diff --git a/.github/workflows/quality-check-dagster-contrib-gemini.yml b/.github/workflows/quality-check-dagster-contrib-gemini.yml new file mode 100644 index 0000000..168fc49 --- /dev/null +++ b/.github/workflows/quality-check-dagster-contrib-gemini.yml @@ -0,0 +1,12 @@ +name: quality-check-dagster-contrib-gemini +on: + pull_request: + types: [opened, synchronize, reopened] + paths: + - 'libraries/dagster-contrib-gemini/**' + +jobs: + check: + uses: ./.github/workflows/template-quality-check.yml + with: + working_directory: ./libraries/dagster-contrib-gemini \ No newline at end of file diff --git a/.github/workflows/release-dagster-contrib-gemini.yml b/.github/workflows/release-dagster-contrib-gemini.yml new file mode 100644 index 0000000..04e91e6 --- /dev/null +++ b/.github/workflows/release-dagster-contrib-gemini.yml @@ -0,0 +1,14 @@ +name: build-and-release-dagster-contrib-gemini + +on: + push: + tags: + - 'dagster_contrib_gemini-*.*.*' + +jobs: + build-and-release-dagster-contrib-gemini: + uses: ./.github/workflows/template-release.yml + with: + library_name: dagster-contrib-gemini + working_directory: ./libraries/dagster-contrib-gemini + secrets: inherit \ No newline at end of file diff --git a/libraries/dagster-contrib-gemini/Makefile b/libraries/dagster-contrib-gemini/Makefile new file mode 100644 index 0000000..1baa6cd --- /dev/null +++ b/libraries/dagster-contrib-gemini/Makefile @@ -0,0 +1,9 @@ +test: + uv run pytest + +ruff: + uv run ruff check --fix . + uv run ruff format . + +check: + uv run pyright diff --git a/libraries/dagster-contrib-gemini/README.md b/libraries/dagster-contrib-gemini/README.md new file mode 100644 index 0000000..dc5bdc4 --- /dev/null +++ b/libraries/dagster-contrib-gemini/README.md @@ -0,0 +1,58 @@ +# dagster-contrib-gemini + +A dagster module that provides integration with [Gemini](https://deepmind.google/technologies/gemini/). + +## Installation + +The `dagster_contrib_gemini` module is available as a PyPI package - install with your preferred python +environment manager (We recommend [uv](https://github.com/astral-sh/uv)). + +``` +source .venv/bin/activate +uv pip install dagster_contrib_gemini +``` + +## Example Usage + +In addition to wrapping the Gemini GenerativeModel class (get_model/get_model_for_asset methods), +this resource logs the usage of the Gemini API to to the asset metadata (both number of calls, and tokens). +This is achieved by wrapping the GenerativeModel.generate_content method. + +Note that the usage will only be logged to the asset metadata from an Asset context - +not from an Op context. +Also note that only the synchronous API usage metadata will be automatically logged - +not the streaming or batching API. + +```python +from dagster import AssetExecutionContext, Definitions, EnvVar, asset, define_asset_job +from dagster_contrib_gemini import GeminiResource + + +@asset(compute_kind="gemini") +def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource): + with gemini.get_model(context) as model: + response = model.generate_content( + "Generate a short sentence on tests" + ) + +defs = Definitions( + assets=[gemini_asset], + resources={ + "gemini": GeminiResource( + api_key=EnvVar("GEMINI_API_KEY"), + generative_model_name="gemini-1.5-flash" + ), + }, +) +``` + + +## Development + +The `Makefile` provides the tools required to test and lint your local installation + +```sh +make test +make ruff +make check +``` \ No newline at end of file diff --git a/libraries/dagster-contrib-gemini/dagster_contrib_gemini/__init__.py b/libraries/dagster-contrib-gemini/dagster_contrib_gemini/__init__.py new file mode 100644 index 0000000..071f9fa --- /dev/null +++ b/libraries/dagster-contrib-gemini/dagster_contrib_gemini/__init__.py @@ -0,0 +1 @@ +from dagster_contrib_gemini.resource import GeminiResource as GeminiResource diff --git a/libraries/dagster-contrib-gemini/dagster_contrib_gemini/resource.py b/libraries/dagster-contrib-gemini/dagster_contrib_gemini/resource.py new file mode 100644 index 0000000..30a3acd --- /dev/null +++ b/libraries/dagster-contrib-gemini/dagster_contrib_gemini/resource.py @@ -0,0 +1,315 @@ +from collections import defaultdict +from contextlib import contextmanager +from functools import wraps +from typing import Optional, Generator, Union +from weakref import WeakKeyDictionary + +from pydantic import Field, PrivateAttr + +from dagster._annotations import experimental, public +from dagster import ( + ConfigurableResource, + DagsterInvariantViolationError, + InitResourceContext, + AssetExecutionContext, + OpExecutionContext, + AssetKey, +) + +import google.generativeai as genai +from google.generativeai import GenerativeModel + +context_to_counters = WeakKeyDictionary() + + +def _add_to_asset_metadata( + context: AssetExecutionContext, usage_metadata: dict, output_name: Optional[str] +): + if context not in context_to_counters: + context_to_counters[context] = defaultdict(lambda: 0) + counters = context_to_counters[context] + + for metadata_key, delta in usage_metadata.items(): + counters[metadata_key] += delta + context.add_output_metadata(dict(counters), output_name) + + +# Unlike in OpenAI - we shouldn't export this wrapper to the user. +# While OpenAI has a lot of endpoints that can be wrapped to log usage, +# Gemini only has one endpoint that provides usage stats (generate_content). +# Gemini's "chat conversation" feature is just a thin wrapper around the same generate_content +# method https://ai.google.dev/gemini-api/docs/text-generation?lang=python#chat +# likewise, the embedding-endpoint also doens't provide any usage stats. +def with_usage_metadata( + context: Union[AssetExecutionContext, OpExecutionContext], + output_name: Optional[str], + func, +): + if not isinstance(context, AssetExecutionContext): + raise DagsterInvariantViolationError( + "The `with_usage_metadata` can only be used when context is of type `AssetExecutionContext`." + ) + + @wraps(func) + def wrapper(*args, **kwargs): + response = func(*args, **kwargs) + usage = response.usage_metadata + usage_metadata = { + "gemini.calls": 1, + "gemini.cached_content_token_count": usage.cached_content_token_count, + "gemini.candidates_token_count": usage.candidates_token_count, + "gemini.prompt_token_count": usage.prompt_token_count, + "gemini.total_token_count": usage.total_token_count, + } + + _add_to_asset_metadata(context, usage_metadata, output_name) + + return response + + return wrapper + + +@public +@experimental +class GeminiResource(ConfigurableResource): + """This resource is a wrapper over the `Gemini library`. + + In addition to wrapping the Gemini GenerativeModel class (get_model/get_model_for_asset methods), + this resource logs the usage of the Gemini API to to the asset metadata (both number of calls, and tokens). + This is achieved by wrapping the model.generate_content method. + + Note that the usage will only be logged to the asset metadata from an Asset context - + not from an Op context. + Also note that only the synchronous API usage metadata will be automatically logged - + not the streaming or batching API. + + Examples: + .. code-block:: python + + from dagster import AssetExecutionContext, Definitions, EnvVar, asset, define_asset_job + from dagster_contrib_gemini import GeminiResource + + + @asset(compute_kind="gemini") + def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource): + with gemini.get_model(context) as model: + response = model.generate_content( + "Generate a short sentence on tests" + ) + + defs = Definitions( + assets=[gemini_asset], + resources={ + "gemini": GeminiResource( + api_key=EnvVar("GEMINI_API_KEY"), + generative_model_name="gemini-1.5-flash" + ), + }, + ) + + """ + + api_key: str = Field( + description=( + "Gemini API key - See https://ai.google.dev/gemini-api/docs/api-key" + ) + ) + + # We call this "generative_model_name" and not "model_name" to avoid pydantic warnings about + # model_* being a protected namespace + generative_model_name: str = Field( + description=( + "A gemini model (e.g. gemini-1.5-flash). select one from https://ai.google.dev/gemini-api/docs/models/gemini" + ) + ) + + _generative_model: GenerativeModel = PrivateAttr() + + @classmethod + def _is_dagster_maintained(cls) -> bool: + return False + + def setup_for_execution(self, context: InitResourceContext) -> None: + # See documentation of the gemini SDK: https://ai.google.dev/api?lang=python + genai.configure(api_key=self.api_key) + self._generative_model = GenerativeModel(model_name=self.generative_model_name) + + # The Gemini library is strange: instead of passing an API-key to the model, you need + # to call the global "configure" function - + # the model will take the API key from this global configuration - and save it under + # the model's _client field. + # But also - this doesn't happen in the GenerativeModel constructor! + # It happens lazily - on the first real API call to Gemini. + # So - in order for the model to actually use the provided API-key (and not with whichever + # api-key was last configurd via the global "configure" function), + # - we need to make an API call after creating the model. + # We use the countTokens API - since it is cost-free and not billable by Google. + # https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/get-token-count#pricing_and_quota + # Code link here: + # https://github.com/google-gemini/generative-ai-python/blob/5a3ba73b363f08b7feb319791f2db6c1d6b00c6f/google/generativeai/generative_models.py#L412-L413 + + self._generative_model.count_tokens("how many tokens in this sentence?") + + @public + @contextmanager + def get_model( + self, context: Union[AssetExecutionContext, OpExecutionContext] + ) -> Generator[GenerativeModel, None, None]: + """Yields a ``gemini.GenerativeModel`` model for interacting with the Gemini API. + + When used in an asset context, the resource wraps the ``model.generate_content`` method + to automatically log its usage to the asset metadata. + + Note that the usage will only be logged to the asset metadata from an Asset context - + not from an Op context. + Also note that only the synchronous API usage metadata will be automatically logged - + not the streaming or batching API. + + :param context: The ``context`` object for computing the op or asset in which ``get_model`` is called. + + Examples: + .. code-block:: python + + from dagster import ( + AssetExecutionContext, + Definitions, + EnvVar, + GraphDefinition, + OpExecutionContext, + asset, + define_asset_job, + op, + ) + from dagster_contrib_gemini import GeminiResource + + # This is an asset using the Gemini resource - usage stats will be logged to + # the asset metadata. + @asset(compute_kind="gemini") + def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource): + with gemini.get_model(context) as model: + response = model.generate_content( + "Generate a short sentence on tests" + ) + + gemini_asset_job = define_asset_job(name="gemini_asset_job", selection="gemini_asset") + + # This is an op using the Gemini resource - usage stats will not be logged. + @op + def gemini_op(context: OpExecutionContext, gemini: GeminiResource): + with gemini.get_model(context) as model: + response = model.generate_content( + "Generate a short sentence on tests" + ) + + gemini_op_job = GraphDefinition(name="gemini_op_job", node_defs=[gemini_op]).to_job() + + defs = Definitions( + assets=[gemini_asset], + jobs=[gemini_asset_job, gemini_op_job], + resources={ + "gemini": GeminiResource( + api_key=EnvVar("GEMINI_API_KEY"), + generative_model_name="gemini-1.5-flash" + ), + }, + ) + """ + yield from self._get_model(context=context, asset_key=None) + + def _wrap_with_usage_metadata( + self, context: AssetExecutionContext, output_name: Optional[str] + ): + self._generative_model.generate_content = with_usage_metadata( + context, output_name, func=self._generative_model.generate_content + ) + + @public + @contextmanager + def get_model_for_asset( + self, context: AssetExecutionContext, asset_key: AssetKey + ) -> Generator[GenerativeModel, None, None]: + """Yields a ``gemini.GenerativeModel`` model for interacting with the Gemini API. + + When using this method, the gemini usage metadata is automatically logged in the + asset materializations associated with the provided ``asset_key``. + This is achieved by wrapping the GenerativeModel.generate_content method. + + This method can only be called when working with assets, + i.e. the provided ``context`` must be of type ``AssetExecutionContext``. + Also note that only the synchronous API usage metadata will be automatically logged - + not the streaming or batching API. + + :param context: the ``context`` object for computing the asset in which ``get_model_for_asset`` is called. + :param asset_key: the ``asset_key`` of the asset for which a materialization should include the metadata. + + Examples: + .. code-block:: python + + from dagster import ( + AssetExecutionContext, + AssetKey, + AssetSpec, + Definitions, + EnvVar, + MaterializeResult, + asset, + define_asset_job, + multi_asset, + ) + from dagster_contrib_gemini import GeminiResource + + + @asset(compute_kind="gemini") + def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource): + with gemini.get_model_for_asset(context, context.asset_key) as model: + response = model.generate_content( + "Generate a short sentence on tests" + ) + + + @multi_asset(specs=[AssetSpec("my_asset1"), AssetSpec("my_asset2")], compute_kind="gemini") + def gemini_multi_asset(context: AssetExecutionContext, gemini: GeminiResource): + with gemini.get_model_for_asset(context, asset_key=AssetKey("my_asset1")) as model: + response = model.generate_content( + "Generate a short sentence on hats" + ) + return ( + MaterializeResult(asset_key="my_asset1", metadata={"some_key": "some_value1"}), + MaterializeResult(asset_key="my_asset2", metadata={"some_key": "some_value2"}), + ) + + + defs = Definitions( + assets=[gemini_asset, gemini_multi_asset], + resources={ + "gemini": GeminiResource( + api_key=EnvVar("GEMINI_API_KEY"), + generative_model_name="gemini-1.5-flash" + ), + }, + ) + + """ + yield from self._get_model(context=context, asset_key=asset_key) + + def _get_model( + self, + context: Union[AssetExecutionContext, OpExecutionContext], + asset_key: Optional[AssetKey] = None, + ) -> Generator[GenerativeModel, None, None]: + if isinstance(context, AssetExecutionContext): + if asset_key is None: + if len(context.assets_def.keys_by_output_name.keys()) > 1: + raise DagsterInvariantViolationError( + "The argument `asset_key` must be specified for multi_asset with more than one asset." + ) + asset_key = context.asset_key + + output_name = context.output_for_asset_key(asset_key) + self._wrap_with_usage_metadata(context=context, output_name=output_name) + + yield self._generative_model + + def teardown_after_execution(self, context: InitResourceContext) -> None: + # The google-generativeai package has no 'close' method. + pass diff --git a/libraries/dagster-contrib-gemini/dagster_contrib_gemini_tests/__init__.py b/libraries/dagster-contrib-gemini/dagster_contrib_gemini_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libraries/dagster-contrib-gemini/dagster_contrib_gemini_tests/test_resource.py b/libraries/dagster-contrib-gemini/dagster_contrib_gemini_tests/test_resource.py new file mode 100644 index 0000000..4f7bfe2 --- /dev/null +++ b/libraries/dagster-contrib-gemini/dagster_contrib_gemini_tests/test_resource.py @@ -0,0 +1,522 @@ +from unittest.mock import MagicMock, patch + +import pytest +from dagster import ( + AssetExecutionContext, + AssetKey, + AssetSelection, + AssetSpec, + Definitions, + OpExecutionContext, + StaticPartitionsDefinition, + asset, + define_asset_job, + graph_asset, + materialize_to_memory, + multi_asset, + op, + job, +) +from dagster._core.errors import DagsterInvariantViolationError +from dagster._core.execution.context.init import build_init_resource_context +from dagster._utils.test import wrap_op_in_graph_and_execute +from dagster_contrib_gemini import GeminiResource + +API_KEY = "xoxp-1234123412341234-12341234-1234" +MODEL_NAME = "gemini-1.5-flash" + + +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_client(mock_configure, mock_model) -> None: + gemini_resource = GeminiResource(api_key=API_KEY, generative_model_name=MODEL_NAME) + gemini_resource.setup_for_execution(build_init_resource_context()) + + mock_context = MagicMock() + with gemini_resource.get_model(mock_context): + mock_configure.assert_called_once_with( + api_key=API_KEY, + ) + mock_model.assert_called_once_with(model_name=MODEL_NAME) + + +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster.OpExecutionContext", autospec=OpExecutionContext) +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_resource_with_op(mock_configure, mock_context, mock_model): + @op + def gemini_op(gemini_resource: GeminiResource): + with gemini_resource.get_model(context=mock_context): + mock_configure.assert_called_once_with(api_key=API_KEY) + mock_model.assert_called_once_with(model_name=MODEL_NAME) + + result = wrap_op_in_graph_and_execute( + gemini_op, + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + assert result.success + + +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_resource_with_asset(mock_configure, mock_context, mock_model): + @asset + def gemini_asset(gemini_resource: GeminiResource): + with gemini_resource.get_model(context=mock_context): + mock_configure.assert_called_once_with(api_key=API_KEY) + mock_model.assert_called_once_with(model_name=MODEL_NAME) + + result = materialize_to_memory( + [gemini_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +@patch("dagster_contrib_gemini.resource.GeminiResource._wrap_with_usage_metadata") +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_resource_with_graph_backed_asset( + mock_configure, mock_context, mock_model, mock_wrapper +): + @op + def message_op(): + return "Say this is a test" + + @op + def gemini_op(gemini_resource: GeminiResource, message: str): + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content(message) + + assert mock_model.called + assert mock_wrapper.called + + @graph_asset + def gemini_asset(): + return gemini_op(message_op()) + + result = materialize_to_memory( + [gemini_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +@patch("dagster_contrib_gemini.resource.GeminiResource._wrap_with_usage_metadata") +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_resource_with_multi_asset( + mock_configure, mock_context, mock_model, mock_wrapper +): + @multi_asset( + specs=[AssetSpec("status"), AssetSpec("result")], + ) + def gemini_multi_asset(gemini_resource: GeminiResource): + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey("status"), + AssetKey("result"), + ] + mock_context.output_for_asset_key.return_value = "result" + + # Test success when asset_key is provided + with gemini_resource.get_model_for_asset( + context=mock_context, asset_key=AssetKey("result") + ) as model: + model.generate_content("Say this is a test") + + assert mock_model.called + assert mock_wrapper.call_count == 1 + mock_wrapper.assert_called_with( + context=mock_context, + output_name="result", + ) + + # Test failure when asset_key is not provided + with pytest.raises(DagsterInvariantViolationError): + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content("Say this is a test") + return None, None + + result = materialize_to_memory( + [gemini_multi_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +@patch("dagster_contrib_gemini.resource.GeminiResource._wrap_with_usage_metadata") +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_resource_with_partitioned_asset( + mock_configure, mock_model, mock_context, mock_wrapper +): + NUM_PARTITION_KEYS = 4 + NUM_ASSET_DEFS = 5 + + gemini_partitions_def = StaticPartitionsDefinition( + [str(j) for j in range(NUM_PARTITION_KEYS)] + ) + + gemini_partitioned_assets = [] + + for i in range(NUM_ASSET_DEFS): + + @asset( + name=f"gemini_partitioned_asset_{i}", + group_name="gemini_partitioned_assets", + partitions_def=gemini_partitions_def, + ) + def gemini_partitioned_asset(gemini_resource: GeminiResource): + assert gemini_resource + + mock_context.output_for_asset_key.return_value = "test" + + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content("Say this is a test") + + assert mock_model.called + assert mock_wrapper.called + mock_wrapper.assert_called_with( + context=mock_context, + output_name="test", + ) + + gemini_partitioned_assets.append(gemini_partitioned_asset) + + defs = Definitions( + assets=gemini_partitioned_assets, + jobs=[ + define_asset_job( + name="gemini_partitioned_asset_job", + selection=AssetSelection.groups("gemini_partitioned_assets"), + partitions_def=gemini_partitions_def, + ) + ], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + for partition_key in gemini_partitions_def.get_partition_keys(): + result = defs.get_job_def("gemini_partitioned_asset_job").execute_in_process( + partition_key=partition_key + ) + assert result.success + + expected_wrapper_call_counts = len(gemini_partitioned_assets) * len( + gemini_partitions_def.get_partition_keys() + ) + assert mock_wrapper.call_count == expected_wrapper_call_counts + + +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_wrapper_with_asset(mock_configure, mock_model, mock_context): + @asset + def gemini_asset(gemini_resource: GeminiResource): + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey("gemini_asset"), + ] + mock_context.asset_key = AssetKey("gemini_asset") + mock_context.output_for_asset_key.return_value = "gemini_asset" + + mock_response = MagicMock() + mock_usage = MagicMock() + + mock_usage.cached_content_token_count = 0 + mock_usage.prompt_token_count = 1 + mock_usage.candidates_token_count = 1 + mock_usage.total_token_count = 2 + + mock_response.usage_metadata = mock_usage + mock_model.return_value.generate_content.return_value = mock_response + + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content("say this is a test") + + mock_context.add_output_metadata.assert_called_with( + metadata={ + "gemini.calls": 1, + "gemini.cached_content_token_count": 0, + "gemini.candidates_token_count": 1, + "gemini.prompt_token_count": 1, + "gemini.total_token_count": 2, + }, + output_name="gemini_asset", + ) + + result = materialize_to_memory( + [gemini_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_wrapper_with_graph_backed_asset( + mock_configure, mock_model, mock_context +): + @op + def message_op(): + return "Say this is a test" + + @op + def gemini_op(gemini_resource: GeminiResource, message: str): + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey("gemini_asset"), + ] + mock_context.asset_key = AssetKey("gemini_asset") + mock_context.output_for_asset_key.return_value = "gemini_asset" + + mock_response = MagicMock() + mock_usage = MagicMock() + + mock_usage.cached_content_token_count = 0 + mock_usage.prompt_token_count = 1 + mock_usage.candidates_token_count = 1 + mock_usage.total_token_count = 2 + + mock_response.usage_metadata = mock_usage + mock_model.return_value.generate_content.return_value = mock_response + + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content(message) + + mock_context.add_output_metadata.assert_called_with( + metadata={ + "gemini.calls": 1, + "gemini.cached_content_token_count": 0, + "gemini.candidates_token_count": 1, + "gemini.prompt_token_count": 1, + "gemini.total_token_count": 2, + }, + output_name="gemini_asset", + ) + + @graph_asset + def gemini_asset(): + return gemini_op(message_op()) + + result = materialize_to_memory( + [gemini_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +# Test that usage metadata is not logged in an op context +@patch("dagster.OpExecutionContext", autospec=OpExecutionContext) +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_wrapper_with_op(mock_configure, mock_model, mock_context): + @op + def gemini_op(gemini_resource: GeminiResource): + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey("gemini_op"), + ] + mock_context.asset_key = AssetKey("gemini_op") + mock_context.output_for_asset_key.return_value = "gemini_op" + + mock_response = MagicMock() + mock_usage = MagicMock() + + mock_usage.cached_content_token_count = 0 + mock_usage.prompt_token_count = 1 + mock_usage.candidates_token_count = 1 + mock_usage.total_token_count = 2 + + mock_response.usage_metadata = mock_usage + mock_model.return_value.generate_content.return_value = mock_response + + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content("Say this is a test") + + assert not mock_context.add_output_metadata.called + + @job + def gemini_job(): + gemini_op() + + defs = Definitions( + jobs=[gemini_job], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + result = defs.get_job_def("gemini_job").execute_in_process() + assert result.success + + +@patch("dagster.AssetExecutionContext", autospec=AssetExecutionContext) +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_wrapper_with_multi_asset(mock_configure, mock_model, mock_context): + @multi_asset( + specs=[AssetSpec("status"), AssetSpec("result")], + ) + def gemini_multi_asset(gemini_resource: GeminiResource): + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey("status"), + AssetKey("result"), + ] + mock_context.output_for_asset_key.return_value = "result" + + mock_response = MagicMock() + mock_usage = MagicMock() + + mock_usage.cached_content_token_count = 0 + mock_usage.prompt_token_count = 1 + mock_usage.candidates_token_count = 1 + mock_usage.total_token_count = 2 + + mock_response.usage_metadata = mock_usage + mock_model.return_value.generate_content.return_value = mock_response + + with gemini_resource.get_model_for_asset( + context=mock_context, asset_key=AssetKey("result") + ) as model: + model.generate_content("Say this is a test") + + mock_context.add_output_metadata.assert_called_with( + metadata={ + "gemini.calls": 1, + "gemini.cached_content_token_count": 0, + "gemini.candidates_token_count": 1, + "gemini.prompt_token_count": 1, + "gemini.total_token_count": 2, + }, + output_name="result", + ) + return None, None + + result = materialize_to_memory( + [gemini_multi_asset], + resources={ + "gemini_resource": GeminiResource( + api_key=API_KEY, generative_model_name=MODEL_NAME + ) + }, + ) + + assert result.success + + +@patch("dagster_contrib_gemini.resource.GenerativeModel") +@patch("dagster_contrib_gemini.resource.genai.configure") +def test_gemini_wrapper_with_partitioned_asset(mock_configure, mock_model): + NUM_PARTITION_KEYS = 4 + NUM_ASSET_DEFS = 5 + + gemini_partitions_def = StaticPartitionsDefinition( + [str(j) for j in range(NUM_PARTITION_KEYS)] + ) + + gemini_partitioned_assets = [] + + for i in range(NUM_ASSET_DEFS): + + @asset( + name=f"gemini_partitioned_asset_{i}", + group_name="gemini_partitioned_assets", + partitions_def=gemini_partitions_def, + ) + def gemini_partitioned_asset(gemini_resource: GeminiResource): + mock_context = MagicMock(autospec=AssetExecutionContext) + mock_context.__class__ = AssetExecutionContext + mock_context.assets_def.keys_by_output_name.keys.return_value = [ + AssetKey(f"gemini_partitioned_asset_{i}"), + ] + mock_context.asset_key = AssetKey(f"gemini_partitioned_asset_{i}") + mock_context.output_for_asset_key.return_value = ( + f"gemini_partitioned_asset_{i}" + ) + + mock_response = MagicMock() + mock_usage = MagicMock() + + mock_usage.cached_content_token_count = 0 + mock_usage.prompt_token_count = 1 + mock_usage.candidates_token_count = 1 + mock_usage.total_token_count = 2 + + mock_response.usage_metadata = mock_usage + mock_model.return_value.generate_content.return_value = mock_response + + with gemini_resource.get_model(context=mock_context) as model: + model.generate_content("Say this is a test") + + mock_context.add_output_metadata.assert_called_with( + { + "gemini.calls": 1, + "gemini.cached_content_token_count": 0, + "gemini.candidates_token_count": 1, + "gemini.prompt_token_count": 1, + "gemini.total_token_count": 2, + }, + f"gemini_partitioned_asset_{i}", + ) + + gemini_partitioned_assets.append(gemini_partitioned_asset) + + defs = Definitions( + assets=gemini_partitioned_assets, + jobs=[ + define_asset_job( + name="gemini_partitioned_asset_job", + selection=AssetSelection.groups("gemini_partitioned_assets"), + partitions_def=gemini_partitions_def, + ) + ], + resources={ + 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