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/*
* Copyright 2017-2022 John Snow Labs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.johnsnowlabs.nlp.embeddings
import com.johnsnowlabs.ml.ai.Nomic
import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
import com.johnsnowlabs.ml.openvino.OpenvinoWrapper
import com.johnsnowlabs.ml.tensorflow._
import com.johnsnowlabs.ml.util.LoadExternalModel.{
loadTextAsset,
modelSanityCheck,
notSupportedEngineError
}
import com.johnsnowlabs.ml.util.{ONNX, Openvino, TensorFlow}
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.annotators.tokenizer.wordpiece.{BasicTokenizer, WordpieceEncoder}
import com.johnsnowlabs.nlp.serialization.MapFeature
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.param._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.slf4j.{Logger, LoggerFactory}
import com.johnsnowlabs.ml.openvino.{OpenvinoWrapper, ReadOpenvinoModel, WriteOpenvinoModel}
/** Sentence embeddings using NomicEmbeddings.
*
* nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI
* text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val embeddings = NomicEmbeddings.pretrained()
* .setInputCols("document")
* .setOutputCol("nomic_embeddings")
* }}}
* The default model is `"nomic_small"`, if no name is provided.
*
* For available pretrained models please see the
* [[https://sparknlp.org/models?q=NomicEmbeddings Models Hub]].
*
* For extended examples of usage, see
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/NomicEmbeddingsTestSpec.scala NomicEmbeddingsTestSpec]].
*
* '''Sources''' :
*
* [[https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf Nomic Embed: Training a Reproducible Long Context Text Embedder]]
*
* [[https://github.com/nomicai/contrastors NomicEmbeddings Github Repository]]
*
* ''' Paper abstract '''
*
* ''This technical report describes the training of nomic-embed-text-v1, the first fully
* reproducible, open-source, open-weights, opendata, 8192 context length English text embedding
* model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short and
* long-context tasks. We release the training code and model weights under an Apache 2 license.
* In contrast with other open-source models, we release a training data loader with 235 million
* curated text pairs that allows for the full replication of nomic-embedtext-v1. You can find
* code and data to replicate the model at https://github.com/nomicai/contrastors.''
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.embeddings.NomicEmbeddings
* import com.johnsnowlabs.nlp.EmbeddingsFinisher
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val embeddings = NomicEmbeddings.pretrained("nomic_small", "en")
* .setInputCols("document")
* .setOutputCol("nomic_embeddings")
*
* val embeddingsFinisher = new EmbeddingsFinisher()
* .setInputCols("nomic_embeddings")
* .setOutputCols("finished_embeddings")
* .setOutputAsVector(true)
*
* val pipeline = new Pipeline().setStages(Array(
* documentAssembler,
* embeddings,
* embeddingsFinisher
* ))
*
* val data = Seq("query: how much protein should a female eat",
* "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." +
* But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" +
* marathon. Check out the chart below to see how much protein you should be eating each day."
*
* ).toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.selectExpr("explode(finished_embeddings) as result").show(1, 80)
* +--------------------------------------------------------------------------------+
* | result|
* +--------------------------------------------------------------------------------+
* |[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
* [[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
* +--------------------------------------------------------------------------------+
* }}}
*
* @see
* [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of transformer
* based embeddings
* @param uid
* required uid for storing annotator to disk
* @groupname anno Annotator types
* @groupdesc anno
* Required input and expected output annotator types
* @groupname Ungrouped Members
* @groupname param Parameters
* @groupname setParam Parameter setters
* @groupname getParam Parameter getters
* @groupname Ungrouped Members
* @groupprio param 1
* @groupprio anno 2
* @groupprio Ungrouped 3
* @groupprio setParam 4
* @groupprio getParam 5
* @groupdesc param
* A list of (hyper-)parameter keys this annotator can take. Users can set and get the
* parameter values through setters and getters, respectively.
*/
class NomicEmbeddings(override val uid: String)
extends AnnotatorModel[NomicEmbeddings]
with HasBatchedAnnotate[NomicEmbeddings]
with WriteTensorflowModel
with WriteOnnxModel
with WriteOpenvinoModel
with HasEmbeddingsProperties
with HasStorageRef
with HasCaseSensitiveProperties
with HasEngine {
/** Annotator reference id. Used to identify elements in metadata or to refer to this annotator
* type
*/
override val inputAnnotatorTypes: Array[String] =
Array(AnnotatorType.DOCUMENT)
override val outputAnnotatorType: AnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
/** ConfigProto from tensorflow, serialized into byte array. Get with
* `config_proto.SerializeToString()`
*
* @group param
*/
val configProtoBytes = new IntArrayParam(
this,
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()")
/** Max sentence length to process (Default: `128`)
*
* @group param
*/
val maxSentenceLength =
new IntParam(this, "maxSentenceLength", "Max sentence length to process")
def sentenceStartTokenId: Int = {
$$(vocabulary)("[CLS]")
}
/** @group setParam */
def sentenceEndTokenId: Int = {
$$(vocabulary)("[SEP]")
}
/** Vocabulary used to encode the words to ids with WordPieceEncoder
*
* @group param
*/
val vocabulary: MapFeature[String, Int] = new MapFeature(this, "vocabulary").setProtected()
/** @group setParam */
def setVocabulary(value: Map[String, Int]): this.type = set(vocabulary, value)
private var _model: Option[Broadcast[Nomic]] = None
def this() = this(Identifiable.randomUID("NOMIC_EMBEDDINGS"))
/** @group setParam */
def setMaxSentenceLength(value: Int): this.type = {
require(
value <= 8192,
"NomicEmbeddings models do not support sequences longer than 8192 because of trainable positional embeddings.")
require(value >= 1, "The maxSentenceLength must be at least 1")
set(maxSentenceLength, value)
this
}
/** @group getParam */
def getMaxSentenceLength: Int = $(maxSentenceLength)
/** @group setParam */
def setModelIfNotSet(
spark: SparkSession,
onnxWrapper: Option[OnnxWrapper],
openvinoWrapper: Option[OpenvinoWrapper]): NomicEmbeddings = {
if (_model.isEmpty) {
_model = Some(
spark.sparkContext.broadcast(
new Nomic(
onnxWrapper,
sentenceStartTokenId = sentenceStartTokenId,
sentenceEndTokenId = sentenceEndTokenId,
openvinoWrapper = openvinoWrapper)))
}
this
}
/** Set Embeddings dimensions for the BERT model Only possible to set this when the first time
* is saved dimension is not changeable, it comes from BERT config file
*
* @group setParam
*/
override def setDimension(value: Int): this.type = {
if (get(dimension).isEmpty)
set(this.dimension, value)
this
}
/** Whether to lowercase tokens or not
*
* @group setParam
*/
override def setCaseSensitive(value: Boolean): this.type = {
if (get(caseSensitive).isEmpty)
set(this.caseSensitive, value)
this
}
setDefault(dimension -> 768, batchSize -> 8, maxSentenceLength -> 128, caseSensitive -> false)
def tokenize(sentences: Seq[Annotation]): Seq[WordpieceTokenizedSentence] = {
val basicTokenizer = new BasicTokenizer($(caseSensitive))
val encoder = new WordpieceEncoder($$(vocabulary))
sentences.map { s =>
val sent = Sentence(
content = s.result,
start = s.begin,
end = s.end,
metadata = Some(s.metadata),
index = s.begin)
val tokens = basicTokenizer.tokenize(sent)
val wordpieceTokens = tokens.flatMap(token => encoder.encode(token))
WordpieceTokenizedSentence(wordpieceTokens)
}
}
/** takes a document and annotations and produces new annotations of this annotator's annotation
* type
*
* @param batchedAnnotations
* Annotations that correspond to inputAnnotationCols generated by previous annotators if any
* @return
* any number of annotations processed for every input annotation. Not necessary one to one
* relationship
*/
override def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]] = {
val allAnnotations = batchedAnnotations
.filter(_.nonEmpty)
.zipWithIndex
.flatMap { case (annotations, i) =>
annotations.filter(_.result.nonEmpty).map(x => (x, i))
}
// Tokenize sentences
val tokenizedSentences = tokenize(allAnnotations.map(_._1))
val processedAnnotations = if (allAnnotations.nonEmpty) {
this.getModelIfNotSet.predict(
sentences = allAnnotations.map(_._1),
tokenizedSentences = tokenizedSentences,
batchSize = $(batchSize),
maxSentenceLength = $(maxSentenceLength))
} else {
Seq()
}
// Group resulting annotations by rows. If there are not sentences in a given row, return empty sequence
batchedAnnotations.indices.map(rowIndex => {
val rowAnnotations = processedAnnotations
// zip each annotation with its corresponding row index
.zip(allAnnotations)
// select the sentences belonging to the current row
.filter(_._2._2 == rowIndex)
// leave the annotation only
.map(_._1)
if (rowAnnotations.nonEmpty)
rowAnnotations
else
Seq.empty[Annotation]
})
}
/** @group getParam */
def getModelIfNotSet: Nomic = _model.get.value
override def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
val suffix = "_nomic"
getEngine match {
case ONNX.name =>
writeOnnxModel(
path,
spark,
getModelIfNotSet.onnxWrapper.get,
suffix,
NomicEmbeddings.onnxFile)
case Openvino.name =>
writeOpenvinoModel(
path,
spark,
getModelIfNotSet.openvinoWrapper.get,
suffix,
NomicEmbeddings.openvinoFile)
case _ =>
throw new Exception(notSupportedEngineError)
}
}
override protected def afterAnnotate(dataset: DataFrame): DataFrame = {
dataset.withColumn(
getOutputCol,
wrapSentenceEmbeddingsMetadata(
dataset.col(getOutputCol),
$(dimension),
Some($(storageRef))))
}
}
trait ReadablePretrainedNomicEmbeddingsModel
extends ParamsAndFeaturesReadable[NomicEmbeddings]
with HasPretrained[NomicEmbeddings] {
override val defaultModelName: Some[String] = Some("nomic_small")
/** Java compliant-overrides */
override def pretrained(): NomicEmbeddings = super.pretrained()
override def pretrained(name: String): NomicEmbeddings = super.pretrained(name)
override def pretrained(name: String, lang: String): NomicEmbeddings =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): NomicEmbeddings =
super.pretrained(name, lang, remoteLoc)
}
trait ReadNomicEmbeddingsDLModel
extends ReadTensorflowModel
with ReadOnnxModel
with ReadOpenvinoModel {
this: ParamsAndFeaturesReadable[NomicEmbeddings] =>
override val tfFile: String = "nomic_tensorflow"
override val onnxFile: String = "nomic_onnx"
override val openvinoFile: String = "nomic_openvino"
def readModel(instance: NomicEmbeddings, path: String, spark: SparkSession): Unit = {
instance.getEngine match {
case ONNX.name =>
val onnxWrapper =
readOnnxModel(path, spark, "_nomic_onnx", zipped = true, useBundle = false, None)
instance.setModelIfNotSet(spark, Some(onnxWrapper), None)
case Openvino.name =>
val openvinoWrapper = readOpenvinoModel(path, spark, "_nomic_ov")
instance.setModelIfNotSet(spark, None, Some(openvinoWrapper))
case _ =>
throw new Exception(notSupportedEngineError)
}
}
addReader(readModel)
def loadSavedModel(
modelPath: String,
spark: SparkSession,
useOpenvino: Boolean = false): NomicEmbeddings = {
val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
val vocabs = loadTextAsset(localModelPath, "vocab.txt").zipWithIndex.toMap
/*Universal parameters for all engines*/
val annotatorModel = new NomicEmbeddings()
.setVocabulary(vocabs)
val modelEngine =
if (useOpenvino)
Openvino.name
else
detectedEngine
annotatorModel.set(annotatorModel.engine, modelEngine)
modelEngine match {
case Openvino.name =>
val ovWrapper: OpenvinoWrapper =
OpenvinoWrapper.read(
spark,
localModelPath,
zipped = false,
useBundle = true,
detectedEngine = detectedEngine)
annotatorModel
.setModelIfNotSet(spark, None, Some(ovWrapper))
case ONNX.name =>
val onnxWrapper =
OnnxWrapper.read(spark, localModelPath, zipped = false, useBundle = true)
annotatorModel
.setModelIfNotSet(spark, Some(onnxWrapper), None)
case _ =>
throw new Exception(notSupportedEngineError)
}
annotatorModel
}
}
/** This is the companion object of [[NomicEmbeddings]]. Please refer to that class for the
* documentation.
*/
object NomicEmbeddings
extends ReadablePretrainedNomicEmbeddingsModel
with ReadNomicEmbeddingsDLModel {
private[NomicEmbeddings] val logger: Logger =
LoggerFactory.getLogger("NomicEmbeddings")
}
