Reference
OpenAI Chat Completion
Reference
OpenAI Chat Completion
If you’re using OpenAI chat completions in Python, you can get set up in just 2 minutes
1
Install the Python SDK
Run pip install athina-logger
2
Configure API key
from athina_logger.api_key import AthinaApiKey
from athina_logger.openai_wrapper import openai
AthinaApiKey.set_api_key(os.getenv('ATHINA_API_KEY'))
openai.api_key = os.getenv('OPENAI_API_KEY')
3
Replace your OpenAI import
from athina_logger.openai_wrapper import openai
4
Add metadata fields
Use OpenAI as your normally would, but optionally add AthinaMeta fields for better segmentation on the platform.
from athina_logger.athina_meta import AthinaMeta
# Use openai.ChatCompletion just as you would normally
# Add fields to AthinaMeta for better segmentation of your data
openai.ChatCompletion.create(
model="gpt-4",
messages=messages,
stream=False,
athina_meta=AthinaMeta(
prompt_slug="yc_rag_v1",
user_query="How much funding does Y Combinator provide?", # For RAG Q&A systems, log the user's query
context={"information": retrieved_documents} # Your retrieved documents
session_id=session_id, # Conversation ID
customer_id=customer_id, # Your Customer's ID
customer_user_id=customer_id, # Your End User's ID
environment=environment, # Environment (production, staging, dev, etc)
external_reference_id="ext_ref_123456",
custom_attributes={
"name": "John",
"age": 30,
"city": "New York"
} # Your custom-attributes
),
)
Note: We support both stream=True and stream=False for OpenAI chat
completions. OpenAI doesn’t provide usage statistics such as prompt and
completion tokens when streaming. However, We overcomes this limitation by
getting these with the help of the tiktoken package, which is designed to work
with all tokenized OpenAI GPT models.