diff --git a/packages/bigframes/notebooks/apps/synthetic_data_generation.ipynb b/packages/bigframes/notebooks/apps/synthetic_data_generation.ipynb index b59777a5da3c..00d30fc8a8a9 100644 --- a/packages/bigframes/notebooks/apps/synthetic_data_generation.ipynb +++ b/packages/bigframes/notebooks/apps/synthetic_data_generation.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -111,7 +111,7 @@ "source": [ "from bigframes.ml.llm import GeminiTextGenerator\n", "\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")" + "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" ] }, { diff --git a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb index c2c85be2b0cf..527d3c4aaacd 100644 --- a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb +++ b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb @@ -29,7 +29,7 @@ "id": "JAPoU8Sm5E6e" }, "source": [ - "# Use BigQuery DataFrames with Generative AI for code generation", + "# Use BigQuery DataFrames with Generative AI for code generation\n", "\n", "\n", "\n", @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "id": "sdjeXFwcHfl7" }, @@ -430,7 +430,7 @@ "source": [ "from bigframes.ml.llm import GeminiTextGenerator\n", "\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")" + "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" ] }, { diff --git a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb index 42dd5a99ac04..2d5bb46d95ed 100644 --- a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb +++ b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Use BigQuery DataFrames to cluster and characterize complaints", + "# Use BigQuery DataFrames to cluster and characterize complaints\n", "\n", "
\n", "\n", @@ -1593,7 +1593,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "id": "mL5P0_3X04dE" }, @@ -1614,7 +1614,7 @@ "source": [ "from bigframes.ml.llm import GeminiTextGenerator\n", "\n", - "q_a_model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")" + "q_a_model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" ] }, { diff --git a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb index 548162b2c6b3..c9fa39926a92 100644 --- a/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb +++ b/packages/bigframes/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb @@ -29,7 +29,7 @@ "id": "EQbZKS7_ooET" }, "source": [ - "# Build a Vector Search application using BigQuery DataFrames (aka BigFrames)", + "# Build a Vector Search application using BigQuery DataFrames (aka BigFrames)\n", "\n", "
\n", "\n", @@ -1451,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1487,7 +1487,7 @@ "source": [ "## gemini model\n", "\n", - "llm_model = bf_llm.GeminiTextGenerator(model_name = \"gemini-2.0-flash-001\") ## replace with other model as needed" + "llm_model = bf_llm.GeminiTextGenerator(model_name = \"gemini-2.5-flash\") ## replace with other model as needed" ] }, { diff --git a/packages/bigframes/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb b/packages/bigframes/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb index e51338c2e8fe..93ac3f31c140 100644 --- a/packages/bigframes/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb +++ b/packages/bigframes/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "id": "UW2fQ2k5Hsic" }, @@ -570,7 +570,7 @@ ], "source": [ "# Define the model\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")\n", + "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")\n", "\n", "# Invoke LLM with prompt\n", "response = predict(zero_shot_prompt, temperature = TEMPERATURE)\n", diff --git a/packages/bigframes/notebooks/generative_ai/large_language_models.ipynb b/packages/bigframes/notebooks/generative_ai/large_language_models.ipynb index e064394c4bd0..4ff9a9d3d237 100644 --- a/packages/bigframes/notebooks/generative_ai/large_language_models.ipynb +++ b/packages/bigframes/notebooks/generative_ai/large_language_models.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -60,7 +60,7 @@ } ], "source": [ - "model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")" + "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" ] }, { diff --git a/packages/bigframes/notebooks/getting_started/bq_dataframes_template.ipynb b/packages/bigframes/notebooks/getting_started/bq_dataframes_template.ipynb index 0970dcedc912..664a3a68d33a 100644 --- a/packages/bigframes/notebooks/getting_started/bq_dataframes_template.ipynb +++ b/packages/bigframes/notebooks/getting_started/bq_dataframes_template.ipynb @@ -1305,13 +1305,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# from bigframes.ml.llm import GeminiTextGenerator\n", "\n", - "# model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")\n", + "# model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")\n", "\n", "# pred = model.predict(df)\n", "# pred" diff --git a/packages/bigframes/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb b/packages/bigframes/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb index fe2d567d1b31..4faff4b8e768 100644 --- a/packages/bigframes/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb +++ b/packages/bigframes/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb @@ -521,7 +521,7 @@ "outputs": [], "source": [ "flattened[\"Transcription\"] = flattened[\"GCS Blob\"].blob.audio_transcribe(\n", - " model_name=\"gemini-2.0-flash-001\",\n", + " model_name=\"gemini-2.5-flash\",\n", " verbose=True,\n", ")\n", "flattened[\"Transcription\"]" diff --git a/packages/bigframes/notebooks/multimodal/multimodal_dataframe.ipynb b/packages/bigframes/notebooks/multimodal/multimodal_dataframe.ipynb index 1d3945c92c34..8f3241259d5f 100644 --- a/packages/bigframes/notebooks/multimodal/multimodal_dataframe.ipynb +++ b/packages/bigframes/notebooks/multimodal/multimodal_dataframe.ipynb @@ -1292,7 +1292,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1339,7 +1339,7 @@ "\n", "transcribed_results = bbq.ai.generate(\n", " prompt=(prompt_text, audio_runtime),\n", - " endpoint=\"gemini-2.0-flash-001\",\n", + " endpoint=\"gemini-2.5-flash\",\n", " model_params={\"generationConfig\": {\"temperature\": 0.0}},\n", ")\n", "\n", diff --git a/packages/bigframes/tests/system/large/blob/test_function.py b/packages/bigframes/tests/system/large/blob/test_function.py index bc09baf268d1..e0996db4212a 100644 --- a/packages/bigframes/tests/system/large/blob/test_function.py +++ b/packages/bigframes/tests/system/large/blob/test_function.py @@ -754,8 +754,8 @@ def test_blob_pdf_chunk_verbose(pdf_mm_df: bpd.DataFrame, bq_connection: str): @pytest.mark.parametrize( "model_name", [ - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ], ) def test_blob_transcribe( @@ -805,8 +805,8 @@ def test_blob_transcribe( @pytest.mark.parametrize( "model_name", [ - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ], ) def test_blob_transcribe_verbose( diff --git a/packages/bigframes/tests/system/large/ml/test_llm.py b/packages/bigframes/tests/system/large/ml/test_llm.py index 357ac2d753ec..638e151ca141 100644 --- a/packages/bigframes/tests/system/large/ml/test_llm.py +++ b/packages/bigframes/tests/system/large/ml/test_llm.py @@ -27,8 +27,6 @@ @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", @@ -56,8 +54,6 @@ def test_create_load_gemini_text_generator_model( @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", @@ -79,8 +75,6 @@ def test_gemini_text_generator_predict_default_params_success( @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", @@ -104,8 +98,6 @@ def test_gemini_text_generator_predict_with_params_success( @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", @@ -131,8 +123,6 @@ def test_gemini_text_generator_multi_cols_predict_success( @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", @@ -176,8 +166,8 @@ def test_gemini_text_generator_predict_output_schema_success( @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ), ) def test_llm_gemini_score(llm_fine_tune_df_default_index, model_name): @@ -205,8 +195,8 @@ def test_llm_gemini_score(llm_fine_tune_df_default_index, model_name): @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ), ) def test_llm_gemini_pro_score_params(llm_fine_tune_df_default_index, model_name): diff --git a/packages/bigframes/tests/system/large/ml/test_multimodal_llm.py b/packages/bigframes/tests/system/large/ml/test_multimodal_llm.py index 69c316e3dacc..6babac261f3c 100644 --- a/packages/bigframes/tests/system/large/ml/test_multimodal_llm.py +++ b/packages/bigframes/tests/system/large/ml/test_multimodal_llm.py @@ -24,8 +24,8 @@ @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ), ) @pytest.mark.flaky(retries=2) @@ -65,7 +65,7 @@ def test_multimodal_embedding_generator_predict_default_params_success( @pytest.mark.parametrize( "model_name", - ("gemini-2.0-flash-001",), + ("gemini-2.5-flash",), ) @pytest.mark.flaky(retries=2) def test_gemini_text_generator_multimodal_structured_output( diff --git a/packages/bigframes/tests/system/large/operations/conftest.py b/packages/bigframes/tests/system/large/operations/conftest.py index 6f64c7552f32..0122c860ca74 100644 --- a/packages/bigframes/tests/system/large/operations/conftest.py +++ b/packages/bigframes/tests/system/large/operations/conftest.py @@ -22,7 +22,7 @@ def gemini_flash_model(session, bq_connection) -> llm.GeminiTextGenerator: return llm.GeminiTextGenerator( session=session, connection_name=bq_connection, - model_name="gemini-2.0-flash-001", + model_name="gemini-2.5-flash", ) diff --git a/packages/bigframes/tests/system/load/test_llm.py b/packages/bigframes/tests/system/load/test_llm.py index 4e8bc65912fb..eec76cf9b679 100644 --- a/packages/bigframes/tests/system/load/test_llm.py +++ b/packages/bigframes/tests/system/load/test_llm.py @@ -41,8 +41,8 @@ def llm_remote_text_df(session, llm_remote_text_pandas_df): @pytest.mark.parametrize( "model_name", ( - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", ), ) def test_llm_gemini_configure_fit( @@ -79,7 +79,7 @@ def test_llm_gemini_configure_fit( @pytest.mark.flaky(retries=2) def test_llm_gemini_w_ground_with_google_search(llm_remote_text_df): - model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001", max_iterations=1) + model = llm.GeminiTextGenerator(model_name="gemini-2.5-flash", max_iterations=1) df = model.predict( llm_remote_text_df["prompt"], ground_with_google_search=True,