schemachat 0.1.1

A unified interface for multiple LLM chat providers with structured output support

SchemaChat

A unified Python interface for multiple LLM chat providers with structured output support using Pydantic models.

Features

• Multi-provider support: OpenAI, Ollama, and OpenRouter APIs
• Structured output: Generate validated responses using Pydantic models
• Factory pattern: Easy provider instantiation and switching
• Advanced context management: Intelligent context size optimization for Ollama
• Fallback model support: Switch models dynamically for error recovery
• Type safety: Full type hints and validation throughout

Installation

pip install schemachat

Quick Start

Basic Text Generation

from schemachat.core.configs.openai import OpenAIConfig
from schemachat.providers.factory import ChatProviderFactory

# Configure OpenAI provider
config = OpenAIConfig(
    api_key="your-openai-api-key",
    base_url="https://api.openai.com/v1",
    model_name="gpt-4",
    fallback_model_name="gpt-3.5-turbo"
)

# Create provider instance
client = ChatProviderFactory.create_provider(config)

# Generate response
response = client.invoke("Hello, world!")
print(response)

Structured Output Generation

from pydantic import BaseModel
from typing import List

class Person(BaseModel):
    name: str
    age: int
    skills: List[str]

# Generate structured response
person = client.invoke_structured(
    "Generate a profile for a Python developer",
    response_model=Person
)

print(f"Name: {person.name}")
print(f"Age: {person.age}")
print(f"Skills: {', '.join(person.skills)}")

Using Ollama Provider

from schemachat.core.configs.ollama import OllamaConfig

# Configure Ollama provider
config = OllamaConfig(
    base_url="http://localhost:11434",
    model_name="llama3.1",
    fallback_model_name="llama3",
    max_num_ctx=128,  # Context size in KB
    num_predict=8192  # Max prediction tokens
)

client = ChatProviderFactory.create_provider(config)
response = client.invoke("Explain quantum computing")

Supported Providers

OpenAI (and OpenAI-compatible APIs)

• Official OpenAI API
• OpenRouter
• Any OpenAI-compatible endpoint

Ollama

• Local Ollama installations
• Advanced context size management
• Automatic token calculation and optimization

Architecture

SchemaChat uses a factory pattern with provider-specific implementations:

• BaseLLMClient: Abstract interface for all providers
• BaseConfig: Configuration base class with validation
• ChatProviderFactory: Factory for creating provider instances
• Provider-specific optimizations: Each provider includes tailored optimizations

Configuration Options

OpenAI Configuration

OpenAIConfig(
    api_key="your-key",
    base_url="https://api.openai.com/v1",
    model_name="gpt-4",
    fallback_model_name="gpt-3.5-turbo",
    max_tokens=8192,
    temperature=0.7,
    top_p=0.9
)

Ollama Configuration

OllamaConfig(
    base_url="http://localhost:11434",
    model_name="llama3.1",
    fallback_model_name="llama3",
    max_num_ctx=128,  # KB
    num_ctx=32768,    # Specific context size
    num_predict=8192  # Max prediction tokens
)

Advanced Features

Provider Information

info = client.get_provider_info()
print(f"Provider: {info['provider_type']}")
print(f"Model: {info['model_name']}")
print(f"Base URL: {info['base_url']}")

Model Switching

# Switch to fallback model
client.use_fallback_model()

# Switch to specific model
client.use_fallback_model("gpt-4-turbo")

Error Handling

try:
    response = client.invoke("Your prompt")
except Exception as e:
    print(f"Error: {e}")
    # Automatically try fallback model
    client.use_fallback_model()
    response = client.invoke("Your prompt")

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yourusername/schemachat.git
cd schemachat

# Install dependencies
uv sync

# Install development dependencies
uv sync --group dev

Running Tests

uv run pytest

Code Formatting

uv run black .
uv run ruff check .

Type Checking

uv run mypy .

Requirements

• Python 3.10+
• pydantic>=2.12.0
• openai>=2.3.0 (for OpenAI providers)
• ollama>=0.6.0 (for Ollama providers)
• tiktoken>=0.12.0 (for token counting)

License

MIT License - see LICENSE file for details.

Contributing

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