docs/usage.md

"""Documentation: Usage Guide"""

Usage Guide - Burme-Coder-Max

Quick Start

Ask a Question

burme-coder ask "Python decorator hta ya py"

Interactive Mode

burme-coder interactive

Train Agent

burme-coder train --data ./data/trajectories

CLI Commands

ask - Ask a single question

burme-coder ask [INSTRUCTION] [OPTIONS]

Options:
  --model TEXT       AI model to use (default: gpt-4)
  --verbose         Show detailed output
  --output, -o      Save response to file

interactive - Start chat mode

burme-coder interactive

Interactive commands:

• exit - Quit
• clear - Clear history
• help - Show help
• history - Show conversation history

train - Train on trajectories

burme-coder train [OPTIONS]

Options:
  --data TEXT      Training data directory (default: ./data/trajectories)
  --epochs INT     Number of epochs (default: 10)
  --batch-size INT Batch size (default: 4)

eval - Evaluate performance

burme-coder eval --data ./data/trajectories

Python API

Import and Use Agent

from burme_coder.core import CoderAgent

agent = CoderAgent(model="gpt-4")
response = agent.generate_response("Python list sorting hta ya")

print(response["response"])

Use Animations

from burme_coder.animations import Spinner, ProgressBar

with Spinner("Loading"):
    do_something()

for i in ProgressBar(range(100)):
    process(i)

Use Thanking System

from burme_coder.ui.thanking import ThankYou

ThankYou.show()

Configuration

Edit .env file:

# API Keys
OPENAI_API_KEY=sk-your-key-here

# Animation Settings
ANIMATION_SPEED=0.05
ANIMATION_COLOR=true

# Cache Settings
CACHE_DIR=./data/cache
CACHE_TTL=3600

Knowledge Base

Search local knowledge:

from burme_coder.knowledge import LocalKB

kb = LocalKB()
results = kb.search("python decorators")

for result in results:
    print(result["snippet"])

Update from web:

from burme_coder.knowledge import WebUpdater

updater = WebUpdater()
updater.update_markdown_files("./data/knowledge/skills")

Examples

See examples/ directory for more examples:

• demo_animation.py - Animation demonstrations
• demo_thanking.py - Thanking system examples
• demo_skill_query.py - Knowledge base queries