Chat Support (LLM Annotator Assistance)

Chat Support adds a collapsible sidebar where annotators can have multi-turn conversations with an LLM about the current annotation instance. The LLM is given context about the task and current text, helping annotators think through difficult cases without giving direct answers.

Quick Start

Add a chat_support section to your YAML config:

chat_support:
  enabled: true
  endpoint_type: ollama
  ai_config:
    model: llama3.2
    temperature: 0.7
    max_tokens: 500

Configuration Reference

chat_support

Field Type Default Description
enabled bool false Enable/disable chat support
endpoint_type string required LLM provider: openai, anthropic, ollama, huggingface, gemini, vllm, openrouter
ai_config dict {} Provider-specific configuration
system_prompt dict {} System prompt customization
ui dict {} UI appearance settings

chat_support.ai_config

Field Type Default Description
model string provider default Model name (e.g., llama3.2, gpt-4o-mini, claude-sonnet-4-20250514)
temperature float 0.7 Response creativity (0.0-2.0)
max_tokens int 500 Maximum response length
api_key string — API key (required for cloud providers)
base_url string — Custom API base URL (for ollama, vllm)
timeout int 30 Request timeout in seconds

chat_support.system_prompt

Field Type Default Description
template string built-in Custom system prompt template

The system prompt template supports these variables:

  • {task_name} — The annotation task name from config
  • {task_description} — The task description
  • {annotation_labels} — Summary of available labels across all schemes
  • {instance_text} — The current instance text being annotated
  • {instance_id} — The current instance ID

chat_support.ui

Field Type Default Description
title string "Ask AI" Sidebar header and toggle button text
placeholder string "Ask about this annotation..." Input placeholder text
sidebar_width int 380 Sidebar width in pixels (200-800)
max_history_per_instance int 50 Max messages stored per instance

Example Configurations

Ollama (Local)

chat_support:
  enabled: true
  endpoint_type: ollama
  ai_config:
    model: llama3.2
    temperature: 0.7
    max_tokens: 500
    base_url: http://localhost:11434

OpenAI

chat_support:
  enabled: true
  endpoint_type: openai
  ai_config:
    model: gpt-4o-mini
    api_key: sk-your-key-here
    temperature: 0.7
    max_tokens: 500

Anthropic

chat_support:
  enabled: true
  endpoint_type: anthropic
  ai_config:
    model: claude-sonnet-4-20250514
    api_key: sk-ant-your-key-here
    temperature: 0.7
    max_tokens: 500

Custom System Prompt

chat_support:
  enabled: true
  endpoint_type: ollama
  ai_config:
    model: llama3.2
  system_prompt:
    template: |
      You are a helpful assistant for the annotation task "{task_name}".

      Task: {task_description}
      Labels: {annotation_labels}

      The annotator is reading: {instance_text}

      Provide guidance without revealing the answer.
      Focus on helping them understand the text.

How It Works

  1. A toggle button appears in the navigation bar (right side)
  2. Clicking it opens a sidebar with a chat interface
  3. The annotator types a question and presses Enter (or clicks Send)
  4. The LLM receives the current instance text and task context as a system prompt
  5. The response appears in the chat
  6. Conversation history is preserved per instance — navigating away and back restores the chat
  7. All chat interactions are saved in user_state.json for research analysis

Data Format

Chat history is stored in user_state.json under each instance's behavioral data:

{
  "instance_id_to_behavioral_data": {
    "instance_1": {
      "chat_history": [
        {
          "role": "user",
          "content": "What does this mean?",
          "timestamp": 1709000000.0,
          "instance_id": "instance_1",
          "response_time_ms": null
        },
        {
          "role": "assistant",
          "content": "This text discusses...",
          "timestamp": 1709000002.5,
          "instance_id": "instance_1",
          "response_time_ms": 2500
        }
      ],
      "interactions": [
        {
          "event_type": "chat_message_sent",
          "target": "chat_sidebar",
          "metadata": {
            "message_length": 22,
            "response_length": 45,
            "response_time_ms": 2500
          }
        }
      ]
    }
  }
}

Analyzing Chat Data

Chat data can be extracted from user_state.json for analysis:

import json

with open("annotation_output/user_state.json") as f:
    user_states = json.load(f)

for username, state in user_states.items():
    bd = state.get("instance_id_to_behavioral_data", {})
    for instance_id, data in bd.items():
        chat = data.get("chat_history", [])
        if chat:
            print(f"User {username}, Instance {instance_id}: {len(chat)} messages")
            for msg in chat:
                print(f"  [{msg['role']}] {msg['content'][:80]}")

Troubleshooting

Chat button doesn't appear: - Verify chat_support.enabled: true in config - Check browser console for errors loading /api/chat/config

"Chat support is not configured" error: - Check server logs for endpoint initialization errors - For Ollama: ensure ollama serve is running - For cloud providers: verify API key is correct

Slow responses: - Increase ai_config.timeout if responses are being cut off - Try a smaller/faster model - For Ollama: ensure the model is already pulled (ollama pull llama3.2)