diff --git a/sdks/python/apache_beam/ml/rag/examples/enrichment_example.py b/sdks/python/apache_beam/ml/rag/examples/enrichment_example.py
new file mode 100644
index 000000000000..d3e9b1ed4c3c
--- /dev/null
+++ b/sdks/python/apache_beam/ml/rag/examples/enrichment_example.py
@@ -0,0 +1,77 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+import apache_beam as beam
+
+import tempfile
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from apache_beam.ml.transforms.base import MLTransform
+from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings
+from transformers import AutoTokenizer
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.ml.rag.types import Chunk, Content
+from apache_beam.ml.rag.enrichment.bigquery_vector_search import BigQueryVectorSearchEnrichmentHandler, BigQueryVectorSearchParameters
+from apache_beam.transforms.enrichment import Enrichment
+
+PROJECT = "<PROJECT>"
+BIGQUERY_TABLE = "<BIGQUERY_TABLE>"
+
+huggingface_embedder = HuggingfaceTextEmbeddings(
+    model_name="sentence-transformers/all-MiniLM-L6-v2")
+tokenizer = AutoTokenizer.from_pretrained(
+    "sentence-transformers/all-MiniLM-L6-v2")
+splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
+    tokenizer,
+    chunk_size=512,
+    chunk_overlap=52,
+)
+
+
+def run_pipeline():
+  with beam.Pipeline() as p:
+
+    # Enrichment
+    _ = (
+        p
+        | beam.Create([
+            Chunk(
+                id="simple_query",
+                content=Content(text="This is a simple test document."),
+                metadata={"language": "en"}),
+            Chunk(
+                id="medical_query",
+                content=Content(text="When did the patient arrive?"),
+                metadata={"language": "en"}),
+        ])
+        | MLTransform(write_artifact_location=tempfile.mkdtemp()).
+        with_transform(huggingface_embedder)
+        | Enrichment(
+            BigQueryVectorSearchEnrichmentHandler(
+                project=PROJECT,
+                vector_search_parameters=BigQueryVectorSearchParameters(
+                    table_name=BIGQUERY_TABLE,
+                    embedding_column='embedding',
+                    columns=['metadata', 'content'],
+                    neighbor_count=3,
+                    metadata_restriction_template=(
+                        "check_metadata(metadata, 'language','{language}')"))))
+        | beam.Map(print))
+
+
+if __name__ == '__main__':
+  run_pipeline()
diff --git a/sdks/python/apache_beam/ml/rag/examples/ingestion_example.py b/sdks/python/apache_beam/ml/rag/examples/ingestion_example.py
new file mode 100644
index 000000000000..c7e1b1affaf8
--- /dev/null
+++ b/sdks/python/apache_beam/ml/rag/examples/ingestion_example.py
@@ -0,0 +1,71 @@
+import apache_beam as beam
+
+import tempfile
+
+from typing import Any, Dict
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from apache_beam.ml.transforms.base import MLTransform
+from apache_beam.ml.rag.chunking.langchain import LangChainChunkingProvider
+from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings
+from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform
+from apache_beam.ml.rag.ingestion.bigquery import BigQueryVectorWriterConfig
+from transformers import AutoTokenizer
+from apache_beam.options.pipeline_options import PipelineOptions
+
+TEMP_LOCATION = "<TEMP_LOCATION>"
+BIGQUERY_TABLE = "<BIGQUERY_TABLE>"
+
+huggingface_embedder = HuggingfaceTextEmbeddings(
+    model_name="sentence-transformers/all-MiniLM-L6-v2")
+tokenizer = AutoTokenizer.from_pretrained(
+    "sentence-transformers/all-MiniLM-L6-v2")
+splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
+    tokenizer,
+    chunk_size=512,
+    chunk_overlap=52,
+)
+
+
+def run_pipeline():
+  with beam.Pipeline(
+      options=PipelineOptions(['--runner=DirectRunner',
+                               f'--temp_location={TEMP_LOCATION}',
+                               '--expansion_service_port=8888'])) as p:
+
+    # Ingestion
+    _ = (
+        p
+        | beam.Create([{
+            'content': 'This is a simple test document. It has multiple sentences. '
+            'We will use it to test basic splitting. ' * 20,
+            'source': 'simple.txt',
+            'language': 'en'
+        },
+                       {
+                           'content': (
+                               'The patient arrived at 2 p.m. yesterday. '
+                               'Initial assessment was completed. '
+                               'Lab results showed normal ranges. '
+                               'Follow-up scheduled for next week.' * 10),
+                           'source': 'medical.txt',
+                           'language': 'en'
+                       }])
+        |
+        MLTransform(write_artifact_location=tempfile.mkdtemp()).with_transform(
+            LangChainChunkingProvider(
+                text_splitter=splitter,
+                document_field="content",
+                metadata_fields=["source", "language"
+                                 ])).with_transform(huggingface_embedder)
+        | VectorDatabaseWriteTransform(
+            BigQueryVectorWriterConfig(
+                write_config={
+                    "table": BIGQUERY_TABLE,
+                    "create_disposition": "CREATE_IF_NEEDED",
+                    "write_disposition": "WRITE_TRUNCATE",
+                })))
+
+
+if __name__ == '__main__':
+  run_pipeline()