DocsGPT/scripts/ingest.py

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import os
import sys
import nltk
import dotenv
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import typer
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import ast
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from collections import defaultdict
from pathlib import Path
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from typing import List, Optional
from langchain.text_splitter import RecursiveCharacterTextSplitter
from parser.file.bulk import SimpleDirectoryReader
from parser.schema.base import Document
from parser.open_ai_func import call_openai_api, get_user_permission
from parser.py2doc import transform_to_docs
from parser.py2doc import extract_functions_and_classes as extract_py
from parser.js2doc import extract_functions_and_classes as extract_js
from parser.java2doc import extract_functions_and_classes as extract_java
dotenv.load_dotenv()
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app = typer.Typer(add_completion=False)
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nltk.download('punkt', quiet=True)
nltk.download('averaged_perceptron_tagger', quiet=True)
#Splits all files in specified folder to documents
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@app.command()
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def ingest(yes: bool = typer.Option(False, "-y", "--yes", prompt=False,
help="Whether to skip price confirmation"),
dir: Optional[List[str]] = typer.Option(["inputs"],
help="""List of paths to directory for index creation.
E.g. --dir inputs --dir inputs2"""),
file: Optional[List[str]] = typer.Option(None,
help="""File paths to use (Optional; overrides dir).
E.g. --file inputs/1.md --file inputs/2.md"""),
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recursive: Optional[bool] = typer.Option(True,
help="Whether to recursively search in subdirectories."),
limit: Optional[int] = typer.Option(None,
help="Maximum number of files to read."),
formats: Optional[List[str]] = typer.Option([".rst", ".md"],
help="""List of required extensions (list with .)
Currently supported: .rst, .md, .pdf, .docx, .csv, .epub, .html"""),
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exclude: Optional[bool] = typer.Option(True, help="Whether to exclude hidden files (dotfiles).")):
"""
Creates index from specified location or files.
By default /inputs folder is used, .rst and .md are parsed.
"""
def process_one_docs(directory, folder_name):
raw_docs = SimpleDirectoryReader(input_dir=directory, input_files=file, recursive=recursive,
required_exts=formats, num_files_limit=limit,
exclude_hidden=exclude).load_data()
raw_docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
# Here we split the documents, as needed, into smaller chunks.
# We do this due to the context limits of the LLMs.
text_splitter = RecursiveCharacterTextSplitter()
docs = text_splitter.split_documents(raw_docs)
# Here we check for command line arguments for bot calls.
# If no argument exists or the yes is not True, then the
# user permission is requested to call the API.
if len(sys.argv) > 1:
if yes:
call_openai_api(docs, folder_name)
else:
get_user_permission(docs, folder_name)
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else:
get_user_permission(docs, folder_name)
folder_counts = defaultdict(int)
folder_names = []
for dir_path in dir:
folder_name = os.path.basename(os.path.normpath(dir_path))
folder_counts[folder_name] += 1
if folder_counts[folder_name] > 1:
folder_name = f"{folder_name}_{folder_counts[folder_name]}"
folder_names.append(folder_name)
for directory, folder_name in zip(dir, folder_names):
process_one_docs(directory, folder_name)
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@app.command()
def convert(dir: Optional[str] = typer.Option("inputs",
help="""Path to directory to make documentation for.
E.g. --dir inputs """),
formats: Optional[str] = typer.Option("py",
help="""Required language.
py, js, java supported for now""")):
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"""
Creates documentation linked to original functions from specified location.
By default /inputs folder is used, .py is parsed.
"""
if formats == 'py':
functions_dict, classes_dict = extract_py(dir)
elif formats == 'js':
functions_dict, classes_dict = extract_js(dir)
elif formats == 'java':
functions_dict, classes_dict = extract_java(dir)
else:
raise Exception("Sorry, language not supported yet")
transform_to_docs(functions_dict, classes_dict, formats, dir)
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if __name__ == "__main__":
app()