mirror of
https://github.com/arc53/DocsGPT
synced 2024-11-17 21:26:26 +00:00
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
import pickle
|
|
import sys
|
|
from argparse import ArgumentParser
|
|
from pathlib import Path
|
|
|
|
import dotenv
|
|
import faiss
|
|
import tiktoken
|
|
from langchain.embeddings import OpenAIEmbeddings
|
|
from langchain.text_splitter import CharacterTextSplitter
|
|
from langchain.vectorstores import FAISS
|
|
|
|
|
|
def num_tokens_from_string(string: str, encoding_name: str) -> int:
|
|
# Function to convert string to tokens and estimate user cost.
|
|
encoding = tiktoken.get_encoding(encoding_name)
|
|
num_tokens = len(encoding.encode(string))
|
|
total_price = ((num_tokens / 1000) * 0.0004)
|
|
return num_tokens, total_price
|
|
|
|
|
|
def call_openai_api():
|
|
# Function to create a vector store from the documents and save it to disk.
|
|
store = FAISS.from_texts(docs, OpenAIEmbeddings(), metadatas=metadatas)
|
|
faiss.write_index(store.index, "docs.index")
|
|
store.index = None
|
|
with open("faiss_store.pkl", "wb") as f:
|
|
pickle.dump(store, f)
|
|
|
|
|
|
def get_user_permission():
|
|
# Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
|
|
# Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
|
|
docs_content = (" ".join(docs))
|
|
tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
|
|
# Here we print the number of tokens and the approx user cost with some visually appealing formatting.
|
|
print(f"Number of Tokens = {format(tokens, ',d')}")
|
|
print(f"Approx Cost = ${format(total_price, ',.2f')}")
|
|
# Here we check for user permission before calling the API.
|
|
user_input = input("Price Okay? (Y/N) \n").lower()
|
|
if user_input == "y":
|
|
call_openai_api()
|
|
elif user_input == "":
|
|
call_openai_api()
|
|
else:
|
|
print("The API was not called. No money was spent.")
|
|
|
|
|
|
# Load .env file
|
|
dotenv.load_dotenv()
|
|
|
|
ap = ArgumentParser("Script for training DocsGPT on .rst documentation files.")
|
|
ap.add_argument("-i", "--inputs",
|
|
type=str,
|
|
default="inputs",
|
|
help="Directory containing documentation files")
|
|
args = ap.parse_args()
|
|
|
|
# Here we load in the data in the format that Notion exports it in.
|
|
ps = list(Path(args.inputs).glob("**/*.rst"))
|
|
|
|
# parse all child directories
|
|
data = []
|
|
sources = []
|
|
for p in ps:
|
|
with open(p) as f:
|
|
data.append(f.read())
|
|
sources.append(p)
|
|
|
|
# Here we split the documents, as needed, into smaller chunks.
|
|
# We do this due to the context limits of the LLMs.
|
|
text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n")
|
|
docs = []
|
|
metadatas = []
|
|
for i, d in enumerate(data):
|
|
splits = text_splitter.split_text(d)
|
|
docs.extend(splits)
|
|
metadatas.extend([{"source": sources[i]}] * len(splits))
|
|
|
|
# Here we check for command line arguments for bot calls.
|
|
# If no argument exists or the permission_bypass_flag argument is not '-y',
|
|
# user permission is requested to call the API.
|
|
if len(sys.argv) > 1:
|
|
permission_bypass_flag = sys.argv[1]
|
|
if permission_bypass_flag == '-y':
|
|
call_openai_api()
|
|
else:
|
|
get_user_permission()
|
|
else:
|
|
get_user_permission()
|