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If you’re using Langchain, you can log your data to Athina with just a few lines of code. All you need to do is to add the CallbackHandler to your LLMChain callbacks.
1

Install Athina Logging SDK

pip install athina-logger
2

Import Athina classes and set API key

from athina_logger.api_key import AthinaApiKey
from athina_logger.langchain_handler import CallbackHandler
 
AthinaApiKey.set_api_key(os.getenv('ATHINA_API_KEY'))
3

Instantiate the `CallbackHandler` with Athina metadata properties

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
)
  • prompt_slug: An identifier for the prompt that is being used.
  • user_query: The query that the user entered to the LLM.
  • environment: The environment in which the LLM is running. For example, production or development.
  • session_id: The session ID of the LLM. This is used to group multiple LLM calls together.
  • customer_id: The ID of the customer that is using the LLM.
  • customer_user_id: The ID of the user that is using the LLM.
  • external_reference_id: The ID of the external reference that is using the LLM.
  • custom_attributes: Any key-value data you want to associate with the LLM call
  • kwargs: Any key-value data you want to associate with the LLM calls in a chain. This key-value data will be stored as context in Athina Server 
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
4

Add `CallbackHandler` to `LLMChain` callbacks

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

​
Supported Models (Without Streaming)

  • text-davinci-003
  • gpt-3.5-turbo
  • gpt-3.5-turbo-0613
  • gpt-3.5-turbo-16k
  • gpt-3.5-turbo-16k-0613
  • gpt-3.5-turbo-1106
  • gpt-4
  • gpt-4-0613
  • gpt-4-32k
  • gpt-4-32k-0613
  • gpt-4-1106-preview
  • meta-llama/Llama-2-13b
  • meta-llama/Llama-2-13b-chat
  • meta-llama/Llama-2-13b-chat-hf
  • meta-llama/Llama-2-13b-hf
  • meta-llama/Llama-2-70b
  • meta-llama/Llama-2-70b-chat
  • meta-llama/Llama-2-70b-chat-hf
  • meta-llama/Llama-2-70b-hf
  • meta-llama/Llama-2-7b
  • meta-llama/Llama-2-7b-chat
  • meta-llama/Llama-2-7b-chat-hf
  • meta-llama/Llama-2-7b-hf
  • claude-2

​
Supported Models (With Streaming)

  • text-davinci-003
  • gpt-3.5-turbo
  • gpt-3.5-turbo-0613
  • gpt-3.5-turbo-16k
  • gpt-3.5-turbo-16k-0613
  • gpt-3.5-turbo-1106
  • gpt-4
  • gpt-4-0613
  • gpt-4-32k
  • gpt-4-32k-0613
  • gpt-4-1106-preview

​
Not using Python?

Reach out to us at hello@athina.ai - we’re happy to add support for other stacks as well if we hear from you.