# API Documentation - Burme-Coder-Max

## Overview

**burme-coder-max** is a Myanmar AI coding agent that provides programming assistance in Burmese language with code examples.

---

## Core Module API

### CoderAgent

Main AI agent for generating coding responses.

```python
from core.agent import CoderAgent

agent = CoderAgent(
    model: str = "gpt-4",           # AI model to use
    temperature: float = 0.7,        # Response creativity
    max_tokens: int = 2048,         # Max response length
    knowledge_dir: Optional[str] = None  # Knowledge base directory
)
```

#### Methods

| Method | Description | Returns |
|--------|-------------|---------|
| `generate_response(instruction, context)` | Generate code response | `Dict` with session_id, response, timestamp |
| `set_system_prompt(prompt)` | Set custom system prompt | `None` |
| `get_trajectory()` | Get conversation for training | `Dict` |
| `save_trajectory(path)` | Save trajectory to file | `None` |
| `reset()` | Reset agent state | `None` |

#### Response Format

```python
{
    "session_id": str,
    "instruction": str,
    "response": str,
    "timestamp": float,
    "model": str
}
```

---

### CodeExecutor

Execute code in various languages.

```python
from core.executor import CodeExecutor

executor = CodeExecutor(
    timeout: int = 30,    # Execution timeout in seconds
    sandbox: bool = True  # Enable sandbox mode
)
```

#### Methods

| Method | Description | Returns |
|--------|-------------|---------|
| `execute(code, language)` | Execute code | `ExecutionResult` |
| `validate_syntax(code, language)` | Check syntax | `Tuple[bool, Optional[str]]` |

#### ExecutionResult

```python
@dataclass
class ExecutionResult:
    success: bool           # Execution success
    output: str            # Execution output
    error: Optional[str]   # Error message
    execution_time: float  # Time taken
```

---

### ResponseValidator

Validate AI generated responses.

```python
from core.validator import ResponseValidator

validator = ResponseValidator()
```

#### Methods

| Method | Description | Returns |
|--------|-------------|---------|
| `validate(response, instruction)` | Validate single response | `ValidationResult` |
| `validate_multiple(responses, instruction)` | Validate multiple | `List[ValidationResult]` |

#### ResponseQuality

```python
class ResponseQuality(Enum):
    EXCELLENT = "excellent"
    GOOD = "good"
    ADEQUATE = "adequate"
    POOR = "poor"
    INVALID = "invalid"
```

---

## Knowledge Module API

### LocalKB

Local knowledge base for markdown files.

```python
from knowledge import LocalKB

kb = LocalKB(base_dir: Optional[str] = None)
```

#### Methods

| Method | Description | Returns |
|--------|-------------|---------|
| `search(query, category)` | Search knowledge | `List[Dict]` |
| `get_content(topic)` | Get topic content | `Optional[str]` |
| `get_all_topics()` | List all topics | `List[str]` |
| `get_random_entry()` | Get random entry | `Optional[Dict]` |

#### Search Result Format

```python
{
    "source": str,      # File name
    "line": int,        # Line number
    "snippet": str,     # Match snippet
    "relevance": float  # Relevance score (0-1)
}
```

---

### WebUpdater

Update knowledge from web sources.

```python
from knowledge import WebUpdater

updater = WebUpdater(cache_dir: Optional[str] = None)
```

#### Methods

| Method | Description | Returns |
|--------|-------------|---------|
| `fetch_content(source)` | Fetch single source | `Optional[str]` |
| `fetch_all()` | Fetch all sources | `Dict[str, str]` |
| `update_markdown_files(path, force)` | Update files | `List[str]` |
| `scrape_url(url, selectors)` | Scrape URL | `Optional[str]` |

---

## Animations Module API

### Spinner

Loading spinner animation.

```python
from animations import Spinner

with Spinner("Loading..."):
    do_something()
```

### ProgressBar

Progress bar for iterations.

```code
from animations import ProgressBar

for i in ProgressBar(range(100), description="Downloading"):
    process(i)
```

### TypingEffect

Typewriter-style text animation.

```python
from animations import TypingEffect

effect = TypingEffect("Hello World", delay=0.05)
effect.animate()
```

### ParticleBurst

Celebration particle effect.

```python
from animations import ParticleBurst

burst = ParticleBurst(count=50)
burst.explode()
```

---

## Thanking Module API

### ThankYou

Simple thank you display.

```python
from ui.thanking import ThankYou

ThankYou.show()                    # Random message
ThankYou.show("Custom message")    # Custom message
ThankYou.show_with_emoji("⭐")     # With emoji
```

### Appreciation

Detailed appreciation display.

```python
from ui.thanking import Appreciation

Appreciation.show(topic="Python")           # With topic
Appreciation.show_banner("Developer")      # Banner style
Appreciation.show_stacked(["Python", "JS"]) # Multiple topics
```

### CreditDisplay

Credits and attribution.

```python
from ui.thanking import CreditDisplay

CreditDisplay.show()       # Full credits
CreditDisplay.show_simple()  # Simple credits
```

---

## CLI Commands API

### ask

```bash
burme-coder ask "instruction" [OPTIONS]

Options:
  --model TEXT       AI model (default: gpt-4)
  --verbose         Verbose output
  --output, -o      Output file
```

### interactive

```bash
burme-coder interactive
```

Interactive commands:
- `exit` - Quit
- `clear` - Clear history
- `history` - Show history
- `help` - Show help
- `/search <query>` - Search knowledge
- `/model <name>` - Switch model
- `/reset` - Reset agent

### train

```bash
burme-coder train --data ./data/trajectories [OPTIONS]

Options:
  --epochs INT      Number of epochs (default: 10)
  --batch-size INT  Batch size (default: 4)
```

### eval

```bash
burme-coder eval --data ./data/trajectories [--verbose]
```

---

## Configuration

### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `OPENAI_API_KEY` | OpenAI API key | - |
| `ANTHROPIC_API_KEY` | Anthropic API key | - |
| `ANIMATION_SPEED` | Animation delay | 0.05 |
| `ANIMATION_COLOR` | Enable colors | true |
| `CACHE_DIR` | Cache directory | ~/.burme_coder/cache |
| `CACHE_TTL` | Cache TTL (seconds) | 3600 |
| `LOG_LEVEL` | Logging level | INFO |

### .env File

```bash
# Copy from example
cp .env.example .env

# Edit with your settings
nano .env
```

---

## Error Handling

### Common Errors

| Error | Cause | Solution |
|-------|-------|----------|
| `SyntaxError` | Invalid code syntax | Check code syntax |
| `TimeoutError` | Execution timeout | Increase timeout |
| `ImportError` | Missing dependencies | Install requirements |
| `APIError` | API key invalid | Verify API key |

### Error Response Format

```python
{
    "error": {
        "code": str,      # Error code
        "message": str,   # Error message
        "details": dict   # Additional details
    }
}
```

---

## Examples

### Basic Usage

```python
from core.agent import CoderAgent
from core.validator import ResponseValidator

# Initialize
agent = CoderAgent(model="gpt-4")
validator = ResponseValidator()

# Generate response
response = agent.generate_response("Python decorator hta ya")

# Validate
result = validator.validate(response["response"], "decorator")
print(f"Quality: {result.quality.value}")
```

### With Animations

```python
from core.agent import CoderAgent
from animations import Spinner

with Spinner("Generating response..."):
    agent = CoderAgent()
    response = agent.generate_response("test")
```

### With Knowledge Base

```python
from core.agent import CoderAgent
from knowledge import LocalKB

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

agent = CoderAgent(knowledge_dir="./data/knowledge")
response = agent.generate_response("decorator")
```