PythonicRAG / aimakerspace /text_utils.py
jeevan
updated
637aeec
# aimakerspace.text_utils.py
import os
from typing import List
from langchain_community.document_loaders import PyPDFLoader
class TextFileLoader:
def __init__(self, path: str, encoding: str = "utf-8"):
self.documents = []
self.path = path
self.encoding = encoding
def load(self):
if os.path.isdir(self.path):
self.load_directory()
elif os.path.isfile(self.path) and self.path.endswith(".txt"):
self.load_file()
else:
raise ValueError(
"Provided path is neither a valid directory nor a .txt file."
)
def load_file(self):
with open(self.path, "r", encoding=self.encoding) as f:
self.documents.append(f.read())
def load_directory(self):
for root, _, files in os.walk(self.path):
for file in files:
if file.endswith(".txt"):
with open(
os.path.join(root, file), "r", encoding=self.encoding
) as f:
self.documents.append(f.read())
def load_documents(self):
self.load()
return self.documents
class PdfFileLoader:
def __init__(self, path: str):
self.documents = []
self.path = path
def load(self):
if os.path.isdir(self.path):
self.load_directory()
elif self.path.endswith(".pdf"):
self.load_file()
else:
raise ValueError(
"Provided path is neither a valid directory nor a .pdf file."
)
def load_file(self):
pdf_loader = PyPDFLoader(self.path)
pdf_pages = pdf_loader.load_and_split() # Defaults to RecursiveCharacterTextSplitter.
self.documents = [page.page_content for page in pdf_pages]
def load_directory(self):
for root, _, files in os.walk(self.path):
for file in files:
if file.endswith(".pdf"):
pdf_loader = PyPDFLoader(file.path)
pdf_pages = pdf_loader.load_and_split()
self.documents.append([page.page_content for page in pdf_pages])
def load_documents(self):
self.load()
return self.documents
class CharacterTextSplitter():
def __init__(
self,
chunk_size: int = 1000,
chunk_overlap: int = 200,
):
assert (
chunk_size > chunk_overlap
), "Chunk size must be greater than chunk overlap"
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
def split(self, text: str) -> List[str]:
chunks = []
for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
chunks.append(text[i : i + self.chunk_size])
return chunks
def split_texts(self, texts: List[str]) -> List[str]:
chunks = []
for text in texts:
chunks.extend(self.split(text))
return chunks