langchain/docs/modules/indexes/document_loaders/examples/microsoft_word.ipynb
Leonid Ganeline 59204a5033
docs: document_loaders improvements (#4200)
- made notebooks consistent: titles, service/format descriptions.
- corrected short names to full names, for example, `Word` -> `Microsoft
Word`
- added missed descriptions
- renamed notebook files to make ToC correctly sorted
2023-05-05 17:44:54 -07:00

209 lines
4.2 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"id": "39af9ecd",
"metadata": {},
"source": [
"# Microsoft Word\n",
"\n",
">[Microsoft Word](https://www.microsoft.com/en-us/microsoft-365/word) is a word processor developed by Microsoft.\n",
"\n",
"This covers how to load `Word` documents into a document format that we can use downstream."
]
},
{
"cell_type": "markdown",
"id": "9438686b",
"metadata": {},
"source": [
"## Using Docx2txt\n",
"\n",
"Load .docx using `Docx2txt` into a document."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7b80ea89",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import Docx2txtLoader"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "99a12031",
"metadata": {},
"outputs": [],
"source": [
"loader = Docx2txtLoader(\"example_data/fake.docx\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b92f68b0",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d83dd755",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "8d40727d",
"metadata": {},
"source": [
"## Using Unstructured"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "721c48aa",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import UnstructuredWordDocumentLoader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9d3d0e35",
"metadata": {},
"outputs": [],
"source": [
"loader = UnstructuredWordDocumentLoader(\"example_data/fake.docx\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "06073f91",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c9adc5cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx'}, lookup_index=0)]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "525d6b67",
"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": "064f9162",
"metadata": {},
"outputs": [],
"source": [
"loader = UnstructuredWordDocumentLoader(\"example_data/fake.docx\", mode=\"elements\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "abefbbdb",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a547c534",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx', 'filename': 'fake.docx', 'category': 'Title'}, lookup_index=0)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
}
],
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}