WARNING: This software is mainly developed according to my personal habits and is still under development. We are not responsible for any data loss that may occur during your use.

LLM Labeling UI is a project fork from Chatbot UI, and made the following modifications to make it more suitable for large language model data labeling tasks.

• The backend code is implemented in python, the frontend code is precompiled, so it can run without a nodejs environment
• The Chatbot UI uses localStorage to save data, with a size limit of 5MB, the LLM Labeling UI can load local data when starting the service, with no size limit
• Web interaction:
  • Browse data in pages, search by keywords, filter by messages count.
  • Directly modify/delete model's response results.
  • Split long conversations into multiple conversations
  • A confirmation button has been added before deleting the conversation message
  • Display the number of messages and token length in the current conversation
  • Allow modify system prompt during the dialogue
  • Replace string in current conversation
• Useful command line tools to help you clean/manage your data, such as language cleaning, duplicate removal, embedding cluster, etc.

pip install llm-labeling-ui

1. Provide OpenAI API Key

You can provide openai api key before start server or configure it later in the web page.

export OPENAI_API_KEY=YOUR_KEY
export OPENAI_ORGANIZATION=YOUR_ORG

2. Start Server

llm-labeling-ui server start --data chatbot-ui-v4-format-history.json --tokenizer meta-llama/Llama-2-7b

• --data: Chatbot-UI-v4 format, here is an example. Before the service starts, a chatbot-ui-v4-format-history.sqlite file will be created based on chatbot-ui-v4-format-history.json. All your modifications on the page will be saved into the sqlite file. If the chatbot-ui-v4-format-history.sqlite file already exists, it will be automatically read.
• --tokenizer is used to display how many tokens the current conversation on the webpage contains. Please note that this is not the token consumed by calling the openai api.

• cluster: Cluster operations, such as create embedding, run cluster, semantic deduplication, etc.
• conversation: Conversation operations, such as remove prefix, remove deduplication, etc
• tag: Add tags to you data, such as lang classification(en,zh..), traditional or simplified chinese classification, etc.

User --help to see more details, such as:

llm-labeling-ui cluster --help

Usage: llm-labeling-ui cluster [OPTIONS] COMMAND [ARGS]...

╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                  │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────────────────────────╮
│ create-embedding  Create embedding                                           │
│ dedup             Delete redundant data in the same clustering result        │
│                   according to certain strategies.                           │
| prune-embedding   Remove embedding not exists in db                          |
│ run               DBSCAN embedding cluster                                   │
│ view              View cluster result                                        │
╰──────────────────────────────────────────────────────────────────────────────