HF_Deploy_RAG / aimakerspace /text_utils.py
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import os
from typing import List
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 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
if __name__ == "__main__":
loader = TextFileLoader("data/KingLear.txt")
loader.load()
splitter = CharacterTextSplitter()
chunks = splitter.split_texts(loader.documents)
print(len(chunks))
print(chunks[0])
print("--------")
print(chunks[1])
print("--------")
print(chunks[-2])
print("--------")
print(chunks[-1])