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import os | |
from pathlib import Path | |
from buster.chatbot import Chatbot, ChatbotConfig | |
TEST_DATA_DIR = Path(__file__).resolve().parent / "data" | |
DOCUMENTS_FILE = os.path.join(str(TEST_DATA_DIR), "document_embeddings_huggingface_subset.tar.gz") | |
def test_chatbot_simple(): | |
hf_transformers_cfg = ChatbotConfig( | |
documents_file=DOCUMENTS_FILE, | |
unknown_prompt="This doesn't seem to be related to the huggingface library. I am not sure how to answer.", | |
embedding_model="text-embedding-ada-002", | |
top_k=3, | |
thresh=0.7, | |
max_words=3000, | |
completion_kwargs={ | |
"temperature": 0, | |
"engine": "text-davinci-003", | |
"max_tokens": 100, | |
}, | |
response_format="slack", | |
text_before_prompt=( | |
"""You are a slack chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python.\n""" | |
"""Make sure to format your answers in Markdown format, including code block and snippets.\n""" | |
"""Do not include any links to urls or hyperlinks in your answers.\n\n""" | |
"""Now answer the following question:\n""" | |
), | |
) | |
chatbot = Chatbot(hf_transformers_cfg) | |
answer = chatbot.process_input("What is a transformer?") | |
assert isinstance(answer, str) | |