OpenAssistant是一个基于聊天的助手，它可以理解任务，可以与第三方系统交互，并动态检索信息

<a href="https://github-com.translate.goog/LAION-AI/Open-Assistant/blob/main/oasst-data/README.md?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en&_x_tr_pto=wapp"></a> # Open Assistant Data Module (oasst_data) ## Installation of oasst_data If you got the exception `ModuleNotFoundError: No module named 'oasst_data'` you first need to install the `oasst_data` package: Run `pip install -e .` in the `oasst-data/` directory of the Open-Assistant repository to install the `oasst_data` python package in editable mode. ## Reading Open-Assistant Export Files Reading jsonl files is in general very simple in Python. To further simplify the process for OA data the `oasst_data` module comes with Pydantic class definitions for validation and helper functions to load and traverse message trees. Code example: ```python # parsing OA data files with oasst_data helpers from oasst_data import read_message_trees, visit_messages_depth_first, ExportMessageNode messages: list[ExportMessageNode] = [] input_file_path = "data_file.jsonl.gz" for tree in read_message_trees(input_file_path): if tree.prompt.lang not in ["en","es"]: # filtering by language tag (optional) continue # example use of depth first tree visitor help function visit_messages_depth_first(tree.prompt, visitor=messages.append, predicate=None) ``` A more comprehensive example of loading all conversation threads ending in assistant replies can be found in the file [oasst_dataset.py](https://github.com/LAION-AI/Open-Assistant/blob/main/model/model_training/custom_datasets/oasst_dataset.py) which is used to load Open-Assistant export data for supervised fine-tuning (training) of our language models. You can also load jsonl data completely without dependencies to `oasst_data` solely with standard python libraries. In this case the json objects are loaded as nested dicts which need to be 'parsed' manually by you: ```python # loading jsonl files without using oasst_data import gzip import json from pathlib import Path input_file_path = Path(input_file_path) if input_file_path.suffix == ".gz": file_in = gzip.open(str(input_file_path), mode="tr", encoding="UTF-8") else: file_in = input_file_path.open("r", encoding="UTF-8") with file_in: # read one object per line for line in file_in: dict_tree = json.loads(line) # manual parsing of data now goes here ... ``` ## Open-Assistant JSON Lines Export Data Format Open-Assistant export data is written as standard [JSON Lines data](https://jsonlines.org/). The generated files are UTF-8 encoded text files with single JSON objects in each line. The files come either uncompressed with the ending `.jsonl` or compressed with the ending `.jsonl.gz`. Three different types of objects can appear in these files: 1. Individual Messages 2. Conversation Threads 3. Message Trees For readability the following JSON examples are shown formatted with indentation on multiple lines although they are be stored without indentation in the actual data file. ### 1. Individual Messages Message objects can be identified by the presence of a `"message_id"` property. In files written by Open-Assistant this property will appear as the first property on the line directly after the opening curly brace. Each message needs at least an id (UUID), message text, a role (either "prompter" or "assistant") and a language tag ([BCP 47](https://en.wikipedia.org/wiki/IETF_language_tag)) like "en" for English. Minimal example of a message: ```json { "message_id": "13714ad5-3161-4ead-9593-7248b0a3f218", "text": "List the pieces of a reinforcement learning system (..)", "role": "prompter", "lang": "en" } ``` Example of a message with more properties: ```json { "message_id": "218440fd-5317-4355-91dc-d001416df62b", "parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4", "user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4", "text": "It was the winter of 2035, and artificial intelligence (..)", "role": "assistant", "lang": "en", "review_count": 3, "review_result": true, "deleted": false, "rank": 0, "synthetic": true, "model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)", "labels": { "spam": { "value": 0.0, "count": 3 }, "lang_mismatch": { "value": 0.0, "count": 3 }, "pii": { "value": 0.0, "count": 3 }, "not_appropriate": { "value": 0.0, "count": 3 }, "hate_speech": { "value": 0.0, "count": 3 }, "sexual_content": { "value": 0.0, "count": 3 }, "quality": { "value": 0.416, "count": 3 }, "toxicity": { "value": 0.16, "count": 3 }, "humor": { "value": 0.0, "count": 3 }, "creativity": { "value": 0.33, "count": 3 }, "violence": { "value": 0.16, "count": 3 } } }, ``` The backend export tool ([export.py](https://github.com/LAION-AI/Open-Assistant/blob/main/backend/export.py)) will generate jsonl files with individual messages when a set of messages is exported that is not a full tree. This is for example the case when filtering messages based on properties like user, deleted, spam or synthetic. Spam messages are those which have a `review_result` that is `false`. ### 2. Conversation Threads Conversation threads are a linear lists of messages. THese objects can be identified by the presence of the `"thread_id"` property which contains the UUID of the last message of the thread (which can be used to reconstruct the thread by returning the list of ancestor messages up to the prompt root message). The message_id of the first message is normally also the id of the message-tree that contains the thread. ```json { "thread_id": "534c7711-afb5-4410-9006-489dc885280e", "thread": [ { "message_id": "14fbb664-a620-45ce-bee4-7c519b16a793", "text": "Why can't we divide by 0? (..)", "role": "prompter", "lang": "en" }, { "message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8", "text": "The reason we cannot divide by zero is because (..)", "role": "assistant", "lang": "en" }, { "message_id": "1c9210e9-af9e-4507-abc5-3b3c7bca4dce", "text": "Can you explain why we created a definition (..)", "role": "prompter", "lang": "en" }, { "message_id": "534c7711-afb5-4410-9006-489dc885280e", "text": "The historical origin of the imaginary (..)", "role": "assistant", "lang": "en" } ] } ``` ### 3. Message Trees Message trees have of a prompt message at the root and can then branch out into multiple different reply branches which each can again have further replies. Message trees can be identified by the `"message_tree_id"` property. The `message_tree_id` always matches the id of the prompt-message. Example of a tree with minimal messages: For clarity only the mandatory elements of the message are shown here. The full export format contains all the message attributes as shown above in the full message example. ```json { "message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793", "tree_state": "ready_for_export", "prompt": { "message_id": "14fbb664-a620-45ce-bee4-7c519b16a793", "text": "Why can't we divide by 0? (..)", "role": "prompter", "lang": "en", "replies": [ { "message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8", "text": "The reason we cannot divide by zero is because (..)", "role": "assistant", "lang": "en", "replies": [ { "message_id": "1c9210e9-af9e-4507-abc5-3b3c7bca4dce", "text": "Can you explain why we created a definition (..)", "role": "prompter", "lang": "en", "replies": [ { "message_id": "534c7711-afb5-4410-9006-489dc885280e", "text": "The historical origin of the imaginary (..)", "role": "assistant", "lang": "en",