claudevdm commented on code in PR #33313: URL: https://github.com/apache/beam/pull/33313#discussion_r1882884174
########## sdks/python/apache_beam/ml/rag/embeddings/base.py: ########## @@ -0,0 +1,44 @@ +# +# 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. + +from apache_beam.ml.transforms.base import EmbeddingTypeAdapter +from apache_beam.ml.rag.types import Embedding + + +def create_rag_adapter() -> EmbeddingTypeAdapter: + """Creates adapter for converting between Chunk and Embedding types. + + The adapter: + - Extracts text from Chunk.content.text for embedding + - Creates Embedding objects from model output + - Preserves Chunk.id and metadata in Embedding + - Sets sparse_embedding to None (dense embeddings only) Review Comment: Oh yeah that is a good point. Makes me wonder if we should just make Embedding a property in the Chunk dataclass like below? ``` @dataclass class Chunk: """Represents a chunk of text with metadata. Attributes: content: The actual content of the chunk id: Unique identifier for the chunk index: Index of this chunk within the original document metadata: Additional metadata about the chunk (e.g., document source) embedding: Embedding of chunk content """ content: Content id: Optional[str] = None index: Optional[int] = None metadata: Optional[Dict[str, Any]] = None embedding: Optional[Embedding] @dataclass class Embedding: dense_embedding: Optional[List[float]] = None sparse_embedding: Optional[Tuple[List[int], List[float]]] = None ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
