[SPARKNLP-1341] Add MultiColumnAssembler (#14743)

* Add AnnotationMerger annotator, docs, and tests

* Add tests cases for readerassembler

* Rename AnnotationMerger to MultiColumnAssembler

---------

Co-authored-by: Devin Ha <devin@trungducha.de>

Author: AbdullahMubeenAnwar

docs/en/annotator_entries/MultiColumnAssembler.md

@@ -0,0 +1,131 @@
+{%- capture title -%}
+MultiColumnAssembler
+{%- endcapture -%}
+
+{%- capture description -%}
+Merges multiple annotation columns into a single annotation column. This is useful when
+multiple annotators produce separate annotation columns (e.g., `document_text`,
+`document_table` from [ReaderAssembler](/docs/en/annotators#readerassembler)) and a downstream
+annotator (e.g., [AutoGGUFVisionModel](/docs/en/annotators#autoggufvisionmodel)) expects a
+single input column containing all annotations.
+
+Annotations from all input columns are collected and concatenated into the output column.
+The output annotator type defaults to `DOCUMENT` but can be configured via
+`setOutputAsAnnotatorType`. Each annotation's metadata is preserved, and a `source_column`
+key is added to track which input column the annotation originated from. All
+annotations from the first input column appear first, then all from the second, and so on.
+
+**Note:** All input columns must use the standard `Annotation` schema. Columns that use the
+`AnnotationImage` schema (e.g., IMAGE-typed columns from `ReaderAssembler`) are **not
+supported** and will cause a validation error.
+
+For more extended examples see the
+[Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/annotation-merger/Merging_Annotation_Columns.ipynb).
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture python_example -%}
+import sparknlp
+from sparknlp.base import *
+from pyspark.ml import Pipeline
+
+documentAssembler1 = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document_text")
+
+documentAssembler2 = DocumentAssembler() \
+    .setInputCol("table") \
+    .setOutputCol("document_table")
+
+multiColumnAssembler = MultiColumnAssembler() \
+    .setInputCols(["document_text", "document_table"]) \
+    .setOutputCol("merged_document")
+
+data = spark.createDataFrame(
+    [("Hello world", "Name | Age\nJohn | 30")],
+    ["text", "table"]
+)
+
+pipeline = Pipeline().setStages([
+    documentAssembler1,
+    documentAssembler2,
+    multiColumnAssembler
+]).fit(data)
+
+result = pipeline.transform(data)
+result.selectExpr("merged_document.result").show(truncate=False)
++--------------------------------+
+|result                          |
++--------------------------------+
+|[Hello world, Name | Age       |
+|John | 30]                      |
++--------------------------------+
+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import spark.implicits._
+import com.johnsnowlabs.nlp.{MultiColumnAssembler, DocumentAssembler}
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler1 = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document_text")
+
+val documentAssembler2 = new DocumentAssembler()
+  .setInputCol("table")
+  .setOutputCol("document_table")
+
+val multiColumnAssembler = new MultiColumnAssembler()
+  .setInputCols("document_text", "document_table")
+  .setOutputCol("merged_document")
+
+val data = Seq(("Hello world", "Name | Age\nJohn | 30"))
+  .toDF("text", "table")
+
+val pipeline = new Pipeline()
+  .setStages(Array(documentAssembler1, documentAssembler2, multiColumnAssembler))
+  .fit(data)
+
+val result = pipeline.transform(data)
+result.selectExpr("merged_document.result").show(false)
++--------------------------------+
+|result                          |
++--------------------------------+
+|[Hello world, Name | Age       |
+|John | 30]                      |
++--------------------------------+
+
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[MultiColumnAssembler](/api/com/johnsnowlabs/nlp/MultiColumnAssembler)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[MultiColumnAssembler](/api/python/reference/autosummary/sparknlp/base/multi_column_assembler/index.html#sparknlp.base.multi_column_assembler.MultiColumnAssembler)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[MultiColumnAssembler](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/MultiColumnAssembler.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+python_api_link=python_api_link
+api_link=api_link
+source_link=source_link
+%}
+

docs/en/annotators.md

@@ -45,6 +45,7 @@ There are two types of Annotators:
 {:.table-model-big}
 |Annotator|Description|Version |
 |---|---|---|
+{% include templates/anno_table_entry.md path="" name="MultiColumnAssembler" summary="Merges multiple annotation columns into a single annotation column. Useful when separate document columns need to be combined for a downstream annotator."%}
 {% include templates/anno_table_entry.md path="" name="AutoGGUFEmbeddings" summary="Annotator that uses the llama.cpp library to generate text embeddings with large language models."%}
 {% include templates/anno_table_entry.md path="" name="AutoGGUFModel" summary="Annotator that uses the llama.cpp library to generate text completions with large language models."%}
 {% include templates/anno_table_entry.md path="" name="AutoGGUFReranker" summary="Annotator that uses the llama.cpp library to rerank text documents based on their relevance to a given query using GGUF-format reranking models."%}