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feat: Update llm.TextEmbeddingGenerator to 005 #1186

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Merged
merged 7 commits into from
Dec 6, 2024
Merged

feat: Update llm.TextEmbeddingGenerator to 005 #1186

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97d9259
docs(bigquery): update minor parts in base.py
shuoweil Dec 3, 2024
c9318d0
docs(bigquery): update minor changes for bigframes/ml/base.py
shuoweil Dec 3, 2024
7b65227
Merge branch 'shuowei-doc-update'
shuoweil Dec 3, 2024
2f1f138
Merge branch 'main' into shuowei-update-textembedding005
Dec 3, 2024
10f1d1a
Merge branch 'main' into shuowei-update-textembedding005
Dec 5, 2024
5b11dfc
udpate docs in semantics.py to match the text-embedding-005 update
shuoweil Dec 5, 2024
35bc0ed
Merge branch 'main' into shuowei-update-textembedding005
Dec 6, 2024
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10 changes: 7 additions & 3 deletions 10 bigframes/ml/llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,9 +47,11 @@
_EMBEDDING_GENERATOR_GECKO_MULTILINGUAL_ENDPOINT,
)

_TEXT_EMBEDDING_005_ENDPOINT = "text-embedding-005"
_TEXT_EMBEDDING_004_ENDPOINT = "text-embedding-004"
_TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT = "text-multilingual-embedding-002"
_TEXT_EMBEDDING_ENDPOINTS = (
_TEXT_EMBEDDING_005_ENDPOINT,
_TEXT_EMBEDDING_004_ENDPOINT,
_TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT,
)
Expand Down Expand Up @@ -606,8 +608,8 @@ class TextEmbeddingGenerator(base.BaseEstimator):

Args:
model_name (str, Default to "text-embedding-004"):
The model for text embedding. Possible values are "text-embedding-004" or "text-multilingual-embedding-002".
text-embedding models returns model embeddings for text inputs.
The model for text embedding. Possible values are "text-embedding-005", "text-embedding-004"
or "text-multilingual-embedding-002". text-embedding models returns model embeddings for text inputs.
text-multilingual-embedding models returns model embeddings for text inputs which support over 100 languages.
Default to "text-embedding-004".
session (bigframes.Session or None):
Expand All @@ -621,7 +623,9 @@ def __init__(
self,
*,
model_name: Literal[
"text-embedding-004", "text-multilingual-embedding-002"
"text-embedding-005",
"text-embedding-004",
"text-multilingual-embedding-002",
] = "text-embedding-004",
session: Optional[bigframes.Session] = None,
connection_name: Optional[str] = None,
Expand Down
1 change: 1 addition & 0 deletions 1 bigframes/ml/loader.py
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Expand Up @@ -71,6 +71,7 @@
llm._CLAUDE_3_SONNET_ENDPOINT: llm.Claude3TextGenerator,
llm._CLAUDE_3_5_SONNET_ENDPOINT: llm.Claude3TextGenerator,
llm._CLAUDE_3_OPUS_ENDPOINT: llm.Claude3TextGenerator,
llm._TEXT_EMBEDDING_005_ENDPOINT: llm.TextEmbeddingGenerator,
llm._TEXT_EMBEDDING_004_ENDPOINT: llm.TextEmbeddingGenerator,
llm._TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT: llm.TextEmbeddingGenerator,
}
Expand Down
6 changes: 3 additions & 3 deletions 6 bigframes/operations/semantics.py
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Expand Up @@ -647,12 +647,12 @@ def search(
>>> bigframes.options.experiments.semantic_operators = True

>>> import bigframes.ml.llm as llm
>>> model = llm.TextEmbeddingGenerator(model_name="text-embedding-004")
>>> model = llm.TextEmbeddingGenerator(model_name="text-embedding-005")
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docs tests fail on semantic. These changes are unnecessary. Maybe just revert it.


>>> df = bpd.DataFrame({"creatures": ["salmon", "sea urchin", "frog", "chimpanzee"]})
>>> df.semantics.search("creatures", "monkey", top_k=1, model=model, score_column='distance')
creatures distance
3 chimpanzee 0.781101
3 chimpanzee 0.635844
<BLANKLINE>
[1 rows x 2 columns]

Expand Down Expand Up @@ -945,7 +945,7 @@ def sim_join(
>>> bigframes.options.experiments.semantic_operators = True

>>> import bigframes.ml.llm as llm
>>> model = llm.TextEmbeddingGenerator(model_name="text-embedding-004")
>>> model = llm.TextEmbeddingGenerator(model_name="text-embedding-005")

>>> df1 = bpd.DataFrame({'animal': ['monkey', 'spider']})
>>> df2 = bpd.DataFrame({'animal': ['scorpion', 'baboon']})
Expand Down
2 changes: 1 addition & 1 deletion 2 notebooks/experimental/semantic_operators.ipynb
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Expand Up @@ -151,7 +151,7 @@
"source": [
"import bigframes.ml.llm as llm\n",
"gemini_model = llm.GeminiTextGenerator(model_name=llm._GEMINI_1P5_FLASH_001_ENDPOINT)\n",
"text_embedding_model = llm.TextEmbeddingGenerator(model_name=\"text-embedding-004\")"
"text_embedding_model = llm.TextEmbeddingGenerator(model_name=\"text-embedding-005\")"
]
},
{
Expand Down
6 changes: 3 additions & 3 deletions 6 owlbot.py
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Expand Up @@ -104,7 +104,7 @@

# Use a custom table of contents since the default one isn't organized well
# enough for the number of classes we have.
assert 1 == s.replace( # publish-docs.sh
assert 1 == s.replace( # publish-docs.sh
[".kokoro/publish-docs.sh"],
(
re.escape("# upload docs")
Expand All @@ -122,14 +122,14 @@
)

# Fixup the documentation.
assert 1 == s.replace( # docs/conf.py
assert 1 == s.replace( # docs/conf.py
["docs/conf.py"],
re.escape("Google Cloud Client Libraries for bigframes"),
"BigQuery DataFrames provides DataFrame APIs on the BigQuery engine",
)

# Don't omit `*/core/*.py` when counting test coverages
assert 1 == s.replace( # .coveragerc
assert 1 == s.replace( # .coveragerc
[".coveragerc"],
re.escape(" */core/*.py\n"),
"",
Expand Down
2 changes: 1 addition & 1 deletion 2 tests/system/large/operations/conftest.py
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Expand Up @@ -29,5 +29,5 @@ def gemini_flash_model(session, bq_connection) -> llm.GeminiTextGenerator:
@pytest.fixture(scope="session")
def text_embedding_generator(session, bq_connection) -> llm.TextEmbeddingGenerator:
return llm.TextEmbeddingGenerator(
session=session, connection_name=bq_connection, model_name="text-embedding-004"
session=session, connection_name=bq_connection, model_name="text-embedding-005"
)
6 changes: 3 additions & 3 deletions 6 tests/system/small/ml/test_llm.py
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Expand Up @@ -196,7 +196,7 @@ def test_text_generator_predict_with_params_success(

@pytest.mark.parametrize(
"model_name",
("text-embedding-004", "text-multilingual-embedding-002"),
("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"),
)
def test_create_load_text_embedding_generator_model(
dataset_id, model_name, session, bq_connection
Expand All @@ -218,7 +218,7 @@ def test_create_load_text_embedding_generator_model(

@pytest.mark.parametrize(
"model_name",
("text-embedding-004", "text-multilingual-embedding-002"),
("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"),
)
@pytest.mark.flaky(retries=2)
def test_text_embedding_generator_predict_default_params_success(
Expand All @@ -236,7 +236,7 @@ def test_text_embedding_generator_predict_default_params_success(

@pytest.mark.parametrize(
"model_name",
("text-embedding-004", "text-multilingual-embedding-002"),
("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"),
)
@pytest.mark.flaky(retries=2)
def test_text_embedding_generator_multi_cols_predict_success(
Expand Down
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