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')