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. Import Athina Logger

Replace your import openai with this:

from athina_logger.api_key import AthinaApiKey
from athina_logger.athina_meta import AthinaMeta
from athina_logger.openai_wrapper import openai
 
client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

3. Set Athina API key

# Initialize the Athina API key somewhere in your code
AthinaApiKey.set_api_key(os.getenv('ATHINA_API_KEY'))

4. Use OpenAI chat completions request as you do normally

Non streaming example:

# Use client.chat.completions.create just as you would normally
# Add fields to AthinaMeta for better segmentation of your data
client.chat.completions.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
    ),
)

Streaming example:

stream = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    stream=True,
    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
    ),
)
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

💡

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.

Frequently Asked Questions

Q. What is AthinaMeta

The AthinaMeta fields are used for segmentation of your data on the dashboard. All these fields are optional, but highly recommended.

class AthinaMeta:
    prompt_slug: Optional[str] = None
    context: Optional[dict] = None
    customer_id: Optional[dict] = None
    customer_user_id: Optional[dict] = None
    session_id: Optional[dict] = None
    user_query: Optional[dict] = None
    environment: Optional[dict] = None
    external_reference_id: Optional[dict] = None
    customer_id: Optional[str] = None
    customer_user_id: Optional[str] = None
    response_time: Optional[int] = None
    custom_attributes: Optional[dict] = None

Q. Is this SDK going to make a proxy request to OpenAI through Athina?

Nope! We know how important your OpenAI inference call is, so we don't want to interfere with that or increase response times.

Instead, we simply make an logging API request to Athina, which is separate from your OpenAI request.

Q. Will this SDK increase my latency?

Nope! The logging call is being made in a background thread as a fire and forget request, so there is almost no additional latency (< 5ms).

Quick Start OpenAI Chat (0.x)