diff --git a/langchain/pyproject.toml b/langchain/pyproject.toml index ac2e77f..72e232c 100644 --- a/langchain/pyproject.toml +++ b/langchain/pyproject.toml @@ -33,7 +33,9 @@ Issues = "https://github.com/vectorize-io/integrations-python/issues" dev = [ "mypy>=1.13.0", "pytest>=8.3.3", + "pytest-asyncio>=0.26.0", "ruff>=0.9.0,<0.10", + "langchain-tests>=0.3.20", ] [tool.ruff.lint] @@ -71,3 +73,5 @@ packages = ["langchain_vectorize"] requires = ["hatchling"] build-backend = "hatchling.build" +[tool.pytest.ini_options] +asyncio_mode = "auto" diff --git a/langchain/tests/conftest.py b/langchain/tests/conftest.py new file mode 100644 index 0000000..529a36c --- /dev/null +++ b/langchain/tests/conftest.py @@ -0,0 +1,214 @@ +import json +import logging +import os +import time +from collections.abc import Iterator +from pathlib import Path +from typing import Literal + +import pytest +import urllib3 +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.exceptions import ApiException +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.retrieve_documents_request import RetrieveDocumentsRequest +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, +) + + +@pytest.fixture(scope="session") +def api_token() -> str: + token = os.getenv("VECTORIZE_TOKEN") + if not token: + msg = "Please set the VECTORIZE_TOKEN environment variable" + raise ValueError(msg) + return token + + +@pytest.fixture(scope="session") +def org_id() -> str: + org = os.getenv("VECTORIZE_ORG") + if not org: + msg = "Please set the VECTORIZE_ORG environment variable" + raise ValueError(msg) + return org + + +@pytest.fixture(scope="session") +def environment() -> Literal["prod", "dev", "local", "staging"]: + env = os.getenv("VECTORIZE_ENV", "prod") + if env not in ["prod", "dev", "local", "staging"]: + msg = "Invalid VECTORIZE_ENV environment variable." + raise ValueError(msg) + return env # type: ignore[return-value] + + +@pytest.fixture(scope="session") +def api_client(api_token: str, environment: str) -> Iterator[ApiClient]: + header_name = None + header_value = None + if environment == "prod": + host = "https://api.vectorize.io/v1" + elif environment == "dev": + host = "https://api-dev.vectorize.io/v1" + elif environment == "local": + host = "http://localhost:3000/api" + header_name = "x-lambda-api-key" + header_value = api_token + else: + host = "https://api-staging.vectorize.io/v1" + + with ApiClient( + Configuration(host=host, access_token=api_token, debug=True), + header_name, + header_value, + ) as api: + yield api + + +@pytest.fixture(scope="session") +def pipeline_id(api_client: ApiClient, org_id: str) -> Iterator[str]: + pipelines = PipelinesApi(api_client) + + connectors_api = SourceConnectorsApi(api_client) + response = connectors_api.create_source_connector( + org_id, + 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 = UploadsApi(api_client) + upload_response = uploads_api.start_file_upload_to_connector( + org_id, + source_connector_id, + StartFileUploadToConnectorRequest( # type: ignore[call-arg] + name="research.pdf", + content_type="application/pdf", + metadata=json.dumps({"created-from-api": True}), + ), + ) + + http = urllib3.PoolManager() + this_dir = Path(__file__).parent + file_path = this_dir / "research.pdf" + + with file_path.open("rb") as f: + http_response = http.request( + "PUT", + upload_response.upload_url, + body=f, + headers={ + "Content-Type": "application/pdf", + "Content-Length": str(file_path.stat().st_size), + }, + ) + if http_response.status != 200: + msg = "Upload failed:" + raise ValueError(msg) + else: + logging.info("Upload successful") + + ai_platforms = AIPlatformConnectorsApi(api_client).get_ai_platform_connectors( + org_id + ) + builtin_ai_platform = next( + c.id for c in ai_platforms.ai_platform_connectors if c.type == "VECTORIZE" + ) + logging.info("Using AI platform %s", builtin_ai_platform) + + 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" + ) + logging.info("Using destination connector %s", builtin_vector_db) + + pipeline_response = pipelines.create_pipeline( + org_id, + PipelineConfigurationSchema( # type: ignore[call-arg] + source_connectors=[ + PipelineSourceConnectorSchema( + id=source_connector_id, + type=SourceConnectorType.FILE_UPLOAD, + config={}, + ) + ], + destination_connector=PipelineDestinationConnectorSchema( + id=builtin_vector_db, + type=DestinationConnectorTypeForPipeline.VECTORIZE, + config={}, + ), + ai_platform_connector=PipelineAIPlatformConnectorSchema( + id=builtin_ai_platform, + type=AIPlatformTypeForPipeline.VECTORIZE, + config=AIPlatformConfigSchema(), + ), + pipeline_name="Test pipeline", + schedule=ScheduleSchema(type=ScheduleSchemaType.MANUAL), + ), + ) + pipeline_id = pipeline_response.data.id + + # Wait for the pipeline to be created + request = RetrieveDocumentsRequest( + question="query", + num_results=2, + ) + start = time.time() + while True: + try: + response = pipelines.retrieve_documents(org_id, pipeline_id, request) + except ApiException as e: + if "503" not in str(e): + raise + else: + docs = response.documents + if len(docs) == 2: + break + if time.time() - start > 180: + msg = "Docs not retrieved in time" + raise RuntimeError(msg) + time.sleep(1) + + logging.info("Created pipeline %s", pipeline_id) + + yield pipeline_id + + try: + pipelines.delete_pipeline(org_id, pipeline_id) + except Exception: + logging.exception("Failed to delete pipeline %s", pipeline_id) diff --git a/langchain/tests/test_langchain_integration_tests.py b/langchain/tests/test_langchain_integration_tests.py new file mode 100644 index 0000000..93f265e --- /dev/null +++ b/langchain/tests/test_langchain_integration_tests.py @@ -0,0 +1,50 @@ +from typing import Literal + +import pytest +from langchain_tests.integration_tests import RetrieversIntegrationTests + +from langchain_vectorize import VectorizeRetriever + + +class TestVectorizeRetrieverIntegration(RetrieversIntegrationTests): + @pytest.fixture(autouse=True) + def setup( + self, + environment: Literal["prod", "dev", "local", "staging"], + api_token: str, + org_id: str, + pipeline_id: str, + ) -> None: + self._environment = environment + self._api_token = api_token + self._org_id = org_id + self._pipeline_id = pipeline_id + + @property + def retriever_constructor(self) -> type[VectorizeRetriever]: + return VectorizeRetriever + + @property + def retriever_constructor_params(self) -> dict: + return { + "environment": self._environment, + "api_token": self._api_token, + "organization": self._org_id, + "pipeline_id": self._pipeline_id, + } + + @property + def retriever_query_example(self) -> str: + return "What are you?" + + @pytest.mark.xfail( + reason="VectorizeRetriever does not support k parameter in constructor" + ) + def test_k_constructor_param(self) -> None: + raise NotImplementedError + + @pytest.mark.xfail( + reason="VectorizeRetriever does not support k parameter in invoke" + ) + def test_invoke_with_k_kwarg(self) -> None: + raise NotImplementedError diff --git a/langchain/tests/test_retrievers.py b/langchain/tests/test_retrievers.py index 024b9a0..9a0828e 100644 --- a/langchain/tests/test_retrievers.py +++ b/langchain/tests/test_retrievers.py @@ -1,165 +1,8 @@ -import json -import logging -import os -import time -from collections.abc import Iterator -from pathlib import Path from typing import Literal -import pytest -import urllib3 -import vectorize_client as v -from vectorize_client import ApiClient - from langchain_vectorize.retrievers import VectorizeRetriever -@pytest.fixture(scope="session") -def api_token() -> str: - token = os.getenv("VECTORIZE_TOKEN") - if not token: - msg = "Please set the VECTORIZE_TOKEN environment variable" - raise ValueError(msg) - return token - - -@pytest.fixture(scope="session") -def org_id() -> str: - org = os.getenv("VECTORIZE_ORG") - if not org: - msg = "Please set the VECTORIZE_ORG environment variable" - raise ValueError(msg) - return org - - -@pytest.fixture(scope="session") -def environment() -> Literal["prod", "dev", "local", "staging"]: - env = os.getenv("VECTORIZE_ENV", "prod") - if env not in ["prod", "dev", "local", "staging"]: - msg = "Invalid VECTORIZE_ENV environment variable." - raise ValueError(msg) - return env - - -@pytest.fixture(scope="session") -def api_client(api_token: str, environment: str) -> Iterator[ApiClient]: - header_name = None - header_value = None - if environment == "prod": - host = "https://api.vectorize.io/v1" - elif environment == "dev": - host = "https://api-dev.vectorize.io/v1" - elif environment == "local": - host = "http://localhost:3000/api" - header_name = "x-lambda-api-key" - header_value = api_token - else: - host = "https://api-staging.vectorize.io/v1" - - with v.ApiClient( - v.Configuration(host=host, access_token=api_token, debug=True), - header_name, - header_value, - ) as api: - yield api - - -@pytest.fixture(scope="session") -def pipeline_id(api_client: v.ApiClient, org_id: str) -> Iterator[str]: - pipelines = v.PipelinesApi(api_client) - - connectors_api = v.SourceConnectorsApi(api_client) - response = connectors_api.create_source_connector( - org_id, - v.CreateSourceConnectorRequest( - v.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) - upload_response = uploads_api.start_file_upload_to_connector( - org_id, - source_connector_id, - v.StartFileUploadToConnectorRequest( - name="research.pdf", - content_type="application/pdf", - metadata=json.dumps({"created-from-api": True}), - ), - ) - - http = urllib3.PoolManager() - this_dir = Path(__file__).parent - file_path = this_dir / "research.pdf" - - with file_path.open("rb") as f: - http_response = http.request( - "PUT", - upload_response.upload_url, - body=f, - headers={ - "Content-Type": "application/pdf", - "Content-Length": str(file_path.stat().st_size), - }, - ) - if http_response.status != 200: - msg = "Upload failed:" - raise ValueError(msg) - else: - logging.info("Upload successful") - - ai_platforms = v.AIPlatformConnectorsApi(api_client).get_ai_platform_connectors( - org_id - ) - builtin_ai_platform = next( - c.id for c in ai_platforms.ai_platform_connectors if c.type == "VECTORIZE" - ) - logging.info("Using AI platform %s", builtin_ai_platform) - - vector_databases = v.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" - ) - logging.info("Using destination connector %s", builtin_vector_db) - - pipeline_response = pipelines.create_pipeline( - org_id, - v.PipelineConfigurationSchema( - source_connectors=[ - v.PipelineSourceConnectorSchema( - id=source_connector_id, - type=v.SourceConnectorType.FILE_UPLOAD, - config={}, - ) - ], - destination_connector=v.PipelineDestinationConnectorSchema( - id=builtin_vector_db, - type="VECTORIZE", - config={}, - ), - ai_platform_connector=v.PipelineAIPlatformConnectorSchema( - id=builtin_ai_platform, - type="VECTORIZE", - config={}, - ), - pipeline_name="Test pipeline", - schedule=v.ScheduleSchema(type="manual"), - ), - ) - pipeline_id = pipeline_response.data.id - logging.info("Created pipeline %s", pipeline_id) - - yield pipeline_id - - try: - pipelines.delete_pipeline(org_id, pipeline_id) - except Exception: - logging.exception("Failed to delete pipeline %s", pipeline_id) - - def test_retrieve_init_args( environment: Literal["prod", "dev", "local", "staging"], api_token: str, @@ -173,21 +16,8 @@ def test_retrieve_init_args( pipeline_id=pipeline_id, num_results=2, ) - start = time.time() - while True: - try: - docs = retriever.invoke(input="What are you?") - if len(docs) == 2: - break - except Exception as e: - if "503" in str(e): - continue - raise RuntimeError(e) from e - - if time.time() - start > 180: - msg = "Docs not retrieved in time" - raise RuntimeError(msg) - time.sleep(1) + docs = retriever.invoke(input="What are you?") + assert len(docs) == 2 def test_retrieve_invoke_args( @@ -197,24 +27,10 @@ def test_retrieve_invoke_args( pipeline_id: str, ) -> None: retriever = VectorizeRetriever(environment=environment, api_token=api_token) - start = time.time() - while True: - try: - docs = retriever.invoke( - input="What are you?", - organization=org_id, - pipeline_id=pipeline_id, - num_results=2, - ) - if len(docs) == 2: - break - - except Exception as e: - if "503" in str(e): - continue - raise RuntimeError(e) from e - if time.time() - start > 180: - msg = "Docs not retrieved in time" - raise RuntimeError(msg) - - time.sleep(1) + docs = retriever.invoke( + input="What are you?", + organization=org_id, + pipeline_id=pipeline_id, + num_results=2, + ) + assert len(docs) == 2 diff --git a/langchain/uv.lock b/langchain/uv.lock index 82abf9a..4f9b8b0 100644 --- a/langchain/uv.lock +++ b/langchain/uv.lock @@ -2,8 +2,16 @@ version = 1 revision = 1 requires-python = ">=3.9" 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