diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index fbbe47aa4a99b..873a98d158c71 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -272,6 +272,7 @@ def _get_configs_for_multi_dirs( # TODO: update to include all packages that rely on standard-tests (all partner packages) # note: won't run on external repo partners dirs_to_run["lint"].add("libs/standard-tests") + dirs_to_run["test"].add("libs/standard-tests") dirs_to_run["test"].add("libs/partners/mistralai") dirs_to_run["test"].add("libs/partners/openai") dirs_to_run["test"].add("libs/partners/anthropic") @@ -279,8 +280,9 @@ def _get_configs_for_multi_dirs( dirs_to_run["test"].add("libs/partners/groq") elif file.startswith("libs/cli"): - # todo: add cli makefile - pass + dirs_to_run["lint"].add("libs/cli") + dirs_to_run["test"].add("libs/cli") + elif file.startswith("libs/partners"): partner_dir = file.split("/")[2] if os.path.isdir(f"libs/partners/{partner_dir}") and [ diff --git a/libs/cli/.gitignore b/libs/cli/.gitignore index 68bc17f9ff210..413adb3d0f4fc 100644 --- a/libs/cli/.gitignore +++ b/libs/cli/.gitignore @@ -158,3 +158,5 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +.integration_test diff --git a/libs/cli/Makefile b/libs/cli/Makefile index 445235c22c841..ac855bd4dc58a 100644 --- a/libs/cli/Makefile +++ b/libs/cli/Makefile @@ -1,8 +1,47 @@ -lint lint_diff: - poetry run poe lint -test: - poetry run poe test +###################### +# LINTING AND FORMATTING +###################### -format: - poetry run poe format +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/cli --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_cli +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) + +test tests: _test _e2e_test + +PYTHON = .venv/bin/python + +_test: + poetry run pytest tests + +# custom integration testing for cli integration flow +# currently ignores vectorstores test because lacks implementation +_e2e_test: + rm -rf .integration_test + mkdir .integration_test + cd .integration_test && \ + python3 -m venv .venv && \ + $(PYTHON) -m pip install --upgrade poetry && \ + $(PYTHON) -m pip install -e .. && \ + $(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLink && \ + cd langchain-parrot-link && \ + poetry install --with lint,typing,test && \ + poetry run pip install -e ../../../standard-tests && \ + make format lint tests && \ + poetry install --with test_integration && \ + rm tests/integration_tests/test_vectorstores.py && \ + make integration_test diff --git a/libs/cli/langchain_cli/dev_scripts.py b/libs/cli/langchain_cli/dev_scripts.py index cccd9689e4482..ebb9331a9c61e 100644 --- a/libs/cli/langchain_cli/dev_scripts.py +++ b/libs/cli/langchain_cli/dev_scripts.py @@ -1,3 +1,4 @@ +# type: ignore """ Development Scripts for template packages """ diff --git a/libs/cli/langchain_cli/integration_template/Makefile b/libs/cli/langchain_cli/integration_template/Makefile index b30039ace1fe0..4c4bbf237d9dc 100644 --- a/libs/cli/langchain_cli/integration_template/Makefile +++ b/libs/cli/langchain_cli/integration_template/Makefile @@ -33,13 +33,13 @@ lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml diff --git a/libs/cli/langchain_cli/integration_template/integration_template/__init__.py b/libs/cli/langchain_cli/integration_template/integration_template/__init__.py index 60d8dac5d007b..44bec467eaa90 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/__init__.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/__init__.py @@ -1,8 +1,11 @@ from importlib import metadata from __module_name__.chat_models import Chat__ModuleName__ +from __module_name__.document_loaders import __ModuleName__Loader from __module_name__.embeddings import __ModuleName__Embeddings -from __module_name__.llms import __ModuleName__LLM +from __module_name__.retrievers import __ModuleName__Retriever +from __module_name__.toolkits import __ModuleName__Toolkit +from __module_name__.tools import __ModuleName__Tool from __module_name__.vectorstores import __ModuleName__VectorStore try: @@ -14,8 +17,11 @@ __all__ = [ "Chat__ModuleName__", - "__ModuleName__LLM", "__ModuleName__VectorStore", "__ModuleName__Embeddings", + "__ModuleName__Loader", + "__ModuleName__Retriever", + "__ModuleName__Toolkit", + "__ModuleName__Tool", "__version__", ] diff --git a/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py b/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py index d299fb2ff26d1..917feb2df0ba2 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py @@ -1,13 +1,19 @@ """__ModuleName__ chat models.""" -from typing import Any, List, Optional +from typing import Any, Dict, Iterator, List, Optional from langchain_core.callbacks import ( CallbackManagerForLLMRun, ) -from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.messages import BaseMessage -from langchain_core.outputs import ChatResult +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import ( + AIMessage, + AIMessageChunk, + BaseMessage, +) +from langchain_core.messages.ai import UsageMetadata +from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from pydantic import Field class Chat__ModuleName__(BaseChatModel): @@ -15,6 +21,8 @@ class Chat__ModuleName__(BaseChatModel): # https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120 """__ModuleName__ chat model integration. + The default implementation echoes the first `parrot_buffer_length` characters of the input. + # TODO: Replace with relevant packages, env vars. Setup: Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``. @@ -258,7 +266,36 @@ class Joke(BaseModel): """ # noqa: E501 - # TODO: This method must be implemented to generate chat responses. + model_name: str = Field(alias="model") + """The name of the model""" + parrot_buffer_length: int + """The number of characters from the last message of the prompt to be echoed.""" + temperature: Optional[float] = None + max_tokens: Optional[int] = None + timeout: Optional[int] = None + stop: Optional[List[str]] = None + max_retries: int = 2 + + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "chat-__package_name_short__" + + @property + def _identifying_params(self) -> Dict[str, Any]: + """Return a dictionary of identifying parameters. + + This information is used by the LangChain callback system, which + is used for tracing purposes make it possible to monitor LLMs. + """ + return { + # The model name allows users to specify custom token counting + # rules in LLM monitoring applications (e.g., in LangSmith users + # can provide per token pricing for their model and monitor + # costs for the given LLM.) + "model_name": self.model_name, + } + def _generate( self, messages: List[BaseMessage], @@ -266,16 +303,101 @@ def _generate( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - raise NotImplementedError() + """Override the _generate method to implement the chat model logic. + + This can be a call to an API, a call to a local model, or any other + implementation that generates a response to the input prompt. + + Args: + messages: the prompt composed of a list of messages. + stop: a list of strings on which the model should stop generating. + If generation stops due to a stop token, the stop token itself + SHOULD BE INCLUDED as part of the output. This is not enforced + across models right now, but it's a good practice to follow since + it makes it much easier to parse the output of the model + downstream and understand why generation stopped. + run_manager: A run manager with callbacks for the LLM. + """ + # Replace this with actual logic to generate a response from a list + # of messages. + last_message = messages[-1] + tokens = last_message.content[: self.parrot_buffer_length] + ct_input_tokens = sum(len(message.content) for message in messages) + ct_output_tokens = len(tokens) + message = AIMessage( + content=tokens, + additional_kwargs={}, # Used to add additional payload to the message + response_metadata={ # Use for response metadata + "time_in_seconds": 3, + }, + usage_metadata={ + "input_tokens": ct_input_tokens, + "output_tokens": ct_output_tokens, + "total_tokens": ct_input_tokens + ct_output_tokens, + }, + ) + ## + + generation = ChatGeneration(message=message) + return ChatResult(generations=[generation]) + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + """Stream the output of the model. + + This method should be implemented if the model can generate output + in a streaming fashion. If the model does not support streaming, + do not implement it. In that case streaming requests will be automatically + handled by the _generate method. + + Args: + messages: the prompt composed of a list of messages. + stop: a list of strings on which the model should stop generating. + If generation stops due to a stop token, the stop token itself + SHOULD BE INCLUDED as part of the output. This is not enforced + across models right now, but it's a good practice to follow since + it makes it much easier to parse the output of the model + downstream and understand why generation stopped. + run_manager: A run manager with callbacks for the LLM. + """ + last_message = messages[-1] + tokens = str(last_message.content[: self.parrot_buffer_length]) + ct_input_tokens = sum(len(message.content) for message in messages) + + for token in tokens: + usage_metadata = UsageMetadata( + { + "input_tokens": ct_input_tokens, + "output_tokens": 1, + "total_tokens": ct_input_tokens + 1, + } + ) + ct_input_tokens = 0 + chunk = ChatGenerationChunk( + message=AIMessageChunk(content=token, usage_metadata=usage_metadata) + ) - # TODO: Implement if Chat__ModuleName__ supports streaming. Otherwise delete method. - # def _stream( - # self, - # messages: List[BaseMessage], - # stop: Optional[List[str]] = None, - # run_manager: Optional[CallbackManagerForLLMRun] = None, - # **kwargs: Any, - # ) -> Iterator[ChatGenerationChunk]: + if run_manager: + # This is optional in newer versions of LangChain + # The on_llm_new_token will be called automatically + run_manager.on_llm_new_token(token, chunk=chunk) + + yield chunk + + # Let's add some other information (e.g., response metadata) + chunk = ChatGenerationChunk( + message=AIMessageChunk(content="", response_metadata={"time_in_sec": 3}) + ) + if run_manager: + # This is optional in newer versions of LangChain + # The on_llm_new_token will be called automatically + run_manager.on_llm_new_token(token, chunk=chunk) + yield chunk # TODO: Implement if Chat__ModuleName__ supports async streaming. Otherwise delete. # async def _astream( @@ -294,8 +416,3 @@ def _generate( # run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, # **kwargs: Any, # ) -> ChatResult: - - @property - def _llm_type(self) -> str: - """Return type of chat model.""" - return "chat-__package_name_short__" diff --git a/libs/cli/langchain_cli/integration_template/integration_template/embeddings.py b/libs/cli/langchain_cli/integration_template/integration_template/embeddings.py index 174c567da8df1..5df2bbfbd402c 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/embeddings.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/embeddings.py @@ -8,7 +8,8 @@ class __ModuleName__Embeddings(Embeddings): # TODO: Replace with relevant packages, env vars. Setup: - Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``. + Install ``__package_name__`` and set environment variable + ``__MODULE_NAME___API_KEY``. .. code-block:: bash @@ -70,21 +71,26 @@ class __ModuleName__Embeddings(Embeddings): """ + def __init__(self, model: str): + self.model = model + def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" - raise NotImplementedError + return [[0.5, 0.6, 0.7] for _ in texts] def embed_query(self, text: str) -> List[float]: """Embed query text.""" - raise NotImplementedError - - # only keep aembed_documents and aembed_query if they're implemented! - # delete them otherwise to use the base class' default - # implementation, which calls the sync version in an executor - async def aembed_documents(self, texts: List[str]) -> List[List[float]]: - """Asynchronous Embed search docs.""" - raise NotImplementedError - - async def aembed_query(self, text: str) -> List[float]: - """Asynchronous Embed query text.""" - raise NotImplementedError + return self.embed_documents([text])[0] + + # optional: add custom async implementations here + # you can also delete these, and the base class will + # use the default implementation, which calls the sync + # version in an async executor: + + # async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + # """Asynchronous Embed search docs.""" + # ... + + # async def aembed_query(self, text: str) -> List[float]: + # """Asynchronous Embed query text.""" + # ... diff --git a/libs/cli/langchain_cli/integration_template/integration_template/llms.py b/libs/cli/langchain_cli/integration_template/integration_template/llms.py deleted file mode 100644 index 2dbe4ac918362..0000000000000 --- a/libs/cli/langchain_cli/integration_template/integration_template/llms.py +++ /dev/null @@ -1,155 +0,0 @@ -"""__ModuleName__ large language models.""" - -from typing import ( - Any, - List, - Optional, -) - -from langchain_core.callbacks import ( - CallbackManagerForLLMRun, -) -from langchain_core.language_models import BaseLLM -from langchain_core.outputs import LLMResult - - -class __ModuleName__LLM(BaseLLM): - """__ModuleName__ completion model integration. - - # TODO: Replace with relevant packages, env vars. - Setup: - Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``. - - .. code-block:: bash - - pip install -U __package_name__ - export __MODULE_NAME___API_KEY="your-api-key" - - # TODO: Populate with relevant params. - Key init args — completion params: - model: str - Name of __ModuleName__ model to use. - temperature: float - Sampling temperature. - max_tokens: Optional[int] - Max number of tokens to generate. - - # TODO: Populate with relevant params. - Key init args — client params: - timeout: Optional[float] - Timeout for requests. - max_retries: int - Max number of retries. - api_key: Optional[str] - __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY. - - See full list of supported init args and their descriptions in the params section. - - # TODO: Replace with relevant init params. - Instantiate: - .. code-block:: python - - from __module_name__ import __ModuleName__LLM - - llm = __ModuleName__LLM( - model="...", - temperature=0, - max_tokens=None, - timeout=None, - max_retries=2, - # api_key="...", - # other params... - ) - - Invoke: - .. code-block:: python - - input_text = "The meaning of life is " - llm.invoke(input_text) - - .. code-block:: python - - # TODO: Example output. - - # TODO: Delete if token-level streaming isn't supported. - Stream: - .. code-block:: python - - for chunk in llm.stream(input_text): - print(chunk) - - .. code-block:: python - - # TODO: Example output. - - .. code-block:: python - - ''.join(llm.stream(input_text)) - - .. code-block:: python - - # TODO: Example output. - - # TODO: Delete if native async isn't supported. - Async: - .. code-block:: python - - await llm.ainvoke(input_text) - - # stream: - # async for chunk in (await llm.astream(input_text)) - - # batch: - # await llm.abatch([input_text]) - - .. code-block:: python - - # TODO: Example output. - """ - - # TODO: This method must be implemented to generate text completions. - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - raise NotImplementedError - - # TODO: Implement if __ModuleName__LLM supports async generation. Otherwise - # delete method. - # async def _agenerate( - # self, - # prompts: List[str], - # stop: Optional[List[str]] = None, - # run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - # **kwargs: Any, - # ) -> LLMResult: - # raise NotImplementedError - - # TODO: Implement if __ModuleName__LLM supports streaming. Otherwise delete method. - # def _stream( - # self, - # prompt: str, - # stop: Optional[List[str]] = None, - # run_manager: Optional[CallbackManagerForLLMRun] = None, - # **kwargs: Any, - # ) -> Iterator[GenerationChunk]: - # raise NotImplementedError - - # TODO: Implement if __ModuleName__LLM supports async streaming. Otherwise delete - # method. - # async def _astream( - # self, - # prompt: str, - # stop: Optional[List[str]] = None, - # run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - # **kwargs: Any, - # ) -> AsyncIterator[GenerationChunk]: - # raise NotImplementedError - - @property - def _llm_type(self) -> str: - """Return type of LLM.""" - return "__package_name_short__-llm" diff --git a/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py b/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py index c48661873c8eb..242103f3a4056 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py @@ -1,7 +1,8 @@ """__ModuleName__ retrievers.""" -from typing import List +from typing import Any, List +from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever @@ -13,7 +14,8 @@ class __ModuleName__Retriever(BaseRetriever): # TODO: Replace with relevant packages, env vars, etc. Setup: - Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``. + Install ``__package_name__`` and set environment variable + ``__MODULE_NAME___API_KEY``. .. code-block:: bash @@ -82,8 +84,24 @@ def format_docs(docs): # TODO: Example output. - """ # noqa: E501 + """ + + k: int = 3 # TODO: This method must be implemented to retrieve documents. - def _get_relevant_documents(self, query: str) -> List[Document]: - raise NotImplementedError() + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any + ) -> List[Document]: + k = kwargs.get("k", self.k) + return [ + Document(page_content=f"Result {i} for query: {query}") for i in range(k) + ] + + # optional: add custom async implementations here + # async def _aget_relevant_documents( + # self, + # query: str, + # *, + # run_manager: AsyncCallbackManagerForRetrieverRun, + # **kwargs: Any, + # ) -> List[Document]: ... diff --git a/libs/cli/langchain_cli/integration_template/integration_template/toolkits.py b/libs/cli/langchain_cli/integration_template/integration_template/toolkits.py index 5a80398891167..b3eda23bd2343 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/toolkits.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/toolkits.py @@ -2,10 +2,10 @@ from typing import List -from langchain_core.tools import BaseTool, BaseToolKit +from langchain_core.tools import BaseTool, BaseToolkit -class __ModuleName__Toolkit(BaseToolKit): +class __ModuleName__Toolkit(BaseToolkit): # TODO: Replace all TODOs in docstring. See example docstring: # https://github.com/langchain-ai/langchain/blob/c123cb2b304f52ab65db4714eeec46af69a861ec/libs/community/langchain_community/agent_toolkits/sql/toolkit.py#L19 """__ModuleName__ toolkit. diff --git a/libs/cli/langchain_cli/integration_template/integration_template/tools.py b/libs/cli/langchain_cli/integration_template/integration_template/tools.py index 57deb006f062a..1904c9865b76c 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/tools.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/tools.py @@ -6,10 +6,10 @@ CallbackManagerForToolRun, ) from langchain_core.tools import BaseTool -from pydantic import BaseModel +from pydantic import BaseModel, Field -class __ModuleName__Input(BaseModel): +class __ModuleName__ToolInput(BaseModel): """Input schema for __ModuleName__ tool. This docstring is **not** part of what is sent to the model when performing tool @@ -18,12 +18,11 @@ class __ModuleName__Input(BaseModel): """ # TODO: Add input args and descriptions. - # a: int = Field(..., description="first number") - # b: int = Field(0, description="second number") - ... + a: int = Field(..., description="first number to add") + b: int = Field(..., description="second number to add") -class __ModuleName__Tool(BaseTool): +class __ModuleName__Tool(BaseTool): # type: ignore[override] """__ModuleName__ tool. Setup: @@ -69,24 +68,26 @@ class __ModuleName__Tool(BaseTool): """The name that is passed to the model when performing tool calling.""" description: str = "TODO: Tool description." """The description that is passed to the model when performing tool calling.""" - args_schema: Type[BaseModel] = __ModuleName__Input + args_schema: Type[BaseModel] = __ModuleName__ToolInput """The schema that is passed to the model when performing tool calling.""" # TODO: Add any other init params for the tool. # param1: Optional[str] # """param1 determines foobar""" - # TODO: Replaced *args with real tool arguments. + # TODO: Replaced (a, b) with real tool arguments. def _run( - self, *args, run_manager: Optional[CallbackManagerForToolRun] = None + self, a: int, b: int, *, run_manager: Optional[CallbackManagerForToolRun] = None ) -> str: - raise NotImplementedError + return str(a + b + 80) # TODO: Implement if tool has native async functionality, otherwise delete. # async def _arun( # self, - # *args, + # a: int, + # b: int, + # *, # run_manager: Optional[AsyncCallbackManagerForToolRun] = None, # ) -> str: # ... diff --git a/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py b/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py index 4236e8c037e73..02ab2c7fccd93 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py @@ -2,8 +2,6 @@ from __future__ import annotations -import asyncio -from functools import partial from typing import ( TYPE_CHECKING, Any, @@ -160,6 +158,8 @@ class __ModuleName__VectorStore(VectorStore): """ # noqa: E501 + _database: dict[str, tuple[Document, list[float]]] = {} + def add_texts( self, texts: Iterable[str], @@ -168,65 +168,70 @@ def add_texts( ) -> List[str]: raise NotImplementedError - async def aadd_texts( - self, - texts: Iterable[str], - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> List[str]: - return await asyncio.get_running_loop().run_in_executor( - None, partial(self.add_texts, **kwargs), texts, metadatas - ) + # optional: add custom async implementations + # async def aadd_texts( + # self, + # texts: Iterable[str], + # metadatas: Optional[List[dict]] = None, + # **kwargs: Any, + # ) -> List[str]: + # return await asyncio.get_running_loop().run_in_executor( + # None, partial(self.add_texts, **kwargs), texts, metadatas + # ) def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: raise NotImplementedError - async def adelete( - self, ids: Optional[List[str]] = None, **kwargs: Any - ) -> Optional[bool]: - raise NotImplementedError + # optional: add custom async implementations + # async def adelete( + # self, ids: Optional[List[str]] = None, **kwargs: Any + # ) -> Optional[bool]: + # raise NotImplementedError def similarity_search( self, query: str, k: int = 4, **kwargs: Any ) -> List[Document]: raise NotImplementedError - async def asimilarity_search( - self, query: str, k: int = 4, **kwargs: Any - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search, query, k=k, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) + # optional: add custom async implementations + # async def asimilarity_search( + # self, query: str, k: int = 4, **kwargs: Any + # ) -> List[Document]: + # # This is a temporary workaround to make the similarity search + # # asynchronous. The proper solution is to make the similarity search + # # asynchronous in the vector store implementations. + # func = partial(self.similarity_search, query, k=k, **kwargs) + # return await asyncio.get_event_loop().run_in_executor(None, func) def similarity_search_with_score( self, *args: Any, **kwargs: Any ) -> List[Tuple[Document, float]]: raise NotImplementedError - async def asimilarity_search_with_score( - self, *args: Any, **kwargs: Any - ) -> List[Tuple[Document, float]]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search_with_score, *args, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) + # optional: add custom async implementations + # async def asimilarity_search_with_score( + # self, *args: Any, **kwargs: Any + # ) -> List[Tuple[Document, float]]: + # # This is a temporary workaround to make the similarity search + # # asynchronous. The proper solution is to make the similarity search + # # asynchronous in the vector store implementations. + # func = partial(self.similarity_search_with_score, *args, **kwargs) + # return await asyncio.get_event_loop().run_in_executor(None, func) def similarity_search_by_vector( self, embedding: List[float], k: int = 4, **kwargs: Any ) -> List[Document]: raise NotImplementedError - async def asimilarity_search_by_vector( - self, embedding: List[float], k: int = 4, **kwargs: Any - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs) - return await asyncio.get_event_loop().run_in_executor(None, func) + # optional: add custom async implementations + # async def asimilarity_search_by_vector( + # self, embedding: List[float], k: int = 4, **kwargs: Any + # ) -> List[Document]: + # # This is a temporary workaround to make the similarity search + # # asynchronous. The proper solution is to make the similarity search + # # asynchronous in the vector store implementations. + # func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs) + # return await asyncio.get_event_loop().run_in_executor(None, func) def max_marginal_relevance_search( self, @@ -238,26 +243,27 @@ def max_marginal_relevance_search( ) -> List[Document]: raise NotImplementedError - async def amax_marginal_relevance_search( - self, - query: str, - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - **kwargs: Any, - ) -> List[Document]: - # This is a temporary workaround to make the similarity search - # asynchronous. The proper solution is to make the similarity search - # asynchronous in the vector store implementations. - func = partial( - self.max_marginal_relevance_search, - query, - k=k, - fetch_k=fetch_k, - lambda_mult=lambda_mult, - **kwargs, - ) - return await asyncio.get_event_loop().run_in_executor(None, func) + # optional: add custom async implementations + # async def amax_marginal_relevance_search( + # self, + # query: str, + # k: int = 4, + # fetch_k: int = 20, + # lambda_mult: float = 0.5, + # **kwargs: Any, + # ) -> List[Document]: + # # This is a temporary workaround to make the similarity search + # # asynchronous. The proper solution is to make the similarity search + # # asynchronous in the vector store implementations. + # func = partial( + # self.max_marginal_relevance_search, + # query, + # k=k, + # fetch_k=fetch_k, + # lambda_mult=lambda_mult, + # **kwargs, + # ) + # return await asyncio.get_event_loop().run_in_executor(None, func) def max_marginal_relevance_search_by_vector( self, @@ -269,15 +275,16 @@ def max_marginal_relevance_search_by_vector( ) -> List[Document]: raise NotImplementedError - async def amax_marginal_relevance_search_by_vector( - self, - embedding: List[float], - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - **kwargs: Any, - ) -> List[Document]: - raise NotImplementedError + # optional: add custom async implementations + # async def amax_marginal_relevance_search_by_vector( + # self, + # embedding: List[float], + # k: int = 4, + # fetch_k: int = 20, + # lambda_mult: float = 0.5, + # **kwargs: Any, + # ) -> List[Document]: + # raise NotImplementedError @classmethod def from_texts( @@ -289,17 +296,18 @@ def from_texts( ) -> VST: raise NotImplementedError - @classmethod - async def afrom_texts( - cls: Type[VST], - texts: List[str], - embedding: Embeddings, - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> VST: - return await asyncio.get_running_loop().run_in_executor( - None, partial(cls.from_texts, **kwargs), texts, embedding, metadatas - ) + # optional: add custom async implementations + # @classmethod + # async def afrom_texts( + # cls: Type[VST], + # texts: List[str], + # embedding: Embeddings, + # metadatas: Optional[List[dict]] = None, + # **kwargs: Any, + # ) -> VST: + # return await asyncio.get_running_loop().run_in_executor( + # None, partial(cls.from_texts, **kwargs), texts, embedding, metadatas + # ) def _select_relevance_score_fn(self) -> Callable[[float], float]: raise NotImplementedError diff --git a/libs/cli/langchain_cli/integration_template/pyproject.toml b/libs/cli/langchain_cli/integration_template/pyproject.toml index efdcb9efeadcd..000a4ece97049 100644 --- a/libs/cli/langchain_cli/integration_template/pyproject.toml +++ b/libs/cli/langchain_cli/integration_template/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] @@ -23,14 +23,16 @@ python = ">=3.9,<4.0" langchain-core = "^0.3.15" [tool.ruff.lint] -select = [ "E", "F", "I", "T201",] +select = ["E", "F", "I", "T201"] [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--strict-markers --strict-config --durations=5" -markers = [ "compile: mark placeholder test used to compile integration tests without running them",] +markers = [ + "compile: mark placeholder test used to compile integration tests without running them", +] asyncio_mode = "auto" [tool.poetry.group.test] @@ -48,11 +50,14 @@ optional = true [tool.poetry.group.dev] optional = true +[tool.poetry.group.dev.dependencies] + [tool.poetry.group.test.dependencies] pytest = "^7.4.3" pytest-asyncio = "^0.23.2" pytest-socket = "^0.7.0" pytest-watcher = "^0.3.4" +langchain-tests = "^0.3.4" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.6" @@ -64,15 +69,3 @@ ruff = "^0.5" [tool.poetry.group.typing.dependencies] mypy = "^1.10" - -[tool.poetry.group.test.dependencies.langchain-core] -path = "../../core" -develop = true - -[tool.poetry.group.dev.dependencies.langchain-core] -path = "../../core" -develop = true - -[tool.poetry.group.typing.dependencies.langchain-core] -path = "../../core" -develop = true diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py index 26b63bd706b5f..c84dafa973abf 100644 --- a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py +++ b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py @@ -1,64 +1,21 @@ """Test Chat__ModuleName__ chat model.""" -from __module_name__.chat_models import Chat__ModuleName__ - - -def test_stream() -> None: - """Test streaming tokens from OpenAI.""" - llm = Chat__ModuleName__() - - for token in llm.stream("I'm Pickle Rick"): - assert isinstance(token.content, str) - - -async def test_astream() -> None: - """Test streaming tokens from OpenAI.""" - llm = Chat__ModuleName__() - - async for token in llm.astream("I'm Pickle Rick"): - assert isinstance(token.content, str) - - -async def test_abatch() -> None: - """Test streaming tokens from Chat__ModuleName__.""" - llm = Chat__ModuleName__() - - result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) - for token in result: - assert isinstance(token.content, str) - +from typing import Type -async def test_abatch_tags() -> None: - """Test batch tokens from Chat__ModuleName__.""" - llm = Chat__ModuleName__() - - result = await llm.abatch( - ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} - ) - for token in result: - assert isinstance(token.content, str) - - -def test_batch() -> None: - """Test batch tokens from Chat__ModuleName__.""" - llm = Chat__ModuleName__() - - result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) - for token in result: - assert isinstance(token.content, str) - - -async def test_ainvoke() -> None: - """Test invoke tokens from Chat__ModuleName__.""" - llm = Chat__ModuleName__() - - result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) - assert isinstance(result.content, str) - - -def test_invoke() -> None: - """Test invoke tokens from Chat__ModuleName__.""" - llm = Chat__ModuleName__() - - result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) - assert isinstance(result.content, str) +from __module_name__.chat_models import Chat__ModuleName__ +from langchain_tests.integration_tests import ChatModelIntegrationTests + + +class TestChatParrotLinkIntegration(ChatModelIntegrationTests): + @property + def chat_model_class(self) -> Type[Chat__ModuleName__]: + return Chat__ModuleName__ + + @property + def chat_model_params(self) -> dict: + # These should be parameters used to initialize your integration for testing + return { + "model": "bird-brain-001", + "temperature": 0, + "parrot_buffer_length": 50, + } diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py index 51937d157d255..f7bd526d04bd5 100644 --- a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py +++ b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py @@ -1,20 +1,16 @@ """Test __ModuleName__ embeddings.""" -from __module_name__.embeddings import __ModuleName__Embeddings +from typing import Type +from __module_name__.embeddings import __ModuleName__Embeddings +from langchain_tests.integration_tests import EmbeddingsIntegrationTests -def test___module_name___embedding_documents() -> None: - """Test cohere embeddings.""" - documents = ["foo bar"] - embedding = __ModuleName__Embeddings() - output = embedding.embed_documents(documents) - assert len(output) == 1 - assert len(output[0]) > 0 +class TestParrotLinkEmbeddingsIntegration(EmbeddingsIntegrationTests): + @property + def embeddings_class(self) -> Type[__ModuleName__Embeddings]: + return __ModuleName__Embeddings -def test___module_name___embedding_query() -> None: - """Test cohere embeddings.""" - document = "foo bar" - embedding = __ModuleName__Embeddings() - output = embedding.embed_query(document) - assert len(output) > 0 + @property + def embedding_model_params(self) -> dict: + return {"model": "nest-embed-001"} diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_llms.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_llms.py deleted file mode 100644 index 9fd6e0a628e94..0000000000000 --- a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_llms.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Test __ModuleName__LLM llm.""" - -from __module_name__.llms import __ModuleName__LLM - - -def test_stream() -> None: - """Test streaming tokens from OpenAI.""" - llm = __ModuleName__LLM() - - for token in llm.stream("I'm Pickle Rick"): - assert isinstance(token, str) - - -async def test_astream() -> None: - """Test streaming tokens from OpenAI.""" - llm = __ModuleName__LLM() - - async for token in llm.astream("I'm Pickle Rick"): - assert isinstance(token, str) - - -async def test_abatch() -> None: - """Test streaming tokens from __ModuleName__LLM.""" - llm = __ModuleName__LLM() - - result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) - for token in result: - assert isinstance(token, str) - - -async def test_abatch_tags() -> None: - """Test batch tokens from __ModuleName__LLM.""" - llm = __ModuleName__LLM() - - result = await llm.abatch( - ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} - ) - for token in result: - assert isinstance(token, str) - - -def test_batch() -> None: - """Test batch tokens from __ModuleName__LLM.""" - llm = __ModuleName__LLM() - - result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) - for token in result: - assert isinstance(token, str) - - -async def test_ainvoke() -> None: - """Test invoke tokens from __ModuleName__LLM.""" - llm = __ModuleName__LLM() - - result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) - assert isinstance(result, str) - - -def test_invoke() -> None: - """Test invoke tokens from __ModuleName__LLM.""" - llm = __ModuleName__LLM() - - result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) - assert isinstance(result, str) diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py new file mode 100644 index 0000000000000..487c4a41cd7ca --- /dev/null +++ b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py @@ -0,0 +1,24 @@ +from typing import Type + +from __module_name__.retrievers import __ModuleName__Retriever +from langchain_tests.integration_tests import ( + RetrieversIntegrationTests, +) + + +class Test__ModuleName__Retriever(RetrieversIntegrationTests): + @property + def retriever_constructor(self) -> Type[__ModuleName__Retriever]: + """Get an empty vectorstore for unit tests.""" + return __ModuleName__Retriever + + @property + def retriever_constructor_params(self) -> dict: + return {"k": 2} + + @property + def retriever_query_example(self) -> str: + """ + Returns a dictionary representing the "args" of an example retriever call. + """ + return "example query" diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py new file mode 100644 index 0000000000000..529bd7fe7ea16 --- /dev/null +++ b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py @@ -0,0 +1,27 @@ +from typing import Type + +from __module_name__.tools import __ModuleName__Tool +from langchain_tests.integration_tests import ToolsIntegrationTests + + +class TestParrotMultiplyToolIntegration(ToolsIntegrationTests): + @property + def tool_constructor(self) -> Type[__ModuleName__Tool]: + return __ModuleName__Tool + + @property + def tool_constructor_params(self) -> dict: + # if your tool constructor instead required initialization arguments like + # `def __init__(self, some_arg: int):`, you would return those here + # as a dictionary, e.g.: `return {'some_arg': 42}` + return {} + + @property + def tool_invoke_params_example(self) -> dict: + """ + Returns a dictionary representing the "args" of an example tool call. + + This should NOT be a ToolCall dict - i.e. it should not + have {"name", "id", "args"} keys. + """ + return {"a": 2, "b": 3} diff --git a/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_vectorstores.py b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_vectorstores.py new file mode 100644 index 0000000000000..2d0820348a3c3 --- /dev/null +++ b/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_vectorstores.py @@ -0,0 +1,37 @@ +from typing import AsyncGenerator, Generator + +import pytest +from __module_name__.vectorstores import __ModuleName__VectorStore +from langchain_core.vectorstores import VectorStore +from langchain_tests.integration_tests import ( + AsyncReadWriteTestSuite, + ReadWriteTestSuite, +) + + +class Test__ModuleName__VectorStoreSync(ReadWriteTestSuite): + @pytest.fixture() + def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore + """Get an empty vectorstore for unit tests.""" + store = __ModuleName__VectorStore() + # note: store should be EMPTY at this point + # if you need to delete data, you may do so here + try: + yield store + finally: + # cleanup operations, or deleting data + pass + + +class Test__ModuleName__VectorStoreAsync(AsyncReadWriteTestSuite): + @pytest.fixture() + async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: # type: ignore + """Get an empty vectorstore for unit tests.""" + store = __ModuleName__VectorStore() + # note: store should be EMPTY at this point + # if you need to delete data, you may do so here + try: + yield store + finally: + # cleanup operations, or deleting data + pass diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py index 70cc82c75dcbf..c83081a44790b 100644 --- a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py +++ b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py @@ -1,8 +1,21 @@ """Test chat model integration.""" +from typing import Type + from __module_name__.chat_models import Chat__ModuleName__ +from langchain_tests.unit_tests import ChatModelUnitTests + +class TestChat__ModuleName__Unit(ChatModelUnitTests): + @property + def chat_model_class(self) -> Type[Chat__ModuleName__]: + return Chat__ModuleName__ -def test_initialization() -> None: - """Test chat model initialization.""" - Chat__ModuleName__() + @property + def chat_model_params(self) -> dict: + # These should be parameters used to initialize your integration for testing + return { + "model": "bird-brain-001", + "temperature": 0, + "parrot_buffer_length": 50, + } diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py index ab4f45a9b7304..5fdf8feb0837d 100644 --- a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py +++ b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py @@ -1,8 +1,16 @@ """Test embedding model integration.""" +from typing import Type + from __module_name__.embeddings import __ModuleName__Embeddings +from langchain_tests.unit_tests import EmbeddingsUnitTests + +class TestParrotLinkEmbeddingsUnit(EmbeddingsUnitTests): + @property + def embeddings_class(self) -> Type[__ModuleName__Embeddings]: + return __ModuleName__Embeddings -def test_initialization() -> None: - """Test embedding model initialization.""" - __ModuleName__Embeddings() + @property + def embedding_model_params(self) -> dict: + return {"model": "nest-embed-001"} diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_imports.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_imports.py deleted file mode 100644 index 1dfa338914189..0000000000000 --- a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,12 +0,0 @@ -from __module_name__ import __all__ - -EXPECTED_ALL = [ - "__ModuleName__LLM", - "Chat__ModuleName__", - "__ModuleName__VectorStore", - "__ModuleName__Embeddings", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_llms.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_llms.py deleted file mode 100644 index 7d53dad10acdd..0000000000000 --- a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_llms.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Test __ModuleName__ Chat API wrapper.""" - -from __module_name__ import __ModuleName__LLM - - -def test_initialization() -> None: - """Test integration initialization.""" - __ModuleName__LLM() diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py new file mode 100644 index 0000000000000..23231276d4125 --- /dev/null +++ b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py @@ -0,0 +1,27 @@ +from typing import Type + +from __module_name__.tools import __ModuleName__Tool +from langchain_tests.unit_tests import ToolsUnitTests + + +class TestParrotMultiplyToolUnit(ToolsUnitTests): + @property + def tool_constructor(self) -> Type[__ModuleName__Tool]: + return __ModuleName__Tool + + @property + def tool_constructor_params(self) -> dict: + # if your tool constructor instead required initialization arguments like + # `def __init__(self, some_arg: int):`, you would return those here + # as a dictionary, e.g.: `return {'some_arg': 42}` + return {} + + @property + def tool_invoke_params_example(self) -> dict: + """ + Returns a dictionary representing the "args" of an example tool call. + + This should NOT be a ToolCall dict - i.e. it should not + have {"name", "id", "args"} keys. + """ + return {"a": 2, "b": 3} diff --git a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_vectorstores.py b/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_vectorstores.py deleted file mode 100644 index 6c044a9a66fb4..0000000000000 --- a/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_vectorstores.py +++ /dev/null @@ -1,6 +0,0 @@ -from __module_name__.vectorstores import __ModuleName__VectorStore - - -def test_initialization() -> None: - """Test integration vectorstore initialization.""" - __ModuleName__VectorStore() diff --git a/libs/cli/langchain_cli/namespaces/integration.py b/libs/cli/langchain_cli/namespaces/integration.py index e78af97c25d97..eca9804358561 100644 --- a/libs/cli/langchain_cli/namespaces/integration.py +++ b/libs/cli/langchain_cli/namespaces/integration.py @@ -6,7 +6,7 @@ import shutil import subprocess from pathlib import Path -from typing import Optional +from typing import Dict, Optional, cast import typer from typing_extensions import Annotated, TypedDict @@ -15,19 +15,17 @@ integration_cli = typer.Typer(no_args_is_help=True, add_completion=False) -Replacements = TypedDict( - "Replacements", - { - "__package_name__": str, - "__module_name__": str, - "__ModuleName__": str, - "__MODULE_NAME__": str, - "__package_name_short__": str, - }, -) +class Replacements(TypedDict): + __package_name__: str + __module_name__: str + __ModuleName__: str + __MODULE_NAME__: str + __package_name_short__: str + __package_name_short_snake__: str -def _process_name(name: str, *, community: bool = False): + +def _process_name(name: str, *, community: bool = False) -> Replacements: preprocessed = name.replace("_", "-").lower() if preprocessed.startswith("langchain-"): @@ -42,7 +40,7 @@ def _process_name(name: str, *, community: bool = False): raise ValueError("Name should not end with `-`.") if preprocessed.find("--") != -1: raise ValueError("Name should not contain consecutive hyphens.") - replacements = { + replacements: Replacements = { "__package_name__": f"langchain-{preprocessed}", "__module_name__": "langchain_" + preprocessed.replace("-", "_"), "__ModuleName__": preprocessed.title().replace("-", ""), @@ -52,7 +50,7 @@ def _process_name(name: str, *, community: bool = False): } if community: replacements["__module_name__"] = preprocessed.replace("-", "_") - return Replacements(replacements) + return replacements @integration_cli.command() @@ -74,16 +72,7 @@ def new( ): """ Creates a new integration package. - - Should be run from libs/partners """ - # confirm that we are in the right directory - if not Path.cwd().name == "partners" or not Path.cwd().parent.name == "libs": - typer.echo( - "This command should be run from the `libs/partners` directory in the " - "langchain-ai/langchain monorepo. Continuing is NOT recommended." - ) - typer.confirm("Are you sure you want to continue?", abort=True) try: replacements = _process_name(name) @@ -104,7 +93,7 @@ def new( "Name of integration in PascalCase", default=replacements["__ModuleName__"] ) - destination_dir = Path.cwd() / replacements["__package_name_short__"] + destination_dir = Path.cwd() / replacements["__package_name__"] if destination_dir.exists(): typer.echo(f"Folder {destination_dir} exists.") raise typer.Exit(code=1) @@ -118,7 +107,7 @@ def new( shutil.move(destination_dir / "integration_template", package_dir) # replacements in files - replace_glob(destination_dir, "**/*", replacements) + replace_glob(destination_dir, "**/*", cast(Dict[str, str], replacements)) # poetry install subprocess.run( @@ -226,4 +215,4 @@ def create_doc( shutil.copy(docs_template, destination_path) # replacements in file - replace_file(destination_path, replacements) + replace_file(destination_path, cast(Dict[str, str], replacements)) diff --git a/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py b/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py index 57fd457f19e4b..51b8b79bd3b6e 100644 --- a/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py +++ b/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py @@ -17,7 +17,7 @@ class ImportExtractor(ast.NodeVisitor): def __init__(self, *, from_package: Optional[str] = None) -> None: """Extract all imports from the given code, optionally filtering by package.""" - self.imports = [] + self.imports: list = [] self.package = from_package def visit_ImportFrom(self, node): @@ -68,7 +68,7 @@ def find_subclasses_in_module(module, classes_: List[Type]) -> List[str]: return subclasses -def _get_all_classnames_from_file(file: str, pkg: str) -> List[Tuple[str, str]]: +def _get_all_classnames_from_file(file: Path, pkg: str) -> List[Tuple[str, str]]: """Extract all class names from a file.""" with open(file, encoding="utf-8") as f: code = f.read() @@ -145,7 +145,7 @@ def find_imports_from_package( return extractor.imports -def _get_current_module(path: str, pkg_root: str) -> str: +def _get_current_module(path: Path, pkg_root: str) -> str: """Convert a path to a module name.""" path_as_pathlib = pathlib.Path(os.path.abspath(path)) relative_path = path_as_pathlib.relative_to(pkg_root).with_suffix("") diff --git a/libs/cli/langchain_cli/namespaces/migrate/main.py b/libs/cli/langchain_cli/namespaces/migrate/main.py index 483fa9f6ea6c7..0956f9d4f683c 100644 --- a/libs/cli/langchain_cli/namespaces/migrate/main.py +++ b/libs/cli/langchain_cli/namespaces/migrate/main.py @@ -4,7 +4,7 @@ import rich import typer -from gritql import run +from gritql import run # type: ignore from typer import Option diff --git a/libs/cli/langchain_cli/utils/find_replace.py b/libs/cli/langchain_cli/utils/find_replace.py index cfea68d3dfaa7..12cb9bb4a13fe 100644 --- a/libs/cli/langchain_cli/utils/find_replace.py +++ b/libs/cli/langchain_cli/utils/find_replace.py @@ 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pytest-watch = "^4.2.0" [tool.poetry.group.lint.dependencies] ruff = "^0.5" +mypy = "^1.13.0" [tool.poetry.group.test.dependencies] +langchain = {path = "../langchain", develop = true} [tool.poetry.group.typing.dependencies] +langchain = {path = "../langchain", develop = true} [tool.poetry.group.test_integration.dependencies] @@ -50,22 +52,11 @@ select = [ "T201", # print ] -[tool.poe.tasks] -test = "poetry run pytest tests" -watch = "poetry run ptw" -version = "poetry version --short" -bump = ["_bump_1", "_bump_2"] -lint = ["_lint", "_check_formatting"] -format = ["_format", "_lint_fix"] - -_bump_2.shell = """sed -i "" "/^__version__ =/c\\ \n__version__ = \\"$version\\"\n" langchain_cli/cli.py""" -_bump_2.uses = { version = "version" } - -_bump_1 = "poetry version patch" -_check_formatting = "poetry run ruff format . --diff" -_lint = "poetry run ruff check ." -_format = "poetry run ruff format ." -_lint_fix = "poetry run ruff check . --fix" +[tool.mypy] +exclude = [ + "langchain_cli/integration_template", + "langchain_cli/package_template", +] [build-system] requires = ["poetry-core"] diff --git a/libs/cli/scripts/generate_migrations.py b/libs/cli/scripts/generate_migrations.py index 38c830443c775..11b5a8a6b0ed3 100644 --- a/libs/cli/scripts/generate_migrations.py +++ b/libs/cli/scripts/generate_migrations.py @@ -1,3 +1,4 @@ +# type: ignore """Script to generate migrations for the migration script.""" import json diff --git a/libs/cli/tests/integration_tests/test_compile.py b/libs/cli/tests/integration_tests/test_compile.py new file mode 100644 index 0000000000000..33ecccdfa0fbd --- /dev/null +++ b/libs/cli/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/cli/tests/unit_tests/migrate/cli_runner/test_cli.py b/libs/cli/tests/unit_tests/migrate/cli_runner/test_cli.py index 4fbeae295bacf..3cc57112ead53 100644 --- a/libs/cli/tests/unit_tests/migrate/cli_runner/test_cli.py +++ b/libs/cli/tests/unit_tests/migrate/cli_runner/test_cli.py @@ -41,6 +41,7 @@ def find_issue(current: Folder, expected: Folder) -> str: return "Unknown" +@pytest.mark.xfail(reason="grit may not be installed in env") def test_command_line(tmp_path: Path) -> None: runner = CliRunner() diff --git a/libs/standard-tests/langchain_tests/integration_tests/__init__.py b/libs/standard-tests/langchain_tests/integration_tests/__init__.py index ed7e5f111c9f4..734af1608c6cb 100644 --- a/libs/standard-tests/langchain_tests/integration_tests/__init__.py +++ b/libs/standard-tests/langchain_tests/integration_tests/__init__.py @@ -11,6 +11,7 @@ "vectorstores", "embeddings", "tools", + "retrievers", ] for module in modules: