dazllm 0.28.0

Simple, unified interface for all major LLMs

dazllm 🚀

Simple, unified interface for all major LLMs

Stop juggling different APIs and libraries. dazllm gives you a clean, consistent way to chat with any LLM - from GPT-4 and Claude to local Ollama or LM Studio models.

Features

✨ Unified API - Same interface for OpenAI, Anthropic, Google, and local models (Ollama, LM Studio) 🔧 Smart Model Selection - Choose by name, type, or let it auto-select
🔐 Secure Configuration - API keys stored safely in system keyring
📝 Structured Output - Get Pydantic models directly from LLM responses
🎨 Image Generation - Create images with DALL-E and more
💻 CLI & Python API - Use from command line or import in your code

Quick Start

Installation

pip install dazllm

Setup

Configure your API keys using keyring:

keyring set dazllm openai_api_key YOUR_OPENAI_KEY
keyring set dazllm anthropic_api_key YOUR_ANTHROPIC_KEY
keyring set dazllm google_api_key YOUR_GOOGLE_KEY
keyring set dazllm ollama_url http://localhost:11434
keyring set dazllm lmstudio_url http://localhost:1234

Check everything is working:

dazllm --check

Usage

Command Line

# Simple chat
dazllm chat "What's the capital of France?"

# Use specific model  
dazllm chat --model openai:gpt-4 "Explain quantum computing"

# Use model type (auto-selects best available)
dazllm chat --model-type paid_best "Write a poem"

# Use provider default
dazllm chat --model openai "Tell me about AI"

# Structured output
dazllm structured "List 3 colors" --schema '{"type":"array","items":{"type":"string"}}'

# Generate images
dazllm image "a red cat wearing a hat" cat.png

# From file
dazllm chat --file prompt.txt --output response.txt

Python API

from dazllm import Llm, ModelType
from pydantic import BaseModel

# Instance-based usage
llm = Llm("openai:gpt-4")
response = llm.chat("Hello!")

# Static/module-level usage
response = Llm.chat("Hello!", model="anthropic:claude-3-5-sonnet-20241022")
response = Llm.chat("Hello!", model_type=ModelType.PAID_BEST)

# Structured output with Pydantic
class ColorList(BaseModel):
    colors: list[str]

result = Llm.chat_structured("List 3 colors", ColorList)
print(result.colors)  # ['red', 'green', 'blue']

# Image generation
Llm.image("a sunset over mountains", "sunset.png")

# Conversation history
conversation = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi there!"},
    {"role": "user", "content": "What's your name?"}
]
response = Llm.chat(conversation, model="ollama:mistral-small")

Model Types

Instead of remembering model names, use semantic types:

• local_small - ~1B parameter models (fast, basic)
• local_medium - ~7B parameter models (good balance)
• local_large - ~14B parameter models (best local quality)
• paid_cheap - Cost-effective cloud models
• paid_best - Highest quality cloud models

Model Format

All models use the format provider:model:

• OpenAI: openai:gpt-4o, openai:gpt-4o-mini, openai:dall-e-3
• Anthropic: anthropic:claude-3-5-sonnet-20241022, anthropic:claude-3-haiku-20240307
• Google: google:gemini-pro, google:gemini-flash
• Ollama: ollama:mistral-small, ollama:llama3:8b, ollama:codellama:7b
• LM Studio: lm-studio:mistral, lm-studio:llama3

You can also use just the provider name (e.g., openai) to use that provider's default model.

Configuration

API keys are stored securely in your system keyring:

# Set API keys
keyring set dazllm openai_api_key YOUR_OPENAI_KEY
keyring set dazllm anthropic_api_key YOUR_ANTHROPIC_KEY
keyring set dazllm google_api_key YOUR_GOOGLE_KEY
keyring set dazllm ollama_url http://localhost:11434
keyring set dazllm lmstudio_url http://localhost:1234

# Set default model (optional)
keyring set dazllm default_model openai:gpt-4o

# Check what's configured
dazllm --check

Examples

Building a Chatbot

from dazllm import Llm

def chatbot():
    llm = Llm.model_named("openai:gpt-4o")
    conversation = []
    
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            break
            
        conversation.append({"role": "user", "content": user_input})
        response = llm.chat(conversation)
        conversation.append({"role": "assistant", "content": response})
        
        print(f"AI: {response}")

chatbot()

Data Extraction

from dazllm import Llm
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int
    city: str

class People(BaseModel):
    people: list[Person]

text = "John Doe, age 30, lives in New York. Jane Smith, age 25, lives in LA."

result = Llm.chat_structured(
    f"Extract people info from: {text}",
    People,
    model="openai:gpt-4o-mini"
)

for person in result.people:
    print(f"{person.name} is {person.age} years old and lives in {person.city}")

Image Generation Pipeline

from dazllm import Llm

# Generate image description
description = Llm.chat(
    "Describe a serene mountain landscape in detail",
    model_type="paid_cheap"
)

# Generate the image
image_path = Llm.image(description, "mountain.png", width=1024, height=768)
print(f"Image saved to {image_path}")

Requirements

• Python 3.8+
• API keys for desired providers (OpenAI, Anthropic, Google)
• Ollama or LM Studio installed for local models

License

MIT License

Contributing

Contributions welcome! Please see the GitHub repository for guidelines.

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dazllm - Making LLMs accessible to everyone! 🚀