Kerb Documentation
The complete toolkit for developers building LLM applications.
Built to drive production ML systems at ApX Machine Learning (apxml.com), available open source.
Contents:
- Getting Started
- Examples
- API Reference
- Core Module
- Agent Module
- Cache Module
- Chunk Module
- Config Module
- Context Module
- Document Module
- Embedding Module
- Evaluation Module
- Fine-Tuning Module
- Generation Module
- Memory Module
- Multimodal Module
- Parsing Module
- Preprocessing Module
- Prompt Module
- Retrieval Module
- Safety Module
- Testing Module
- Tokenizer Module
- Module Overview
- Modules Reference
- Core Module
- Agent Module
- Cache Module
- Chunk Module
- Config Module
- Context Module
- Document Module
- Embedding Module
- Evaluation Module
- Fine-Tuning Module
- Generation Module
- Memory Module
- Multimodal Module
- Parsing Module
- Preprocessing Module
- Prompt Module
- Retrieval Module
- Safety Module
- Testing Module
- Tokenizer Module
Overview
Simple
Advanced LLM techniques made simple. Clean, easy-to-use interfaces for complex operations.
Lightweight
Only install what you need. Kerb is modular, no unnecessary dependencies.
Compatible
Works with any LLM project. Kerb is a toolkit, not a framework. Use it alongside your existing stack.
Installation
# Install everything
pip install kerb[all]
# Or install specific modules
pip install kerb[generation] kerb[embeddings] kerb[evaluation]
Quick Start
from kerb.generation import generate, ModelName, LLMProvider
from kerb.prompt import render_template
# Generate with any provider, easy config change.
response = generate(
"Explain quantum computing",
model=ModelName.GPT_4O_MINI,
provider=LLMProvider.OPENAI
)
print(f"Response: {response.content}")
print(f"Tokens: {response.usage.total_tokens}")
print(f"Cost: ${response.cost:.6f}")
Modules
Everything you need to build LLM applications.