date_collected stringclasses 1
value | repo_name stringlengths 6 116 | file_name stringlengths 2 220 | file_contents stringlengths 13 357k | prompts list |
|---|---|---|---|---|
2024-01-10 | SBrandeis/transformers | src~transformers~models~openai~modeling_tf_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License... | [] |
2024-01-10 | Lucete28/TradeTrend | TT_runfile~update_naver_raw.py | from airflow.models.variable import Variable
import openai
import pandas as pd
openai.api_key = Variable.get("gpt_api_key")
Target_list = Variable.get("Target_list")
values = [tuple(item.strip("()").split(",")) for item in Target_list.split("),")]
values = [(x[0].strip(), x[1].strip()) for x in values]
err_report =... | [
"PLACEHOLDER PLACEHOLDER ๊ด๋ จ ๋ด์ค๊ธฐ์ฌ ์ ๋ชฉ์ธ๋ฐ PLACEHOLDER ์ฃผ์์ ๋ฏธ์น ๊ธ์ ๋์ ํ๊ท ์ 0์์ 1์ฌ์ด ์์ซ์ ๋์๋ฆฌ๊น์ง ๋ํ๋ด float๊ฐ๋ง"
] |
2024-01-10 | LilithHafner/ai | integrated_ai.py | import openai
openai.api_key = "sk-..."
# GPT AI
def ai(prompt):
response = openai.Completion.create(
engine="code-davinci-002",
prompt=prompt,
temperature=0,
max_tokens=256,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop="<end>"
)
return response.cho... | [
"This is a question and answer bot that has oracles to various external tools including python, google, and others\n\n<user input>what time is it<end>\n<pyhton eval>time.ctime()<end>\n<python eval result>Traceback (most recent call last):\n File \"/Users/x/Documents/integrated_ai.py\", line 26, in python\n retu... |
2024-01-10 | Kororinpas/Lit_Tool | document_util.py | def get_split_documents(docs, chunk_size, chunk_overlap):
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,chunk_overlap=chunk_overlap)
return text_splitter.split_documents(docs) | [] |
2024-01-10 | Kororinpas/Lit_Tool | literature_test.py | import streamlit as st
import sys
class StreamlitWriter:
def write(self, text):
st.write(text.strip())
### This the function about streamlit
def Vector_Databse():
st.write("Vector Database")
choose = st.radio("Choose using an existing database or upload a new one.",
["Using ... | [
"\n Given the document and query, find PLACEHOLDER sentences in the document that are most similar in meaning to the query. \n Return the sentences, the meta source of the sentences and the cosine similarity scores. \n If no similar sentences is found, return the sentence with highest cosine siliarity scor... |
2024-01-10 | Kororinpas/Lit_Tool | pdf_retrieval.py | from operator import itemgetter
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.document_loaders import DataFrameLoader, PyMuPDFLoader
import os
import f... | [
"sample_introes",
"words_limit",
"format_instructions",
"\n I have extracted text from the initial pages of a Journal of Economic Literature (JEL) PDF file. I require assistance in extracting \n specific details, namely: article title, author, abstract and keywords section. Pleas... |
2024-01-10 | Kororinpas/Lit_Tool | pdf_documents.py | from pdf_metadata import get_pdf_metadata
from pdf_metadata_llm import get_pdf_metadata_using_llm
def get_pdf_documents(pdf_files):
from langchain.document_loaders import PyMuPDFLoader,DirectoryLoader,UnstructuredPDFLoader
docs =[]
import re
for pdf_fullpath in pdf_files:
metadata = get_pdf_metadata(pdf... | [] |
2024-01-10 | Kororinpas/Lit_Tool | pdf_metadata_llm.py | from doi import get_doi
from document_util import get_split_documents
def get_pdf_metadata_using_llm(doc):
import re
doc[0].page_content = re.sub('\n+',' ',doc[0].page_content.strip())
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# text_splitter = RecursiveCharacterTextSplitter(... | [] |
2024-01-10 | Kororinpas/Lit_Tool | cosine_match.py | def search_cosine_similarity(query,split_docs,embeddings): ##query-str,split_docs-list,embeddings-embeddings()
split_docs_content = [content['content'] for content in split_docs]
embed_docs = embeddings.embed_documents(split_docs_content)
embed_query= embeddings.embed_query(query)
from openai.embe... | [] |
2024-01-10 | Kororinpas/Lit_Tool | embedding_function.py | def get_embedding_function():
from langchain.embeddings import HuggingFaceEmbeddings
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device':device}
return HuggingFaceEmbeddings(model_name=model_name... | [] |
2024-01-10 | Kororinpas/Lit_Tool | doi.py | def get_doi(abstract):
from kor.extraction import create_extraction_chain
from kor.nodes import Object, Text, Number
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) # type: ignore
schema = Object(
id="doi",
description="doi is a digital ide... | [] |
2024-01-10 | jied-O/Jids-Garage | langchainagentstest.py | from langchain import OpenAI
from langchain.chains import LLMChain
from langchain.chains import PALChain
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.agents import load_tools
from ogbujipt.config import openai_emulation
from ogbujipt.model_style.alpaca import prep... | [
"What is the capital of {place}?"
] |
2024-01-10 | tarunsamanta2k20/quivr | backend~parsers~audio.py | import os
import tempfile
import time
from io import BytesIO
from tempfile import NamedTemporaryFile
import openai
from fastapi import UploadFile
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import Document
from langchain.text_splitter... | [] |
2024-01-10 | sshh12/llm_optimize | llm_optimize~optimize.py | from typing import Callable, Optional, Tuple, List
import re
import openai
from langchain.input import print_text
from langchain.prompts.chat import (
SystemMessage,
HumanMessage,
AIMessage,
)
from llm_optimize import llm, constants
# The numeric score and the LLM-facing representation
ScoreTuple = Tuple... | [] |
2024-01-10 | xiahan4956/Auto_Claude_100k | autogpt~llm~api_manager.py | from __future__ import annotations
from typing import List, Optional
import openai
from openai import Model
from autogpt.config import Config
from autogpt.llm.base import CompletionModelInfo, MessageDict
from autogpt.llm.providers.openai import OPEN_AI_MODELS
from autogpt.logs import logger
from autogpt.singleton im... | [] |
2024-01-10 | xiahan4956/Auto_Claude_100k | autogpt~llm~utils~claude.py | from autogpt.config import Config
import time
import openai
import json
CFG = Config()
openai.api_key = CFG.openai_api_key
MAX_TOKEN_ONCE = 100000
CONTINUE_PROMPT = "... continue"
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
def _sendReq(anthropic, prompt, max_tokens_to_sample):
print("----------... | [
"f\"{question} {anthropic.AI_PROMPT}",
"1. You will receive a JSON string, and your task is to extract information from it and return it as a JSON object. 2.Use function's json schema to extrct.Please notice the format 3. Be aware that the given JSON may contain errors, so you may need to infer the fields and t... |
2024-01-10 | pkrack/asp | asp~ppo_patched.py | import warnings
from typing import Any, Dict, Optional, Type, TypeVar, Union
import numpy as np
import torch as th
from gymnasium import spaces
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, M... | [] |
2024-01-10 | jongio/chat-with-your-data-solution-accelerator | backend~utilities~orchestrator~Strategies.py | from enum import Enum
class OrchestrationStrategy(Enum):
OPENAI_FUNCTION = 'openai_function'
LANGCHAIN = 'langchain'
def get_orchestrator(orchestration_strategy: str):
if orchestration_strategy == OrchestrationStrategy.OPENAI_FUNCTION.value:
from .OpenAIFunctions import OpenAIFunctionsOrchestrator... | [] |
2024-01-10 | jongio/chat-with-your-data-solution-accelerator | backend~utilities~document_chunking~Layout.py | from typing import List
from .DocumentChunkingBase import DocumentChunkingBase
from langchain.text_splitter import MarkdownTextSplitter
from .Strategies import ChunkingSettings
from ..common.SourceDocument import SourceDocument
class LayoutDocumentChunking(DocumentChunkingBase):
def __init__(self) -> None:
... | [] |
2024-01-10 | jongio/chat-with-your-data-solution-accelerator | backend~utilities~helpers~LLMHelper.py | import openai
from typing import List
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from .EnvHelper import EnvHelper
class LLMHelper:
def __init__(self):
env_helper: EnvHelp... | [] |
2024-01-10 | pcc2k00/HousingPriceTrend | HousingPriceTrendMetaphor.py | import openai
import yaml
from metaphor_python import Metaphor
with open("pass.yml") as f:
content = f.read()
my_credentials = yaml.load(content, Loader=yaml.FullLoader)
openai.api_key = my_credentials["openAi"]
metaphor = Metaphor(my_credentials["metaphor"])
USER_QUESTION = "Recent housing price in Seattle"
... | [] |
2024-01-10 | romain-cambonie/openxcom-mod-generator | src~chat~ask_for_visual_proposition.py | from openai import OpenAI
from openai.types.chat import ChatCompletion
def ask_for_concept_art(
client: OpenAI,
character_story: str,
art_style_description: str,
) -> str:
system_prompt = (
"Generate a comprehensive and vivid visual concept art of a character for a piece of artwork. "
... | [
"Generate a comprehensive and vivid visual concept art of a character for a piece of artwork. The character should fit within a distinct theme and style, and the description must be detailed enough to guide an artist in creating a dynamic and engaging image.Here are the guidelines for your description:Theme and Set... |
2024-01-10 | romain-cambonie/openxcom-mod-generator | src~dalle~call_dalle_and_save_image.py | import requests
from openai import OpenAI
from pathlib import Path
from typing import Optional
from openai.types import ImagesResponse
def call_dalle_and_save_image(prompt: str, client: OpenAI, output_file_path: Path) -> Optional[Path]:
try:
# Generate image using OpenAI client
response: ImagesRe... | [] |
2024-01-10 | romain-cambonie/openxcom-mod-generator | src~chat~ask_for_dalle_character_prompt.py | from openai import OpenAI
from openai.types.chat import ChatCompletion
def ask_for_dalle_character_prompt(
client: OpenAI,
concept_art_description: str,
) -> str:
system_prompt = (
"You're given a detailed concept art description of a character. Your task is to condense this description into a "
... | [
"Transform the above concept art description into a succinct DALL-E prompt.",
"You're given a detailed concept art description of a character. Your task is to condense this description into a succinct, vivid DALL-E prompt.The DALL-E prompt should accurately capture the key visual elements and artistic style descr... |
2024-01-10 | romain-cambonie/openxcom-mod-generator | src~chat~ask_for_origin_story.py | from openai import OpenAI
from openai.types.chat import ChatCompletion
def ask_for_origin_story(
client: OpenAI,
character_name: str,
equipment_description: str,
appearance_description: str,
) -> str:
system_prompt = (
"You are tasked with creating a short origin story for a fictional char... | [
"You are tasked with creating a short origin story for a fictional character. You will receive three key pieces of information: (1) the character's name, (2) a YAML payload detailing the character's equipment, and (3) an image that shows some characteristics of the character's appearance. Your job is to weave these... |
2024-01-10 | outlines-dev/outlines | outlines~models~__init__.py | """Module that contains all the models integrated in outlines.
We group the models in submodules by provider instead of theme (completion, chat
completion, diffusers, etc.) and use routing functions everywhere else in the
codebase.
"""
from .awq import awq
from .exllamav2 import exl2
from .gptq import gptq
from .llam... | [] |
2024-01-10 | ball2004244/Pinecone-Hackathon-23-Backend | logic~pinecone_db.py | '''
This file contains the logic for storing and querying data from Pinecone.
'''
from typing import List
from langchain.vectorstores import Pinecone
from langchain.chains.summarize import load_summarize_chain
from langchain.llms import GooglePalm
from langchain.embeddings.google_palm import GooglePalmEmbeddings
from l... | [] |
2024-01-10 | TheoKanning/crossword | crossword~clues.py | import json
import os
import openai
def convert_raw_clues(raw_filename, output_filename):
"""
Reads raw clue info from raw_filename, formats it to match GPT-3's fine-tune input, and writes it to output_filename
Raw clues are formatted like "Up in the air : ALOFT"
"""
with open(output_filename, ... | [
"f\"Answer: {answer.lower()}\\nClue:"
] |
2024-01-10 | NusretOzates/langchain_retrieval_qa_bot | data_loaders.py | import re
from itertools import chain
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders import PyPDFLoader, TextLoader, UnstructuredURLLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import RecursiveCharacterTextSplitter
f... | [] |
2024-01-10 | Antrozhuk/telegramChatGPTBot | src~telegram_bot.py | import telegram.constants as constants
from telegram import Update
from telegram.ext import ApplicationBuilder, ContextTypes, CommandHandler, MessageHandler, filters
from src.openai_helper import OpenAIHelper
from src.logger import Logger
class ChatGPT3TelegramBot:
"""
Class representing a Chat-GPT3 Telegram... | [] |
2024-01-10 | aws-samples/aurora-postgresql-pgvector | DAT303~02_QuestionAndAnswering~rag_app.py | # Import libraries
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.vectorstores.pgvector import PGVector
from langchain.memory import ConversationSummaryBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css
from langchain.text_splitter import Rec... | [] |
2024-01-10 | WuQingYi20/InteractiveStory | wsgi.py | from flask import Flask, render_template, jsonify, request
import openai
import re
from prompts import prompts
from dotenv import load_dotenv
import os
# Load the .env file
load_dotenv()
app = Flask(__name__)
initialCall = True
currentDescription = ""
# Initialize OpenAI API with your API key
openai.api_key = os.g... | [
"\n",
"PLACEHOLDER PLACEHOLDER",
"PLACEHOLDER",
"originalStory + \"\\n\" + prompts['next-page']['story']",
"next-page",
"originalStory + \"\\n\" + prompts['next-page']['summary']",
"originalStory + response_story.choices[0].message['content'] + \"\\n\" + prompts['next-page']['choices']",
"content",
... |
2024-01-10 | yamdereneko/ymbot | src~chatGPT~Chat_GPT_API.py | # -*- coding: utf-8 -*-
import asyncio
import nonebot
from pydantic import BaseModel
from httpx import AsyncClient
import src.Data.jx3_Redis as redis
import openai
class Response(BaseModel):
"""่ฟๅๆฐๆฎๆจกๅ"""
id: str
"""็ถๆ็ """
object: str
created: int
model: str
choices: list
"""่ฟๅๆถๆฏๅญ็ฌฆไธฒ""... | [] |
2024-01-10 | kaistAI/SelFee | data_augmentation~call_openai_multiprocessing_sharegpt.py | from concurrent.futures import ProcessPoolExecutor
import argparse
import multiprocessing
import openai
from time import sleep
from random import random
import nltk
nltk.download('punkt')
from nltk import tokenize
import json
import fcntl
from typing import List
import os
from tenacity import (
retry,
sto... | [
"Revise the answer based on your own critique within 500 words. Your revision should be simple and clear, so do not add any rhetorics such as apology for the past mistake. Write as if the revised answer is the first try.\nRevision:",
"PLACEHOLDER",
"Revise the answer based on your own critique within 500 words.... |
2024-01-10 | kaistAI/SelFee | evaluation~gpt4_automatic_evaluation.py | """This code is sourced from 4960ca7 commit of https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/eval_gpt_review.py"""
import argparse
import json
import os
import time
import openai
import tqdm
import ray
import shortuuid
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__n... | [
"prompt_template",
"system_prompt"
] |
2024-01-10 | kaistAI/SelFee | data_augmentation~call_openai_multiprocessing_flan.py | from concurrent.futures import ProcessPoolExecutor
import argparse
import multiprocessing
import openai
from time import sleep
from random import random
import nltk
nltk.download('punkt')
from nltk import tokenize
import json
import fcntl
from typing import List
import os
from tenacity import (
retry,
sto... | [
"Here is the answer:\nPLACEHOLDER\n",
"Revise the answer based on your own critique with minimal edits. Your revision should be simple and clear, so do not add any rhetorics such as apology for the past mistake. Write as if the revised answer is the first try.\nRevision:",
"Here is a revised proposed answer:\nP... |
2024-01-10 | kaistAI/SelFee | data_augmentation~call_openai_multiprocessing_alpaca.py | from concurrent.futures import ProcessPoolExecutor
import argparse
import multiprocessing
import openai
from time import sleep
from random import random
import nltk
nltk.download('punkt')
from nltk import tokenize
import json
import fcntl
from typing import List
import os
from tenacity import (
retry,
sto... | [
"Revise the answer based on your own critique within 500 words. Your revision should be simple and clear, so do not add any rhetorics such as apology for the past mistake. Write as if the revised answer is the first try.\nRevision:",
"PLACEHOLDER",
"PLACEHOLDERHere is a proposed answer:\nPLACEHOLDER\n\nAre ther... |
2024-01-10 | Neelesh99/KnowledgeSpaces | LLMServer~construct_index.py | import os
from llama_index import VectorStoreIndex, LLMPredictor, PromptHelper, Document, \
StringIterableReader, SlackReader, LangchainEmbedding, ServiceContext
from langchain import HuggingFaceHub, HuggingFacePipeline
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbedd... | [] |
2024-01-10 | MikeRock51/african_cuisines_recipe_api | models~chat~chatProvider.py | #!/usr/bin/env python3
from openai import OpenAI
import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI key missing")
client = OpenAI(api_key=api_key)
def getChatResponse(chatHistory):
try:
completion = client.chat.comp... | [] |
2024-01-10 | MikeRock51/african_cuisines_recipe_api | chatbot~yishu_cli.py | #!/usr/bin/env python3
from openai import OpenAI
from termcolor import colored
import os
# Load environment variables from a .env file
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI key missing")
client = OpenAI(api_key=api_key)
def m... | [
"Your name is Yishu. You are a food and nutrition specialist bot. You provide expert assistance on all matters related to food, nutrition and health"
] |
2024-01-10 | goldenNormal/meeting-summary | utils_llm_models.py | from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import (
HumanMessage,
AIMessage,
SystemMessage
)
import time
import os
OPENAI_API_KEY,API_BASE = os.getenv('OPENAI_API_KEY'),os.getenv('API_BASE')
from langchain.chat_models import ChatOpenAI
from jinja2 i... | [] |
2024-01-10 | jhmatthews/alpro | alpro~models.py | import numpy as np
from scipy.interpolate import interp1d
import types
import os
from scipy.integrate import simps
class units:
'''
class containing some units. Should probably use astropy units
but I find them a bit annoying.
'''
def __init__(self):
self.kpc = 3.0857e21
self.pc =... | [] |
2024-01-10 | gpt4plugins/autogen | test~test_code.py | import sys
import os
import pytest
import autogen
from autogen.code_utils import (
UNKNOWN,
extract_code,
execute_code,
infer_lang,
improve_code,
improve_function,
)
KEY_LOC = "notebook"
OAI_CONFIG_LIST = "OAI_CONFIG_LIST"
here = os.path.abspath(os.path.dirname(__file__))
# def test_find_code... | [] |
2024-01-10 | Sunbird-VA/sakhi_api_service | jadupitara_ingest_data.py | import requests
import json
import os.path
import openai
from gpt_index import SimpleDirectoryReader
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import Recur... | [
"['Collection']",
"contentType"
] |
2024-01-10 | Sunbird-VA/sakhi_api_service | query_with_langchain.py | import logging
import openai
from gpt_index import SimpleDirectoryReader
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
fr... | [
"question",
"[]",
"[PLACEHOLDER, PLACEHOLDER]",
"\n Write the same question as user input and make it more descriptive without adding new information and without making the facts incorrect.\n\n User: {question}\n Rephrased User input:"
] |
2024-01-10 | BoChenGroup/PyDPM | pydpm~metric~topic_coherence.py | #!/usr/bin/python3
# -*- coding: utf-8 -*-
# Author: Xinyang Liu <lxy771258012@163.com>
# License: BSD-3-Clause
import numpy as np
from gensim.test.utils import common_corpus, common_dictionary
from gensim.models.coherencemodel import CoherenceModel
"""
Examples
---------
One way of using this feature is through prov... | [] |
2024-01-10 | dlt-hub/qdrant_dlt_rag | unstructured_to_qdrant.py | import getpass
import os
from dotenv import load_dotenv
load_dotenv()
OPENAI_API_KEY= os.getenv("OPENAI_API_KEY")
import os
import logging
from typing import Dict, List
# Configure logging
logging.basicConfig(level=logging.INFO)
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai impo... | [] |
2024-01-10 | dlt-hub/qdrant_dlt_rag | ragas_custom.py | """An implementation of the Ragas metric
"""
from deepeval.metrics import BaseMetric
from deepeval.test_case import LLMTestCase
import warnings
class ContextualPrecisionMetric(BaseMetric):
"""This metric checks the contextual precision using Ragas"""
def __init__(
self,
minimum_score: float =... | [] |
2024-01-10 | dlt-hub/qdrant_dlt_rag | evals.py | import logging
from typing import Dict, Tuple, Optional
from openai import OpenAI
from ragas_custom import RagasMetric
from deepeval.test_case import LLMTestCase
from dotenv import load_dotenv
load_dotenv()
import os
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=OPENAI_API_KEY)
from qdrant_cl... | [
"You are a helpful assistant."
] |
2024-01-10 | nik-55/learning-ml | ml-3~pdf-project~answer.py | from langchain.prompts import PromptTemplate
from pypdf import PdfReader
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import LLMChain, ConversationalRetrievalChain
from langchain.text_splitter import Chara... | [
"Give the answer to the question: {question} based on the following text: {content}"
] |
2024-01-10 | AlekHesa/Function_Call | db_sampling.py | import openai
import os
import requests
from tenacity import retry,wait_random_exponential,stop_after_attempt
from termcolor import colored
from dotenv import dotenv_values
import sqlite3
GPT_MODEL = "gpt-3.5-turbo-0613"
config = dotenv_values(".env")
openai.api_key= config['OPENAI_API_KEY']
@retry(wait=wait_rando... | [
"Query: PLACEHOLDER\n the previous query received the error PLACEHOLDER.\n Please return a fixed SQL query in plain text.\n Your response should consist of only the sql query with the separator sql_start at the beginning and sql_end at the end\n ... |
2024-01-10 | Slice-Labs/hackathon-2020-reddit-nlp | topics.py | import numpy as np
import gensim.corpora as corpora
from gensim.models import CoherenceModel
from gensim.models import ldamulticore
import multiprocessing as mp
DEFAULT_WORKERS = max(1, mp.cpu_count() - 1)
def create_id2word(tokenized_docs, filter_no_below=10, filter_no_above=0.5):
id2word = corpora.Dictionary(to... | [] |
2024-01-10 | aidanandrews22/Lecture-Recorder | Lecture~src~window.py | from gi.repository import Gtk
from .gi_composites import GtkTemplate
import openai
from openai import OpenAI()
from google.cloud import speech
from google.cloud import language_v1
from google.cloud import texttospeech
from pydub import AudioSegment
from pydub.playback import play
from datetime import datetime
import... | [
"You are a helpful assistant for Aidan. Your task is to correct any spelling discrepancies in the transcribed text. Only add necessary punctuation such as periods, commas, and capitalization, and use only the context provided. You can not generate text based on the input, you may only correct the input punctuationa... |
2024-01-10 | riccardobl/chat-jme | ingest~indexbuilder.py | import os
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import ... | [] |
2024-01-10 | riccardobl/chat-jme | query~discoursequery.py |
import os,json
import hashlib
from langchain.docstore.document import Document
import requests
import markdownify
from langchain import OpenAI, PromptTemplate, LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTe... | [] |
2024-01-10 | riccardobl/chat-jme | TorchEmbeddings.py | from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from sentence_transformers import SentenceTransformer, models
import torch
import numpy as np
import threading
from torch... | [] |
2024-01-10 | riccardobl/chat-jme | Summary.py | from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
from bs4 import BeautifulSoup
import gc
import mi... | [] |
2024-01-10 | riccardobl/chat-jme | bot.py | import os
import utils
import traceback
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
import langchain
from langchain.cache import InMemoryCache
from langchain.llms import OpenAI
from langchain.chains.conversati... | [
"prompts/openai.text-davinci-003.txt",
"question",
"prompts/PLACEHOLDER.PLACEHOLDER.txt"
] |
2024-01-10 | riccardobl/chat-jme | SmartCache.py | from langchain.cache import BaseCache
import os
import utils
from embeddings import EmbeddingsManager
import json
from typing import Any, Dict, List, Optional, Tuple
from langchain.schema import Generation
import time
import pickle
from Summary import Summary
import uuid
RETURN_VAL_TYPE = List[Generation]
cla... | [] |
2024-01-10 | riccardobl/chat-jme | OpenAICachedEmbeddings.py | """Wrapper around OpenAI embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.embeddings.openai import OpenAIEmbeddings
from typing i... | [] |
2024-01-10 | riccardobl/chat-jme | ingest~website.py | #Ingest website pages
import requests
from bs4 import BeautifulSoup
import hashlib
from langchain.docstore.document import Document
import time
from . import indexbuilder
class Website(indexbuilder.IndexBuilder):
def __init__(self,config,options):
super().__init__(config,options)
self.index=[
... | [] |
2024-01-10 | riccardobl/chat-jme | ingest~source.py | # Clone the repo and ingest all the java and markdown files
import hashlib
from langchain.docstore.document import Document
import os
import re
from . import indexbuilder
import time
class Source(indexbuilder.IndexBuilder) :
def __init__(self,config,options,githubRepo, branch,includeFiles):
super().__init__... | [] |
2024-01-10 | zwssunny/chatgpt-on-wechat | voice~factory.py | """
voice factory
"""
def create_voice(voice_type):
"""
create a voice instance
:param voice_type: voice type code
:return: voice instance
"""
if voice_type == "baidu":
from voice.baidu.baidu_voice import BaiduVoice
return BaiduVoice()
elif voice_type == "google":
... | [] |
2024-01-10 | rkaganda/minecraft_explore_bot | observe_bot.py | from javascript import require, On, Once, AsyncTask, once, off
import math
import logging
import json
import bot_functions
import bot_tasks
import config
import db
from db import BotDB
import openai
# logger init
logger = logging.getLogger('bot')
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(filename=c... | [
"heard - PLACEHOLDER",
"PLACEHOLDER spawned",
"digging completed.",
"goal reached.",
"processing task...",
"can't see target"
] |
2024-01-10 | pytorch/vision | torchvision~datasets~country211.py | from pathlib import Path
from typing import Callable, Optional
from .folder import ImageFolder
from .utils import download_and_extract_archive, verify_str_arg
class Country211(ImageFolder):
"""`The Country211 Data Set <https://github.com/openai/CLIP/blob/main/data/country211.md>`_ from OpenAI.
This dataset ... | [] |
2024-01-10 | Siddhartha90/The-Aubergine-index | sentimentAnalysis.py | import openai
import os, json
openai.api_key = os.environ.get("OPENAI_API_KEY")
def sentimentAnalysis(reviews, keyword):
# reviews = """
# 0: Loved it!! Awesome service. The food was so good that I didn't have time to take too many pictures. The service was impeccable and very attentive. It was overall a very din e... | [
"You are answering questions on the following reviews```PLACEHOLDER```",
"Given this keyword ```PLACEHOLDER, Reply with how the related sentiment is for the given result. Use lateral thinking, for example, if it's implied all they sell is steak, that's probably gluten free",
"[10]",
"You are an assistant that... |
2024-01-10 | pilievwm/desc | category.py | import json
import time
import openai
from collections import defaultdict
import requests
import validators
from helpers import *
import time
import re
import html
import textwrap
from bs4 import BeautifulSoup
from request_counter import count_requests, global_counter, get
from datetime import datetime
from flask_socke... | [
"The output must be a coherent and detailed description of the category in question in strictly and valid HTML format. It should be written in PLACEHOLDER. The output should contains valid HTML code except tags like H1, newline, body and other main tags.For the headings use H3 and before each heading add one additi... |
2024-01-10 | smallv0221/datasets | datasets~openwebtext~openwebtext.py | # coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | [] |
2024-01-10 | sbyebss/monge_map_solver | src~models~img2text_model.py | import torch
from dalle2_pytorch import OpenAIClipAdapter
from dalle2_pytorch.dalle2_pytorch import l2norm
from dalle2_pytorch.optimizer import get_optimizer
from torchmetrics.classification.accuracy import Accuracy
import wandb
from src.models.base_model import BaseModule
from src.viz.points import plot_histogram
tr... | [] |
2024-01-10 | sbyebss/monge_map_solver | src~models~text2img_model.py | import os
import cv2
import torch
from dalle2_pytorch import OpenAIClipAdapter
from dalle2_pytorch.dalle2_pytorch import l2norm
from dalle2_pytorch.optimizer import get_optimizer
from PIL import Image, ImageDraw, ImageFont
from torchvision.utils import make_grid, save_image
from src.callbacks.txt2img_callbacks import... | [] |
2024-01-10 | gucky92/Auto-GPT | tests~integration~conftest.py | import os
import openai.api_requestor
import pytest
from pytest_mock import MockerFixture
from tests.conftest import PROXY
from tests.vcr.vcr_filter import before_record_request, before_record_response
BASE_VCR_CONFIG = {
"record_mode": "new_episodes",
"before_record_request": before_record_request,
"bef... | [] |
2024-01-10 | norrishuang/private-llm-qa-bot | doc_preprocess~QA_auto_generator.py | import os
import re
import argparse
import openai
import json
from tqdm import tqdm
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import MarkdownTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
# you need to install these packages: pypdf, tqdm, openai, l... | [
"\nHere is one page of {product}'s manual document\n```\n{page}\n```\nPlease automatically generate as many questions as possible based on this manual document, and follow these rules:\n1. \"{product}\"\" should be contained in every question\n2. questions start with \"Question:\"\n3. answers begin with \"Answer:\"... |
2024-01-10 | norrishuang/private-llm-qa-bot | deploy~lambda~intention~intention.py | import json
import os
import logging
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain import ... | [
"Q: ",
"instruction",
"ๅ็ญไธๅ้ๆฉ้ข๏ผ\n\nPLACEHOLDER\n\n\"Q: \"PLACEHOLDER\"๏ผ่ฟไธช้ฎ้ข็ๆ้ฎๆๅพๆฏๅฅ๏ผๅฏ้้กน[PLACEHOLDER]\nA: ",
"options",
"{instruction}\n\n{fewshot}\n\n\"Q: \"{query}\"๏ผ่ฟไธช้ฎ้ข็ๆ้ฎๆๅพๆฏๅฅ๏ผๅฏ้้กน[{options}]\nA: "
] |
2024-01-10 | norrishuang/private-llm-qa-bot | code~intention_detect~intention.py | import json
import os
import logging
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain import ... | [
"Q: ",
"instruction",
"ๅ็ญไธๅ้ๆฉ้ข๏ผ\n\nPLACEHOLDER\n\n\"Q: \"PLACEHOLDER\"๏ผ่ฟไธช้ฎ้ข็ๆ้ฎๆๅพๆฏๅฅ๏ผๅฏ้้กน[PLACEHOLDER]\nA: ",
"options",
"{instruction}\n\n{fewshot}\n\n\"Q: \"{query}\"๏ผ่ฟไธช้ฎ้ข็ๆ้ฎๆๅพๆฏๅฅ๏ผๅฏ้้กน[{options}]\nA: "
] |
2024-01-10 | norrishuang/private-llm-qa-bot | code~aos_write_job.py | #!/usr/bin/env python
# coding: utf-8
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers
import boto3
import random
import json
from awsglue.utils import getResolvedOptions
import sys
import hashlib
import datetime
import re
import os
import itertools
from bs4 import BeautifulSoup
fr... | [
"0"
] |
2024-01-10 | Bhardwaj-python/J.A.R.V.I.S. | J.A.R.V.I.S~Brain~AIBrain.py | import openai
fileopen = open("D:\\Bhardwaj\\J.A.R.V.I.S\\Data\\Api.txt")
API = fileopen.read()
fileopen.close()
def ReplyBrain(question, chat_log=None):
file_path = "D:\\Bhardwaj\\J.A.R.V.I.S\\Database\\chat_log.txt"
with open(file_path, "r") as file:
chat_log_template = file.read()
if chat_log ... | [
"PLACEHOLDER You : PLACEHOLDER\nJ.A.R.V.I.S. : "
] |
2024-01-10 | Bhardwaj-python/J.A.R.V.I.S. | J.A.R.V.I.S~Brain~Qna.py | #Api Key
fileopen = open("D:\\Bhardwaj\\J.A.R.V.I.S\\Data\\Api.txt")
API = fileopen.read()
fileopen.close()
#Modules
import openai
#Coding
openai.api_key = API
completion = openai.Completion()
def QuestionAnswer(question, chat_log=None):
file_path = "D:\\Bhardwaj\\J.A.R.V.I.S\\Database\\chat_log.txt"
with o... | [
"PLACEHOLDER You : PLACEHOLDER\nJ.A.R.V.I.S. : "
] |
2024-01-10 | emrgnt-cmplxty/quantgpt | quantgpt~core~data~cache.py | import logging
import os
import pickle
import time
from enum import Enum
from typing import Any
import openai
from quantgpt.financial_tools.utils import home_path
logger = logging.getLogger(__name__)
class DataCache:
def __init__(
self,
cache_file=None,
initial_prompt_file=None,
... | [
"You are Bloomberg GPT, a Large Language Model which specializes in understanding financial data."
] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~central_intelligence.py | import logging
from repo_gpt.agents.base_agent import BaseAgent
from repo_gpt.agents.code_writer import CodeWritingAgent
from repo_gpt.agents.repo_comprehender import RepoUnderstandingAgent
from repo_gpt.file_handler.generic_code_file_handler import PythonFileHandler
from repo_gpt.openai_service import OpenAIService
f... | [
"You are an expert software engineer writing code in a repository. The user gives you a plan detailing how the code needs to be updated. You implement the code changes using functions. Ask clarifying questions.",
"You are an expert software engineer. You have a few helper agents that help you understand and write... |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~code_manager~code_processor.py | import logging
from itertools import islice
from typing import List
import numpy as np
import pandas as pd
import tiktoken
from tqdm import tqdm
from ..console import verbose_print
from ..file_handler.abstract_handler import ParsedCode
from ..openai_service import OpenAIService, tokens_from_string
logger = logging.g... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~simple_memory_store.py | import json
import logging
import openai
import tiktoken
from tenacity import ( # for exponential backoff
retry,
stop_after_attempt,
wait_random_exponential,
)
from repo_gpt.openai_service import num_tokens_from_messages, num_tokens_from_string
class MemoryStore:
summary_prompt = """*Briefly* summa... | [
"Finally, ...",
"You are an expert technical writer.",
"*Briefly* summarize this partial conversation about programming.\n Include less detail about older parts and more detail about the most recent messages.\n Start a new paragraph every time the topic changes!\n\n This is only part of a longer conver... |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~code_manager~code_manager.py | import logging
import os
import pickle
from pathlib import Path
import pandas as pd
from tqdm import tqdm
from ..console import verbose_print
from ..openai_service import OpenAIService
from .code_dir_extractor import CodeDirectoryExtractor
from .code_processor import CodeProcessor
logger = logging.getLogger(__name__... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~test_generator.py | import os
import openai as openai
from .code_manager.abstract_extractor import LanguageHandler
from .openai_service import GPT_3_MODELS, GPT_4_MODELS, num_tokens_from_messages
class TestGenerator:
TEMPERATURE = 0.4 # temperature = 0 can sometimes get stuck in repetitive loops, so we use 0.4
def __init__(
... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~autogen~repo_qna.py | import logging
import os
import re
from pathlib import Path
import autogen
import pytest
from repo_gpt.agents.autogen.user_proxy_function_call_agent import (
UserProxyFunctionCallAgent,
)
from repo_gpt.agents.repo_comprehender import get_relative_path_directory_structure
from repo_gpt.code_manager.abstract_extrac... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~repo_comprehender.py | # Refactored RepoUnderstandingAgent using the ParentAgent
import logging
import os
from pathlib import Path
from pathspec import PathSpec
from pathspec.patterns import GitWildMatchPattern
from tqdm import tqdm
from repo_gpt.agents.base_agent import BaseAgent
from repo_gpt.file_handler.generic_code_file_handler import... | [
"You are an expert software engineer on a specific code repository. Users ask you how they can implement something in their codebase. You first use your tools to search and understand the codebase and then figure out how to implement the users' task in the repository.\n **DO NOT** communicate with the user direc... |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~code_writer.py | import logging
from pathlib import Path
from repo_gpt.agents.base_agent import BaseAgent
from repo_gpt.file_handler.generic_code_file_handler import PythonFileHandler
from repo_gpt.openai_service import OpenAIService
from repo_gpt.search_service import SearchService
logger = logging.getLogger(__name__)
class CodeWr... | [
"You are an expert software engineer writing code in a repository. The user gives you a plan detailing how the code needs to be updated. You implement the code changes using functions. Ask clarifying questions.\n **DO NOT** respond to the user directly. Use the functions instead.\n ",
"{'type': 'string', '... |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~autogen~user_proxy_function_call_agent.py | import json
import logging
import autogen
try:
from termcolor import colored
except ImportError:
def colored(x, *args, **kwargs):
return x
logger = logging.getLogger(__name__)
class UserProxyFunctionCallAgent(autogen.UserProxyAgent):
def __init__(self, *args, **kwargs):
super().__init... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~vscode_prompt_service.py | import json
from dataclasses import asdict, dataclass
from enum import Enum
from typing import Union
from repo_gpt.openai_service import OpenAIService
from repo_gpt.prompt_service import PromptService
from repo_gpt.search_service import SearchService
class Status(Enum):
SUCCESS = "SUCCESS"
ERROR = "ERROR"
... | [] |
2024-01-10 | shruti222patel/repo-gpt | src~repo_gpt~agents~base_agent.py | import inspect
import json
import logging
from abc import ABC, abstractmethod
import openai
import tiktoken
from tenacity import ( # for exponential backoff
retry,
stop_after_attempt,
wait_random_exponential,
)
from repo_gpt.agents.simple_memory_store import MemoryStore
logger = logging.getLogger(__name... | [
"Continue"
] |
2024-01-10 | dborodin836/TF2-GPTChatBot | gui~log_window.py | import os
import sys
import time
import tkinter as tk
from tkinter.ttk import Checkbutton
import openai
import ttkbootstrap as ttk
from ttkbootstrap import Style
from services.chatgpt import send_gpt_completion_request
from utils.bans import ban_player, list_banned_players, unban_player
from utils.bot_state import st... | [
"gpt3 ",
"Type your commands here... Or start with 'help' command"
] |
2024-01-10 | bhargavkakadiya/llm-app | app_html.py | import sys
from langchain.llms import OpenAI
from langchain.document_loaders import WebBaseLoader
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import OpenAIEmbedding... | [
"You are an AI assistant for answering questions for the job post and advice users to assess job based on the description.\n You are given the following extracted parts of a webpage of job post and a question. Provide a conversational answer.\n If you don't know the answer, just say \"Hmm, I'm not sure.\" Don... |
2024-01-10 | bhargavkakadiya/llm-app | app_pdf.py | import sys
from langchain.llms import OpenAI
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.chains.summarize import load_summarize_chain
# Load the document
loader = UnstructuredPDFLoader(str(sys.argv[1]))
data = loader.load()
llm = OpenAI(temperature=0)
chain = load_summarize_chain(llm, ... | [] |
2024-01-10 | joshmlove/pdfReaderAI | pdfReaderAI.py | import openai
import pdfplumber
import constants
# Load the OpenAI API key
openai.api_key = constants.APIKEY
# Read your own data from the PDF file
with pdfplumber.open('Josh_Love_Resume copy.pdf') as pdf:
data = ' '.join(page.extract_text() for page in pdf.pages)
# Function to use the OpenAI API to answer queri... | [
"PLACEHOLDER\n\nPLACEHOLDER"
] |
2024-01-10 | ankitrana2709/CS50 | chat~chatter.py | import openai
import os
# Set up the OpenAI API key
openai.api_key = "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"
# Set up the initial conversation prompt
conversation_prompt = "Hello, I'm a chatbot. Which article you want today?"
# Set up the API parameters
model_engine = "davinci"
max_tokens = 150
# Star... | [
"Hello, I'm a chatbot. Which article you want today?\n\nUser: Write an article about PLACEHOLDER\nBot:",
"Hello, I'm a chatbot. Which article you want today?",
"conversation_prompt12842c18-8928-44a0-8550-527da9fe5a43\n\nUser: Write an article about PLACEHOLDER\nBot:"
] |
2024-01-10 | msuliot/open_ai_fine_tuning | full_automatic.py | import requests
import time
import openai
import datetime
import json
import sys
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def validate_file(filename):
try:
with open(filename, 'r') as file:
lines = file.readli... | [
"Where do I mail my check?",
"You are a helpful and professional customer service representative"
] |
2024-01-10 | msuliot/open_ai_fine_tuning | step2_upload_file.py | import openai
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def main():
ft_file = openai.File.create(file=open("data.jsonl", "rb"), purpose='fine-tune')
print(ft_file)
print("Here is the training file id you need for Step 4 =... | [] |
2024-01-10 | msuliot/open_ai_fine_tuning | step5_model_validation.py | import openai
import datetime
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def pretty_table(f):
print(f"\n{'ID':<33} {'Created At':<22} {'Finished At':<22} {'Status':<13} {'Fine Tuned Model'} ")
print('-' * 140)
for job in f... | [] |
2024-01-10 | msuliot/open_ai_fine_tuning | cleanup.py | import openai
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def delete_file(file_id):
try:
openai.File.delete(file_id)
print("File deleted successfully")
except Exception as e:
print("Error deleting file: ... | [] |
2024-01-10 | msuliot/open_ai_fine_tuning | step3_file_validation.py | import openai
import datetime
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def pretty_table(f):
print(f"\n{'ID':<33} {'Purpose':<20} {'Status':<12} {'Created At'}")
print('-' * 88)
for file in f['data']:
created_at =... | [] |
2024-01-10 | msuliot/open_ai_fine_tuning | step4_create_finetuned_model.py | import openai
# get keys from .env file
import os
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
def main():
##### You will need to replace the TRAINING_FILE_ID with the one you got from the previous step.
ft_job = openai.FineTuningJob.create(training_file="TRAINING... | [] |
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