Add Xorbits Dataframe as a Document Loader (#7319)

- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source
computing framework that makes it easy to scale data science and machine
learning workloads in parallel. Xorbits can leverage multi cores or GPUs
to accelerate computation on a single machine, or scale out up to
thousands of machines to support processing terabytes of data.

- This PR added support for the Xorbits document loader, which allows
langchain to leverage Xorbits to parallelize and distribute the loading
of data.
- Dependencies: This change requires the Xorbits library to be installed
in order to be used.
`pip install xorbits`
- Request for review: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/Xorbitsio

Co-authored-by: Bagatur <baskaryan@gmail.com>

tests/integration_tests/document_loaders/test_xorbits.py

@@ -0,0 +1,64 @@
+import pytest
+
+from langchain.document_loaders import XorbitsLoader
+from langchain.schema import Document
+
+try:
+    import xorbits  # noqa: F401
+
+    xorbits_installed = True
+except ImportError:
+    xorbits_installed = False
+
+
+@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
+def test_load_returns_list_of_documents() -> None:
+    import xorbits.pandas as pd
+
+    data = {
+        "text": ["Hello", "World"],
+        "author": ["Alice", "Bob"],
+        "date": ["2022-01-01", "2022-01-02"],
+    }
+    loader = XorbitsLoader(pd.DataFrame(data))
+    docs = loader.load()
+    assert isinstance(docs, list)
+    assert all(isinstance(doc, Document) for doc in docs)
+    assert len(docs) == 2
+
+
+@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
+def test_load_converts_dataframe_columns_to_document_metadata() -> None:
+    import xorbits.pandas as pd
+
+    data = {
+        "text": ["Hello", "World"],
+        "author": ["Alice", "Bob"],
+        "date": ["2022-01-01", "2022-01-02"],
+    }
+    loader = XorbitsLoader(pd.DataFrame(data))
+    docs = loader.load()
+    expected = {
+        "author": ["Alice", "Bob"],
+        "date": ["2022-01-01", "2022-01-02"],
+    }
+    for i, doc in enumerate(docs):
+        assert doc.metadata["author"] == expected["author"][i]
+        assert doc.metadata["date"] == expected["date"][i]
+
+
+@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
+def test_load_uses_page_content_column_to_create_document_text() -> None:
+    import xorbits.pandas as pd
+
+    data = {
+        "text": ["Hello", "World"],
+        "author": ["Alice", "Bob"],
+        "date": ["2022-01-01", "2022-01-02"],
+    }
+    sample_data_frame = pd.DataFrame(data)
+    sample_data_frame = sample_data_frame.rename(columns={"text": "dummy_test_column"})
+    loader = XorbitsLoader(sample_data_frame, page_content_column="dummy_test_column")
+    docs = loader.load()
+    assert docs[0].page_content == "Hello"
+    assert docs[1].page_content == "World"