Email 集成

使用 LangChain Python 集成 Email 文档加载器。

本 notebook 展示how to load email (.eml) or Microsoft Outlook (.msg) files.

Please see Unstructured for more instructions on setting up Unstructured locally, including setting up required system dependencies.

Using unstructured

pip install -qU unstructured

from langchain_community.document_loaders import UnstructuredEmailLoader

loader = UnstructuredEmailLoader("./example_data/fake-email.eml")

data = loader.load()

data

[Document(page_content='This is a test email to use for unit tests.\n\nImportant points:\n\nRoses are red\n\nViolets are blue', metadata={'source': './example_data/fake-email.eml'})]

Retain elements

在底层，Unstructured 为不同的文本块创建不同的 “elements”。默认情况下我们将它们组合在一起，但你可以通过指定 mode="elements" 轻松保持这种分离。

loader = UnstructuredEmailLoader("example_data/fake-email.eml", mode="elements")

data = loader.load()

data[0]

Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'file_directory': 'example_data', 'filename': 'fake-email.eml', 'last_modified': '2022-12-16T17:04:16-05:00', 'sent_from': ['Matthew Robinson [[e-mail]](mailto:[e-mail])'], 'sent_to': ['Matthew Robinson [[e-mail]](mailto:[e-mail])'], 'subject': 'Test Email', 'languages': ['eng'], 'filetype': 'message/rfc822', 'category': 'NarrativeText'})

Processing attachments

You can process attachments with UnstructuredEmailLoader by setting process_attachments=True in the constructor. By default, attachments will be partitioned using the partition function from unstructured. You can use a different partitioning function by passing the function to the attachment_partitioner kwarg.

loader = UnstructuredEmailLoader(
    "example_data/fake-email.eml",
    mode="elements",
    process_attachments=True,
)

data = loader.load()

data[0]

Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'file_directory': 'example_data', 'filename': 'fake-email.eml', 'last_modified': '2022-12-16T17:04:16-05:00', 'sent_from': ['Matthew Robinson [[e-mail]](mailto:[e-mail])'], 'sent_to': ['Matthew Robinson [[e-mail]](mailto:[e-mail])'], 'subject': 'Test Email', 'languages': ['eng'], 'filetype': 'message/rfc822', 'category': 'NarrativeText'})

Using OutlookMessageLoader

pip install -qU extract_msg

from langchain_community.document_loaders import OutlookMessageLoader

loader = OutlookMessageLoader("example_data/fake-email.msg")

data = loader.load()

data[0]

Document(page_content='This is a test email to experiment with the MS Outlook MSG Extractor\r\n\r\n\r\n-- \r\n\r\n\r\nKind regards\r\n\r\n\r\n\r\n\r\nBrian Zhou\r\n\r\n', metadata={'source': 'example_data/fake-email.msg', 'subject': 'Test for TIF files', 'sender': 'Brian Zhou [[e-mail]](mailto:[e-mail])', 'date': datetime.datetime(2013, 11, 18, 0, 26, 24, tzinfo=zoneinfo.ZoneInfo(key='America/Los_Angeles'))})

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