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community[patch]: add NotebookLoader unit test (#17721)
Thank you for contributing to LangChain! - **Description:** added unit tests for NotebookLoader. Linked PR: https://github.com/langchain-ai/langchain/pull/17614 - **Issue:** [#17614](https://github.com/langchain-ai/langchain/pull/17614) - **Twitter handle:** @paulodoestech - [x] Pass lint and test: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified to check that you're passing lint and testing. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ - [x] Add tests and docs: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: lachiewalker <lachiewalker1@hotmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
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@ -25,7 +25,11 @@ def concatenate_cells(
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"""
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cell_type = cell["cell_type"]
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source = cell["source"]
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output = cell["outputs"]
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if include_outputs:
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try:
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output = cell["outputs"]
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except KeyError:
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pass
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if include_outputs and cell_type == "code" and output:
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if "ename" in output[0].keys():
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@ -58,14 +62,13 @@ def concatenate_cells(
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def remove_newlines(x: Any) -> Any:
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"""Recursively remove newlines, no matter the data structure they are stored in."""
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import pandas as pd
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if isinstance(x, str):
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return x.replace("\n", "")
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elif isinstance(x, list):
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return [remove_newlines(elem) for elem in x]
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elif isinstance(x, pd.DataFrame):
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return x.applymap(remove_newlines)
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elif isinstance(x, dict):
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return {k: remove_newlines(v) for (k, v) in x.items()}
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else:
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return x
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@ -104,29 +107,29 @@ class NotebookLoader(BaseLoader):
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self,
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) -> List[Document]:
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"""Load documents."""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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"pandas is needed for Notebook Loader, "
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"please install with `pip install pandas`"
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)
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p = Path(self.file_path)
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with open(p, encoding="utf8") as f:
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d = json.load(f)
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data = pd.json_normalize(d["cells"])
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filtered_data = data[["cell_type", "source", "outputs"]]
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if self.remove_newline:
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filtered_data = filtered_data.applymap(remove_newlines)
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filtered_data = [
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{k: v for (k, v) in cell.items() if k in ["cell_type", "source", "outputs"]}
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for cell in d["cells"]
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]
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text = filtered_data.apply(
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lambda x: concatenate_cells(
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x, self.include_outputs, self.max_output_length, self.traceback
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),
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axis=1,
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).str.cat(sep=" ")
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if self.remove_newline:
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filtered_data = list(map(remove_newlines, filtered_data))
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text = "".join(
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list(
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map(
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lambda x: concatenate_cells(
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x, self.include_outputs, self.max_output_length, self.traceback
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),
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filtered_data,
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)
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)
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)
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metadata = {"source": str(p)}
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@ -0,0 +1,85 @@
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import json
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from pytest_mock import MockerFixture
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from langchain_community.document_loaders.notebook import NotebookLoader
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def test_initialization() -> None:
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loader = NotebookLoader(path="./testfile.ipynb")
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assert loader.file_path == "./testfile.ipynb"
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def test_load_no_outputs(mocker: MockerFixture) -> None:
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mock_notebook_content = {
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": ["# Test notebook\n", "This is a test notebook."],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": ["Hello World!\n"],
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}
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],
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}
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]
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}
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mocked_cell_type = mock_notebook_content["cells"][0]["cell_type"]
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mocked_source = mock_notebook_content["cells"][0]["source"]
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# Convert the mock notebook content to a JSON string
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mock_notebook_content_str = json.dumps(mock_notebook_content)
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# Mock the open function & json.load functions
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mocker.patch("builtins.open", mocker.mock_open(read_data=mock_notebook_content_str))
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mocker.patch("json.load", return_value=mock_notebook_content)
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loader = NotebookLoader(path="./testfile.ipynb")
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docs = loader.load()
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assert len(docs) == 1
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assert docs[0].page_content == f"'{mocked_cell_type}' cell: '{mocked_source}'\n\n"
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assert docs[0].metadata == {"source": "testfile.ipynb"}
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def test_load_with_outputs(mocker: MockerFixture) -> None:
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mock_notebook_content: dict = {
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"cells": [
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{
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"cell_type": "code",
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"metadata": {},
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"source": ["# Test notebook\n", "This is a test notebook."],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": ["Hello World!\n"],
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}
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],
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}
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]
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}
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mocked_cell_type = mock_notebook_content["cells"][0]["cell_type"]
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mocked_source = mock_notebook_content["cells"][0]["source"]
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mocked_output = mock_notebook_content["cells"][0]["outputs"][0]["text"]
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# Convert the mock notebook content to a JSON string
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mock_notebook_content_str = json.dumps(mock_notebook_content)
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# Mock the open function & json.load functions
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mocker.patch("builtins.open", mocker.mock_open(read_data=mock_notebook_content_str))
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mocker.patch("json.load", return_value=mock_notebook_content)
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loader = NotebookLoader(path="./testfile.ipynb", include_outputs=True)
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docs = loader.load()
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assert len(docs) == 1
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expected_content = (
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f"'{mocked_cell_type}' cell: '{mocked_source}'\n"
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f" with output: '{mocked_output}'\n\n"
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)
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assert docs[0].page_content == expected_content
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assert docs[0].metadata == {"source": "testfile.ipynb"}
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