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 | normand1/HyperFeeder | podcastTextGenerationApp~podcastIntroPlugins~utilities~podcastIntroWriter.py | from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
import os
class PodcastIntroWriter:
def writeIntro(self, allStoryTitles, podcastName, typeOfPodcast):
llm = OpenAI(
model=os.getenv("OPENAI_MODEL_SUMMARY"),
max_toke... | [
"INTRO_TEMPLATE_STRING",
"typeOfPodcast",
"podcastName",
"allStoryTitles"
] |
2024-01-10 | normand1/HyperFeeder | podcastTextGenerationApp~podcastOutroWriterPlugins~outroWriterPlugin.py | import os
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from podcastOutroWriterPlugins.baseOutroWriterPlugin import BaseOutroWriterPlugin
class OutroWriterPlugin(BaseOutroWriterPlugin):
def identify(self) -> str:
return "🎸 outro write... | [
"OUTRO_TEMPLATE_STRING",
"introText"
] |
2024-01-10 | normand1/HyperFeeder | podcastTextGenerationApp~podcastSummaryPlugins~storySummaryPlugin.py | import os
from podcastSummaryPlugins.baseSummaryPlugin import BaseSummaryPlugin
from langchain import OpenAI
from langchain.docstore.document import Document
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import PromptTemplate
class StorySummaryPlugin(BaseSummaryPlugin):
def i... | [
"Write a detailed summary of the following:\n {text}\n DETAILED SUMMARY:"
] |
2024-01-10 | normand1/HyperFeeder | podcastTextGenerationApp~podcastSegmentWriterPlugins~utilities~storySegmentWriter.py | import os
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
class StorySegmentWriter:
def writeSegmentFromSummary(self, storySummary):
llm = OpenAI(
model=os.getenv("OPENAI_MODEL_SUMMARY"),
max_tokens=int(os.getenv(... | [
"storySummary",
"SEGMENT_WRITER_STRING"
] |
2024-01-10 | platisd/sycophant | sycophant.py | #!/usr/bin/env python3
import sys
import argparse
import json
import re
from io import BytesIO
from datetime import datetime, timedelta
from pathlib import Path
from openai import OpenAI
from bs4 import BeautifulSoup
from PIL import Image
from jinja2 import Environment, FileSystemLoader
import yaml
import requests
... | [
"PLACEHOLDER\nPLACEHOLDER",
"PLACEHOLDER\n\n```\nPLACEHOLDER\n\nPLACEHOLDER\n```",
"You are a helpful assistant who summarizes news articles",
"Rewrite the following article but keep the last paragraph if it includes attribution to the original articles and the links to them:\n",
"Keep the last paragraph if... |
2024-01-10 | protella/chatgpt-bots | bot_functions.py | from openai import OpenAI
import os
from dotenv import load_dotenv
from textwrap import dedent
import base64
from io import BytesIO
from PIL import Image
from copy import deepcopy
load_dotenv() # load auth tokens from .env file
# Default models: https://platform.openai.com/docs/models
GPT_MODEL = 'gpt-4-1106-previe... | [
"[{'type': 'text', 'text': ''}]",
" Based on the DALL-E 3 prompt, I can imagine an image like this: PLACEHOLDER \n While I can't create or view images,\n I can describe what such an image might look like or the ideas it might convey.\n ... |
2024-01-10 | bytjn1416124/gpt3-jabebot | jabebot.py | from dotenv import load_dotenv
from random import choice
from flask import Flask, request
import os
import openai
load_dotenv()
#openai.api_key = os.getenv("OPENAI_API_KEY")
openai.api_key = "your-openai-api-key-here"
completion = openai.Completion()
start_sequence = "\nJabe:"
restart_sequence = "\n\nPerson:"
sessio... | [
"PLACEHOLDER\n\nPerson:: PLACEHOLDERstart_sequencedf9497ed-610c-495d-ac47-20b073407a88:",
"You are talking to Jabe, GPT3 bot influencer who was mentored by Elon Musk in the past. Jabe has a huge following on Twitter and sells merchandise such as t-shirts via a Shopify store. He also published funny videos on Yout... |
2024-01-10 | lewislf/ai-want-coffee | predict~rewritten~coffee_assistant.py | from openai import OpenAI
from threading import Thread, Event
from queue import Queue
import argparse
from agent import GPTVisionAgent
from image_handler import ImageHandler
from time import sleep
from cv2 import waitKey
system_prompt = (
"Você se chama Clio e é uma Inteligência Computacional Autônoma (ICA) "
... | [
"Você se chama Clio e é uma Inteligência Computacional Autônoma (ICA) do laboratório de Computação de Alto Desempenho (LCAD) da Universidade Federal do Espírito Santo (UFES).\nVocê é uma barista muito prestativa e é responsável por instruir o processo de fazer café coado da forma mais detalhada possível e em qualqu... |
2024-01-10 | lewislf/ai-want-coffee | predict~AgentLegacy.py | import base64
from openai import OpenAI
client = OpenAI()
talk = [
{'role': 'system', 'content': 'Você está em uma cozinha e deve trabalhar para fazer café. Para isso foi '
'atribuído a você um corpo robótico de tamanho semelhante ao humano '
'que... | [
"Você está em uma cozinha e deve trabalhar para fazer café. Para isso foi atribuído a você um corpo robótico de tamanho semelhante ao humano que responderá de forma precisa às suas instruções desde que você se expresse da forma correta. Você deverá responder no seguinte formato:\n# comentário\nfunção(argumento)\nO ... |
2024-01-10 | lewislf/ai-want-coffee | predict~rewritten~coffee_agent_v2.py | from openai import OpenAI
def main():
client = OpenAI()
if __name__ == "__main__":
main() | [] |
2024-01-10 | lewislf/ai-want-coffee | predict~rewritten~coffee_agent.py | from openai import OpenAI
from agent import GPTVisionAgent
system_prompt = (
"Você está em uma cozinha e deve trabalhar para fazer café. Para isso foi "
"atribuído a você um corpo robótico de tamanho semelhante ao humano "
"que responderá de forma precisa às suas instruções desde que você se expresse da f... | [
"Você está em uma cozinha e deve trabalhar para fazer café. Para isso foi atribuído a você um corpo robótico de tamanho semelhante ao humano que responderá de forma precisa às suas instruções desde que você se expresse da forma correta. Você deverá responder no seguinte formato:\n# comentário\nfunção(argumento)\nO ... |
2024-01-10 | lewislf/ai-want-coffee | predict~gpt4vision.py | import openai
import requests
import json
import base64
from api_key import OPENAI_API_KEY
def set_pre_configuration(prompt=None):
openai.api_key = OPENAI_API_KEY
if prompt is None:
prompt = [
{
'role': 'system',
'content': (
"Você se ch... | [
"Dada a tarefa, sugira uma tarefa equivalente que possa ser realizada no processo de fazer café.\n Tarefa original: PLACEHOLDER.",
"\n Com base na resposta do usuário e na tarefa fornecida, determine a intenção do usuário.\n Se a resposta do usuário sugerir o desejo de capturar uma imagem, classifique a ... |
2024-01-10 | Kaptan-Usama/pdf_answerer_chatbot | pdf_answerer.py | import streamlit as st
import PyPDF2
import io
import openai
import docx2txt
import pyperclip
import os
from PyPDF2 import PdfMerger
st.set_page_config(page_title="PDF Question Answerer", page_icon="📄")
st.markdown("""
<style>
div[data-baseweb="input"] > div {
background: rgba(0,0,0,0.1) !im... | [
"text84b2eaec-1d0f-4b70-bd51-0f517285bf48\nQuestion: PLACEHOLDER\nAnswer:",
"text879bacb8-d48a-4426-b455-f7de55cc89cf\nQuestion: PLACEHOLDER\nAnswer:",
"\nQuestion: PLACEHOLDER\nAnswer:"
] |
2024-01-10 | danielpatrickhug/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 | IndexFziQ/CLSEG | src~run_generation_sample_csl.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_cl_cr.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_cl_so.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_baseline_ft.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_baseline_pt.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_sample_cfg.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_sample_sct.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_cl_is.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_cl_all.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_baseline_gcl.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_csl.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | IndexFziQ/CLSEG | src~run_generation_sample_tpl.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 c... | [] |
2024-01-10 | adrianmarino/thesis-paper | lib~recommender~chatbot~movie~movie_recommendations_output_parser.py | from langchain.schema import BaseOutputParser
from bunch import Bunch
from typing import List
import util as ut
import re
import logging
from pydantic import BaseModel, PrivateAttr
class MovieRecommendationsOutputParser(BaseOutputParser[List[str]]):
__list_size: int = PrivateAttr(True)
def __init__(self, lis... | [] |
2024-01-10 | adrianmarino/thesis-paper | lib~recommender~chatbot~stateless~chat_bot_response_factory.py | from .chat_bot_response import ChatBotResponse
import logging
from langchain_core.messages import SystemMessage
class ChatBotResponseFactory:
def __init__(self, output_parser, template_factory):
self._output_parser = output_parser
self._template_factory = template_factory
self._logger ... | [] |
2024-01-10 | adrianmarino/thesis-paper | lib~model~llm~chain_builder.py | from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import Ollama
from langchain.chat_models import ChatOllama
from langchain.prompts import ChatPromptTemplate
class OllamaModelBuilder:
@staticmethod
def ch... | [
"[('system', PLACEHOLDER), ('human', '{request}')]"
] |
2024-01-10 | TheItCrOw/R.O.B.E.R.T. | src~training~rob_test_pipeline.py | from db import db
from torchmetrics.text.rouge import ROUGEScore
from nltk.translate.bleu_score import sentence_bleu
import torch
import time
import random
import sys
import numpy as np
import json
import os
import gc
import openai
from datetime import datetime
from chat_gpt3 import chat_gpt3
sys.path.append(os.path.j... | [
"inp",
"\nA student is having a conversation with Rob, the virtual reality assistant. This is the chat history:\n[HISTORY]\n\nRob knows the following:\n[CONTEXT]\n\nRob continued the dialog with:\n[ANSWER]\n\nRate Robs answer with a number from 1 to 10. Focus heavily on whether the answer has correct information ... |
2024-01-10 | civrealm/civrealm | src~civrealm~agents~civ_autogpt~GPTAgent.py | import os
import openai
import time
import random
import json
import requests
import warnings
from func_timeout import func_timeout
from func_timeout import FunctionTimedOut
from civrealm.agents.civ_autogpt.utils.num_tokens_from_messages import num_tokens_from_messages
from civrealm.agents.civ_autogpt.utils.interact_w... | [
"You should only respond in JSON format as described",
"The former chat history can be summarized as: \n",
"src/civrealm/agents/civ_autogpt/prompts/state_prompt.txt",
"src/civrealm/agents/civ_autogpt/prompts/task_prompt.txt",
"You should only use the given commands!",
"PLACEHOLDER Now you get the needed i... |
2024-01-10 | AI-Jie01/auto-evaluator | auto-evaluator.py | import os
import json
import time
import pypdf
import random
import itertools
import text_utils
import pandas as pd
import altair as alt
import streamlit as st
from io import StringIO
from langchain.llms import Anthropic
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.ch... | [
"`Gradeing style prompt`",
"Descriptive"
] |
2024-01-10 | ITM-Kitware/align-system | align_system~cli~run_align_system.py | import sys
import json
from rich.highlighter import JSONHighlighter
from align_system.utils import logging
from align_system.interfaces.cli_builder import build_interfaces
from align_system.algorithms.llm_baseline import LLMBaseline
from align_system.algorithms.llama_index import LlamaIndex
from align_system.similari... | [
"unstructured"
] |
2024-01-10 | ITM-Kitware/align-system | align_system~algorithms~llama_index.py | from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import (
VectorStoreIndex,
SimpleDirectoryReader,
LangchainEmbedding,
ServiceContext,
)
from llama_index.llms import HuggingFaceLLM
from llama_index.prompts.prompts import SimpleInputPrompt
from llama_index.llm_predictor... | [
"\n",
"Write a response that appropriately completes the request.\n\n",
"\nPLACEHOLDER",
"{query_str}",
"scenario",
"\nIdentify the integer index of the choice that best completes the request.\n",
"Below is an instruction that describes a task. ",
"### Instruction:\n{query_str}\n\n### Response:",
"C... |
2024-01-10 | ITM-Kitware/align-system | align_system~cli~run_action_based_align_system.py | import sys
import json
from rich.highlighter import JSONHighlighter
from align_system.utils import logging
from align_system.interfaces.cli_builder import build_interfaces
from align_system.algorithms.llm_baseline import LLMBaseline
from align_system.algorithms.llama_index import LlamaIndex
from align_system.similari... | [
"unstructured",
"mission",
"casualties"
] |
2024-01-10 | jaredbradley243/docsgpt | scripts~code_docs_gen.py | import ast
import json
from pathlib import Path
import dotenv
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
dotenv.load_dotenv()
ps = list(Path("inputs").glob("**/*.py"))
data = []
sources = []
for p in ps:
with open(p) as f:
data.append(f.read())
sources.append(p)
... | [
"Code: \n{code}, \nDocumentation: "
] |
2024-01-10 | jaredbradley243/docsgpt | application~parser~py2doc.py | import ast
import os
from pathlib import Path
import tiktoken
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
def find_files(directory):
files_list = []
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
... | [
"Code: \n{code}, \nDocumentation: ",
"functions_names",
"class_name",
"Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: "
] |
2024-01-10 | phasetr/generative-ai | fundamentals~tts.py | import os
import traceback
from uuid_extensions import uuid7str
from openai import OpenAI
class TTS:
"""_summary_
適当なテキストから音声ファイルを作る。
"""
def __init__(self, uuid) -> None:
self.uuid = uuid
self.output_directory_name = os.environ.get(
"OUTPUT_DIRECTORY_NAME", "output")
... | [] |
2024-01-10 | phasetr/generative-ai | 2023-08-21-lang-chain-streamlit~Chapter04~get_costs.py | from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
llm = OpenAI(model_name="gpt-3.5-turbo")
with get_openai_callback() as cb:
result = llm("Tell me a joke")
print(cb)
| [] |
2024-01-10 | phasetr/generative-ai | 2023-12-21-Modern_Generative_AI_with_ChatGPT_and_OpenAI_Models~Chapter10-Enterprise_use_cases~code~medical_smart_search_app.py | import os
import streamlit as st
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
from langchain.vectorstores.faiss import FAISS
with open('secrets.toml', 'r') as f:... | [] |
2024-01-10 | phasetr/generative-ai | 2023-11-26-hackathon-note~1_text_to_mp3.py | import os
from uuid_extensions import uuid7str
from openai import OpenAI
from pathlib import Path
sample_text_name = "sample1.txt"
is_exist = os.path.exists(sample_text_name)
if not is_exist:
print(f"{sample_text_name}を作成してください。")
exit()
print("テキストを読み込みます。")
text = ""
with open(sample_text_name, mode="r", en... | [] |
2024-01-10 | phasetr/generative-ai | fundamentals~poll.py | from openai import OpenAI
from uuid_extensions import uuid7str
import dotenv
import os
# APIキーの設定
dotenv.load_dotenv()
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
class OpenAIAdapter:
def __init__(self, file_root) -> None:
self.file_root = file_root
self.output_directory_name = os.e... | [] |
2024-01-10 | phasetr/generative-ai | fundamentals~stt.py | import traceback
from openai import OpenAI
import os
class STT:
def __init__(self) -> None:
self.output_directory_name = os.environ.get(
"OUTPUT_DIRECTORY_NAME", "output")
# ディレクトリがなければ作る
if not os.path.exists(self.output_directory_name):
os.mkdir(self.output_direct... | [] |
2024-01-10 | phasetr/generative-ai | 2023-12-21-Modern_Generative_AI_with_ChatGPT_and_OpenAI_Models~Chapter10-Enterprise_use_cases~code~call_center_app.py | import sys
import requests
import os
import numpy as np
import toml
from streamlit_chat import message
import streamlit as st
import openai
with open('secrets.toml', 'r') as f:
config = toml.load(f)
openai.api_type = "azure"
openai.api_key = config['OPENAI_API_KEY']
openai.api_base = config['OPENAI_API_BASE']
ope... | [
"Elaborate a list of remediations to get to the following improvement: PLACEHOLDER",
"Operator: Good morning, thank you for calling the auto insurance company, my name is John, how can I assist you today?\nCustomer: Yes, hi, I just noticed a dent on the side of my car and I have no idea how it got there. There we... |
2024-01-10 | phasetr/generative-ai | 2023-12-21-Modern_Generative_AI_with_ChatGPT_and_OpenAI_Models~Chapter10-Enterprise_use_cases~code~contract_analyzer_app.py | import sys
import toml
import streamlit as st
import openai
with open('secrets.toml', 'r') as f:
config = toml.load(f)
openai.api_type = "azure"
openai.api_key = config['OPENAI_API_KEY']
openai.api_base = config['OPENAI_API_BASE']
openai.api_version = "2022-12-01"
contract = """
This Contract for Services ("Agr... | [
"\n\nThis Contract for Services (\"Agreement\") is entered into as of [date], by and between Company A (\"Company\") and Company B (\"Service Provider\").\n1.\tServices Provided. Service Provider agrees to provide the following services to Company (the \"Services\"): The Service Provider agrees to provide consultin... |
2024-01-10 | phasetr/generative-ai | 2023-11-26-hackathon-note~2_img_to_mp3.py | import os
from uuid_extensions import uuid7str
from openai import OpenAI
from pathlib import Path
import base64
def encode_image(image_path):
"""画像をbase64にエンコードする"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
sample_img_name = "img/1.jpg"
is... | [
"[{'type': 'text', 'text': '日本語で説明してください'}, {'type': 'image_url', 'image_url': 'data:image/jpeg;base64,PLACEHOLDER'}]"
] |
2024-01-10 | Azure-Samples/flask-app-on-azure-functions | FlaskApp~__init__.py | from flask import Flask
# Always use relative import for custom module
from .package.module import MODULE_VALUE
app = Flask(__name__)
@app.route("/")
def index():
return (
"Try /hello/Chris for parameterized Flask route.\n"
"Try /module for module import guidance"
)
@app.route("/hello/<name>... | [] |
2024-01-10 | Crazykrai/MakeUC2023 | search.py | import os
import openai
import json
import re
from googleapiclient.discovery import build
from dotenv import load_dotenv
from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
load_dotenv()
apiKey = os.getenv('GOOGLE_API_KEY')
seId = os.getenv('GOOGLE_CSE_ID')
openai.api_key = os.getenv('GPT_KEY')
... | [
"Create a 2 sentence summary of a website's content using the given text from the website alongside the URL: PLACEHOLDER - PLACEHOLDER"
] |
2024-01-10 | aweidner/ScryBot | scrybot~api~search.py | import sys
import asyncio
import openai
async def search(query):
completion = await openai.ChatCompletion.acreate(
model="ft:gpt-3.5-turbo-0613:personal::8MPbjnyY",
messages=[
{"role": "system", "content": "Act as a scryfall api bot that accepts a user query and translates it into a se... | [
"Act as a scryfall api bot that accepts a user query and translates it into a search URL. Output only the url."
] |
2024-01-10 | cancelself/geist | geist.py | import os
import sys
import glob
import json
import openai
import pickle
import getpass
from datetime import datetime
def load_chat_history(file_path):
try:
with open(file_path, 'rb') as f:
chat_history = pickle.load(f)
except FileNotFoundError:
chat_history = []
return chat_hi... | [
"{'role': 'user', 'content': 'PLACEHOLDERPLACEHOLDER', 'name': PLACEHOLDER}",
"content",
"PLACEHOLDERPLACEHOLDER"
] |
2024-01-10 | maioria/chatgpt-talkieai | talkieai-server~app~ai~chat_gpt_ai.py | from typing import List, Dict
import json
from pydantic import BaseModel
from app.ai.interfaces import SystemBaseAI, MessageInvokeDTO
from app.ai.models import *
from app.core.logging import logging
class ChatGPTInvokeDTO(BaseModel):
messages: List[Dict]
model: str = 'gpt-3.5-turbo'
temperature: float = 0... | [] |
2024-01-10 | jasonthewhale/Indigenous_AI | story_generator.py | import streamlit as st
import pandas as pd
import numpy as np
import openai
openai.organization = "org-tlNrDekRRlExHL1gWb7oCHPD"
openai.api_key = st.secrets["OPENAI_API_KEY"]
df = pd.read_csv('./datasets/indigenous_map.csv')
for _, row in df.iterrows():
language_name = row['Language']
new_url = f"https://m... | [
"\nHere is the info about an indigenous language in QLD. Help me create a short and brief, but fascinating story involves language name, introduction, pronunciation, Synonyms, Common words. Also set the background or scene of the story as value of \"Locations\", describing a story bsaed on image attribution. Pls ke... |
2024-01-10 | wshao12/ChatGPT | src~revChatGPT~ProxyServer.py | """
Fetches cookies from chat.openai.com and returns them (Flask)
"""
from OpenAIAuth.Cloudflare import Cloudflare
from flask import Flask, request, jsonify
import tls_client
import json
app = Flask(__name__)
session = tls_client.Session(
client_identifier="chrome_108"
)
# Get cloudflare cookies
cf_clearance, us... | [] |
2024-01-10 | abuzarmahmood/KatzGPT | katz_gpt_test.py | """
https://levelup.gitconnected.com/langchain-for-multiple-pdf-files-87c966e0c032
"""
from langchain.document_loaders import PyPDFLoader, PyPDFDirectoryLoader
from glob import glob
import os
from tqdm import tqdm
from joblib import Parallel, delayed
from pickle import dump, load
from langchain.embeddings import Ope... | [
"question",
"You are an AI assistant for answering questions about systems neuroscience, specifically taste processing.\nYou are given the following extracted parts of a long document and a question. Provide a conversational answer.\nIf you don't know the answer, just say \"Hmm, I'm not sure.\" Don't try to make ... |
2024-01-10 | aishvi-g/healthease | templates~telebot~medease.py | import cohere
import os
from health_data import health_database, train_data
from azure.ai.translation.text import TextTranslationClient, TranslatorCredential
from azure.ai.translation.text.models import InputTextItem
api_key = os.environ['API_KEY']
azure_key = os.environ['azure_key']
endpoints = os.environ['endpoint']... | [] |
2024-01-10 | sahuidhsu/GPT-KUnit-coder | display_messages.py | import toml
import openai
log_file = open("message_log.txt", "w")
with open("config.toml") as config_file:
config = toml.load(config_file)
if not config["OPENAI_API_KEY"]:
print("ERROR! Please set your OPENAI_API_KEY in config.toml")
exit()
def write_output(msg):
print(msg)
log_file.write(msg + ... | [] |
2024-01-10 | chuanyang-Zheng/Progressive-Hint | main_clean.py | import copy
import os
import time
import jsonlines
import openai
import json
import re
import numpy as np
from utils import delete_extra_zero,_strip_string
import argparse
from statistics import mean
from collections import Counter
import traceback
# OpenAI Key
openai.api_key = "Put Your Key Here"
def find_ma... | [
"PLACEHOLDER\n\nQ: PLACEHOLDER\nA:",
"PLACEHOLDER\n\nQuestion: PLACEHOLDER\nA:",
"[]",
"Follow the given examples and answer the question."
] |
2024-01-10 | rick-love/lc-playground | news_parser.py | import datetime
from config import get_OpenAI, get_NewsAPIKey
from openai import OpenAI
from newsapi import NewsApiClient
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
# Import Pydantic
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseMo... | [] |
2024-01-10 | rick-love/lc-playground | chains.py | from config import get_OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Exception a... | [
"How do you say good afternoon in {language}?",
"language"
] |
2024-01-10 | rick-love/lc-playground | day4_parsers.py | from OpenAI_Training.config import get_OpenAI
from openai import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Exception as e:
raise Exception(f"Error setting API key for OpenAI: {e}")... | [
"\nFrom the following email, please extract the following information:\nUser_Id: what is the user id?\n\nImport_ID: what is the import id?\nstart_time: what is the start time?\nend_time: what is the end time?\nerrors: what are the errors? If there are multiple errors, please list them all in square brackets as an a... |
2024-01-10 | rick-love/lc-playground | chains_story.py | from config import get_OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Exception a... | [
"location",
"name",
"\nAs a childrens book author, write a simple and short (90 words) story lullaby based on the location\n{location}\nand the main character\n{name}\n\nSTORY:\n",
"\nTranslate the {story} to {language}.\n\nMake sure the translation is simple and fun to read for children.\n\nTRANSLATION:\n",
... |
2024-01-10 | rick-love/lc-playground | day3.py | from OpenAI_Training.config import get_api_key
from openai import OpenAI
from langchain.prompts import FewShotPromptTemplate,PromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import HumanMessage, SystemMe... | [
"\nI really want to travel to {location}. What should I do there?\n\nRespond with one short answer.\n\n",
"location",
"Input: {noun}\nOutput",
"Lisbon",
"format_instructions",
"You are a nice AI bot that helps a user figure out what to eat in one short sentence",
"Give the location an item is usually fo... |
2024-01-10 | rick-love/lc-playground | memory.py | from config import get_OpenAI
from openai import OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_... | [] |
2024-01-10 | rick-love/lc-playground | doesnt_work_speech2text.py | from pathlib import Path
from OpenAI_Training.config import get_api_key
from openai import OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_api_key()
except Exception as e:
raise Exception(f"Err... | [] |
2024-01-10 | rick-love/lc-playground | getWeather.py | # This file is used to get the weather for a given location
from config import get_OpenAI, get_OpenWeatherAPIKey
from langchain.llms import OpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Exception as e:
raise E... | [] |
2024-01-10 | rick-love/lc-playground | pydantic_parser.py | from config import get_OpenAI
from openai import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
#import Pydantic
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, field_validator, validator
from typing import List
# S... | [
"\nFrom the following email, please extract the following information:\nUser_Id: what is the user id?\n\nImport_ID: what is the import id?\nstart_time: what is the start time?\nend_time: what is the end time?\nerrors: what are the errors? If there are multiple errors, please list them all in square brackets as an a... |
2024-01-10 | rick-love/lc-playground | getNews.py | from config import get_NewsAPIKey, get_OpenAI
from newsapi import NewsApiClient
from openai import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
# Set the API key for NewsAPI
try:
newsapi = NewsApiClient(api_key=get_NewsAPIKey())
except Exception as e:
ra... | [] |
2024-01-10 | rick-love/lc-playground | story_generator.py | from config import get_OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Exception a... | [
"location",
"name",
"\n Translate the {story} to {language}.\n\n Make sure the translation is simple and fun to read for children.\n\n TRANSLATION:\n ",
"\n As a childrens book author, write a simple and short (90 words) story lullaby based on the location\n {location}\n ... |
2024-01-10 | rick-love/lc-playground | blogGenerator.py | from OpenAI_Training.config import get_OpenAI
from openai import OpenAI
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
from langchain.memory import ConversationBufferMemory
from langchain.utilities import WikipediaAPIWrapper
from lan... | [
"Write a Blog title about {topic}",
"Enter your topic:",
"Write a Blog article based on this title: {title} while also leveraging this wikipedia research: {wikipedia_research}. The article should be less 500 words long."
] |
2024-01-10 | rick-love/lc-playground | languageConverter.py | from langchain.llms import OpenAI
import streamlit as st
from config import get_OpenAI
from langchain.prompts import PromptTemplate
template = """
Below is a text message that maybe poorly written.
Your goal is to:
- Properly format the text
- Convert the text to the desired tone
- Convert the text to the desired lang... | [
"Yo! Your order has shipped.",
"Ich schreibe Ihnen, um Sie darüber zu informieren, dass das von Ihnen bestellte Produkt versandt wurde.",
"tone",
"Hey! Just wanted to let you know that your order has been shipped.",
"Le escribo para informarle que el producto que ha pedido ha sido enviado.",
"\nBelow is a... |
2024-01-10 | rick-love/lc-playground | starter.py | from config import get_OpenAI
from openai import OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
from langchain.memory import ConversationBufferMemory
import streamlit as st
# Set ... | [] |
2024-01-10 | rick-love/lc-playground | promptTemplates.py | from OpenAI_Training.config import get_OpenAI, get_PineCone
from openai import OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import streamlit as st
# Set the API key for OpenAI
try:
OpenAI.api_key = get_OpenAI()
except Excep... | [
"\n\nTranslates from English to German in a nice tone.\n{human_input}\n\n"
] |
2024-01-10 | BDSI-Utwente/steers | ingest~deprecated~04-topics_openai.py | from dotenv import load_dotenv
load_dotenv(".env")
from database import *
import openai
from openai.error import RateLimitError
import os
import random
import backoff
from peewee import DataError
openai.api_key = os.getenv("OPENAI_APIKEY")
# prepare topic getter with exponential backoff baked in
@backoff.on_exceptio... | [
"You are a topic extraction engine. When you get a message, you will reply with a comma-separated list of up to 8 topics and concepts that are most relevant to that message."
] |
2024-01-10 | BDSI-Utwente/steers | ingest~deprecated~03-categories_openai.py | from dotenv import load_dotenv
load_dotenv(".env")
from database import *
import openai
from openai.error import RateLimitError
import os
import random
import backoff
from typing import List
from peewee import DataError
openai.api_key = os.getenv("OPENAI_APIKEY")
# prepare category getter with exponential backoff ba... | [
"You are an academic library classification engine. When you get a message, you will reply with a comma-separated list of academic domains that best fit the thesis described in the message."
] |
2024-01-10 | mikelapierre/chat-with-your-data-solution-accelerator | code~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 | mikelapierre/chat-with-your-data-solution-accelerator | code~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 | mikelapierre/chat-with-your-data-solution-accelerator | code~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 | zharry29/curious_code_prompts | datasets~imdb~imdb_old.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
parser = argpar... | [
"PLACEHOLDER PLACEHOLDER.\n\n",
"Movie Expressed Sentiment"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~cnn_dailymail~cnn_dailymail.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from transformers import AutoTokenizer
from tqdm import tqdm
from rouge import FilesRouge
parser = argparse.ArgumentParser()
parser.add_argument('--prompt', requ... | [
"cnn_dailymail/3.0.0",
"PLACEHOLDERPLACEHOLDER\n\nAnswer: PLACEHOLDER\n\n\n",
"{highlights}",
"2_or_3_sentences",
"PLACEHOLDERPLACEHOLDERPLACEHOLDER\n\n\n"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~Winogrande~winogrande.py | import os
import argparse
import ast
import pickle
import random
import time
import numpy as np
import openai
from sklearn.metrics import accuracy_score, f1_score
from tqdm import tqdm
import utils
class Winogrande():
def __init__(self, templates):
self.apply_template = templates
def build_text_pr... | [
"PLACEHOLDERPLACEHOLDER\n\n",
"'''\nThis is a coference resolution task. There will be a '_' in a given sentence and options will be provided. You need to choose from given options and fill in the '_'.\n'''\n\n",
"None",
"\n\n\n",
"\n\nAnswer: "
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~imdb~imdb.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
from scipy.stats... | [
"PLACEHOLDERPLACEHOLDER\n\n\n",
"PLACEHOLDER PLACEHOLDER.\n\n",
"Movie Expressed Sentiment"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~OpenPI-v2~code-prompts~class_prefix.py | import openai
from utils import build_prompt
class EntityStateGeneration():
'''function to generate entity state changes given the goal, context, and current step of a procedure. '''
def __init__(self):
pass
def gpt4(self, prompt):
res = openai.Completion.create(
e... | [] |
2024-01-10 | zharry29/curious_code_prompts | datasets~HotpotQA~hotpotqa.py | import time
import json
import utils
import random
import pickle
import openai
import argparse
import numpy as np
from tqdm import tqdm
from datasets import load_dataset
from sklearn.metrics import accuracy_score
class HotpotQA():
def __init__(self, apply_template):
self.apply_template = apply_template
... | [
"./code-prompts/comment_prefix.py",
"PLACEHOLDERPLACEHOLDER\n\n",
"None",
"./code-prompts/class_prefix.py",
"\n\n\n",
"\n\nAnswer: "
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~wikihow_temporal~wikihow_temporal.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import csv
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
impor... | [
"(a) ",
"You are trying to placeholder. You need to do two things:\n(a) PLACEHOLDER\n(b) PLACEHOLDER\nThe first thing to do is",
"(b) ",
"PLACEHOLDER PLACEHOLDER\n\n",
"PLACEHOLDERPLACEHOLDER\n\n\n"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~xsum~xsum.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from transformers import AutoTokenizer
from tqdm import tqdm
from rouge import FilesRouge
parser = argparse.ArgumentParser()
parser.add_argument('--prompt', requ... | [
"{highlights}",
"PLACEHOLDERPLACEHOLDER PLACEHOLDER\n\n\n",
"DOC_tldr",
"PLACEHOLDERPLACEHOLDERPLACEHOLDER\n\n"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~mmlu~mmlu.py | import argparse
import openai
import os
import numpy as np
import pandas as pd
import time
from crop import crop
choices = ["A", "B", "C", "D"]
def softmax(x):
z = x - max(x)
numerator = np.exp(z)
denominator = np.sum(numerator)
softmax = numerator/denominator
return softmax
def format_subject(s... | [
"\n{}. {}",
"PLACEHOLDERPLACEHOLDER",
"\nAnswer:",
"The following are multiple choice questions (with answers) about {}.\n\n",
" {}\n\n"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~squad~squad.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from transformers import AutoTokenizer
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('--prompt', required=True, type=str, help='Ei... | [
"{context}",
"{answer}",
"PLACEHOLDERPLACEHOLDER PLACEHOLDER\n\n",
"question",
"PLACEHOLDERPLACEHOLDERPLACEHOLDER\n\n",
"Questions with Context +unanswerable",
"context",
"{question}"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~wikihow_goal_step~wikihow_goal_step.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import csv
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
impor... | [
"Given an action: PLACEHOLDER\nWhat is the most likely goal of that action?\n(a) PLACEHOLDER\n(b) PLACEHOLDER\n(c) PLACEHOLDER\n(d) PLACEHOLDER\nThe most likely goal is: ",
"PLACEHOLDER PLACEHOLDER\n\n",
" ",
"goalPLACEHOLDER",
"PLACEHOLDERPLACEHOLDER\n\n\n"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~OpenPI-v2~openpi.py | import re
import ast
import json
import time
import utils
import pickle
import random
import openai
import argparse
import numpy as np
from tqdm import tqdm
from datasets import load_dataset
from sklearn.metrics import accuracy_score
class OpenPI():
def __init__(self, metadata, apply_template):
self.metad... | [
"./code-prompts/comment_prefix.py",
"\n",
"[]",
"PLACEHOLDER\n\n",
"- The PLACEHOLDER of PLACEHOLDER is PLACEHOLDER before and PLACEHOLDER afterwards.\n",
"PLACEHOLDERPLACEHOLDER",
"PLACEHOLDERGoal: PLACEHOLDER\n\n",
"./code-prompts/class_prefix.py",
"['P L A C E H O L D E R']",
"Goal: PLACEHOLDER... |
2024-01-10 | zharry29/curious_code_prompts | datasets~HellaSWAG~hellaswag.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
import backoff
... | [
"PLACEHOLDER\n\nAnswer: PLACEHOLDER\n\n\n",
"PLACEHOLDERPLACEHOLDER\n\n\n",
"how_ends"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~yelp~yelp.py | import argparse
import openai
from datasets import load_dataset
import random
random.seed(29)
from promptsource.templates import DatasetTemplates
import time
from sklearn.metrics import accuracy_score
import pickle
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
from scipy.stats... | [
"yelp_review_full",
"PLACEHOLDER PLACEHOLDER.\n\n",
"PLACEHOLDERPLACEHOLDER\n\n\n",
"based_on_that"
] |
2024-01-10 | zharry29/curious_code_prompts | datasets~ANLI~anli.py | import time
import json
import utils
import pickle
import random
import openai
import argparse
import numpy as np
from tqdm import tqdm
from datasets import load_dataset
from sklearn.metrics import accuracy_score
random.seed(29)
class ANLI():
def __init__(self, apply_template, idx):
self.apply_template =... | [
"./code-prompts/comment_prefix.py",
"PLACEHOLDERPLACEHOLDER\n\n",
"None",
"./code-prompts/class_prefix.py",
"\n\n\n",
"\n\nAnswer: "
] |
2024-01-10 | jitingxu1/llama_index | llama_index~evaluation~dataset_generation.py | """Dataset generation from documents"""
from __future__ import annotations
import re
from typing import List, Optional
from llama_index import (
Document,
SummaryIndex,
ServiceContext,
)
from llama_index.llms.openai import OpenAI
from llama_index.prompts.base import BasePromptTemplate, PromptTemplate
fro... | [
"Context information is below.\n\"\n\"\n---------------------\n{context_str}\n---------------------\n\"\n\"Given the context information and not prior knowledge.\n\"\n\"generate only questions based on the below query.\n\"\n\"{query_str}\n\"\n"
] |
2024-01-10 | yamyyao/langchain | libs~experimental~langchain_experimental~comprehend_moderation~pii.py | import asyncio
from typing import Any, Dict, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPiiError,
)
class ComprehendPII:
def __init__(
self,
client: Any,
callback: Optional[Any] = None,
unique_id: Optional[str] = No... | [] |
2024-01-10 | yamyyao/langchain | libs~langchain~langchain~vectorstores~elasticsearch.py | import logging
import uuid
from abc import ABC, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import... | [] |
2024-01-10 | yamyyao/langchain | libs~langchain~tests~integration_tests~vectorstores~test_xata.py | """Test Xata vector store functionality.
Before running this test, please create a Xata database by following
the instructions from:
https://python.langchain.com/docs/integrations/vectorstores/xata
"""
import os
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings... | [] |
2024-01-10 | yamyyao/langchain | libs~experimental~langchain_experimental~comprehend_moderation~toxicity.py | import asyncio
import importlib
from typing import Any, List, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationToxicityError,
)
class ComprehendToxicity:
def __init__(
self,
client: Any,
callback: Optional[Any] = None,
un... | [] |
2024-01-10 | yamyyao/langchain | libs~langchain~langchain~memory~readonly.py | from typing import Any, Dict, List
from langchain.schema import BaseMemory
class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
"""Return memory variables."""
ret... | [] |
2024-01-10 | huqianghui/aigc-langchain-bot | common~bing_search_multi_market.py | """Util that calls Bing Search.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
"""
from typing import Dict, List
import requests
from langchain.utilities import BingSearchAPIWrapper
class BingSearchAPIMultiMarketW... | [] |
2024-01-10 | aahn33/llm-summary | map_and_refine~refine.py | from langchain.chat_models import ChatOpenAI
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import CharacterTextSplitter
from langchain.callbacks import get_openai_callback
import tikt... | [
"\n Your assignment is to expand an existing summary by adding new information that follows it. Here's the current summary up to a specified point:\n\n {existing}\n\n Now, consider the following content which occurs after the existing summary:\n\n {text}\n\n Evaluate the additiona... |
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