provider-hub 0.2.0

Unified LLM provider interface for multi-agent systems

Provider Hub

Unified LLM provider interface for multi-agent systems.

Table of Contents

• Installation
• Quick Start
• Core Features
• API Reference
• Supported Models
• Testing

Installation

pip install provider_hub

Quick Start

1. Environment Setup

Create a .env file in your project root:

OPENAI_API_KEY=your-openai-api-key
DEEPSEEK_API_KEY=your-deepseek-api-key  
DASHSCOPE_API_KEY=your-qwen-api-key
ARK_API_KEY=your-doubao-api-key

2. Basic Usage

from provider_hub import LLM

# Simple text chat
llm = LLM(
    model="doubao-seed-1-6-250615", 
    temperature=0.7,
    top_p=0.9,
    max_tokens=100,
    timeout=30
)
response = llm.chat("Hello, how are you?")
print(response.content)

Core Features

📝 Text Processing

All models support standard text chat functionality with configurable parameters.

from provider_hub import LLM

llm = LLM(
    model="qwen-plus",
    temperature=0.7,
    top_p=0.9,
    max_tokens=100,
    timeout=30
)
response = llm.chat("Explain quantum computing")
print(response.content)

🖼️ Vision Processing

Process both local images and URLs with vision-capable models.

from provider_hub import LLM, ChatMessage, prepare_image_content

# Vision model setup
vision_llm = LLM(
    model="qwen-vl-plus",
    temperature=0.5,
    max_tokens=150,
    timeout=60
)

# Process local image
image_content = prepare_image_content("path/to/your/image.jpg")

# Or process image URL
# image_content = prepare_image_content("https://example.com/image.jpg")

messages = [ChatMessage(
    role="user",
    content=[
        {"type": "text", "text": "What do you see in this image?"},
        image_content
    ]
)]
response = vision_llm.chat(messages)
print(response.content)

🧠 Reasoning Mode

Enable step-by-step reasoning for complex problem solving.

from provider_hub import LLM

# DeepSeek reasoning
deepseek_reasoning = LLM(
    model="deepseek-reasoner", 
    thinking=True,
    temperature=0.3,
    max_tokens=200,
    timeout=60
)

# Qwen reasoning  
qwen_reasoning = LLM(
    model="qwen3-max-preview",
    thinking=True,
    temperature=0.5,
    max_tokens=180,
    timeout=50
)

# Doubao reasoning
doubao_reasoning = LLM(
    model="doubao-seed-1-6-250615",
    thinking={"type": "enabled"},
    temperature=0.4,
    max_tokens=200,
    timeout=45
)

response = qwen_reasoning.chat("Calculate 15 * 23 step by step")
print(response.content)

⚡ OpenAI GPT-5 Reasoning Effort

GPT-5 models support adjustable reasoning intensity through the chat method.

from provider_hub import LLM

gpt5_reasoning = LLM(
    model="gpt-5",
    max_tokens=200,
    timeout=40
)

response = gpt5_reasoning.chat(
    "Solve this complex problem step by step",
    reasoning_effort="high"  # Options: "low", "medium", "high"
)
print(response.content)

API Reference

Parameters

Parameter	Type	Description	Range/Options
model	string	Model identifier	See Supported Models
temperature	float	Controls randomness	0.0-2.0 (0=deterministic, 2=very creative)
top_p	float	Nucleus sampling threshold	0.0-1.0
max_tokens	int	Maximum response length	Positive integer
timeout	int	Request timeout	Seconds (default: 30)
thinking	bool/dict	Enable reasoning mode	Provider-specific format
reasoning_effort	string	GPT-5 reasoning intensity	"low", "medium", "high"

Parameter Support by Provider

Parameter	OpenAI	DeepSeek	Qwen	Doubao	Notes
temperature	✅	✅	✅	✅	GPT-5 series limited to 1.0
top_p	✅	✅	✅	✅	Full support
max_tokens	✅	✅	✅	✅	GPT-5 auto-converts to max_completion_tokens
timeout	✅	✅	✅	✅	Full support
thinking	❌	✅	✅	✅	Model-specific availability
reasoning_effort	✅	❌	❌	❌	GPT-5 series only

Provider-Specific Notes

OpenAI

• GPT-5 series: Only support temperature=1.0, use reasoning_effort instead of thinking
• GPT-4.1: Full parameter support
• Reasoning: Use reasoning_effort="high" for complex problems

DeepSeek

• Thinking support: Only deepseek-reasoner model
• Format: thinking=True
• Best for: Mathematical and logical reasoning

Qwen

• Thinking support: Only qwen3-* models (qwen3-max-preview, qwen3-coder-plus, qwen3-coder-flash)
• Format: thinking=True
• Vision models: qwen-vl-max, qwen-vl-plus support image processing

Doubao

• Thinking support: All models
• Format: thinking={"type": "enabled"}
• Vision support: doubao-seed-1-6-vision-250815

Classes

LLM

Main interface for all LLM providers.

llm = LLM(
    model="model-name",
    temperature=0.7,
    max_tokens=100,
    # ... other parameters
)

Methods:

• chat(messages, **kwargs) → ChatResponse: Send messages and get response
  • reasoning_effort (OpenAI GPT-5 only): "low", "medium", "high"

ChatMessage

Container for structured messages.

message = ChatMessage(
    role="user",  # "user", "assistant", "system"
    content="text or list of content items"
)

ChatResponse

Response object from LLM.

Attributes:

• content: Response text
• model: Model used
• usage: Token usage statistics
• finish_reason: Completion reason

Utility Functions

prepare_image_content(image_input)

Prepares image content for vision models.

# Local file
image_content = prepare_image_content("./image.jpg")

# URL
image_content = prepare_image_content("https://example.com/image.jpg")

test_connection()

Quick connectivity test for available models.

from provider_hub import test_connection
test_connection()

Supported Models

OpenAI

• gpt-5
• gpt-5-mini
• gpt-5-nano
• gpt-4.1

DeepSeek

• deepseek-chat
• deepseek-reasoner

Qwen

• qwen3-max-preview
• qwen-plus
• qwen-flash
• qwen3-coder-plus
• qwen3-coder-flash
• qwen-vl-max
• qwen-vl-plus

Doubao

• doubao-seed-1-6-250615
• doubao-seed-1-6-vision-250815
• doubao-seed-1-6-flash-250828

Testing

Quick Test

Test connectivity to available models:

from provider_hub import test_connection
test_connection()

Comprehensive Test Suite

Run full model testing with detailed reports:

python test_connection.py

This generates:

• test_report.json - Machine-readable results
• test_report.md - Human-readable report