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import os
from typing import List
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_core.documents.base import Document
from policy_rag.data_models import DocList
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
# Text Loading
class DocLoader:
docs: DocList = DocList([]).root
def load(self, path: str) -> List[Document]:
if path.endswith('.pdf'):
loader = PyMuPDFLoader(path)
self.docs.extend(loader.load())
else:
print(f'Skipping {path} - not PDF')
return self.docs
def load_dir(self, dir_path: str) -> List[Document]:
for doc_name in os.listdir(dir_path):
doc_path = os.path.join(dir_path, doc_name)
self.load(doc_path)
return self.docs
# Text Splitting
def get_recursive_token_chunks(
docs: List[Document],
model_name: str = 'gpt-4',
chunk_size: int = 150,
chunk_overlap: int = 0
) -> List[Document]:
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
model_name=model_name,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
return text_splitter.split_documents(docs)
def get_semantic_chunks(
docs: List[Document],
embedding_model: OpenAIEmbeddings,
breakpoint_type: str = 'gradient'
) -> List[Document]:
text_splitter = SemanticChunker(
embeddings=embedding_model,
breakpoint_threshold_type=breakpoint_type
)
return text_splitter.split_documents(docs) |