File size: 2,152 Bytes
ee3a625 d380674 ee3a625 |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
# setting device on GPU if available, else CPU
import os
import sys
from timeit import default_timer as timer
from typing import List
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.chroma import Chroma
from langchain.vectorstores.faiss import FAISS
from app_modules.init import app_init, get_device_types
from app_modules.llm_summarize_chain import SummarizeChain
def load_documents(source_pdfs_path, urls) -> List:
loader = PyPDFDirectoryLoader(source_pdfs_path, silent_errors=True)
documents = loader.load()
if urls is not None and len(urls) > 0:
for doc in documents:
source = doc.metadata["source"]
filename = source.split("/")[-1]
for url in urls:
if url.endswith(filename):
doc.metadata["url"] = url
break
return documents
def split_chunks(documents: List, chunk_size, chunk_overlap) -> List:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
return text_splitter.split_documents(documents)
llm_loader = app_init(False)[0]
source_pdfs_path = (
sys.argv[1] if len(sys.argv) > 1 else os.environ.get("SOURCE_PDFS_PATH")
)
chunk_size = sys.argv[2] if len(sys.argv) > 2 else os.environ.get("CHUNCK_SIZE")
chunk_overlap = sys.argv[3] if len(sys.argv) > 3 else os.environ.get("CHUNK_OVERLAP")
sources = load_documents(source_pdfs_path, None)
print(f"Splitting {len(sources)} PDF pages in to chunks ...")
chunks = split_chunks(
sources, chunk_size=int(chunk_size), chunk_overlap=int(chunk_overlap)
)
print(f"Summarizing {len(chunks)} chunks ...")
start = timer()
summarize_chain = SummarizeChain(llm_loader)
result = summarize_chain.call_chain(
{"input_documents": chunks},
None,
None,
True,
)
end = timer()
print(f"Completed in {end - start:.3f}s")
print("\n\n***Summary:")
print(result["output_text"])
|