649 lines
25 KiB
Python
649 lines
25 KiB
Python
import asyncio
|
||
import json
|
||
import logging
|
||
import sys
|
||
|
||
import ftfy
|
||
import requests
|
||
from azure.identity import DefaultAzureCredential
|
||
from langchain_community.document_loaders import (
|
||
AzureAIDocumentIntelligenceLoader,
|
||
BSHTMLLoader,
|
||
CSVLoader,
|
||
Docx2txtLoader,
|
||
OutlookMessageLoader,
|
||
PyPDFLoader,
|
||
TextLoader,
|
||
YoutubeLoader,
|
||
)
|
||
from langchain_core.documents import Document
|
||
from open_webui.env import AIOHTTP_CLIENT_SESSION_SSL, GLOBAL_LOG_LEVEL, REQUESTS_VERIFY
|
||
from open_webui.retrieval.loaders.datalab_marker import DatalabMarkerLoader
|
||
from open_webui.retrieval.loaders.external_document import ExternalDocumentLoader
|
||
from open_webui.retrieval.loaders.mineru import MinerULoader
|
||
from open_webui.retrieval.loaders.mistral import MistralLoader
|
||
from open_webui.retrieval.loaders.paddleocr_vl import PaddleOCRVLLoader
|
||
|
||
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
|
||
log = logging.getLogger(__name__)
|
||
|
||
known_source_ext = [
|
||
'go',
|
||
'py',
|
||
'java',
|
||
'sh',
|
||
'bat',
|
||
'ps1',
|
||
'cmd',
|
||
'js',
|
||
'ts',
|
||
'css',
|
||
'cpp',
|
||
'hpp',
|
||
'h',
|
||
'c',
|
||
'cs',
|
||
'sql',
|
||
'log',
|
||
'ini',
|
||
'pl',
|
||
'pm',
|
||
'r',
|
||
'dart',
|
||
'dockerfile',
|
||
'env',
|
||
'php',
|
||
'hs',
|
||
'hsc',
|
||
'lua',
|
||
'nginxconf',
|
||
'conf',
|
||
'm',
|
||
'mm',
|
||
'plsql',
|
||
'perl',
|
||
'rb',
|
||
'rs',
|
||
'db2',
|
||
'scala',
|
||
'bash',
|
||
'swift',
|
||
'vue',
|
||
'svelte',
|
||
'ex',
|
||
'exs',
|
||
'erl',
|
||
'tsx',
|
||
'jsx',
|
||
'hs',
|
||
'lhs',
|
||
'json',
|
||
'yaml',
|
||
'yml',
|
||
'toml',
|
||
]
|
||
|
||
|
||
class ExcelLoader:
|
||
"""Fallback Excel loader using pandas when unstructured is not installed."""
|
||
|
||
def __init__(self, file_path):
|
||
self.file_path = file_path
|
||
|
||
def load(self) -> list[Document]:
|
||
import pandas as pd
|
||
|
||
text_parts = []
|
||
xls = pd.ExcelFile(self.file_path)
|
||
for sheet_name in xls.sheet_names:
|
||
df = pd.read_excel(xls, sheet_name=sheet_name)
|
||
text_parts.append(f'Sheet: {sheet_name}\n{df.to_string(index=False)}')
|
||
return [
|
||
Document(
|
||
page_content='\n\n'.join(text_parts),
|
||
metadata={'source': self.file_path},
|
||
)
|
||
]
|
||
|
||
|
||
class PptxLoader:
|
||
"""Fallback PowerPoint loader using python-pptx when unstructured is not installed."""
|
||
|
||
def __init__(self, file_path):
|
||
self.file_path = file_path
|
||
|
||
def load(self) -> list[Document]:
|
||
from pptx import Presentation
|
||
|
||
prs = Presentation(self.file_path)
|
||
text_parts = []
|
||
for i, slide in enumerate(prs.slides, 1):
|
||
slide_texts = []
|
||
for shape in slide.shapes:
|
||
if shape.has_text_frame:
|
||
slide_texts.append(shape.text_frame.text)
|
||
if slide_texts:
|
||
text_parts.append(f'Slide {i}:\n' + '\n'.join(slide_texts))
|
||
return [
|
||
Document(
|
||
page_content='\n\n'.join(text_parts),
|
||
metadata={'source': self.file_path},
|
||
)
|
||
]
|
||
|
||
|
||
class TikaLoader:
|
||
def __init__(self, url, file_path, mime_type=None, extract_images=None):
|
||
self.url = url
|
||
self.file_path = file_path
|
||
self.mime_type = mime_type
|
||
|
||
self.extract_images = extract_images
|
||
|
||
def load(self) -> list[Document]:
|
||
with open(self.file_path, 'rb') as f:
|
||
data = f.read()
|
||
|
||
if self.mime_type is not None:
|
||
headers = {'Content-Type': self.mime_type}
|
||
else:
|
||
headers = {}
|
||
|
||
if self.extract_images == True:
|
||
headers['X-Tika-PDFextractInlineImages'] = 'true'
|
||
|
||
endpoint = self.url
|
||
if not endpoint.endswith('/'):
|
||
endpoint += '/'
|
||
endpoint += 'tika/text'
|
||
|
||
r = requests.put(endpoint, data=data, headers=headers, verify=REQUESTS_VERIFY)
|
||
|
||
if r.ok:
|
||
raw_metadata = r.json()
|
||
text = raw_metadata.get('X-TIKA:content', '<No text content found>').strip()
|
||
|
||
if 'Content-Type' in raw_metadata:
|
||
headers['Content-Type'] = raw_metadata['Content-Type']
|
||
|
||
log.debug('Tika extracted text: %s', text)
|
||
|
||
return [Document(page_content=text, metadata=headers)]
|
||
else:
|
||
raise Exception(f'Error calling Tika: {r.reason}')
|
||
|
||
|
||
class DoclingLoader:
|
||
def __init__(self, url, api_key=None, file_path=None, mime_type=None, params=None):
|
||
self.url = url.rstrip('/')
|
||
self.api_key = api_key
|
||
self.file_path = file_path
|
||
self.mime_type = mime_type
|
||
|
||
self.params = params or {}
|
||
|
||
def load(self) -> list[Document]:
|
||
with open(self.file_path, 'rb') as f:
|
||
headers = {}
|
||
if self.api_key:
|
||
headers['X-Api-Key'] = f'{self.api_key}'
|
||
|
||
r = requests.post(
|
||
f'{self.url}/v1/convert/file',
|
||
files={
|
||
'files': (
|
||
self.file_path,
|
||
f,
|
||
self.mime_type or 'application/octet-stream',
|
||
)
|
||
},
|
||
data={
|
||
'image_export_mode': 'placeholder',
|
||
**self.params,
|
||
},
|
||
headers=headers,
|
||
verify=AIOHTTP_CLIENT_SESSION_SSL,
|
||
)
|
||
if r.ok:
|
||
result = r.json()
|
||
document_data = result.get('document', {})
|
||
text = document_data.get('md_content', '<No text content found>')
|
||
|
||
metadata = {'Content-Type': self.mime_type} if self.mime_type else {}
|
||
|
||
log.debug('Docling extracted text: %s', text)
|
||
return [Document(page_content=text, metadata=metadata)]
|
||
else:
|
||
error_msg = f'Error calling Docling API: {r.reason}'
|
||
if r.text:
|
||
try:
|
||
error_data = r.json()
|
||
if 'detail' in error_data:
|
||
error_msg += f' - {error_data["detail"]}'
|
||
except Exception:
|
||
error_msg += f' - {r.text}'
|
||
raise Exception(f'Error calling Docling: {error_msg}')
|
||
|
||
|
||
class Loader:
|
||
def __init__(self, engine: str = '', **kwargs):
|
||
self.engine = engine
|
||
self.user = kwargs.get('user', None)
|
||
self.kwargs = kwargs
|
||
|
||
def load(self, filename: str, file_content_type: str, file_path: str) -> list[Document]:
|
||
loader = self._get_loader(filename, file_content_type, file_path)
|
||
docs = loader.load()
|
||
return [Document(page_content=ftfy.fix_text(doc.page_content), metadata=doc.metadata) for doc in docs]
|
||
|
||
async def aload(self, filename: str, file_content_type: str, file_path: str) -> list[Document]:
|
||
"""
|
||
Async wrapper around `load`.
|
||
|
||
Document loaders dispatched by `_get_loader` (PyMuPDF, Unstructured,
|
||
python-docx, Tika, etc.) are uniformly synchronous and CPU/IO-bound.
|
||
Calling `load` directly from an async handler would block the event
|
||
loop for the entire parse — minutes for large PDFs. This offloads
|
||
the work to a worker thread so the loop stays responsive.
|
||
"""
|
||
return await asyncio.to_thread(self.load, filename, file_content_type, file_path)
|
||
|
||
def _is_text_file(self, file_ext: str, file_content_type: str) -> bool:
|
||
return file_ext in known_source_ext or (
|
||
file_content_type
|
||
and file_content_type.find('text/') >= 0
|
||
# Avoid text/html files being detected as text
|
||
and not file_content_type.find('html') >= 0
|
||
)
|
||
|
||
def _detect_text_encoding(self, file_path: str) -> str:
|
||
"""Detect the encoding of a text file with CJK-aware fallbacks.
|
||
|
||
Langchain's ``TextLoader`` uses chardet internally when
|
||
``autodetect_encoding=True``, but chardet frequently misidentifies
|
||
CJK encodings (e.g. GB18030 detected as GB2312 or even Cyrillic).
|
||
This method replaces that by:
|
||
|
||
1. Trying UTF-8 first (fast path for the vast majority of files).
|
||
2. Using chardet as a *hint* to prioritise the right CJK codec
|
||
family, but mapping subset names to their superset
|
||
(e.g. GB2312 → gb18030).
|
||
3. Validating that decoded text actually contains CJK characters,
|
||
guarding against codecs that "succeed" but produce garbage.
|
||
4. Falling back to latin-1 (always valid, ftfy fixes mojibake later).
|
||
"""
|
||
try:
|
||
with open(file_path, 'rb') as f:
|
||
raw = f.read()
|
||
except OSError:
|
||
return 'utf-8'
|
||
|
||
if not raw:
|
||
return 'utf-8'
|
||
|
||
# Fast path: most files are UTF-8
|
||
try:
|
||
raw.decode('utf-8')
|
||
return 'utf-8'
|
||
except UnicodeDecodeError:
|
||
pass
|
||
|
||
# Use chardet as a hint, not as ground truth
|
||
import chardet
|
||
|
||
detected = chardet.detect(raw)
|
||
detected_enc = (detected.get('encoding') or '').lower().replace('-', '').replace('_', '')
|
||
|
||
# Map chardet's detected encoding to the correct superset codec.
|
||
# chardet often reports GB2312 for content that is actually GB18030;
|
||
# GB18030 is a strict superset of both GB2312 and GBK.
|
||
_ENC_FAMILY = {
|
||
'gb2312': 'gb18030',
|
||
'gb18030': 'gb18030',
|
||
'gbk': 'gb18030',
|
||
'big5': 'big5',
|
||
'euckr': 'euc-kr',
|
||
'eucjp': 'euc-jp',
|
||
'iso2022jp': 'euc-jp',
|
||
'shiftjis': 'shift_jis',
|
||
}
|
||
|
||
# Build priority list: chardet-hinted codec first, then remaining CJK
|
||
base_order = ['gb18030', 'big5', 'euc-kr', 'euc-jp']
|
||
hinted = _ENC_FAMILY.get(detected_enc)
|
||
if hinted and hinted in base_order:
|
||
ordered = [hinted] + [e for e in base_order if e != hinted]
|
||
else:
|
||
ordered = base_order
|
||
|
||
for enc in ordered:
|
||
try:
|
||
text = raw.decode(enc)
|
||
if text.strip() and self._has_cjk_characters(text):
|
||
log.info(
|
||
'Detected encoding %s for %s (chardet guessed %s)',
|
||
enc,
|
||
file_path,
|
||
detected.get('encoding'),
|
||
)
|
||
return enc
|
||
except (UnicodeDecodeError, LookupError):
|
||
continue
|
||
|
||
# If chardet gave a non-CJK answer that isn't in our family map,
|
||
# try it directly — it might be a valid Western encoding.
|
||
chardet_encoding = detected.get('encoding')
|
||
if chardet_encoding:
|
||
try:
|
||
raw.decode(chardet_encoding)
|
||
log.info(
|
||
'Using chardet-detected encoding %s for %s',
|
||
chardet_encoding,
|
||
file_path,
|
||
)
|
||
return chardet_encoding
|
||
except (UnicodeDecodeError, LookupError):
|
||
pass
|
||
|
||
# latin-1 is the ultimate fallback: every byte 0x00–0xFF is valid.
|
||
# ftfy.fix_text() (applied downstream) repairs most mojibake that
|
||
# results from treating Windows-1252 content as Latin-1.
|
||
log.info('Falling back to latin-1 encoding for %s', file_path)
|
||
return 'latin-1'
|
||
|
||
@staticmethod
|
||
def _has_cjk_characters(text: str, threshold: float = 0.05) -> bool:
|
||
"""Check if decoded text contains a meaningful proportion of CJK characters.
|
||
|
||
This guards against codecs that technically "succeed" but decode the
|
||
bytes into wrong Unicode codepoints (e.g. PUA chars, random symbols).
|
||
A genuine CJK document should have at least ``threshold`` fraction of
|
||
its non-whitespace characters in CJK Unicode blocks.
|
||
"""
|
||
if not text:
|
||
return False
|
||
|
||
cjk_count = 0
|
||
total = 0
|
||
for ch in text:
|
||
if ch.isspace():
|
||
continue
|
||
total += 1
|
||
cp = ord(ch)
|
||
if (
|
||
0x4E00 <= cp <= 0x9FFF # CJK Unified Ideographs
|
||
or 0x3400 <= cp <= 0x4DBF # CJK Extension A
|
||
or 0x20000 <= cp <= 0x2A6DF # CJK Extension B
|
||
or 0x2A700 <= cp <= 0x2B73F # CJK Extension C
|
||
or 0x2B740 <= cp <= 0x2B81F # CJK Extension D
|
||
or 0xF900 <= cp <= 0xFAFF # CJK Compatibility Ideographs
|
||
or 0x3000 <= cp <= 0x303F # CJK Symbols and Punctuation
|
||
or 0x3040 <= cp <= 0x309F # Hiragana
|
||
or 0x30A0 <= cp <= 0x30FF # Katakana
|
||
or 0xAC00 <= cp <= 0xD7AF # Hangul Syllables
|
||
or 0xFF00 <= cp <= 0xFFEF # Halfwidth and Fullwidth Forms
|
||
):
|
||
cjk_count += 1
|
||
|
||
if total == 0:
|
||
return False
|
||
|
||
return (cjk_count / total) >= threshold
|
||
|
||
def _get_loader(self, filename: str, file_content_type: str, file_path: str):
|
||
file_ext = filename.split('.')[-1].lower()
|
||
|
||
if (
|
||
self.engine == 'external'
|
||
and self.kwargs.get('EXTERNAL_DOCUMENT_LOADER_URL')
|
||
and self.kwargs.get('EXTERNAL_DOCUMENT_LOADER_API_KEY')
|
||
):
|
||
loader = ExternalDocumentLoader(
|
||
file_path=file_path,
|
||
url=self.kwargs.get('EXTERNAL_DOCUMENT_LOADER_URL'),
|
||
api_key=self.kwargs.get('EXTERNAL_DOCUMENT_LOADER_API_KEY'),
|
||
mime_type=file_content_type,
|
||
user=self.user,
|
||
)
|
||
elif self.engine == 'tika' and self.kwargs.get('TIKA_SERVER_URL'):
|
||
if self._is_text_file(file_ext, file_content_type):
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
else:
|
||
loader = TikaLoader(
|
||
url=self.kwargs.get('TIKA_SERVER_URL'),
|
||
file_path=file_path,
|
||
extract_images=self.kwargs.get('PDF_EXTRACT_IMAGES'),
|
||
)
|
||
elif (
|
||
self.engine == 'datalab_marker'
|
||
and self.kwargs.get('DATALAB_MARKER_API_KEY')
|
||
and file_ext
|
||
in [
|
||
'pdf',
|
||
'xls',
|
||
'xlsx',
|
||
'ods',
|
||
'doc',
|
||
'docx',
|
||
'odt',
|
||
'ppt',
|
||
'pptx',
|
||
'odp',
|
||
'html',
|
||
'epub',
|
||
'png',
|
||
'jpeg',
|
||
'jpg',
|
||
'webp',
|
||
'gif',
|
||
'tiff',
|
||
]
|
||
):
|
||
api_base_url = self.kwargs.get('DATALAB_MARKER_API_BASE_URL', '')
|
||
if not api_base_url or api_base_url.strip() == '':
|
||
api_base_url = 'https://www.datalab.to/api/v1/marker' # https://github.com/open-webui/open-webui/pull/16867#issuecomment-3218424349
|
||
|
||
loader = DatalabMarkerLoader(
|
||
file_path=file_path,
|
||
api_key=self.kwargs['DATALAB_MARKER_API_KEY'],
|
||
api_base_url=api_base_url,
|
||
additional_config=self.kwargs.get('DATALAB_MARKER_ADDITIONAL_CONFIG'),
|
||
use_llm=self.kwargs.get('DATALAB_MARKER_USE_LLM', False),
|
||
skip_cache=self.kwargs.get('DATALAB_MARKER_SKIP_CACHE', False),
|
||
force_ocr=self.kwargs.get('DATALAB_MARKER_FORCE_OCR', False),
|
||
paginate=self.kwargs.get('DATALAB_MARKER_PAGINATE', False),
|
||
strip_existing_ocr=self.kwargs.get('DATALAB_MARKER_STRIP_EXISTING_OCR', False),
|
||
disable_image_extraction=self.kwargs.get('DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION', False),
|
||
format_lines=self.kwargs.get('DATALAB_MARKER_FORMAT_LINES', False),
|
||
output_format=self.kwargs.get('DATALAB_MARKER_OUTPUT_FORMAT', 'markdown'),
|
||
)
|
||
elif self.engine == 'docling' and self.kwargs.get('DOCLING_SERVER_URL'):
|
||
if self._is_text_file(file_ext, file_content_type):
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
else:
|
||
# Build params for DoclingLoader
|
||
params = self.kwargs.get('DOCLING_PARAMS', {})
|
||
if not isinstance(params, dict):
|
||
try:
|
||
params = json.loads(params)
|
||
except json.JSONDecodeError:
|
||
log.error('Invalid DOCLING_PARAMS format, expected JSON object')
|
||
params = {}
|
||
|
||
loader = DoclingLoader(
|
||
url=self.kwargs.get('DOCLING_SERVER_URL'),
|
||
api_key=self.kwargs.get('DOCLING_API_KEY', None),
|
||
file_path=file_path,
|
||
mime_type=file_content_type,
|
||
params=params,
|
||
)
|
||
elif (
|
||
self.engine == 'document_intelligence'
|
||
and self.kwargs.get('DOCUMENT_INTELLIGENCE_ENDPOINT') != ''
|
||
and (
|
||
file_ext in ['pdf', 'docx', 'ppt', 'pptx']
|
||
or file_content_type
|
||
in [
|
||
'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
|
||
'application/vnd.ms-powerpoint',
|
||
'application/vnd.openxmlformats-officedocument.presentationml.presentation',
|
||
]
|
||
)
|
||
):
|
||
if self.kwargs.get('DOCUMENT_INTELLIGENCE_KEY') != '':
|
||
loader = AzureAIDocumentIntelligenceLoader(
|
||
file_path=file_path,
|
||
api_endpoint=self.kwargs.get('DOCUMENT_INTELLIGENCE_ENDPOINT'),
|
||
api_key=self.kwargs.get('DOCUMENT_INTELLIGENCE_KEY'),
|
||
api_model=self.kwargs.get('DOCUMENT_INTELLIGENCE_MODEL'),
|
||
)
|
||
else:
|
||
loader = AzureAIDocumentIntelligenceLoader(
|
||
file_path=file_path,
|
||
api_endpoint=self.kwargs.get('DOCUMENT_INTELLIGENCE_ENDPOINT'),
|
||
azure_credential=DefaultAzureCredential(),
|
||
api_model=self.kwargs.get('DOCUMENT_INTELLIGENCE_MODEL'),
|
||
)
|
||
elif self.engine == 'mineru' and file_ext in self.kwargs.get('MINERU_FILE_EXTENSIONS', ['pdf']):
|
||
mineru_timeout = self.kwargs.get('MINERU_API_TIMEOUT', 300)
|
||
if mineru_timeout:
|
||
try:
|
||
mineru_timeout = int(mineru_timeout)
|
||
except ValueError:
|
||
mineru_timeout = 300
|
||
|
||
loader = MinerULoader(
|
||
file_path=file_path,
|
||
api_mode=self.kwargs.get('MINERU_API_MODE', 'local'),
|
||
api_url=self.kwargs.get('MINERU_API_URL', 'http://localhost:8000'),
|
||
api_key=self.kwargs.get('MINERU_API_KEY', ''),
|
||
params=self.kwargs.get('MINERU_PARAMS', {}),
|
||
timeout=mineru_timeout,
|
||
)
|
||
elif (
|
||
self.engine == 'mistral_ocr'
|
||
and self.kwargs.get('MISTRAL_OCR_API_KEY') != ''
|
||
and file_ext in ['pdf'] # Mistral OCR currently only supports PDF and images
|
||
):
|
||
loader = MistralLoader(
|
||
base_url=self.kwargs.get('MISTRAL_OCR_API_BASE_URL'),
|
||
api_key=self.kwargs.get('MISTRAL_OCR_API_KEY'),
|
||
file_path=file_path,
|
||
)
|
||
elif self.engine == 'paddleocr_vl' and self.kwargs.get('PADDLEOCR_VL_TOKEN') != '':
|
||
loader = PaddleOCRVLLoader(
|
||
api_url=self.kwargs.get('PADDLEOCR_VL_BASE_URL'),
|
||
token=self.kwargs.get('PADDLEOCR_VL_TOKEN'),
|
||
file_path=file_path,
|
||
)
|
||
else:
|
||
if file_ext == 'pdf':
|
||
loader = PyPDFLoader(
|
||
file_path,
|
||
extract_images=self.kwargs.get('PDF_EXTRACT_IMAGES'),
|
||
mode=self.kwargs.get('PDF_LOADER_MODE', 'page'),
|
||
)
|
||
elif file_ext == 'csv':
|
||
loader = CSVLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
elif file_ext == 'rst':
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredRSTLoader
|
||
|
||
loader = UnstructuredRSTLoader(file_path, mode='elements')
|
||
except ImportError:
|
||
log.warning(
|
||
"The 'unstructured' package is not installed. "
|
||
'Falling back to plain text loading for .rst file. '
|
||
'Install it with: pip install unstructured'
|
||
)
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
elif file_ext == 'xml':
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredXMLLoader
|
||
|
||
loader = UnstructuredXMLLoader(file_path)
|
||
except ImportError:
|
||
log.warning(
|
||
"The 'unstructured' package is not installed. "
|
||
'Falling back to plain text loading for .xml file. '
|
||
'Install it with: pip install unstructured'
|
||
)
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
elif file_ext in ['htm', 'html']:
|
||
loader = BSHTMLLoader(file_path, open_encoding='unicode_escape')
|
||
elif file_ext == 'md':
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
elif file_content_type == 'application/epub+zip':
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredEPubLoader
|
||
|
||
loader = UnstructuredEPubLoader(file_path)
|
||
except ImportError:
|
||
raise ValueError(
|
||
"Processing .epub files requires the 'unstructured' package. "
|
||
'Install it with: pip install unstructured'
|
||
)
|
||
elif (
|
||
file_content_type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
|
||
or file_ext == 'docx'
|
||
):
|
||
loader = Docx2txtLoader(file_path)
|
||
elif file_ext == 'doc' or file_content_type == 'application/msword':
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredWordDocumentLoader
|
||
|
||
loader = UnstructuredWordDocumentLoader(file_path)
|
||
except ImportError:
|
||
raise ValueError(
|
||
"Processing .doc files requires the 'unstructured' package. "
|
||
'Install it with: pip install unstructured'
|
||
)
|
||
elif file_content_type in [
|
||
'application/vnd.ms-excel',
|
||
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
|
||
] or file_ext in ['xls', 'xlsx']:
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredExcelLoader
|
||
|
||
loader = UnstructuredExcelLoader(file_path)
|
||
except ImportError:
|
||
log.warning(
|
||
"The 'unstructured' package is not installed. "
|
||
'Falling back to pandas for Excel file loading. '
|
||
'Install unstructured for better results: pip install unstructured'
|
||
)
|
||
loader = ExcelLoader(file_path)
|
||
elif file_content_type in [
|
||
'application/vnd.ms-powerpoint',
|
||
'application/vnd.openxmlformats-officedocument.presentationml.presentation',
|
||
] or file_ext in ['ppt', 'pptx']:
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredPowerPointLoader
|
||
|
||
loader = UnstructuredPowerPointLoader(file_path)
|
||
except ImportError:
|
||
log.warning(
|
||
"The 'unstructured' package is not installed. "
|
||
'Falling back to python-pptx for PowerPoint file loading. '
|
||
'Install unstructured for better results: pip install unstructured'
|
||
)
|
||
loader = PptxLoader(file_path)
|
||
elif file_ext == 'msg':
|
||
loader = OutlookMessageLoader(file_path)
|
||
elif file_ext == 'odt':
|
||
try:
|
||
from langchain_community.document_loaders import UnstructuredODTLoader
|
||
|
||
loader = UnstructuredODTLoader(file_path)
|
||
except ImportError:
|
||
raise ValueError(
|
||
"Processing .odt files requires the 'unstructured' package. "
|
||
'Install it with: pip install unstructured'
|
||
)
|
||
elif self._is_text_file(file_ext, file_content_type):
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
else:
|
||
loader = TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
|
||
|
||
return loader
|