olingo-llm-parser 0.1.0

Elegant Jinja2-based chat template parser for AI applications

🤖 Olingo LLM Parser

Elegant Jinja2-based chat template parser for AI applications

Transform your Jinja2 templates into structured chat conversations for OpenAI API spec. Perfect for OpenAI, Anthropic, and other chat-based language models.

✨ Features

• 🎯 Simple & Elegant: Clean syntax with custom {% chat %} blocks
• 🔧 Jinja2 Powered: Full support for variables, loops, conditionals
• 📋 Schema Integration: Automatic JSON schema loading for structured responses
• 🚀 AI Ready: Perfect for OpenAI, Anthropic, and other chat APIs
• 🎨 Flexible: Use anywhere - scripts, web apps, AI agents
• 📦 Minimal Dependencies: Just Jinja2, nothing else

🚀 Quick Start

Installation

pip install olingo-llm-parser

Basic Usage

1. Create a template (chat_template.jinja):

{% chat role="system" %}
You are a helpful AI assistant specializing in {{ domain }}.
{% endchat %}

{% chat role="user" %}
{{ user_question }}
{% endchat %}

2. Parse and use:

from olingo_llm_parser import parse_chat_template

# Parse template with variables
messages = parse_chat_template("chat_template.jinja", {
    "domain": "data science",
    "user_question": "What is machine learning?"
})

# Use with OpenAI
import openai
response = openai.chat.completions.create(
    model="gpt-4",
    messages=messages
)

Output:

[
    {"role": "system", "content": "You are a helpful AI assistant specializing in data science."},
    {"role": "user", "content": "What is machine learning?"}
]

🎨 Advanced Examples

With Loops and Conditions

{% chat role="system" %}
You are an expert {{ role }}.
{% endchat %}

{% for example in examples %}
{% chat role="user" %}
Example: {{ example.question }}
{% endchat %}

{% chat role="assistant" %}
{{ example.answer }}
{% endchat %}
{% endfor %}

{% chat role="user" %}
Now answer: {{ final_question }}
{% endchat %}

With JSON Schema

from olingo_llm_parser import parse_chat_and_schema

# Parse both template and schema
messages, response_format = parse_chat_and_schema(
    template_path="template.jinja",
    schema_path="response_schema.json",
    variables={"topic": "AI"}
)

# Use with OpenAI structured output
response = openai.chat.completions.create(
    model="gpt-4",
    messages=messages,
    response_format=response_format  # Automatic structured JSON
)

Schema-Aware Templates

Your templates can even reference the schema:

{% chat role="system" %}
Respond in JSON format following this schema:
{{ json_schema }}
{% endchat %}

{% chat role="user" %}
Generate data about {{ topic }}.
{% endchat %}

📚 API Reference

Core Functions

parse_chat_template(template_path, variables=None)

Parse a Jinja2 template containing {% chat %} blocks.

Parameters:

• template_path (str): Path to your .jinja template file
• variables (dict, optional): Variables to pass to the template

Returns:

• List[Dict[str, str]]: List of message dictionaries with 'role' and 'content'

load_json_schema(schema_path)

Load a JSON schema and format it for AI APIs.

Parameters:

• schema_path (str): Path to your JSON schema file

Returns:

• Dict: Formatted response_format for AI APIs

parse_chat_and_schema(template_path, schema_path=None, variables=None)

Parse both template and schema together.

Parameters:

• template_path (str): Path to your .jinja template file
• schema_path (str, optional): Path to your JSON schema file
• variables (dict, optional): Variables to pass to the template

Returns:

• Tuple[List[Dict], Dict]: (messages, response_format) ready for AI APIs

🏗️ Template Syntax

Chat Blocks

{% chat role="system" %}
Your system message here
{% endchat %}

{% chat role="user" %}
Your user message here  
{% endchat %}

{% chat role="assistant" %}
Your assistant message here
{% endchat %}

Variables

{% chat role="user" %}
Hello {{ name }}, tell me about {{ topic }}.
{% endchat %}

Loops

{% for item in items %}
{% chat role="user" %}
Process this: {{ item }}
{% endchat %}
{% endfor %}

Conditionals

{% chat role="system" %}
{% if expert_mode %}
You are an expert assistant with advanced capabilities.
{% else %}
You are a helpful general assistant.
{% endif %}
{% endchat %}

🎯 Use Cases

• AI Chat Applications: Structure conversations for any chat API
• Prompt Engineering: Create reusable, dynamic prompt templates
• AI Agents: Build complex conversational flows
• Data Generation: Generate structured training data
• API Integration: Seamless integration with OpenAI, Anthropic, etc.

🤝 Why Choose Olingo LLM Parser?

Feature	Olingo LLM Parser	Manual String Building	Other Solutions
Readability	✅ Clean template syntax	❌ Messy string concat	⚠️ Complex APIs
Maintainability	✅ Separate logic & content	❌ Mixed code/content	⚠️ Vendor lock-in
Flexibility	✅ Full Jinja2 power	❌ Limited logic	⚠️ Restricted features
AI Integration	✅ Perfect API format	❌ Manual formatting	⚠️ API-specific
Schema Support	✅ Built-in JSON schema	❌ No schema support	❌ No schema support

📋 Requirements

• Python 3.8+
• Jinja2 3.0+

🛠️ Development

# Clone repository
git clone https://github.com/ollsoft-ai/olingo-llm-parser.git
cd olingo-llm-parser

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
isort .

📄 License

MIT License - see LICENSE file for details.

🌟 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📞 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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Made with ❤️ by the Ollsoft Team

⭐ Star us on GitHub • 📦 PyPI Package