diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 33bf19e..4b7ea58 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -45,7 +45,7 @@ jobs: run: uv run ruff format --check - name: Run ruff check run: uv run ruff check - # - name: Run mypy - # run: uv run mypy . + - name: Run mypy + run: uv run mypy . - name: Minimize uv cache run: uv cache prune --ci diff --git a/langchain/langchain_vectorize/retrievers.py b/langchain/langchain_vectorize/retrievers.py index 3559f42..a3afa22 100644 --- a/langchain/langchain_vectorize/retrievers.py +++ b/langchain/langchain_vectorize/retrievers.py @@ -4,20 +4,18 @@ from typing import TYPE_CHECKING, Any, Literal, Optional -import vectorize_client from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever from typing_extensions import override -from vectorize_client import ( - ApiClient, - Configuration, - PipelinesApi, - RetrieveDocumentsRequest, -) +from vectorize_client.api.pipelines_api import PipelinesApi +from vectorize_client.api_client import ApiClient +from vectorize_client.configuration import Configuration +from vectorize_client.models.retrieve_documents_request import RetrieveDocumentsRequest if TYPE_CHECKING: from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.runnables import RunnableConfig + from vectorize_client.models.document import Document as VectorizeDocument _METADATA_FIELDS = { "relevancy", @@ -122,7 +120,7 @@ def format_docs(docs): metadata_filters: list[dict[str, Any]] = [] """The metadata filters to apply when retrieving the documents.""" - _pipelines: PipelinesApi | None = None + _pipelines: PipelinesApi = _NOT_SET # type: ignore[assignment] @override def model_post_init(self, /, context: Any) -> None: @@ -146,7 +144,7 @@ def model_post_init(self, /, context: Any) -> None: self._pipelines = PipelinesApi(api) @staticmethod - def _convert_document(document: vectorize_client.models.Document) -> Document: + def _convert_document(document: VectorizeDocument) -> Document: metadata = {field: getattr(document, field) for field in _METADATA_FIELDS} return Document(id=document.id, page_content=document.text, metadata=metadata) @@ -162,14 +160,29 @@ def _get_relevant_documents( rerank: bool | None = None, metadata_filters: list[dict[str, Any]] | None = None, ) -> list[Document]: - request = RetrieveDocumentsRequest( + request = RetrieveDocumentsRequest( # type: ignore[call-arg] question=query, num_results=num_results or self.num_results, rerank=rerank or self.rerank, metadata_filters=metadata_filters or self.metadata_filters, ) + organization_ = organization or self.organization + if not organization_: + msg = ( + "Organization must be set either at initialization " + "or in the invoke method." + ) + raise ValueError(msg) + pipeline_id_ = pipeline_id or self.pipeline_id + if not pipeline_id_: + msg = ( + "Pipeline ID must be set either at initialization " + "or in the invoke method." + ) + raise ValueError(msg) + response = self._pipelines.retrieve_documents( - organization or self.organization, pipeline_id or self.pipeline_id, request + organization_, pipeline_id_, request ) return [self._convert_document(doc) for doc in response.documents] @@ -181,9 +194,10 @@ def invoke( *, organization: str = "", pipeline_id: str = "", - num_results: int = _NOT_SET, - rerank: bool = _NOT_SET, - metadata_filters: list[dict[str, Any]] = _NOT_SET, + num_results: int = _NOT_SET, # type: ignore[assignment] + rerank: bool = _NOT_SET, # type: ignore[assignment] + metadata_filters: list[dict[str, Any]] = _NOT_SET, # type: ignore[assignment] + **_kwargs: Any, ) -> list[Document]: """Invoke the retriever to get relevant documents. @@ -218,16 +232,15 @@ def invoke( query = "what year was breath of the wild released?" docs = retriever.invoke(query, num_results=2) """ - kwargs = {} if organization: - kwargs["organization"] = organization + _kwargs["organization"] = organization if pipeline_id: - kwargs["pipeline_id"] = pipeline_id + _kwargs["pipeline_id"] = pipeline_id if num_results is not _NOT_SET: - kwargs["num_results"] = num_results + _kwargs["num_results"] = num_results if rerank is not _NOT_SET: - kwargs["rerank"] = rerank + _kwargs["rerank"] = rerank if metadata_filters is not _NOT_SET: - kwargs["metadata_filters"] = metadata_filters + _kwargs["metadata_filters"] = metadata_filters - return super().invoke(input, config, **kwargs) + return super().invoke(input, config, **_kwargs) diff --git a/langchain/pyproject.toml b/langchain/pyproject.toml index ac2e77f..095cc02 100644 --- a/langchain/pyproject.toml +++ b/langchain/pyproject.toml @@ -31,7 +31,7 @@ Issues = "https://github.com/vectorize-io/integrations-python/issues" [dependency-groups] dev = [ - "mypy>=1.13.0", + "mypy>=1.17.1,<1.18", "pytest>=8.3.3", "ruff>=0.9.0,<0.10", ] @@ -59,6 +59,8 @@ flake8-annotations.mypy-init-return = true [tool.mypy] strict = true +strict_bytes = true +enable_error_code = "deprecated" warn_unreachable = true pretty = true show_error_codes = true diff --git a/langchain/tests/test_retrievers.py b/langchain/tests/test_retrievers.py index 024b9a0..f5d5ea6 100644 --- a/langchain/tests/test_retrievers.py +++ b/langchain/tests/test_retrievers.py @@ -8,8 +8,42 @@ import pytest import urllib3 -import vectorize_client as v -from vectorize_client import ApiClient +from vectorize_client.api.ai_platform_connectors_api import AIPlatformConnectorsApi +from vectorize_client.api.destination_connectors_api import DestinationConnectorsApi +from vectorize_client.api.pipelines_api import PipelinesApi +from vectorize_client.api.source_connectors_api import SourceConnectorsApi +from vectorize_client.api.uploads_api import UploadsApi +from vectorize_client.api_client import ApiClient +from vectorize_client.configuration import Configuration +from vectorize_client.models.ai_platform_config_schema import AIPlatformConfigSchema +from vectorize_client.models.ai_platform_type_for_pipeline import ( + AIPlatformTypeForPipeline, +) +from vectorize_client.models.create_source_connector_request import ( + CreateSourceConnectorRequest, +) +from vectorize_client.models.destination_connector_type_for_pipeline import ( + DestinationConnectorTypeForPipeline, +) +from vectorize_client.models.file_upload import FileUpload +from vectorize_client.models.pipeline_ai_platform_connector_schema import ( + PipelineAIPlatformConnectorSchema, +) +from vectorize_client.models.pipeline_configuration_schema import ( + PipelineConfigurationSchema, +) +from vectorize_client.models.pipeline_destination_connector_schema import ( + PipelineDestinationConnectorSchema, +) +from vectorize_client.models.pipeline_source_connector_schema import ( + PipelineSourceConnectorSchema, +) +from vectorize_client.models.schedule_schema import ScheduleSchema +from vectorize_client.models.schedule_schema_type import ScheduleSchemaType +from vectorize_client.models.source_connector_type import SourceConnectorType +from vectorize_client.models.start_file_upload_to_connector_request import ( + StartFileUploadToConnectorRequest, +) from langchain_vectorize.retrievers import VectorizeRetriever @@ -38,7 +72,7 @@ def environment() -> Literal["prod", "dev", "local", "staging"]: if env not in ["prod", "dev", "local", "staging"]: msg = "Invalid VECTORIZE_ENV environment variable." raise ValueError(msg) - return env + return env # type: ignore[return-value] @pytest.fixture(scope="session") @@ -56,8 +90,8 @@ def api_client(api_token: str, environment: str) -> Iterator[ApiClient]: else: host = "https://api-staging.vectorize.io/v1" - with v.ApiClient( - v.Configuration(host=host, access_token=api_token, debug=True), + with ApiClient( + Configuration(host=host, access_token=api_token, debug=True), header_name, header_value, ) as api: @@ -65,24 +99,22 @@ def api_client(api_token: str, environment: str) -> Iterator[ApiClient]: @pytest.fixture(scope="session") -def pipeline_id(api_client: v.ApiClient, org_id: str) -> Iterator[str]: - pipelines = v.PipelinesApi(api_client) +def pipeline_id(api_client: ApiClient, org_id: str) -> Iterator[str]: + pipelines = PipelinesApi(api_client) - connectors_api = v.SourceConnectorsApi(api_client) + connectors_api = SourceConnectorsApi(api_client) response = connectors_api.create_source_connector( org_id, - v.CreateSourceConnectorRequest( - v.FileUpload(name="from api", type="FILE_UPLOAD") - ), + CreateSourceConnectorRequest(FileUpload(name="from api", type="FILE_UPLOAD")), ) source_connector_id = response.connector.id logging.info("Created source connector %s", source_connector_id) - uploads_api = v.UploadsApi(api_client) + uploads_api = UploadsApi(api_client) upload_response = uploads_api.start_file_upload_to_connector( org_id, source_connector_id, - v.StartFileUploadToConnectorRequest( + StartFileUploadToConnectorRequest( # type: ignore[call-arg] name="research.pdf", content_type="application/pdf", metadata=json.dumps({"created-from-api": True}), @@ -109,7 +141,7 @@ def pipeline_id(api_client: v.ApiClient, org_id: str) -> Iterator[str]: else: logging.info("Upload successful") - ai_platforms = v.AIPlatformConnectorsApi(api_client).get_ai_platform_connectors( + ai_platforms = AIPlatformConnectorsApi(api_client).get_ai_platform_connectors( org_id ) builtin_ai_platform = next( @@ -117,9 +149,9 @@ def pipeline_id(api_client: v.ApiClient, org_id: str) -> Iterator[str]: ) logging.info("Using AI platform %s", builtin_ai_platform) - vector_databases = v.DestinationConnectorsApi( - api_client - ).get_destination_connectors(org_id) + vector_databases = DestinationConnectorsApi(api_client).get_destination_connectors( + org_id + ) builtin_vector_db = next( c.id for c in vector_databases.destination_connectors if c.type == "VECTORIZE" ) @@ -127,26 +159,26 @@ def pipeline_id(api_client: v.ApiClient, org_id: str) -> Iterator[str]: pipeline_response = pipelines.create_pipeline( org_id, - v.PipelineConfigurationSchema( + PipelineConfigurationSchema( # type: ignore[call-arg] source_connectors=[ - v.PipelineSourceConnectorSchema( + PipelineSourceConnectorSchema( id=source_connector_id, - type=v.SourceConnectorType.FILE_UPLOAD, + type=SourceConnectorType.FILE_UPLOAD, config={}, ) ], - destination_connector=v.PipelineDestinationConnectorSchema( + destination_connector=PipelineDestinationConnectorSchema( id=builtin_vector_db, - type="VECTORIZE", + type=DestinationConnectorTypeForPipeline.VECTORIZE, config={}, ), - ai_platform_connector=v.PipelineAIPlatformConnectorSchema( + ai_platform_connector=PipelineAIPlatformConnectorSchema( id=builtin_ai_platform, - type="VECTORIZE", - config={}, + type=AIPlatformTypeForPipeline.VECTORIZE, + config=AIPlatformConfigSchema(), ), pipeline_name="Test pipeline", - schedule=v.ScheduleSchema(type="manual"), + schedule=ScheduleSchema(type=ScheduleSchemaType.MANUAL), ), ) pipeline_id = 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