mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-03 03:59:42 +00:00
cli: standard tests in cli, test that they run, skip vectorstore tests (#28521)
This commit is contained in:
Erick Friis
GitHub
@@ -1,64 +1,21 @@
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"""Test Chat__ModuleName__ chat model."""
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from typing import Type
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from __module_name__.chat_models import Chat__ModuleName__
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from langchain_tests.integration_tests import ChatModelIntegrationTests
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def test_stream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = Chat__ModuleName__()
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class TestChatParrotLinkIntegration(ChatModelIntegrationTests):
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@property
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def chat_model_class(self) -> Type[Chat__ModuleName__]:
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return Chat__ModuleName__
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for token in llm.stream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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async def test_astream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = Chat__ModuleName__()
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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async def test_abatch() -> None:
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"""Test streaming tokens from Chat__ModuleName__."""
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llm = Chat__ModuleName__()
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_abatch_tags() -> None:
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"""Test batch tokens from Chat__ModuleName__."""
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llm = Chat__ModuleName__()
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token.content, str)
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def test_batch() -> None:
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"""Test batch tokens from Chat__ModuleName__."""
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llm = Chat__ModuleName__()
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_ainvoke() -> None:
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"""Test invoke tokens from Chat__ModuleName__."""
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llm = Chat__ModuleName__()
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result.content, str)
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def test_invoke() -> None:
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"""Test invoke tokens from Chat__ModuleName__."""
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llm = Chat__ModuleName__()
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result.content, str)
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@property
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def chat_model_params(self) -> dict:
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# These should be parameters used to initialize your integration for testing
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return {
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"model": "bird-brain-001",
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"temperature": 0,
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"parrot_buffer_length": 50,
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}
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@@ -1,20 +1,16 @@
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"""Test __ModuleName__ embeddings."""
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from typing import Type
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from __module_name__.embeddings import __ModuleName__Embeddings
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from langchain_tests.integration_tests import EmbeddingsIntegrationTests
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def test___module_name___embedding_documents() -> None:
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"""Test cohere embeddings."""
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documents = ["foo bar"]
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embedding = __ModuleName__Embeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) > 0
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class TestParrotLinkEmbeddingsIntegration(EmbeddingsIntegrationTests):
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@property
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def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
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return __ModuleName__Embeddings
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def test___module_name___embedding_query() -> None:
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"""Test cohere embeddings."""
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document = "foo bar"
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embedding = __ModuleName__Embeddings()
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output = embedding.embed_query(document)
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assert len(output) > 0
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@property
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def embedding_model_params(self) -> dict:
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return {"model": "nest-embed-001"}
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@@ -1,64 +0,0 @@
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"""Test __ModuleName__LLM llm."""
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from __module_name__.llms import __ModuleName__LLM
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def test_stream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = __ModuleName__LLM()
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for token in llm.stream("I'm Pickle Rick"):
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assert isinstance(token, str)
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async def test_astream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = __ModuleName__LLM()
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token, str)
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async def test_abatch() -> None:
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"""Test streaming tokens from __ModuleName__LLM."""
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llm = __ModuleName__LLM()
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token, str)
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async def test_abatch_tags() -> None:
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"""Test batch tokens from __ModuleName__LLM."""
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llm = __ModuleName__LLM()
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token, str)
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def test_batch() -> None:
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"""Test batch tokens from __ModuleName__LLM."""
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llm = __ModuleName__LLM()
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token, str)
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async def test_ainvoke() -> None:
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"""Test invoke tokens from __ModuleName__LLM."""
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llm = __ModuleName__LLM()
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result, str)
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def test_invoke() -> None:
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"""Test invoke tokens from __ModuleName__LLM."""
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llm = __ModuleName__LLM()
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result, str)
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@@ -0,0 +1,24 @@
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from typing import Type
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from __module_name__.retrievers import __ModuleName__Retriever
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from langchain_tests.integration_tests import (
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RetrieversIntegrationTests,
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)
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class Test__ModuleName__Retriever(RetrieversIntegrationTests):
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@property
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def retriever_constructor(self) -> Type[__ModuleName__Retriever]:
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"""Get an empty vectorstore for unit tests."""
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return __ModuleName__Retriever
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@property
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def retriever_constructor_params(self) -> dict:
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return {"k": 2}
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@property
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def retriever_query_example(self) -> str:
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"""
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Returns a dictionary representing the "args" of an example retriever call.
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"""
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return "example query"
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@@ -0,0 +1,27 @@
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from typing import Type
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from __module_name__.tools import __ModuleName__Tool
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from langchain_tests.integration_tests import ToolsIntegrationTests
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class TestParrotMultiplyToolIntegration(ToolsIntegrationTests):
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@property
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def tool_constructor(self) -> Type[__ModuleName__Tool]:
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return __ModuleName__Tool
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@property
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def tool_constructor_params(self) -> dict:
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# if your tool constructor instead required initialization arguments like
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# `def __init__(self, some_arg: int):`, you would return those here
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# as a dictionary, e.g.: `return {'some_arg': 42}`
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return {}
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@property
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def tool_invoke_params_example(self) -> dict:
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"""
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Returns a dictionary representing the "args" of an example tool call.
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This should NOT be a ToolCall dict - i.e. it should not
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have {"name", "id", "args"} keys.
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"""
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return {"a": 2, "b": 3}
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@@ -0,0 +1,37 @@
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from typing import AsyncGenerator, Generator
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import pytest
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from __module_name__.vectorstores import __ModuleName__VectorStore
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from langchain_core.vectorstores import VectorStore
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from langchain_tests.integration_tests import (
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AsyncReadWriteTestSuite,
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ReadWriteTestSuite,
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)
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class Test__ModuleName__VectorStoreSync(ReadWriteTestSuite):
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@pytest.fixture()
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def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore
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"""Get an empty vectorstore for unit tests."""
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store = __ModuleName__VectorStore()
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# note: store should be EMPTY at this point
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# if you need to delete data, you may do so here
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try:
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yield store
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finally:
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# cleanup operations, or deleting data
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pass
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class Test__ModuleName__VectorStoreAsync(AsyncReadWriteTestSuite):
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@pytest.fixture()
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async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: # type: ignore
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"""Get an empty vectorstore for unit tests."""
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store = __ModuleName__VectorStore()
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# note: store should be EMPTY at this point
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# if you need to delete data, you may do so here
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try:
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yield store
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finally:
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# cleanup operations, or deleting data
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pass
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@@ -1,8 +1,21 @@
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"""Test chat model integration."""
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from typing import Type
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from __module_name__.chat_models import Chat__ModuleName__
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from langchain_tests.unit_tests import ChatModelUnitTests
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def test_initialization() -> None:
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"""Test chat model initialization."""
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Chat__ModuleName__()
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class TestChat__ModuleName__Unit(ChatModelUnitTests):
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@property
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def chat_model_class(self) -> Type[Chat__ModuleName__]:
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return Chat__ModuleName__
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@property
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def chat_model_params(self) -> dict:
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# These should be parameters used to initialize your integration for testing
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return {
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"model": "bird-brain-001",
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"temperature": 0,
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"parrot_buffer_length": 50,
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}
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@@ -1,8 +1,16 @@
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"""Test embedding model integration."""
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from typing import Type
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from __module_name__.embeddings import __ModuleName__Embeddings
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from langchain_tests.unit_tests import EmbeddingsUnitTests
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def test_initialization() -> None:
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"""Test embedding model initialization."""
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__ModuleName__Embeddings()
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class TestParrotLinkEmbeddingsUnit(EmbeddingsUnitTests):
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@property
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def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
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return __ModuleName__Embeddings
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@property
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def embedding_model_params(self) -> dict:
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return {"model": "nest-embed-001"}
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@@ -1,12 +0,0 @@
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from __module_name__ import __all__
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EXPECTED_ALL = [
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"__ModuleName__LLM",
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"Chat__ModuleName__",
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"__ModuleName__VectorStore",
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"__ModuleName__Embeddings",
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]
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def test_all_imports() -> None:
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assert sorted(EXPECTED_ALL) == sorted(__all__)
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@@ -1,8 +0,0 @@
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"""Test __ModuleName__ Chat API wrapper."""
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from __module_name__ import __ModuleName__LLM
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def test_initialization() -> None:
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"""Test integration initialization."""
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__ModuleName__LLM()
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@@ -0,0 +1,27 @@
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from typing import Type
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from __module_name__.tools import __ModuleName__Tool
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from langchain_tests.unit_tests import ToolsUnitTests
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class TestParrotMultiplyToolUnit(ToolsUnitTests):
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@property
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def tool_constructor(self) -> Type[__ModuleName__Tool]:
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return __ModuleName__Tool
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@property
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def tool_constructor_params(self) -> dict:
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# if your tool constructor instead required initialization arguments like
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# `def __init__(self, some_arg: int):`, you would return those here
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# as a dictionary, e.g.: `return {'some_arg': 42}`
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return {}
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@property
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def tool_invoke_params_example(self) -> dict:
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"""
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Returns a dictionary representing the "args" of an example tool call.
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This should NOT be a ToolCall dict - i.e. it should not
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have {"name", "id", "args"} keys.
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"""
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return {"a": 2, "b": 3}
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@@ -1,6 +0,0 @@
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from __module_name__.vectorstores import __ModuleName__VectorStore
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def test_initialization() -> None:
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"""Test integration vectorstore initialization."""
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__ModuleName__VectorStore()
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