internet_ml/internet_ml/NLP/no_context/QA.py

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# type: ignore
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from typing import Any, List, Tuple
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import os
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import sys
from pathlib import Path
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import dotenv
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import openai
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from transformers import pipeline
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sys.path.append(str(Path(__file__).parent.parent.parent) + "/tools/NLP/data")
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sys.path.append(str(Path(__file__).parent.parent.parent) + "/tools/NLP")
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sys.path.append(str(Path(__file__).parent.parent.parent) + "/tools")
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sys.path.append(str(Path(__file__).parent.parent.parent) + "/utils")
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import config
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import internet
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from ChatGPT import Chatbot
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dotenv.load_dotenv()
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def answer(
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query: str,
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model: str = "openai-chatgpt",
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GOOGLE_SEARCH_API_KEY: str = "",
GOOGLE_SEARCH_ENGINE_ID: str = "",
OPENAI_API_KEY: str = "",
CHATGPT_SESSION_TOKEN: str = "",
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CHATGPT_CONVERSATION_ID: str = "",
CHATGPT_PARENT_ID: str = "",
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) -> tuple[Any, list[str]]:
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# if environment keys are not given, assume it is in env
if GOOGLE_SEARCH_API_KEY == "":
GOOGLE_SEARCH_API_KEY = str(os.environ.get("GOOGLE_SEARCH_API_KEY"))
if GOOGLE_SEARCH_ENGINE_ID == "":
GOOGLE_SEARCH_ENGINE_ID = str(os.environ.get("GOOGLE_SEARCH_ENGINE_ID"))
if OPENAI_API_KEY == "":
OPENAI_API_KEY = str(os.environ.get("OPENAI_API_KEY"))
openai.api_key = OPENAI_API_KEY
if CHATGPT_SESSION_TOKEN == "":
CHATGPT_SESSION_TOKEN = str(os.environ.get("CHATGPT_SESSION_TOKEN"))
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if CHATGPT_CONVERSATION_ID == "":
CHATGPT_CONVERSATION_ID = str(os.environ.get("CHATGPT_CONVERSATION_ID"))
if CHATGPT_PARENT_ID == "":
CHATGPT_PARENT_ID = str(os.environ.get("CHATGPT_PARENT_ID"))
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"""
model naming convention
# Open-AI models:
include prefix openai-*
# HuggingFace
include prefix hf-*
#
"""
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if not (model.startswith("openai-") or model.startswith("hf-")):
model = "openai-chatgpt" # Default
if model.startswith("openai-"):
if model == "openai-chatgpt":
# ChatGPT
results: tuple[list[str], list[str]] = internet.Google(
query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
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).google()
# print(' '.join(filter(lambda x: isinstance(x, str), results[0]))[:4000])
prompt = f"Using the context: {' '.join(filter(lambda x: isinstance(x, str), results[0]))[:3000]} and answer the question with the context above and previous knowledge: \"{query}\". Also write long answers or essays if asked."
print(prompt)
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chatbot = Chatbot(
{"session_token": CHATGPT_SESSION_TOKEN},
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conversation_id=None,
parent_id=None,
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)
response = chatbot.ask(
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prompt=prompt,
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conversation_id=None,
parent_id=None,
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)
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return (response["message"], results[1])
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else:
if model == "openai-text-davinci-003":
results: tuple[list[str], list[str]] = internet.Google(
query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
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).google()
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context = " ".join(results[0])
context[: (4097 - len(query) - 10)]
response = openai.Completion.create(
model="text-davinci-003",
prompt=f"{context} Q: {query}",
max_tokens=len(context),
n=1,
stop=None,
temperature=0.5,
)
return (response.choices[0].text, results[1])
# TODO: add suport later
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else:
model = model.replace("hf-", "", 1)
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results: tuple[list[str], list[str]] = internet.Google(
query, GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
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).google()
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qa_model = pipeline("question-answering", model=model)
response = qa_model(question=query, context=" ".join(results[0]))
return (response["answer"], results[1])
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print(
answer(
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query="Best original song in 80th Golden Globe award 2023?",
model="openai-chatgpt",
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)
)