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This PR adds a minimal document indexer abstraction. The goal of this abstraction is to allow developers to create custom retrievers that also have a standard indexing API and allow updating the document content in them. The abstraction comes with a test suite that can verify that the indexer implements the correct semantics. This is an iteration over a previous PRs (https://github.com/langchain-ai/langchain/pull/24364). The main difference is that we're sub-classing from BaseRetriever in this iteration and as so have consolidated the sync and async interfaces. The main problem with the current design is that runt time search configuration has to be specified at init rather than provided at run time. We will likely resolve this issue in one of the two ways: (1) Define a method (`get_retriever`) that will allow creating a retriever at run time with a specific configuration.. If we do this, we will likely break the subclass on BaseRetriever (2) Generalize base retriever so it can support structured queries --------- Co-authored-by: Erick Friis <erick@langchain.dev> |
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langchain_standard_tests | ||
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tests | ||
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poetry.lock | ||
pyproject.toml | ||
README.md |
langchain-standard-tests
This is an INTERNAL library for the LangChain project. It contains the base classes for a standard set of tests.
Installation
This package will NOT be regularly published to pypi. It is intended to be installed directly from github at test time.
Pip:
```bash
pip install git+https://github.com/langchain-ai/langchain.git#subdirectory=libs/standard-tests
```
Poetry:
```bash
poetry add git+https://github.com/langchain-ai/langchain.git#subdirectory=libs/standard-tests
```
Usage
To add standard tests to an integration package's e.g. ChatModel, you need to create
- A unit test class that inherits from ChatModelUnitTests
- An integration test class that inherits from ChatModelIntegrationTests
tests/unit_tests/test_standard.py
:
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_standard_tests.unit_tests import ChatModelUnitTests
from langchain_parrot_chain import ChatParrotChain
class TestParrotChainStandard(ChatModelUnitTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatParrotChain
tests/integration_tests/test_standard.py
:
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
from langchain_parrot_chain import ChatParrotChain
class TestParrotChainStandard(ChatModelIntegrationTests):
@pytest.fixture
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatParrotChain
Reference
The following fixtures are configurable in the test classes. Anything not marked as required is optional.
chat_model_class
(required): The class of the chat model to be testedchat_model_params
: The keyword arguments to pass to the chat model constructorchat_model_has_tool_calling
: Whether the chat model can call tools. By default, this is set tohasattr(chat_model_class, 'bind_tools)
chat_model_has_structured_output
: Whether the chat model can structured output. By default, this is set tohasattr(chat_model_class, 'with_structured_output')