olingo-llm-parser 0.1.6

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_template_and_schema

# Parse both template and schema from files
messages, response_format = parse_template_and_schema(
    template="template.jinja",
    schema="response_schema.json",
    variables={"topic": "AI"}
)

# Or use strings/dictionaries directly
template_string = """{% chat role="system" %}
You are an expert on {{ topic }}.
{% endchat %}"""

schema_dict = {"type": "object", "properties": {"answer": {"type": "string"}}}

messages, response_format = parse_template_and_schema(
    template=template_string,
    schema=schema_dict,
    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 %}

Flexible Input Types

The new parse_template_and_schema function supports multiple input formats:

from olingo_llm_parser import parse_template_and_schema

# Template as file path, schema as file path  
messages, format = parse_template_and_schema("template.jinja", "schema.json")

# Template as string, schema as dictionary
template_str = """{% chat role="user" %}Hello!{% endchat %}"""
schema_dict = {"type": "string"}
messages, format = parse_template_and_schema(template_str, schema_dict)

# Template as string, schema as JSON string
schema_json = '{"type": "object", "properties": {"name": {"type": "string"}}}'
messages, format = parse_template_and_schema(template_str, schema_json)

# Template only (no schema)
messages, format = parse_template_and_schema(template_str)  # format will be None

📚 API Reference

Core Functions

parse_chat_template(template, variables=None)

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

Parameters:

• template (str|Path): Either a file path or template string content
• 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_template_and_schema(template, schema=None, variables=None)

Parse both template and schema together with flexible input types.

Parameters:

• template (str|Path): Either a file path or template string content
• schema (str|Path|Dict, optional): Either a file path, JSON string, or schema dictionary
• 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 .

📦 Building and Publishing New Versions

1. Update Version Number

🎯 Recommended: Automated Version Bumping

We use bump2version for automated version management:

# Install bump2version (one-time setup)
pip install bump2version

# Bump patch version (0.1.2 → 0.1.3)
bump2version patch

# Bump minor version (0.1.3 → 0.2.0)  
bump2version minor

# Bump major version (0.2.0 → 1.0.0)
bump2version major

This automatically:

• ✅ Updates version in olingo_llm_parser/__init__.py
• ✅ Creates a git commit with the version bump
• ✅ Creates a git tag (e.g., v0.1.3)
• ✅ Single source of truth (version only in __init__.py)

Manual Alternative: If you prefer manual updates, just edit the version in olingo_llm_parser/__init__.py:

__version__ = "0.1.3"  # Update this line only

Semantic Versioning Guidelines:

• Patch (0.1.1 → 0.1.2): Bug fixes, no breaking changes
• Minor (0.1.2 → 0.2.0): New features, backward compatible
• Major (0.2.0 → 1.0.0): Breaking changes

2. Install Build Tools

pip install build twine

3. Run Tests

Make sure all tests pass before publishing:

python -m pytest -v

4. Clean Previous Builds

# PowerShell (Windows)
Remove-Item -Recurse -Force dist, build, *.egg-info -ErrorAction SilentlyContinue

# Bash (Linux/Mac)
rm -rf dist/ build/ *.egg-info/

5. Build the Package

python -m build

This creates two files in dist/:

• olingo-llm-parser-X.X.X.tar.gz (source distribution)
• olingo_llm_parser-X.X.X-py3-none-any.whl (wheel distribution)

7. Upload to PyPI

Once verified on TestPyPI:

python -m twine upload dist/*

8. Tag the Release

git add .
git commit -m "chore: bump version to X.X.X"
git tag vX.X.X
git push origin main --tags

🔐 PyPI Authentication

Option A: API Token (Recommended)

1. Go to PyPI Account Settings
2. Create an API token
3. Use __token__ as username and your token as password

Option B: Configure ~/.pypirc

[pypi]
username = __token__
password = pypi-your-token-here

[testpypi]
username = __token__
password = pypi-your-testpypi-token-here

📄 License

MIT License - see LICENSE file for details.

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Made with ❤️ by the Ollsoft Team