langchain/libs/standard-tests
Eugene Yurtsev 41dfad5104
core[minor]: Introduce DocumentIndex abstraction (#25062)
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>
2024-08-05 18:06:33 +00:00
..
langchain_standard_tests core[minor]: Introduce DocumentIndex abstraction (#25062) 2024-08-05 18:06:33 +00:00
scripts standard-tests: a standard unit and integration test set (#20182) 2024-04-09 12:43:00 -07:00
tests standard-tests[patch]: Add pytest assert rewrites (#24408) 2024-07-18 21:41:11 +00:00
Makefile standard-tests: a standard unit and integration test set (#20182) 2024-04-09 12:43:00 -07:00
poetry.lock standard-tests[minor]: Add standard tests for cache (#23357) 2024-06-24 15:15:03 +00:00
pyproject.toml standard-tests[minor]: Add standard tests for cache (#23357) 2024-06-24 15:15:03 +00:00
README.md standard-tests: a standard unit and integration test set (#20182) 2024-04-09 12:43:00 -07:00

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

  1. A unit test class that inherits from ChatModelUnitTests
  2. 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 tested
  • chat_model_params: The keyword arguments to pass to the chat model constructor
  • chat_model_has_tool_calling: Whether the chat model can call tools. By default, this is set to hasattr(chat_model_class, 'bind_tools)
  • chat_model_has_structured_output: Whether the chat model can structured output. By default, this is set to hasattr(chat_model_class, 'with_structured_output')