basicpytorch / app.py
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Adding injest code to app.py
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#!/usr/bin/env python3
import os
import glob
from typing import List
from dotenv import load_dotenv
from multiprocessing import Pool
from tqdm import tqdm
from langchain.document_loaders import (
CSVLoader,
EverNoteLoader,
PDFMinerLoader,
TextLoader,
UnstructuredEmailLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from constants import CHROMA_SETTINGS
load_dotenv()
# Load environment variables
persist_directory = os.environ.get('PERSIST_DIRECTORY')
source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
chunk_size = 500
chunk_overlap = 50
# Custom document loaders
class MyElmLoader(UnstructuredEmailLoader):
"""Wrapper to fallback to text/plain when default does not work"""
def load(self) -> List[Document]:
"""Wrapper adding fallback for elm without html"""
try:
try:
doc = UnstructuredEmailLoader.load(self)
except ValueError as e:
if 'text/html content not found in email' in str(e):
# Try plain text
self.unstructured_kwargs["content_source"]="text/plain"
doc = UnstructuredEmailLoader.load(self)
else:
raise
except Exception as e:
# Add file_path to exception message
raise type(e)(f"{self.file_path}: {e}") from e
return doc
# Map file extensions to document loaders and their arguments
LOADER_MAPPING = {
".csv": (CSVLoader, {}),
# ".docx": (Docx2txtLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".eml": (MyElmLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".pdf": (PDFMinerLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
".txt": (TextLoader, {"encoding": "utf8"}),
# Add more mappings for other file extensions and loaders as needed
}
def load_single_document(file_path: str) -> Document:
ext = "." + file_path.rsplit(".", 1)[-1]
if ext in LOADER_MAPPING:
loader_class, loader_args = LOADER_MAPPING[ext]
loader = loader_class(file_path, **loader_args)
return loader.load()[0]
raise ValueError(f"Unsupported file extension '{ext}'")
def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
"""
Loads all documents from the source documents directory, ignoring specified files
"""
all_files = []
for ext in LOADER_MAPPING:
all_files.extend(
glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
)
filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
with Pool(processes=os.cpu_count()) as pool:
results = []
with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
for i, doc in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
results.append(doc)
pbar.update()
return results
def process_documents(ignored_files: List[str] = []) -> List[Document]:
"""
Load documents and split in chunks
"""
print(f"Loading documents from {source_directory}")
documents = load_documents(source_directory, ignored_files)
if not documents:
print("No new documents to load")
exit(0)
print(f"Loaded {len(documents)} new documents from {source_directory}")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
texts = text_splitter.split_documents(documents)
print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
return texts
def does_vectorstore_exist(persist_directory: str) -> bool:
"""
Checks if vectorstore exists
"""
if os.path.exists(os.path.join(persist_directory, 'index')):
if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
# At least 3 documents are needed in a working vectorstore
if len(list_index_files) > 3:
return True
return False
def main():
# Create embeddings
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
if does_vectorstore_exist(persist_directory):
# Update and store locally vectorstore
print(f"Appending to existing vectorstore at {persist_directory}")
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
collection = db.get()
texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
print(f"Creating embeddings. May take some minutes...")
db.add_documents(texts)
else:
# Create and store locally vectorstore
print("Creating new vectorstore")
texts = process_documents()
print(f"Creating embeddings. May take some minutes...")
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
db.persist()
db = None
print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
if __name__ == "__main__":
main()
import gradio as gr
import torch
import os
def greet(name):
return torch.rand(5)
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
if 'HUGGINGFACE_SPACE_ID' in os.environ:
# Running in Hugging Face Space
gradio_share = False
else:
# Running on desktop
gradio_share = True
iface.launch(share=gradio_share)