Bulk Export Documents or Conversation History

Your data is yours. Export it for detailed analysis of your user conversations, or to move to another service.

Export all conversations from your project

To export all conversations, click Analysis then click Download.

Obtain all conversations that anyone has had in your project, including any and all users. Of course, only the Owner or Admins of a project can access these sensitive details.

Data format

• If a user is authenticated when chatting, we include their email address. Otherwise null.

• The format mirrors OpenAI's Conversation spec, e.g. below. See OpenAI's docs for details https://platform.openai.com/docs/api-reference/chat/create.

• In addition, each assistant message includes contexts that were (potentially) used to answer the question. We always include a maximum of 80 contexts per assitant response.

# Data format modeled after Chat API https://platform.openai.com/docs/api-reference/chat/create
"model": "gpt-4",
"messages": [
    {
        "role": "system",
        "content": "Your system prompt here"
    },
    {
        "role": "user",
        "content": "What is in these documents?"
    }
],
... etc

How to read Conversation History

import jsonlines
import pprint

filename = 'myProject-convo_history.jsonl'
with jsonlines.open(filename) as f:
    data = list(f)

print(len(data))
pprint.pprint(data[0])

Example of a single row:

{'convo': {'folderId': None,
           'id': '03a9ffb3-5bde-4766-a4eb-66dff42ed8ac',
           'messages': [{'content': 'Contrast Shakespeare against Kierkegaard..',
                         'contexts': [],
                         'role': 'user'},
                        {'content': , "While Shakespeare's works explore the complexities of "
                                      'human nature through vivid characters and timeless '
                                      "themes, Kierkegaard's philosophical writings delve "
                                      'into the depths of individual existence, faith, and '
                                      'the human condition, making them distinct yet equally '
                                      'profound in their examination of the human '
                                      'experience.',
                         'contexts': [{'base_url': 'http://kastanday.com',
                                       'course_name ': 'test-video-ingest-21',
                                       'pagenumber': '',
                                       'readable_filename': 'Kastan Day – I '
                                                            'love coding, '
                                                            'drones and '
                                                            'podcasts.',
                                       's3_path': '',
                                       'text': 'Skip to content\n'
                                               'I solve real world problems '
                                               'with machine learning.\n'
                                               'Swarthmore college president '
                                               'Val Smith asked me to speak to '
                                               'incoming students at '
                                               'orientation 2019. View my talk '
                                               'on startups, failure and '
                                               'creating your own system of '
                                               'happiness.\n'
                                               'Working at NASA’s Autonomy '
                                               'incubator, read about my work '
                                               'here.\n'
                                               'Currently\n'
                                               '\n'
                                               'Masters in Computer Science '
                                               'from UIUC\n'
                                               'Specialization in applied '
                                               'machine learning, ML-ops, and '
                                               'distributed ML training.\n'
                                               'Expected grad May, 2023.\n'
                                               '\n'
                                               'National Center for\xa0'
                                               'Supercomputing Applications '
                                               '(NCSA)\n'
                                               'Research Assistant, Oct '
                                               '21-Present.\n'
                                               'Funded by the NSF & IBM '
                                               'Research.\n'
                                               '\n'
                                               'I implemented distributed ML '
                                               'training on a GPU '
                                               'supercomputer (25 Nvidia DGX '
                                               'nodes, 200 A100 GPUs) to scale '
                                               'up the research of domain '
                                               'experts in biology and '
                                               'physics.\n'
                                               '\n'
                                               'Distributed (HPC) Systems\n'
                                               'Data & Model Sharding '
                                               'Parallelism\n'
                                               'Pipeline & Tensor Parallelism\n'
                                               'PyTorch Lightning\n'
                                               'Mesh Tensorflow\n'
                                               'Ray.io\n'
                                               'FairScale\n'
                                               'Horovod\n'
                                               'Dask\n'
                                               'Docker\n'
                                       }
                               ]
                         ],
                         'role': 'assistant'},
           'model': {'id': 'gpt-4-0613', 'name': 'GPT-4-0613'},
           'name': 'How did Kastan win argonne?',
           'prompt': 'You are ChatGPT, a large language model trained by '
                     "OpenAI. Follow the user's instructions carefully. "
                     'Respond using markdown.',
           'temperature': 0.4,
           'user_email': '[e-mail]'},
 'convo_id': '03a9ffb3-5bde-4766-a4eb-66dff42ed8ac',
 'course_name': 'test-video-ingest-21',
 'created_at': '2023-08-14T16:35:40.508062-07:00',
 'id': 3476,
 'user_email': '[e-mail]'}

Export all Documents

Export all documents from the bottom of the "Materials" page.

Download the post-processed text and vector embeddings (OpenAI Ada-002) used by the LLM. The export format is JSON Lines (.JSONL). To minimize data transfer costs, exporting original files (PDFs, etc.) is only available for individual documents.