Retrieve events based on filters

import honeyhive
from honeyhive.models import components, operations

s = honeyhive.HoneyHive(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
)


res = s.events.get_events(request=operations.GetEventsRequestBody(
    project='<value>',
    filters=[
        components.EventFilter(
            field='event_type',
            value='model',
            operator=components.Operator.IS,
            type=components.Type.STRING,
        ),
    ],
))

if res.object is not None:
    # handle response
    pass

{
  "events": [
    {
      "project_id": "New Project",
      "source": "playground",
      "session_id": "caf77ace-3417-4da4-944d-f4a0688f3c23",
      "event_id": "7f22137a-6911-4ed3-bc36-110f1dde6b66",
      "parent_id": "caf77ace-3417-4da4-944d-f4a0688f3c23",
      "event_type": "model",
      "event_name": "Model Completion",
      "config": {
        "model": "gpt-3.5-turbo",
        "version": "v0.1 - Fork",
        "provider": "openai",
        "hyperparameters": {
          "temperature": 0,
          "top_p": 1,
          "max_tokens": 1000,
          "presence_penalty": 0,
          "frequency_penalty": 0,
          "stop": [],
          "n": 1
        },
        "template": [
          {
            "role": "system",
            "content": "Answer the user's question only using provided context.\n\nContext: {{ context }}"
          },
          {
            "role": "user",
            "content": "{{question}}"
          }
        ],
        "type": "chat"
      },
      "children_ids": [],
      "inputs": {
        "context": "Hello world",
        "question": "What is in the context?",
        "chat_history": [
          {
            "role": "system",
            "content": "Answer the user's question only using provided context.\n\nContext: Hello world"
          },
          {
            "role": "user",
            "content": "What is in the context?"
          }
        ]
      },
      "outputs": {
        "role": "assistant",
        "content": "Hello world"
      },
      "error": null,
      "start_time": "2024-04-01 22:38:19",
      "end_time": "2024-04-01 22:38:19",
      "duration": 824.8056,
      "metadata": {
        "cost": 0.00008,
        "completion_tokens": 23,
        "prompt_tokens": 35,
        "total_tokens": 58
      },
      "feedback": {},
      "metrics": {
        "Answer Faithfulness": 5,
        "Answer Faithfulness_explanation": "The AI assistant's answer is a concise and accurate description of Ramp's API. It provides a clear explanation of what the API does and how developers can use it to integrate Ramp's financial services into their own applications. The answer is faithful to the provided context.",
        "Number of words": 18
      },
      "user_properties": {
        "user": "google-oauth2|111840237613341303366"
      }
    }
  ],
  "totalEvents": 123
}