airtrain 0.1.25

A platform for building and deploying AI agents with structured skills

Airtrain

A powerful platform for building and deploying AI agents with structured skills and capabilities.

Features

• Structured Skills: Build modular AI skills with defined input/output schemas
• Multiple LLM Integrations: Built-in support for OpenAI and Anthropic models
• Structured Outputs: Parse LLM responses into structured Pydantic models
• Credential Management: Secure handling of API keys and credentials
• Type Safety: Full type hints and Pydantic model support
• Image Support: Handle image inputs for multimodal models
• Error Handling: Robust error handling and logging

Installation

pip install airtrain

Quick Start

1. Basic OpenAI Chat

from airtrain.integrations.openai.skills import OpenAIChatSkill, OpenAIInput

# Initialize the skill
skill = OpenAIChatSkill()

# Create input
input_data = OpenAIInput(
    user_input="Explain quantum computing in simple terms.",
    system_prompt="You are a helpful teacher.",
    max_tokens=500,
    temperature=0.7
)

# Get response
result = skill.process(input_data)
print(result.response)
print(f"Tokens Used: {result.usage['total_tokens']}")

2. Anthropic Claude Integration

from airtrain.integrations.anthropic.skills import AnthropicChatSkill, AnthropicInput

# Initialize the skill
skill = AnthropicChatSkill()

# Create input
input_data = AnthropicInput(
    user_input="Explain the theory of relativity.",
    system_prompt="You are a physics expert.",
    model="claude-3-opus-20240229",
    temperature=0.3
)

# Get response
result = skill.process(input_data)
print(result.response)
print(f"Usage: {result.usage}")

3. Structured Output with OpenAI

from pydantic import BaseModel
from typing import List
from airtrain.integrations.openai.skills import OpenAIParserSkill, OpenAIParserInput

# Define your response model
class PersonInfo(BaseModel):
    name: str
    age: int
    occupation: str
    skills: List[str]

# Initialize the parser skill
parser_skill = OpenAIParserSkill()

# Create input with response model
input_data = OpenAIParserInput(
    user_input="Tell me about John Doe, a 30-year-old software engineer who specializes in Python and AI",
    system_prompt="Extract structured information about the person.",
    response_model=PersonInfo
)

# Get structured response
result = parser_skill.process(input_data)
person_info = result.parsed_response
print(f"Name: {person_info.name}")
print(f"Skills: {', '.join(person_info.skills)}")

Error Handling

All skills include built-in error handling:

from airtrain.core.skills import ProcessingError

try:
    result = skill.process(input_data)
except ProcessingError as e:
    print(f"Processing failed: {e}")

Advanced Features

• Image Analysis Support
• Function Calling
• Custom Validators
• Async Processing
• Token Usage Tracking

For more examples and detailed documentation, visit our documentation.

Documentation

For detailed documentation, visit our documentation site.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

0.1.15

• Added conversation support for Deepseek, Togehter AI, Fireworks AI, Gemini, Groq, Cerebras and Sambanova.
• Added Change Log

Notes

The changelog format is based on Keep a Changelog, and this project adheres to Semantic Versioning.