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#!/usr/bin/env python3
import os
import glob
from typing import List
from multiprocessing import Pool
from tqdm import tqdm
from langchain.document_loaders import (
CSVLoader,
EverNoteLoader,
PyMuPDFLoader,
TextLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.docstore.document import Document
from config import (
CHROMA_SETTINGS,
DOCUMENTS_PATH,
PERSIST_DIRECTORY,
CHUNK_SIZE,
CHUNK_OVERLAP,
)
# Map file extensions to document loaders and their arguments
LOADER_MAPPING = {
".csv": (CSVLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".pdf": (PyMuPDFLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
".txt": (TextLoader, {"encoding": "utf8"}),
}
def load_single_document(file_path: str) -> List[Document]:
print(file_path)
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()
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, docs in enumerate(
pool.imap_unordered(load_single_document, filtered_files)
):
results.extend(docs)
pbar.update()
return results
def process_documents(ignored_files: List[str] = []) -> List[Document]:
"""
Load documents and split in chunks
"""
print(f"Loading documents from {DOCUMENTS_PATH}")
documents = load_documents(DOCUMENTS_PATH, ignored_files)
if not documents:
print("No new documents to load")
return []
print(f"Loaded {len(documents)} new documents from {DOCUMENTS_PATH}")
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 create_vectorstore():
# Create embeddings
embeddings = OpenAIEmbeddings()
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"]]
)
if not texts:
return
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()
if not texts:
return
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!")
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