|
length: values.length,
|
|
nullCount: values.length,
|
|
nullBitmap,
|
|
});
|
|
return (0, apache_arrow_1.makeVector)(data);
|
|
}
|
|
}
|
|
// No need for inference, let Arrow create it
|
|
if (type instanceof apache_arrow_1.Int) {
|
|
if (apache_arrow_1.DataType.isInt(type) && type.bitWidth === 64) {
|
|
// wrap in BigInt to avoid bug: https://github.com/apache/arrow/issues/40051
|
|
values = values.map((v) => {
|
|
if (v === null) {
|
|
return v;
|
|
}
|
|
else if (typeof v === "bigint") {
|
|
return v;
|
|
}
|
|
else if (typeof v === "number") {
|
|
return BigInt(v);
|
|
}
|
|
else {
|
|
return v;
|
|
}
|
|
});
|
|
}
|
|
else {
|
|
// Similarly, bigint isn't supported for 16 or 32-bit ints.
|
|
values = values.map((v) => {
|
|
if (typeof v == "bigint") {
|
|
return Number(v);
|
|
}
|
|
else {
|
|
return v;
|
|
}
|
|
});
|
|
}
|
|
}
|
|
return vectorFromArray(values, type);
|
|
}
|
|
if (values.length === 0) {
|
|
throw Error("makeVector requires at least one value or the type must be specfied");
|
|
}
|
|
const sampleValue = values.find((val) => val !== null && val !== undefined);
|
|
if (sampleValue === undefined) {
|
|
throw Error("makeVector cannot infer the type if all values are null or undefined");
|
|
}
|
|
if (Array.isArray(sampleValue)) {
|
|
// Default Arrow inference doesn't handle list types
|
|
return makeListVector(values);
|
|
}
|
|
else if (Buffer.isBuffer(sampleValue)) {
|
|
// Default Arrow inference doesn't handle Buffer
|
|
return vectorFromArray(values, new apache_arrow_1.Binary());
|
|
}
|
|
else if (!(stringAsDictionary ?? false) &&
|
|
(typeof sampleValue === "string" || sampleValue instanceof String)) {
|
|
// If the type is string then don't use Arrow's default inference unless dictionaries are requested
|
|
// because it will always use dictionary encoding for strings
|
|
return vectorFromArray(values, new apache_arrow_1.Utf8());
|
|
}
|
|
else {
|
|
// Convert a JS array of values to an arrow vector
|
|
return vectorFromArray(values);
|
|
}
|
|
}
|
|
/** Helper function to apply embeddings from metadata to an input table */
|
|
async function applyEmbeddingsFromMetadata(table, schema) {
|
|
const registry = (0, registry_1.getRegistry)();
|
|
const functions = await registry.parseFunctions(schema.metadata);
|
|
const columns = Object.fromEntries(table.schema.fields.map((field) => [
|
|
field.name,
|
|
table.getChild(field.name),
|
|
]));
|
|
for (const functionEntry of functions.values()) {
|
|
const sourceColumn = columns[functionEntry.sourceColumn];
|
|
const destColumn = functionEntry.vectorColumn ?? "vector";
|
|
if (sourceColumn === undefined) {
|
|
throw new Error(`Cannot apply embedding function because the source column '${functionEntry.sourceColumn}' was not present in the data`);
|
|
}
|
|
// Check if destination column exists and handle accordingly
|
|
if (columns[destColumn] !== undefined) {
|
|
const existingColumn = columns[destColumn];
|
|
// If the column exists but is all null, we can fill it with embeddings
|
|
if (existingColumn.nullCount !== existingColumn.length) {
|
|
// Column has non-null values, skip embedding application
|
|
continue;
|
|
}
|
|
}
|
|
if (table.batches.length > 1) {
|
|
throw new Error("Internal error: `makeArrowTable` unexpectedly created a table with more than one batch");
|
|
}
|
|
const values = sourceColumn.toArray();
|
|
const vectors = await functionEntry.function.computeSourceEmbeddings(values);
|
|
if (vectors.length !== values.length) {
|
|
throw new Error("Embedding function did not return an embedding for each input element");
|
|
}
|
|
let destType;
|
|
const dtype = schema.fields.find((f) => f.name === destColumn).type;
|
|
if (isFixedSizeList(dtype)) {
|
|
destType = (0, sanitize_1.sanitizeType)(dtype);
|
|
}
|
|
else {
|
|
throw new Error("Expected FixedSizeList as datatype for vector field, instead got: " +
|
|
dtype);
|
|
}
|
|
const vector = makeVector(vectors, destType);
|
|
columns[destColumn] = vector;
|
|
}
|
|
// Add any missing columns from the schema as null vectors
|
|
for (const field of schema.fields) {
|
|
if (!(field.name in columns)) {
|
|
const nullValues = new Array(table.numRows).fill(null);
|
|
columns[field.name] = makeVector(nullValues, field.type, undefined, field.nullable);
|
|
}
|
|
}
|
|
const newTable = new apache_arrow_1.Table(columns);
|
|
return alignTable(newTable, schema);
|
|
}
|
|
/** Helper function to apply embeddings to an input table */
|
|
async function applyEmbeddings(table, embeddings, schema) {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = (0, sanitize_1.sanitizeSchema)(schema);
|
|
}
|
|
if (schema?.metadata.has("embedding_functions")) {
|
|
return applyEmbeddingsFromMetadata(table, schema);
|
|
}
|
|
else if (embeddings == null || embeddings === undefined) {
|
|
return table;
|
|
}
|
|
let schemaMetadata = schema?.metadata || new Map();
|
|
if (!(embeddings == null || embeddings === undefined)) {
|
|
const registry = (0, registry_1.getRegistry)();
|
|
const embeddingMetadata = registry.getTableMetadata([embeddings]);
|
|
schemaMetadata = new Map([...schemaMetadata, ...embeddingMetadata]);
|
|
}
|
|
// Convert from ArrowTable to Record<String, Vector>
|
|
const colEntries = [...Array(table.numCols).keys()].map((_, idx) => {
|
|
const name = table.schema.fields[idx].name;
|
|
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
|
|
const vec = table.getChildAt(idx);
|
|
return [name, vec];
|
|
});
|
|
const newColumns = Object.fromEntries(colEntries);
|
|
const sourceColumn = newColumns[embeddings.sourceColumn];
|
|
const destColumn = embeddings.vectorColumn ?? "vector";
|
|
const innerDestType = embeddings.function.embeddingDataType() ?? new apache_arrow_1.Float32();
|
|
if (sourceColumn === undefined) {
|
|
throw new Error(`Cannot apply embedding function because the source column '${embeddings.sourceColumn}' was not present in the data`);
|
|
}
|
|
if (table.numRows === 0) {
|
|
if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
|
|
// We have an empty table and it already has the embedding column so no work needs to be done
|
|
// Note: we don't return an error like we did below because this is a common occurrence. For example,
|
|
// if we call convertToTable with 0 records and a schema that includes the embedding
|
|
return table;
|
|
}
|
|
const dimensions = embeddings.function.ndims();
|
|
if (dimensions !== undefined) {
|
|
const destType = newVectorType(dimensions, innerDestType);
|
|
newColumns[destColumn] = makeVector([], destType);
|
|
}
|
|
else if (schema != null) {
|
|
const destField = schema.fields.find((f) => f.name === destColumn);
|
|
if (destField != null) {
|
|
newColumns[destColumn] = makeVector([], destField.type, undefined, destField.nullable);
|
|
}
|
|
else {
|
|
throw new Error(`Attempt to apply embeddings to an empty table failed because schema was missing embedding column '${destColumn}'`);
|
|
}
|
|
}
|
|
else {
|
|
throw new Error("Attempt to apply embeddings to an empty table when the embeddings function does not specify `embeddingDimension`");
|
|
}
|
|
}
|
|
else {
|
|
// Check if destination column exists and handle accordingly
|
|
if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
|
|
const existingColumn = newColumns[destColumn];
|
|
// If the column exists but is all null, we can fill it with embeddings
|
|
if (existingColumn.nullCount !== existingColumn.length) {
|
|
// Column has non-null values, skip embedding application and return table as-is
|
|
let newTable = new apache_arrow_1.Table(newColumns);
|
|
if (schema != null) {
|
|
newTable = alignTable(newTable, schema);
|
|
}
|
|
return new apache_arrow_1.Table(new apache_arrow_1.Schema(newTable.schema.fields, schemaMetadata), newTable.batches);
|
|
}
|
|
}
|
|
if (table.batches.length > 1) {
|
|
throw new Error("Internal error: `makeArrowTable` unexpectedly created a table with more than one batch");
|
|
}
|
|
const values = sourceColumn.toArray();
|
|
const vectors = await embeddings.function.computeSourceEmbeddings(values);
|
|
if (vectors.length !== values.length) {
|
|
throw new Error("Embedding function did not return an embedding for each input element");
|
|
}
|
|
const destType = newVectorType(vectors[0].length, innerDestType);
|
|
newColumns[destColumn] = makeVector(vectors, destType);
|
|
}
|
|
let newTable = new apache_arrow_1.Table(newColumns);
|
|
if (schema != null) {
|
|
if (schema.fields.find((f) => f.name === destColumn) === undefined) {
|
|
throw new Error(`When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`);
|
|
}
|
|
newTable = alignTable(newTable, schema);
|
|
}
|
|
newTable = new apache_arrow_1.Table(new apache_arrow_1.Schema(newTable.schema.fields, schemaMetadata), newTable.batches);
|
|
return newTable;
|
|
}
|
|
/**
|
|
* Convert an Array of records into an Arrow Table, optionally applying an
|
|
* embeddings function to it.
|
|
*
|
|
* This function calls `makeArrowTable` first to create the Arrow Table.
|
|
* Any provided `makeTableOptions` (e.g. a schema) will be passed on to
|
|
* that call.
|
|
*
|
|
* The embedding function will be passed a column of values (based on the
|
|
* `sourceColumn` of the embedding function) and expects to receive back
|
|
* number[][] which will be converted into a fixed size list column. By
|
|
* default this will be a fixed size list of Float32 but that can be
|
|
* customized by the `embeddingDataType` property of the embedding function.
|
|
*
|
|
* If a schema is provided in `makeTableOptions` then it should include the
|
|
* embedding columns. If no schema is provded then embedding columns will
|
|
* be placed at the end of the table, after all of the input columns.
|
|
*/
|
|
async function convertToTable(data, embeddings, makeTableOptions) {
|
|
let processedData = data;
|
|
// If we have a schema with embedding metadata, we need to preprocess the data
|
|
// to ensure all nested fields are present
|
|
if (makeTableOptions?.schema &&
|
|
makeTableOptions.schema.metadata?.has("embedding_functions")) {
|
|
processedData = ensureNestedFieldsExist(data, makeTableOptions.schema);
|
|
}
|
|
const table = makeArrowTable(processedData, makeTableOptions);
|
|
return await applyEmbeddings(table, embeddings, makeTableOptions?.schema);
|
|
}
|
|
/** Creates the Arrow Type for a Vector column with dimension `dim` */
|
|
function newVectorType(dim, innerType) {
|
|
// in Lance we always default to have the elements nullable, so we need to set it to true
|
|
// otherwise we often get schema mismatches because the stored data always has schema with nullable elements
|
|
const children = new apache_arrow_1.Field("item", (0, sanitize_1.sanitizeType)(innerType), true);
|
|
return new apache_arrow_1.FixedSizeList(dim, children);
|
|
}
|
|
/**
|
|
* Serialize an Array of records into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will call `convertToTable` and pass on `embeddings` and `schema`
|
|
*
|
|
* `schema` is required if data is empty
|
|
*/
|
|
async function fromRecordsToBuffer(data, embeddings, schema) {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = (0, sanitize_1.sanitizeSchema)(schema);
|
|
}
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
const writer = apache_arrow_1.RecordBatchFileWriter.writeAll(table);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
/**
|
|
* Serialize an Array of records into a buffer using the Arrow IPC Stream serialization
|
|
*
|
|
* This function will call `convertToTable` and pass on `embeddings` and `schema`
|
|
*
|
|
* `schema` is required if data is empty
|
|
*/
|
|
async function fromRecordsToStreamBuffer(data, embeddings, schema) {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = (0, sanitize_1.sanitizeSchema)(schema);
|
|
}
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
const writer = apache_arrow_1.RecordBatchStreamWriter.writeAll(table);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
async function fromTableToBuffer(table, embeddings, schema) {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = (0, sanitize_1.sanitizeSchema)(schema);
|
|
}
|
|
const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
|
|
const writer = apache_arrow_1.RecordBatchFileWriter.writeAll(tableWithEmbeddings);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC File serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
async function fromDataToBuffer(data, embeddings, schema) {
|
|
if (schema !== undefined && schema !== null) {
|
|
schema = (0, sanitize_1.sanitizeSchema)(schema);
|
|
}
|
|
if (isArrowTable(data)) {
|
|
const table = (0, sanitize_1.sanitizeTable)(data);
|
|
// If we have a schema with embedding functions, we need to ensure all columns exist
|
|
// before applying embeddings, since applyEmbeddingsFromMetadata expects all columns
|
|
// to be present in the table
|
|
if (schema && schema.metadata?.has("embedding_functions")) {
|
|
const alignedTable = alignTableToSchema(table, schema);
|
|
return fromTableToBuffer(alignedTable, embeddings, schema);
|
|
}
|
|
else {
|
|
return fromTableToBuffer(table, embeddings, schema);
|
|
}
|
|
}
|
|
else {
|
|
const table = await convertToTable(data, embeddings, { schema });
|
|
return fromTableToBuffer(table);
|
|
}
|
|
}
|
|
/**
|
|
* Read a single record batch from a buffer.
|
|
*
|
|
* Returns null if the buffer does not contain a record batch
|
|
*/
|
|
async function fromBufferToRecordBatch(data) {
|
|
const iter = await apache_arrow_1.RecordBatchFileReader.readAll(Buffer.from(data)).next()
|
|
.value;
|
|
const recordBatch = iter?.next().value;
|
|
return recordBatch || null;
|
|
}
|
|
/**
|
|
* Create a buffer containing a single record batch
|
|
*/
|
|
async function fromRecordBatchToBuffer(batch) {
|
|
const writer = new apache_arrow_1.RecordBatchFileWriter().writeAll([batch]);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
/**
|
|
* Serialize an Arrow Table into a buffer using the Arrow IPC Stream serialization
|
|
*
|
|
* This function will apply `embeddings` to the table in a manner similar to
|
|
* `convertToTable`.
|
|
*
|
|
* `schema` is required if the table is empty
|
|
*/
|
|
async function fromTableToStreamBuffer(table, embeddings, schema) {
|
|
const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
|
|
const writer = apache_arrow_1.RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
|
|
return Buffer.from(await writer.toUint8Array());
|
|
}
|
|
/**
|
|
* Reorder the columns in `batch` so that they agree with the field order in `schema`
|
|
*/
|
|
function alignBatch(batch, schema) {
|
|
const alignedChildren = [];
|
|
for (const field of schema.fields) {
|
|
const indexInBatch = batch.schema.fields?.findIndex((f) => f.name === field.name);
|
|
if (indexInBatch < 0) {
|
|
throw new Error(`The column ${field.name} was not found in the Arrow Table`);
|
|
}
|
|
alignedChildren.push(batch.data.children[indexInBatch]);
|
|
}
|
|
const newData = (0, apache_arrow_1.makeData)({
|
|
type: new apache_arrow_1.Struct(schema.fields),
|
|
length: batch.numRows,
|
|
nullCount: batch.nullCount,
|
|
children: alignedChildren,
|
|
});
|
|
return new apache_arrow_1.RecordBatch(schema, newData);
|
|
}
|
|
/**
|
|
* Reorder the columns in `table` so that they agree with the field order in `schema`
|
|
*/
|
|
function alignTable(table, schema) {
|
|
const alignedBatches = table.batches.map((batch) => alignBatch(batch, schema));
|
|
return new apache_arrow_1.Table(schema, alignedBatches);
|
|
}
|
|
/**
|
|
* Create an empty table with the given schema
|
|
*/
|
|
function createEmptyTable(schema) {
|
|
return new apache_arrow_1.Table((0, sanitize_1.sanitizeSchema)(schema));
|
|
}
|
|
function validateSchemaEmbeddings(schema, data, embeddings) {
|
|
const fields = [];
|
|
const missingEmbeddingFields = [];
|
|
// First we check if the field is a `FixedSizeList`
|
|
// Then we check if the data contains the field
|
|
// if it does not, we add it to the list of missing embedding fields
|
|
// Finally, we check if those missing embedding fields are `this._embeddings`
|
|
// if they are not, we throw an error
|
|
for (let field of schema.fields) {
|
|
if (isFixedSizeList(field.type)) {
|
|
field = (0, sanitize_1.sanitizeField)(field);
|
|
if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
|
|
// Check if there's an embedding function registered for this field
|
|
let hasEmbeddingFunction = false;
|
|
// Check schema metadata for embedding functions
|
|
if (schema.metadata.has("embedding_functions")) {
|
|
const embeddings = JSON.parse(schema.metadata.get("embedding_functions"));
|
|
// biome-ignore lint/suspicious/noExplicitAny: we don't know the type of `f`
|
|
if (embeddings.find((f) => f["vectorColumn"] === field.name)) {
|
|
hasEmbeddingFunction = true;
|
|
}
|
|
}
|
|
// Check passed embedding function parameter
|
|
if (embeddings && embeddings.vectorColumn === field.name) {
|
|
hasEmbeddingFunction = true;
|
|
}
|
|
// If the field is nullable AND there's no embedding function, allow undefined/omitted values
|
|
if (field.nullable && !hasEmbeddingFunction) {
|
|
fields.push(field);
|
|
}
|
|
else {
|
|
// Either not nullable OR has embedding function - require explicit values
|
|
if (hasEmbeddingFunction) {
|
|
// Don't add to missingEmbeddingFields since this is expected to be filled by embedding function
|
|
fields.push(field);
|
|
}
|
|
else {
|
|
missingEmbeddingFields.push(field);
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
fields.push(field);
|
|
}
|
|
}
|
|
else {
|
|
fields.push(field);
|
|
}
|
|
}
|
|
if (missingEmbeddingFields.length > 0 && embeddings === undefined) {
|
|
throw new Error(`Table has embeddings: "${missingEmbeddingFields
|
|
.map((f) => f.name)
|
|
.join(",")}", but no embedding function was provided`);
|
|
}
|
|
return new apache_arrow_1.Schema(fields, schema.metadata);
|
|
}
|
|
/**
|
|
* Ensures that all nested fields defined in the schema exist in the data,
|
|
* filling missing fields with null values.
|
|
*/
|
|
function ensureNestedFieldsExist(data, schema) {
|
|
return data.map((row) => {
|
|
const completeRow = {};
|
|
for (const field of schema.fields) {
|
|
if (field.name in row) {
|
|
if (field.type.constructor.name === "Struct" &&
|
|
row[field.name] !== null &&
|
|
row[field.name] !== undefined) {
|
|
// Handle nested struct
|
|
const nestedValue = row[field.name];
|
|
completeRow[field.name] = ensureStructFieldsExist(nestedValue, field.type);
|
|
}
|
|
else {
|
|
// Non-struct field or null struct value
|
|
completeRow[field.name] = row[field.name];
|
|
}
|
|
}
|
|
else {
|
|
// Field is missing from the data - set to null
|
|
completeRow[field.name] = null;
|
|
}
|
|
}
|
|
return completeRow;
|
|
});
|
|
}
|
|
/**
|
|
* Recursively ensures that all fields in a struct type exist in the data,
|
|
* filling missing fields with null values.
|
|
*/
|
|
function ensureStructFieldsExist(data, structType) {
|
|
const completeStruct = {};
|
|
for (const childField of structType.children) {
|
|
if (childField.name in data) {
|
|
if (childField.type.constructor.name === "Struct" &&
|
|
data[childField.name] !== null &&
|
|
data[childField.name] !== undefined) {
|
|
// Recursively handle nested struct
|
|
completeStruct[childField.name] = ensureStructFieldsExist(data[childField.name], childField.type);
|
|
}
|
|
else {
|
|
// Non-struct field or null struct value
|
|
completeStruct[childField.name] = data[childField.name];
|
|
}
|
|
}
|
|
else {
|
|
// Field is missing - set to null
|
|
completeStruct[childField.name] = null;
|
|
}
|
|
}
|
|
return completeStruct;
|
|
}
|
|
// Matches format of https://github.com/lancedb/lance/blob/main/rust/lance/src/arrow/json.rs
|
|
function dataTypeToJson(dataType) {
|
|
switch (dataType.typeId) {
|
|
// For primitives, matches https://github.com/lancedb/lance/blob/e12bb9eff2a52f753668d4b62c52e4d72b10d294/rust/lance-core/src/datatypes.rs#L185
|
|
case apache_arrow_1.Type.Null:
|
|
return { type: "null" };
|
|
case apache_arrow_1.Type.Bool:
|
|
return { type: "bool" };
|
|
case apache_arrow_1.Type.Int8:
|
|
return { type: "int8" };
|
|
case apache_arrow_1.Type.Int16:
|
|
return { type: "int16" };
|
|
case apache_arrow_1.Type.Int32:
|
|
return { type: "int32" };
|
|
case apache_arrow_1.Type.Int64:
|
|
return { type: "int64" };
|
|
case apache_arrow_1.Type.Uint8:
|
|
return { type: "uint8" };
|
|
case apache_arrow_1.Type.Uint16:
|
|
return { type: "uint16" };
|
|
case apache_arrow_1.Type.Uint32:
|
|
return { type: "uint32" };
|
|
case apache_arrow_1.Type.Uint64:
|
|
return { type: "uint64" };
|
|
case apache_arrow_1.Type.Int: {
|
|
const bitWidth = dataType.bitWidth;
|
|
const signed = dataType.isSigned;
|
|
const prefix = signed ? "" : "u";
|
|
return { type: `${prefix}int${bitWidth}` };
|
|
}
|
|
case apache_arrow_1.Type.Float: {
|
|
switch (dataType.precision) {
|
|
case apache_arrow_1.Precision.HALF:
|
|
return { type: "halffloat" };
|
|
case apache_arrow_1.Precision.SINGLE:
|
|
return { type: "float" };
|
|
case apache_arrow_1.Precision.DOUBLE:
|
|
return { type: "double" };
|
|
}
|
|
throw Error("Unsupported float precision");
|
|
}
|
|
case apache_arrow_1.Type.Float16:
|
|
return { type: "halffloat" };
|
|
case apache_arrow_1.Type.Float32:
|
|
return { type: "float" };
|
|
case apache_arrow_1.Type.Float64:
|
|
return { type: "double" };
|
|
case apache_arrow_1.Type.Utf8:
|
|
return { type: "string" };
|
|
case apache_arrow_1.Type.Binary:
|
|
return { type: "binary" };
|
|
case apache_arrow_1.Type.LargeUtf8:
|
|
return { type: "large_string" };
|
|
case apache_arrow_1.Type.LargeBinary:
|
|
return { type: "large_binary" };
|
|
case apache_arrow_1.Type.List:
|
|
return {
|
|
type: "list",
|
|
fields: [fieldToJson(dataType.children[0])],
|
|
};
|
|
case apache_arrow_1.Type.FixedSizeList: {
|
|
const fixedSizeList = dataType;
|
|
return {
|
|
type: "fixed_size_list",
|
|
fields: [fieldToJson(fixedSizeList.children[0])],
|
|
length: fixedSizeList.listSize,
|
|
};
|
|
}
|
|
case apache_arrow_1.Type.Struct:
|
|
return {
|
|
type: "struct",
|
|
fields: dataType.children.map(fieldToJson),
|
|
};
|
|
case apache_arrow_1.Type.Date: {
|
|
const unit = dataType.unit;
|
|
return {
|
|
type: unit === apache_arrow_1.DateUnit.DAY ? "date32:day" : "date64:ms",
|
|
};
|
|
}
|
|
case apache_arrow_1.Type.Timestamp: {
|
|
const timestamp = dataType;
|
|
const timezone = timestamp.timezone || "-";
|
|
return {
|
|
type: `timestamp:${timestamp.unit}:${timezone}`,
|
|
};
|
|
}
|
|
case apache_arrow_1.Type.Decimal: {
|
|
const decimal = dataType;
|
|
return {
|
|
type: `decimal:${decimal.bitWidth}:${decimal.precision}:${decimal.scale}`,
|
|
};
|
|
}
|
|
case apache_arrow_1.Type.Duration: {
|
|
const duration = dataType;
|
|
return { type: `duration:${duration.unit}` };
|
|
}
|
|
case apache_arrow_1.Type.FixedSizeBinary: {
|
|
const byteWidth = dataType.byteWidth;
|
|
return { type: `fixed_size_binary:${byteWidth}` };
|
|
}
|
|
case apache_arrow_1.Type.Dictionary: {
|
|
const dict = dataType;
|
|
const indexType = dataTypeToJson(dict.indices);
|
|
const valueType = dataTypeToJson(dict.valueType);
|
|
return {
|
|
type: `dict:${valueType.type}:${indexType.type}:false`,
|
|
};
|
|
}
|
|
}
|
|
throw new Error("Unsupported data type");
|
|
}
|
|
function fieldToJson(field) {
|
|
return {
|
|
name: field.name,
|
|
type: dataTypeToJson(field.type),
|
|
nullable: field.nullable,
|
|
metadata: field.metadata,
|
|
};
|
|
}
|
|
function alignTableToSchema(table, targetSchema) {
|
|
const existingColumns = new Map();
|
|
// Map existing columns
|
|
for (const field of table.schema.fields) {
|
|
existingColumns.set(field.name, table.getChild(field.name));
|
|
}
|
|
// Create vectors for all fields in target schema
|
|
const alignedColumns = {};
|
|
for (const field of targetSchema.fields) {
|
|
if (existingColumns.has(field.name)) {
|
|
// Column exists, use it
|
|
alignedColumns[field.name] = existingColumns.get(field.name);
|
|
}
|
|
else {
|
|
// Column missing, create null vector
|
|
alignedColumns[field.name] = createNullVector(field, table.numRows);
|
|
}
|
|
}
|
|
// Create new table with aligned schema and columns
|
|
return new apache_arrow_1.Table(targetSchema, alignedColumns);
|
|
}
|
|
function createNullVector(field, numRows) {
|
|
if (field.type.constructor.name === "Struct") {
|
|
// For struct types, create a struct with null fields
|
|
const structType = field.type;
|
|
const childVectors = structType.children.map((childField) => createNullVector(childField, numRows));
|
|
// Create struct data
|
|
const structData = (0, apache_arrow_1.makeData)({
|
|
type: structType,
|
|
length: numRows,
|
|
nullCount: 0,
|
|
children: childVectors.map((v) => v.data[0]),
|
|
});
|
|
return (0, apache_arrow_1.makeVector)(structData);
|
|
}
|
|
else {
|
|
// For other types, create a vector of nulls
|
|
const nullBitmap = new Uint8Array(Math.ceil(numRows / 8));
|
|
// All bits are 0, meaning all values are null
|
|
const data = (0, apache_arrow_1.makeData)({
|
|
type: field.type,
|
|
length: numRows,
|
|
nullCount: numRows,
|
|
nullBitmap,
|
|
});
|
|
return (0, apache_arrow_1.makeVector)(data);
|
|
}
|
|
}
|