Helicone

import os
HELICONE_API_KEY = os.environ["HELICONE_API_KEY"]
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

import os
from openai import OpenAI
import uuid
import requests

update_headers = {
    'Authorization': f'Bearer {HELICONE_API_KEY}',
    'Content-Type': 'application/json',
}

client = OpenAI(
api_key=OPENAI_API_KEY,  # Replace with your OpenAI API key
base_url="http://oai.hconeai.com/v1",  # Set the API endpoint
default_headers= {  # Optionally set default headers or set per request (see below)
    "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
}
)

data = [
{
    "question": "What causes diabetes?",
    "context": "Diabetes is a metabolic disorder characterized by high blood sugar levels. It is primarily caused by a combination of genetic and environmental factors, including obesity and lack of physical activity.",
},
{
    "question": "What is the capital of France?",
    "context": "Paris is the capital of France. It is a place where people speak French and enjoy baguettes. I once heard that the Eiffel Tower was built by aliens, but don\'t quote me on that.",
},
{
    "question": "How is pneumonia treated?",
    "context": "Pneumonia is an infection that inflames the air sacs in one or both lungs. It is typically treated with antibiotics, rest, and supportive care. The choice of antibiotics depends on the type of pneumonia and its severity.",
}
]

def create_prompt(question, context):
prompt = f"""
Context information is below.
---------------------
{context}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {question}
Answer:
"""
return prompt

def generate_responses(prompt):
response = client.chat.completions.create(
model="gpt-4",
messages=[
    {"role": "user", "content": prompt}
],
extra_headers={ # Can also attach headers per request
    "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
    "Helicone-Request-Id": f"{my_helicone_request_id}"
},
).choices[0].message.content
return response

from uptrain import EvalLLM, Evals, ResponseMatching

eval_llm = EvalLLM(openai_api_key = OPENAI_API_KEY)

def uptrain_evaluate(item):
res = eval_llm.evaluate(
    project_name = "Helicone-Demo",
    data = item,
    checks = [
    Evals.RESPONSE_CONCISENESS,
    Evals.RESPONSE_RELEVANCE,
    Evals.RESPONSE_COMPLETENESS_WRT_CONTEXT,
    Evals.FACTUAL_ACCURACY,
    ]
)
return res

results = []
for index in range(len(data)):

question = data[index]['question']
context = data[index]['context']    

prompt = create_prompt(question, context)

my_helicone_request_id = str(uuid.uuid4())

response = generate_responses(prompt)

eval_data = [
    {
        'question': question,
        'context': context, 
        'response': response, 
    }
]


result = uptrain_evaluate(eval_data)
results.append(result)

for i in result[0].keys():
    if i.startswith('score') or i.startswith('explanation'):
        json_data = {
                        'key': i,
                        'value': str(result[0][i]),
                    }
        status = requests.put(f'https://api.hconeai.com/v1/request/{my_helicone_request_id}/property', headers=update_headers, json=json_data)