> ## Documentation Index
> Fetch the complete documentation index at: https://docs.athina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Langchain

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.

***

<Steps>
  <Step title="Install Athina Logging SDK">
    `pip install athina-logger`
  </Step>

  <Step title="Import Athina classes and set API key">
    ```python
    from athina_logger.api_key import AthinaApiKey
    from athina_logger.langchain_handler import CallbackHandler
     
    AthinaApiKey.set_api_key(os.getenv('ATHINA_API_KEY'))
    ```
  </Step>

  <Step title="Instantiate the `CallbackHandler` with Athina metadata properties">
    ```python
    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

    ```python
    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
    ```
  </Step>

  <Step title="Add `CallbackHandler` to `LLMChain` callbacks">
    ```python
    chain = LLMChain(
      llm=ChatOpenAI(...otherProperties, callbacks=[athina_handler]),
      prompt=chat_prompt,
    )
    chain.run('OpenAI')
    ```
  </Step>
</Steps>

### Supported Models (Without Streaming)[](#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)[](#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?[](#not-using-python)

Reach out to us at [hello@athina.ai](mailto:hello@athina.ai) - we're happy to add support for other stacks as well if we hear from you.