langchain/docs/docs/integrations/document_loaders/email.ipynb
Erick Friis 7bc100fd43
docs: integration package pip installs (#15762)
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{
"cells": [
{
"cell_type": "markdown",
"id": "9fdbd55d",
"metadata": {},
"source": [
"# Email\n",
"\n",
"This notebook shows how to load email (`.eml`) or `Microsoft Outlook` (`.msg`) files."
]
},
{
"cell_type": "markdown",
"id": "89caa348",
"metadata": {},
"source": [
"## Using Unstructured"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "226e50aa-407d-43d9-a81d-f6706298b10c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%pip install --upgrade --quiet unstructured"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "40cd9806",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_community.document_loaders import UnstructuredEmailLoader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2d20b852",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "579fa702",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "90c1d899",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[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'})]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "8bf50cba",
"metadata": {},
"source": [
"### Retain Elements\n",
"\n",
"Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b9592eaf",
"metadata": {},
"outputs": [],
"source": [
"loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\", mode=\"elements\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0b16d03f",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d7bdc5e5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'filename': 'fake-email.eml', 'file_directory': 'example_data', 'date': '2022-12-16T17:04:16-05:00', 'filetype': 'message/rfc822', 'sent_from': ['Matthew Robinson <mrobinson@unstructured.io>'], 'sent_to': ['Matthew Robinson <mrobinson@unstructured.io>'], 'subject': 'Test Email', 'category': 'NarrativeText'})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
},
{
"cell_type": "markdown",
"id": "5021f20a",
"metadata": {},
"source": [
"### Processing Attachments\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6539f166",
"metadata": {},
"outputs": [],
"source": [
"loader = UnstructuredEmailLoader(\n",
" \"example_data/fake-email.eml\",\n",
" mode=\"elements\",\n",
" process_attachments=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "aebead38",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ddeb60f4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'filename': 'fake-email.eml', 'file_directory': 'example_data', 'date': '2022-12-16T17:04:16-05:00', 'filetype': 'message/rfc822', 'sent_from': ['Matthew Robinson <mrobinson@unstructured.io>'], 'sent_to': ['Matthew Robinson <mrobinson@unstructured.io>'], 'subject': 'Test Email', 'category': 'NarrativeText'})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
},
{
"cell_type": "markdown",
"id": "6a074515",
"metadata": {},
"source": [
"## Using OutlookMessageLoader"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "058e670e-9964-44ee-b888-44f23ffb9310",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet extract_msg"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1e7a8444",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import OutlookMessageLoader"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "77a055e6",
"metadata": {},
"outputs": [],
"source": [
"loader = OutlookMessageLoader(\"example_data/fake-email.msg\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "789882de",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "46aa0632",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"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={'subject': 'Test for TIF files', 'sender': 'Brian Zhou <brizhou@gmail.com>', 'date': 'Mon, 18 Nov 2013 16:26:24 +0800'})"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b223ce2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
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"nbformat": 4,
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}