provider-hub 0.4.2

Unified interface for accessing multiple LLM providers

Provider Hub

Unified interface for accessing multiple LLM providers.

---

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")

# Create a message with text + image
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)

You can also include multiple images in the same request:

# Prepare local images
image_content1 = prepare_image_content("path/to/your/image1.jpg")
image_content2 = prepare_image_content("path/to/your/image2.jpg")
# Add more images as needed

# Create a message with text + multiple images
messages = [
    ChatMessage(
        role="user",
        content=[
            {"type": "text", "text": "What do you see in these images?"},
            image_content1,
            image_content2,
            # Add additional image_content items here
        ]
    )
]

---

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)

---

System Prompt

Set a default system prompt when initializing LLM.

from provider_hub import LLM

# Simple string system prompt
llm = LLM(
    model="doubao-seed-1-6-250615", 
    temperature=0.7,
    top_p=0.9,
    max_tokens=100,
    timeout=30,
    system_prompt="You are a concise assistant."
)
response = llm.chat("Explain recursion in one paragraph")
print(response.content)

# Structured system prompt (list of role/content dicts)
llm2 = LLM(
    model="doubao-seed-1-6-250615", 
    temperature=0.7,
    top_p=0.9,
    max_tokens=100,
    timeout=30,
    system_prompt=[{"role": "system", "content": "You are a helpful assistant."}]
)
response2 = llm2.chat("Summarize the API")
print(response2.content)

Notes:

• Do not pass system role message directly to chat(...). Use system_prompt instead.
• For list format, role must be "system".

---

Streaming Mode

Only supported by Doubao and Qwen models.

This library provides two mechanisms for controlling streaming:

• stream (bool):

  • True: Returns an iterator yielding partial chunk dictionaries as the model generates output.
  • False: Returns a final ChatResponse object (default).

• stream_options (dict[str, bool]): Additional options for streaming responses. Only set this when stream=True.

  • Example: {"include_usage": True} → includes the total token usage in the last line of the output.

from provider_hub import LLM

# Streaming with token usage included
llm = LLM(
    model="doubao-seed-1-6-250615", 
    temperature=0.7,
    top_p=0.9,
    max_tokens=100,
    timeout=30,
    stream=True,
    stream_options={"include_usage": True}
)

response = llm.chat("Hello, how are you?")

for chunk in response:
    if chunk.choices:
        for choice in chunk.choices:
            if choice.delta.content:
                print(choice.delta.content, end='', flush=True)
    # Print only total token usage data
    else:
        print("\n", chunk.usage)

Notes:

• Not all providers or models support streaming. Always check a model’s capabilities before enabling streaming to avoid runtime errors.
• stream_options is provider-specific. Refer to the provider’s documentation for supported keys.

---

OpenAI GPT-5 Reasoning Effort

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

from provider_hub import LLM

# GPT-5 with high reasoning effort
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
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 only

---

Provider-Specific Notes

OpenAI

• GPT-5 series: only supports temperature=1.0; use reasoning_effort.
• GPT-4.1: full parameter support.

DeepSeek

• Thinking support only in deepseek-reasoner model (thinking=True).

Qwen

• Thinking support only in 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 supported by all models (thinking={"type": "enabled"}).
• Vision supported in doubao-seed-1-6-vision-250815.

---

Utility Functions

• prepare_image_content(image_input)

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

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

---

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
• qwen3-max
• qwen3-vl-plus
• qwen3-omni-flash
• qwen3-235b-a22b
• qwen3-vl-235b-a22b-thinking

Doubao

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

---

Testing

-t, --test Run full connection tests.

# Test all models
provider-hub -t

# Test one model
provider-hub -t Doubao doubao-seed-1-6-250615

# Test one model with reasoning (when supported)
provider-hub -t Doubao doubao-seed-1-6-250615 -k

-q, --quick-test Run lightweight connectivity checks.

# Quick test all models
provider-hub -q

# Quick test a single provider
provider-hub -q Doubao

-k, --thinking Enable reasoning mode during a test. Used together with -t.

# Test all models with reasoning
provider-hub -t -k