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Add ingestion and enrichment examples.
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sdks/python/apache_beam/ml/rag/examples/enrichment_example.py
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# | ||
# 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. | ||
# | ||
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import apache_beam as beam | ||
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import tempfile | ||
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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 | ||
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PROJECT = "<PROJECT>" | ||
BIGQUERY_TABLE = "<BIGQUERY_TABLE>" | ||
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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, | ||
) | ||
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def run_pipeline(): | ||
with beam.Pipeline() as p: | ||
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# 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)) | ||
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if __name__ == '__main__': | ||
run_pipeline() |
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sdks/python/apache_beam/ml/rag/examples/ingestion_example.py
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import apache_beam as beam | ||
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import tempfile | ||
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from typing import Any, Dict | ||
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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 | ||
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TEMP_LOCATION = "<TEMP_LOCATION>" | ||
BIGQUERY_TABLE = "<BIGQUERY_TABLE>" | ||
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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, | ||
) | ||
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def run_pipeline(): | ||
with beam.Pipeline( | ||
options=PipelineOptions(['--runner=DirectRunner', | ||
f'--temp_location={TEMP_LOCATION}', | ||
'--expansion_service_port=8888'])) as p: | ||
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# 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' | ||
}]) | ||
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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", | ||
}))) | ||
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if __name__ == '__main__': | ||
run_pipeline() |