# from deepeval.tracing import trace, TraceType
# from openai import OpenAI

# client = OpenAI()


# class Chatbot:
#     def __init__(self):
#         pass

#     @trace(type=TraceType.LLM, name="OpenAI", model="gpt-4")
#     def llm(self, input):
#         response = client.chat.completions.create(
#             model="gpt-4",
#             messages=[
#                 {
#                     "role": "system",
#                     "content": "You are a helpful assistant.",
#                 },
#                 {"role": "user", "content": input},
#             ],
#         )
#         return response.choices[0].message.content

#     @trace(
#         type=TraceType.EMBEDDING,
#         name="Embedding",
#         model="text-embedding-ada-002",
#     )
#     def get_embedding(self, input):
#         response = (
#             client.embeddings.create(
#                 input=input, model="text-embedding-ada-002"
#             )
#             .data[0]
#             .embedding
#         )
#         return response

#     @trace(type=TraceType.RETRIEVER, name="Retriever")
#     def retriever(self, input=input):
#         embedding = self.get_embedding(input)

#         # Replace this with an actual vector search that uses embedding
#         list_of_retrieved_nodes = ["Retrieval Node 1", "Retrieval Node 2"]
#         return list_of_retrieved_nodes

#     @trace(type=TraceType.TOOL, name="Search")
#     def search(self, input):
#         # Replace this with an actual function that searches the web
#         title_of_the_top_search_results = "Search Result: " + input
#         return title_of_the_top_search_results

#     @trace(type=TraceType.TOOL, name="Format")
#     def format(self, retrieval_nodes, input):
#         prompt = "You are a helpful assistant, based on the following information: \n"
#         for node in retrieval_nodes:
#             prompt += node + "\n"
#         prompt += "Generate an unbiased response for " + input + "."
#         return prompt

#     @trace(type=TraceType.AGENT, name="Chatbot")
#     def query(self, user_input=input):
#         top_result_title = self.search(user_input)
#         retrieval_results = self.retriever(top_result_title)
#         prompt = self.format(retrieval_results, top_result_title)
#         return self.llm(prompt)


# import pytest
# from deepeval import assert_test
# from deepeval.test_case import LLMTestCase
# from deepeval.metrics import HallucinationMetric

# chatbot = Chatbot()


# def test_hallucination():
#     context = [
#         "Be a natural-born citizen of the United States.",
#         "Be at least 35 years old.",
#         "Have been a resident of the United States for 14 years.",
#     ]
#     input = "What are the requirements to be president?"

#     metric = HallucinationMetric(threshold=0.8)
#     test_case = LLMTestCase(
#         input=input,
#         actual_output=chatbot.query(user_input=input),
#         context=context,
#     )
#     assert_test(test_case, [metric])