import os import sys import nltk import dotenv import typer import ast from collections import defaultdict from pathlib import Path 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 get_classes, get_functions, transform_to_docs dotenv.load_dotenv() app = typer.Typer(add_completion=False) nltk.download('punkt', quiet=True) nltk.download('averaged_perceptron_tagger', quiet=True) #Splits all files in specified folder to documents @app.command() 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"""), 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"""), 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) 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) @app.command() def convert(): ps = list(Path("inputs").glob("**/*.py")) data = [] sources = [] for p in ps: with open(p) as f: data.append(f.read()) sources.append(p) functions_dict = {} classes_dict = {} c1 = 0 for code in data: functions = get_functions(ast.parse(code)) source = str(sources[c1]) functions_dict[source] = functions classes = get_classes(code) classes_dict[source] = classes c1 += 1 transform_to_docs(functions_dict, classes_dict) if __name__ == "__main__": app()