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ingest.py
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ingest.py
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import logging
import os
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
import click
import torch
from langchain.docstore.document import Document
from langchain.text_splitter import Language, RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from utils import get_embeddings
from constants import (
CHROMA_SETTINGS,
DOCUMENT_MAP,
EMBEDDING_MODEL_NAME,
INGEST_THREADS,
PERSIST_DIRECTORY,
SOURCE_DIRECTORY,
)
def file_log(logentry):
file1 = open("file_ingest.log", "a")
file1.write(logentry + "\n")
file1.close()
print(logentry + "\n")
def load_single_document(file_path: str) -> Document:
# Loads a single document from a file path
try:
file_extension = os.path.splitext(file_path)[1]
loader_class = DOCUMENT_MAP.get(file_extension)
if loader_class:
file_log(file_path + " loaded.")
loader = loader_class(file_path)
else:
file_log(file_path + " document type is undefined.")
raise ValueError("Document type is undefined")
return loader.load()[0]
except Exception as ex:
file_log("%s loading error: \n%s" % (file_path, ex))
return None
def load_document_batch(filepaths):
logging.info("Loading document batch")
# create a thread pool
with ThreadPoolExecutor(len(filepaths)) as exe:
# load files
futures = [exe.submit(load_single_document, name) for name in filepaths]
# collect data
if futures is None:
file_log(name + " failed to submit")
return None
else:
data_list = [future.result() for future in futures]
# return data and file paths
return (data_list, filepaths)
def load_documents(source_dir: str) -> list[Document]:
# Loads all documents from the source documents directory, including nested folders
paths = []
for root, _, files in os.walk(source_dir):
for file_name in files:
print("Importing: " + file_name)
file_extension = os.path.splitext(file_name)[1]
source_file_path = os.path.join(root, file_name)
if file_extension in DOCUMENT_MAP.keys():
paths.append(source_file_path)
# Have at least one worker and at most INGEST_THREADS workers
n_workers = min(INGEST_THREADS, max(len(paths), 1))
chunksize = round(len(paths) / n_workers)
docs = []
with ProcessPoolExecutor(n_workers) as executor:
futures = []
# split the load operations into chunks
for i in range(0, len(paths), chunksize):
# select a chunk of filenames
filepaths = paths[i : (i + chunksize)]
# submit the task
try:
future = executor.submit(load_document_batch, filepaths)
except Exception as ex:
file_log("executor task failed: %s" % (ex))
future = None
if future is not None:
futures.append(future)
# process all results
for future in as_completed(futures):
# open the file and load the data
try:
contents, _ = future.result()
docs.extend(contents)
except Exception as ex:
file_log("Exception: %s" % (ex))
return docs
def split_documents(documents: list[Document]) -> tuple[list[Document], list[Document]]:
# Splits documents for correct Text Splitter
text_docs, python_docs = [], []
for doc in documents:
if doc is not None:
file_extension = os.path.splitext(doc.metadata["source"])[1]
if file_extension == ".py":
python_docs.append(doc)
else:
text_docs.append(doc)
return text_docs, python_docs
@click.command()
@click.option(
"--device_type",
default="cuda" if torch.cuda.is_available() else "cpu",
type=click.Choice(
[
"cpu",
"cuda",
"ipu",
"xpu",
"mkldnn",
"opengl",
"opencl",
"ideep",
"hip",
"ve",
"fpga",
"ort",
"xla",
"lazy",
"vulkan",
"mps",
"meta",
"hpu",
"mtia",
],
),
help="Device to run on. (Default is cuda)",
)
def main(device_type):
# Load documents and split in chunks
logging.info(f"Loading documents from {SOURCE_DIRECTORY}")
documents = load_documents(SOURCE_DIRECTORY)
text_documents, python_documents = split_documents(documents)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
python_splitter = RecursiveCharacterTextSplitter.from_language(
language=Language.PYTHON, chunk_size=880, chunk_overlap=200
)
texts = text_splitter.split_documents(text_documents)
texts.extend(python_splitter.split_documents(python_documents))
logging.info(f"Loaded {len(documents)} documents from {SOURCE_DIRECTORY}")
logging.info(f"Split into {len(texts)} chunks of text")
"""
(1) Chooses an appropriate langchain library based on the enbedding model name. Matching code is contained within fun_localGPT.py.
(2) Provides additional arguments for instructor and BGE models to improve results, pursuant to the instructions contained on
their respective huggingface repository, project page or github repository.
"""
embeddings = get_embeddings(device_type)
logging.info(f"Loaded embeddings from {EMBEDDING_MODEL_NAME}")
db = Chroma.from_documents(
texts,
embeddings,
persist_directory=PERSIST_DIRECTORY,
client_settings=CHROMA_SETTINGS,
)
if __name__ == "__main__":
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s", level=logging.INFO
)
main()