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