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c07fa76 5b9ca00 c07fa76 1e72b5a 314c1d9 1e72b5a c07fa76 2bc1685 5b9ca00 c07fa76 1e72b5a 7ca39a5 c07fa76 1e72b5a c07fa76 314c1d9 5b9ca00 c07fa76 1e72b5a c07fa76 1e72b5a c07fa76 5b9ca00 c07fa76 1e72b5a 5b9ca00 c07fa76 1e72b5a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import os
import tempfile
import requests
from fastapi import HTTPException
# from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader
from langchain_community.document_loaders import PyMuPDFLoader, Docx2txtLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_text_splitters.sentence_transformers import SentenceTransformersTokenTextSplitter # give better results but slow can use later for project
from langchain.schema import Document
MODEL_DIR = os.path.join("/tmp", "e5-large-v2")
def load_and_chunk(url: str) -> list[Document]:
print(url)
resp = requests.get(url)
if resp.status_code != 200:
raise HTTPException(400, "Could not download document")
content_type = resp.headers.get("Content-Type", "").lower()
url_lower = url.lower()
if "application/pdf" in content_type or ".pdf" in url_lower:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
tmp.write(resp.content)
tmp_path = tmp.name
try:
loader = PyMuPDFLoader(tmp_path)
docs = loader.load_and_split()
finally:
os.remove(tmp_path)
elif (
"application/vnd.openxmlformats-officedocument.wordprocessingml.document" in content_type
or ".docx" in url_lower
):
with tempfile.NamedTemporaryFile(delete=False, suffix=".docx") as tmp:
tmp.write(resp.content)
tmp_path = tmp.name
try:
loader = Docx2txtLoader(tmp_path)
docs = loader.load_and_split()
finally:
os.remove(tmp_path)
elif "text/plain" in content_type or ".txt" in url_lower:
text = resp.content.decode("utf-8", errors="ignore")
docs = [Document(page_content=text)]
else:
raise HTTPException(400, f"Unsupported document type: {content_type}")
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=120,separators=["\n\n", "\n", ".", " ", ""])
# splitter = SentenceTransformersTokenTextSplitter(model_name = MODEL_DIR,tokens_per_chunk=500, chunk_overlap=80)
return splitter.split_documents(docs)
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