Add langchain dep to ML tests.

sdks/python/apache_beam/ml/rag/chunking/langchain_test.py

@@ -16,13 +16,15 @@
 
 """Tests for apache_beam.ml.rag.chunking.langchain."""
 
+import functools
 import unittest
 
 import apache_beam as beam
-from apache_beam.ml.rag.types import Chunk
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import BeamAssertException
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.testing.util import is_not_empty
 
 try:
   from apache_beam.ml.rag.chunking.langchain import LangChainChunker
@@ -41,13 +43,10 @@
   TRANSFORMERS_AVAILABLE = False
 
 
-def chunk_equals(expected, actual):
-  """Custom equality function for Chunk objects."""
-  if not isinstance(expected, Chunk) or not isinstance(actual, Chunk):
-    return False
-  return (
-      expected.content == actual.content and expected.index == actual.index and
-      expected.metadata == actual.metadata)
+def assert_true(elements, assert_fn, error_message_fn):
+  if not assert_fn(elements):
+    raise BeamAssertException(error_message_fn(elements))
+  return True
 
 
 @unittest.skipIf(not LANGCHAIN_AVAILABLE, 'langchain is not installed.')
@@ -83,9 +82,15 @@ def test_no_metadata_fields(self):
           | provider.get_ptransform_for_processing())
       chunks_count = chunks | beam.combiners.Count.Globally()
 
-      assert_that(chunks_count, lambda x: x[0] > 0, 'Has chunks')
+      assert_that(chunks_count, is_not_empty(), 'Has chunks')
 
-      assert_that(chunks, lambda x: all(c.metadata == {} for c in x))
+      assert_that(
+          chunks,
+          functools.partial(
+              assert_true,
+              assert_fn=lambda x: (all(c.metadata == {} for c in x)),
+              error_message_fn=lambda x: f"Expected empty metadata, actual {x}")
+      )
 
   def test_multiple_metadata_fields(self):
     """Test chunking with multiple metadata fields."""
@@ -94,6 +99,7 @@ def test_multiple_metadata_fields(self):
         document_field='content',
         metadata_fields=['source', 'language'],
         text_splitter=splitter)
+    expected_metadata = {'source': 'simple.txt', 'language': 'en'}
 
     with TestPipeline() as p:
       chunks = (
@@ -102,18 +108,20 @@ def test_multiple_metadata_fields(self):
           | provider.get_ptransform_for_processing())
       chunks_count = chunks | beam.combiners.Count.Globally()
 
-      assert_that(chunks_count, lambda x: x[0] > 0, 'Has chunks')
+      assert_that(chunks_count, is_not_empty(), 'Has chunks')
       assert_that(
           chunks,
-          lambda x: all(
-              c.metadata == {
-                  'source': 'simple.txt', 'language': 'en'
-              } for c in x))
+          functools.partial(
+              assert_true,
+              assert_fn=lambda x: all(
+                  c.metadata == expected_metadata for c in x),
+              error_message_fn=lambda x:
+              f"Expected metadata {expected_metadata}, actual {x}"))
 
   def test_recursive_splitter_no_overlap(self):
     """Test RecursiveCharacterTextSplitter with no overlap."""
     splitter = RecursiveCharacterTextSplitter(
-        chunk_size=30, chunk_overlap=0, separators=[". "])
+        chunk_size=30, chunk_overlap=0, separators=[".", " "])
     provider = LangChainChunker(
         document_field='content',
         metadata_fields=['source'],
@@ -126,8 +134,14 @@ def test_recursive_splitter_no_overlap(self):
           | provider.get_ptransform_for_processing())
       chunks_count = chunks | beam.combiners.Count.Globally()
 
-      assert_that(chunks_count, lambda x: x[0] > 0, 'Has chunks')
-      assert_that(chunks, lambda x: all(len(c.content.text) <= 30 for c in x))
+      assert_that(chunks_count, is_not_empty(), 'Has chunks')
+      assert_that(
+          chunks,
+          functools.partial(
+              assert_true,
+              assert_fn=lambda x: all(len(c.content.text) <= 30 for c in x),
+              error_message_fn=lambda x: f"Expected len(chunk) <= 30, \
+                 actual {[len(c.content.text) for c in x]}"))
 
   @unittest.skipIf(not TRANSFORMERS_AVAILABLE, "transformers not available")
   def test_huggingface_tokenizer_splitter(self):
@@ -155,13 +169,13 @@ def check_token_lengths(chunks):
           # Verify each chunk's token length is within limits
           num_tokens = len(tokenizer.encode(chunk.content.text))
           if not num_tokens <= 10:
-            raise AssertionError(
+            raise BeamAssertException(
                 f"Chunk has {num_tokens} tokens, expected <= 10")
         return True
 
       chunks_count = chunks | beam.combiners.Count.Globally()
 
-      assert_that(chunks_count, lambda x: x[0] > 0, 'Has chunks')
+      assert_that(chunks_count, is_not_empty(), 'Has chunks')
       assert_that(chunks, check_token_lengths)
 
   def test_invalid_document_field(self):

sdks/python/setup.py

@@ -486,6 +486,7 @@ def get_portability_package_data():
           'ml_test': [
               'datatable',
               'embeddings',
+              'langchain',
               'onnxruntime',
               'sentence-transformers',
               'skl2onnx',
@@ -505,6 +506,7 @@ def get_portability_package_data():
               'datatable',
               'embeddings',
               'onnxruntime',
+              'langchain',
               'sentence-transformers',
               'skl2onnx',
               'pillow',