ape-openai 1.0.6
OpenAI integration for APE (AI Programmatic Execution)
License: MIT
Requires Python: >=3.11

# ape-openai

OpenAI integration for APE (AI Programmatic Execution).

## What is ape-openai?

**ape-openai** bridges APE's deterministic validation layer with OpenAI's function calling API. It prevents hallucinations in function parameters by enforcing strict type checking and constraints before execution.

## Why ape-openai?

OpenAI's function calling is powerful but can be unreliable:
- Function parameters can be incorrectly formatted
- Type mismatches cause runtime errors
- Missing required fields break execution
- No validation before calling your code

**ape-openai solves this** by adding APE as a validation layer:

```
OpenAI → JSON parameters → APE validation → Deterministic execution ✓
```

## Installation

```bash
# Core package (schema conversion + execution)
pip install ape-openai

# With OpenAI SDK (for code generation)
pip install ape-openai[openai]

# Development dependencies
pip install ape-openai[dev]
```

**Prerequisites:**
- Python >= 3.11
- ape-lang >= 0.2.0

## Test Coverage

✅ **All tests passing**

- **Total tests: 49**
- Last verified via pytest discovery

See [../ape/docs/APE_TESTING_GUARANTEES.md](../ape/docs/APE_TESTING_GUARANTEES.md) for details on what these tests guarantee.

The test suite covers:
- Schema conversion (APE → OpenAI)
- Executor (OpenAI → APE runtime)
- Utils (error formatting, validation)
- End-to-end integration
- Generator (NL → APE code)

To verify test counts:
```bash
pytest packages/ape-openai/tests --collect-only -q
```

## Quick Start

```python
from openai import OpenAI
from ape_openai import ApeOpenAIFunction

# 1. Create Ape task file
# calculator.ape:
# task add:
#     inputs: a: Integer, b: Integer
#     outputs: sum: Integer
#     constraints: a > 0, b > 0
#     steps: sum = a + b

# 2. Load as OpenAI function
func = ApeOpenAIFunction.from_ape_file("calculator.ape", "add")

# 3. Get OpenAI function schema
function_schema = func.to_openai_function()

# 4. Use with OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Add 5 and 3"}],
    functions=[function_schema],
    function_call="auto"
)

# 5. Execute with APE validation
if response.choices[0].message.function_call:
    func_call = response.choices[0].message.function_call
    result = func.execute(func_call.arguments)
    print(f"Result: {result}")  # 8
```

## API Reference

### Schema Conversion

**`ape_task_to_openai_schema(task: ApeTask) -> dict`**

Converts APE task to OpenAI function schema.

```python
from ape_openai import ape_task_to_openai_schema, ApeTask

task = ApeTask(
    name="calculate_tax",
    inputs={"amount": "float", "rate": "float"},
    output="float",
    description="Calculate tax amount"
)

schema = ape_task_to_openai_schema(task)
# {
#     "name": "calculate_tax",
#     "description": "Calculate tax amount",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "amount": {"type": "number"},
#             "rate": {"type": "number"}
#         },
#         "required": ["amount", "rate"]
#     }
# }
```

### Execution

**`execute_openai_call(module, function_name, arguments) -> Any`**

Execute OpenAI function call with APE validation.

```python
from ape import compile
from ape_openai import execute_openai_call

module = compile("calculator.ape")
result = execute_openai_call(module, "add", '{"a": 5, "b": 3}')
```

### High-Level Wrapper

**`ApeOpenAIFunction`**

Complete integration wrapper.

```python
func = ApeOpenAIFunction.from_ape_file("calc.ape", "multiply")

# Get schema
schema = func.to_openai_function()

# Execute
result = func.execute({"a": 4, "b": 7})
```

## Features

- ✅ **Schema conversion**: APE → OpenAI function format
- ✅ **Type validation**: Strict parameter checking
- ✅ **Error handling**: Clear error messages
- ✅ **Code generation**: Natural language → APE (experimental)
- ✅ **Full type support**: String, Integer, Float, Boolean, List, Dict

## Type Mapping

| Ape Type | OpenAI Type |
|----------|-------------|
| String   | string      |
| Integer  | integer     |
| Float    | number      |
| Boolean  | boolean     |
| List     | array       |
| Dict     | object      |

## Advanced Usage

### Code Generation

Generate APE code from natural language:

```python
from ape_openai import generate_ape_from_nl

code = generate_ape_from_nl(
    "Create a function that calculates compound interest",
    model="gpt-4o"
)
print(code)
```

### Error Handling

```python
from ape_openai import ApeOpenAIFunction
from ape import ApeExecutionError

func = ApeOpenAIFunction.from_ape_file("calc.ape", "divide")

try:
    result = func.execute({"a": 10, "b": 0})
except ApeExecutionError as e:
    print(f"Execution failed: {e}")
```

## Examples

See the `examples/` directory for complete examples:
- Basic function calling
- Multi-function conversations
- Error handling patterns
- Code generation workflows

## Development

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .

# Type checking
mypy src/
```

## License

MIT License - see LICENSE file for details.

## Links

- [APE Language](https://github.com/Quynah/ape-lang)
- [Documentation](https://github.com/Quynah/ape-lang/tree/main/packages/ape-openai)
- [Issues](https://github.com/Quynah/ape-lang/issues)