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How this addresses that need: Produces a : repeating structure from the full record. Side effects of this change: * None Relevant ticket(s): * None --- embeddings/strategies/full_record.py | 13 +++++++++++-- tests/test_strategies.py | 4 +--- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/embeddings/strategies/full_record.py b/embeddings/strategies/full_record.py index 5dae9d6..a602372 100644 --- a/embeddings/strategies/full_record.py +++ b/embeddings/strategies/full_record.py @@ -9,5 +9,14 @@ class FullRecordStrategy(BaseStrategy): STRATEGY_NAME = "full_record" def extract_text(self, timdex_record: dict) -> str: - """Serialize the entire transformed_record as JSON.""" - return json.dumps(timdex_record) + """Serialize the entire TIMDEX record. + + The final string form is: + : + : + ... + """ + final_string = "" + for k, v in timdex_record.items(): + final_string += f"{k}: {json.dumps(v)}\n" + return final_string diff --git a/tests/test_strategies.py b/tests/test_strategies.py index ede712a..439b2e4 100644 --- a/tests/test_strategies.py +++ b/tests/test_strategies.py @@ -1,5 +1,3 @@ -import json - import pytest from embeddings.strategies.base import BaseStrategy @@ -14,7 +12,7 @@ def test_full_record_strategy_extracts_text(): text = strategy.extract_text(timdex_record) - assert text == json.dumps(timdex_record) + assert text == """timdex_record_id: "test-123"\ntitle: ["Test Title"]\n""" assert strategy.STRATEGY_NAME == "full_record" From ed45be020a1acdffe145bff73c4d661268bfb1d1 Mon Sep 17 00:00:00 2001 From: Graham Hukill Date: Fri, 14 Nov 2025 09:45:30 -0500 Subject: [PATCH 3/4] Add chunk_size env var and configuration Why these changes are being introduced: Now that we can support multiprocessing, we should expose the chunk_size parameter for inference. How this addresses that need: The env var TE_CHUNK_SIZE is added that will set the chunk_size configuration for inference. Side effects of this change: * When multiprocessing is used, and TE_CHUNK_SIZE is set, it will be used for inference. Relevant ticket(s): * None --- README.md | 18 +++++++++++++----- .../models/os_neural_sparse_doc_v3_gte.py | 13 ++++++++----- 2 files changed, 21 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 7ff9fc9..5d8db3c 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,13 @@ WORKSPACE=### Set to `dev` for local development, this will be set to `stage` an TE_MODEL_URI=# HuggingFace model URI TE_MODEL_PATH=# Path where the model will be downloaded to and loaded from HF_HUB_DISABLE_PROGRESS_BARS=#boolean to use progress bars for HuggingFace model downloads; defaults to 'true' in deployed contexts +# inference performance tuning +TE_TORCH_DEVICE=# defaults to 'cpu', but can be set to 'mps' for Apple Silicon, or theoretically 'cuda' for GPUs +TE_BATCH_SIZE=# batch size for each inference worker, defaults to 32 +TE_NUM_WORKERS=# number of parallel model inference workers, defaults to 1 +TE_CHUNK_SIZE=# number of batches each parallel worker grabs; no effect if TE_NUM_WORKERS=1 +OMP_NUM_THREADS=# torch env var that sets thread usage during inference, default is not setting and using torch defaults +MKL_NUM_THREADS=# torch env var that sets thread usage during inference, default is not setting and using torch defaults ``` ## Configuring an Embedding Model @@ -106,14 +113,15 @@ Options: [required] --model-path PATH Path where the model will be downloaded to and loaded from, e.g. '/path/to/model'. [required] - -d, --dataset-location PATH TIMDEX dataset location, e.g. + --dataset-location PATH TIMDEX dataset location, e.g. 's3://timdex/dataset', to read records from. - [required] - --run-id TEXT TIMDEX ETL run id. [required] + --run-id TEXT TIMDEX ETL run id. --run-record-offset INTEGER TIMDEX ETL run record offset to start from, - default = 0. [required] + default = 0. --record-limit INTEGER Limit number of records after --run-record- - offset, default = None (unlimited). [required] + offset, default = None (unlimited). + --input-jsonl TEXT Optional filepath to JSONLines file containing + TIMDEX records to create embeddings from. --strategy [full_record] Pre-embedding record transformation strategy. Repeatable to apply multiple strategies. [required] diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py index 769bf6a..d995903 100644 --- a/embeddings/models/os_neural_sparse_doc_v3_gte.py +++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py @@ -191,11 +191,11 @@ def create_embeddings( # read env vars for configurations num_workers = int(os.getenv("TE_NUM_WORKERS", "1")) - batch_size = int( - os.getenv("TE_BATCH_SIZE", "32") - ) # sentence-transformers default + batch_size = int(os.getenv("TE_BATCH_SIZE", "32")) + chunk_size_env = os.getenv("TE_CHUNK_SIZE") + chunk_size = int(chunk_size_env) if chunk_size_env else None - # configure device and worker pool based on number of workers requested + # configure for inference if num_workers > 1 or self.device == "mps": device = None pool = self._model.start_multi_process_pool( @@ -206,17 +206,20 @@ def create_embeddings( pool = None logger.info( f"Num workers: {num_workers}, batch size: {batch_size}, " - f"device: {device}, pool: {pool}" + f"chunk size: {chunk_size, }device: {device}, pool: {pool}" ) # get sparse vector embedding for input text(s) + inference_start = time.perf_counter() sparse_vectors = self._model.encode_document( texts, batch_size=batch_size, device=device, pool=pool, save_to_cpu=True, + chunk_size=chunk_size, ) + logger.info(f"Inference elapsed: {time.perf_counter()-inference_start}s") sparse_vectors = cast("list[Tensor]", sparse_vectors) for i, embedding_input in enumerate(embedding_inputs_list): From ee1c26f1268d84ceee86ab239a4e2bc38e655b52 Mon Sep 17 00:00:00 2001 From: Graham Hukill Date: Fri, 14 Nov 2025 09:49:54 -0500 Subject: [PATCH 4/4] Do not save sparse vectors for OSNeuralSparseDocV3GTE Why these changes are being introduced: Our initial pass with the embedding class OSNeuralSparseDocV3GTE was to save both the sparse vector and the decoded token:weights. Each sparse vector was the length of the model vocabulary, about 30k, with mostly zeros. While technically this could be used for analysis beyond just the decoded token:weights given to OpenSearch, the data transfer and storage overhead exceeds any known use cases at the moment. How this addresses that need: The OSNeuralSparseDocV3GTE embedding model is updated to not include the sparse vector for the Embedding.embedding_vector property on output. This can easily be turned on later, with an inline code comment showing how to toggle that back on. Side effects of this change: * No sparse vectors are stored for now, storage is decreased. Relevant ticket(s): * None --- embeddings/embedding.py | 4 ++-- embeddings/models/os_neural_sparse_doc_v3_gte.py | 7 +++++-- tests/test_os_neural_sparse_doc_v3_gte.py | 6 +++--- 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/embeddings/embedding.py b/embeddings/embedding.py index 302c895..0fca7c8 100644 --- a/embeddings/embedding.py +++ b/embeddings/embedding.py @@ -48,8 +48,8 @@ class Embedding: run_record_offset: int model_uri: str embedding_strategy: str - embedding_vector: list[float] - embedding_token_weights: dict + embedding_vector: list[float] | None + embedding_token_weights: dict | None timestamp: datetime.datetime = field( default_factory=lambda: datetime.datetime.now(datetime.UTC) diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py index d995903..8ed33a9 100644 --- a/embeddings/models/os_neural_sparse_doc_v3_gte.py +++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py @@ -247,8 +247,11 @@ def _get_embedding_from_sparse_vector( decoded_token_weights = cast("list[tuple[str, float]]", decoded_token_weights) embedding_token_weights = dict(decoded_token_weights) - # prepare sparse vector for JSON serialization - embedding_vector = sparse_vector.to_dense().tolist() + # # prepare sparse vector for JSON serialization + # NOTE: at this time we are NOT including the sparse vector for output. This + # block can be uncommented in the future to include it when wanted. + # embedding_vector = sparse_vector.to_dense().tolist() # noqa: ERA001 + embedding_vector = None return Embedding( timdex_record_id=embedding_input.timdex_record_id, diff --git a/tests/test_os_neural_sparse_doc_v3_gte.py b/tests/test_os_neural_sparse_doc_v3_gte.py index e49353e..1936e01 100644 --- a/tests/test_os_neural_sparse_doc_v3_gte.py +++ b/tests/test_os_neural_sparse_doc_v3_gte.py @@ -217,7 +217,7 @@ def test_create_embedding_returns_embedding_object(tmp_path): assert embedding.run_record_offset == 42 assert embedding.model_uri == model.model_uri assert embedding.embedding_strategy == "title_only" - assert embedding.embedding_vector == pytest.approx([0.1, 0.2]) + assert embedding.embedding_vector is None assert embedding.embedding_token_weights == {"sum": pytest.approx(0.3)} @@ -257,6 +257,6 @@ def test_create_embeddings_consumes_iterator_and_returns_embeddings( assert len(embeddings) == 2 assert embeddings[0].timdex_record_id == "id-1" - assert embeddings[0].embedding_vector == pytest.approx([0.1, 0.2]) + assert embeddings[0].embedding_vector is None assert embeddings[1].timdex_record_id == "id-2" - assert embeddings[1].embedding_vector == pytest.approx([0.3, 0.4]) + assert embeddings[1].embedding_vector is None