Langchain

from athina_logger.api_key import AthinaApiKey
from athina_logger.langchain_handler import CallbackHandler
 
AthinaApiKey.set_api_key(os.getenv('ATHINA_API_KEY'))

athina_handler = CallbackHandler(
    prompt_slug='customer-query-prompt/v1',
    user_query='I would like to get a refund on a pair of shoes I purchased online',
    environment='production',
    session_id='1234',
    customer_id='nike-usa',
    customer_user_id='tim@apple.com',
    external_reference_id='your-reference-id',
    custom_attributes= {
        "loggedBy": "John Doe",
        "age": 24,
        "isAdmin": true,
        "references": null
        # any other attribute to be logged
    }
    kwargs: Any, # Any key-value data you want to associate with the LLM calls in a chain
)

Sample kwargs:
context1 = "Germany is located in central europe"
context2 = "Berlin is the capital of Germany"
 
This will be stored as:
 
{
    "context1": "Germany is located in central europe",
    "context2": "Berlin is the capital of Germany"
}
 
This will be perceived as retrieved context

chain = LLMChain(
    llm=ChatOpenAI(...otherProperties, callbacks=[athina_handler]),
    prompt=chat_prompt,
)
chain.run('OpenAI')