pixigpt 0.1.5

Production-grade Python client for the PixiGPT API

PixiGPT Python Client

Production-grade Python client for the PixiGPT API.

Features

• 🚀 High Performance: Connection pooling with 100 connections
• 🔄 Smart Retries: Exponential backoff (0.1s → 0.8s)
• ⏱️ Timeouts: 30s default, fully configurable
• 🎯 Type Hints: Full typing support for modern Python
• 📦 Minimal Dependencies: Just requests + urllib3
• 🔧 OpenAI Compatible: Familiar API surface
• 🧠 Chain of Thought: Server-extracted CoT reasoning in reasoning_content
• 🛠️ Tool Calling: Full OpenAI-compatible function calling support

Installation

pip install pixigpt

Quick Start

from pixigpt import Client, ChatCompletionRequest, Message

client = Client("sk-proj-YOUR_API_KEY", "https://pixigpt.com/v1")

# Option 1: With assistant personality
response = client.create_chat_completion(
    ChatCompletionRequest(
        assistant_id="your-assistant-id",  # Optional
        messages=[Message(role="user", content="Hello!")],
    )
)

# Option 2: Pure OpenAI mode (no assistant)
response = client.create_chat_completion(
    ChatCompletionRequest(
        messages=[
            Message(role="system", content="You are helpful"),
            Message(role="user", content="Hello!"),
        ],
    )
)

print(response.choices[0].message.content)

# Access chain of thought reasoning (if enable_thinking=true)
if response.choices[0].reasoning_content:
    print(f"Reasoning: {response.choices[0].reasoning_content}")

Configuration

from pixigpt import Client

# Custom timeout and retries
client = Client(
    api_key="sk-proj-...",
    base_url="https://pixigpt.com/v1",
    timeout=60,        # 60 second timeout
    max_retries=5,     # Retry up to 5 times
)

# Custom session
import requests
session = requests.Session()
session.proxies = {"http": "http://proxy:8080"}

client = Client(
    api_key="sk-proj-...",
    base_url="https://pixigpt.com/v1",
    session=session,
)

API Methods

Vision & Moderation

Image/video analysis and content moderation:

from pixigpt import (
    VisionAnalyzeRequest,
    VisionTagsRequest,
    VisionOCRRequest,
    VisionVideoRequest,
    ModerationTextRequest,
    ModerationMediaRequest,
)

# Image analysis
response = client.analyze_image(
    VisionAnalyzeRequest(
        image_url="https://example.com/image.jpg",
        user_prompt="Describe this in detail.",
    )
)

# Tag generation
response = client.analyze_image_for_tags(
    VisionTagsRequest(image_url="https://example.com/image.jpg")
)

# OCR text extraction
response = client.extract_text(
    VisionOCRRequest(image_url="https://example.com/document.jpg")
)

# Video analysis (< 10MB)
response = client.analyze_video(
    VisionVideoRequest(
        video_url="https://example.com/video.mp4",
        user_prompt="Describe what happens.",
    )
)

# Text moderation (11 categories)
response = client.moderate_text(
    ModerationTextRequest(prompt="text to moderate")
)
# Returns: category (SAFE, SEXUAL_ADULT, UNDERAGE_SEXUAL, etc.) + score (0.0-1.0)

# Image/video moderation
response = client.moderate_media(
    ModerationMediaRequest(
        media_url="https://example.com/image.jpg",
        is_video=False,
    )
)

Moderation Categories:

• CRITICAL: UNDERAGE_SEXUAL (priority), JAILBREAK, SUICIDE_SELF_HARM, PII, COPYRIGHT_VIOLATION
• WARNING: VIOLENT, ILLEGAL_ACTS, UNETHICAL, HATE_SPEECH
• ALLOWED: SEXUAL_ADULT (explicit only), SAFE (everything else)

Chat Completions (Stateless)

from pixigpt import ChatCompletionRequest, Message

# Basic completion
response = client.create_chat_completion(
    ChatCompletionRequest(
        assistant_id=assistant_id,  # Optional - omit for pure OpenAI mode
        messages=[
            Message(role="user", content="What's the weather?"),
        ],
        temperature=0.7,
        max_tokens=2000,
        enable_thinking=True,  # Enable chain of thought (default: True)
    )
)

# Access response
print(response.choices[0].message.content)
print(f"Tokens: {response.usage.total_tokens}")

# Access reasoning (server-provided, automatically extracted from <think> tags)
if response.choices[0].reasoning_content:
    print(f"Reasoning: {response.choices[0].reasoning_content}")

Tool Calling (Function Calling)

from pixigpt import ChatCompletionRequest, Message

# Define tools (OpenAI format)
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"}
                },
                "required": ["location"]
            }
        }
    }
]

# Request with tools
response = client.create_chat_completion(
    ChatCompletionRequest(
        messages=[
            Message(role="system", content="You are helpful"),
            Message(role="user", content="What's the weather in Paris?"),
        ],
        tools=tools,
    )
)

# Check if model wants to call a tool
if response.choices[0].finish_reason == "tool_calls":
    for tool_call in response.choices[0].message.tool_calls:
        print(f"Tool: {tool_call.function.name}")
        print(f"Args: {tool_call.function.arguments}")

        # Execute tool, then send result back
        result = {"temperature": 18, "conditions": "cloudy"}

        response = client.create_chat_completion(
            ChatCompletionRequest(
                messages=[
                    Message(role="system", content="You are helpful"),
                    Message(role="user", content="What's the weather in Paris?"),
                    response.choices[0].message,  # Assistant's tool call
                    Message(
                        role="tool",
                        content=json.dumps(result),
                        tool_call_id=tool_call.id,
                    ),
                ],
                tools=tools,
            )
        )

Threads (Async with Memory)

# Create thread
thread = client.create_thread()

# Add message
msg = client.create_message(thread.id, "user", "Hello!")

# Run assistant
run = client.create_run(thread.id, assistant_id, enable_thinking=True)

# Wait for completion (message included in response!)
completed_run = client.wait_for_run(thread.id, run.id)

# Access the assistant's response directly
content = completed_run.message.content[0].text["value"]
print(f"assistant: {content}")

# Access reasoning if available
if completed_run.message.reasoning_content:
    print(f"Reasoning: {completed_run.message.reasoning_content}")

# Access tool execution results (Pixi tools only - when assistant has tools_config=null)
if completed_run.message.sources:
    for src in completed_run.message.sources:
        print(f"Source [{src.tool_name}]: {src.title} - {src.url}")

if completed_run.message.media:
    for media in completed_run.message.media:
        print(f"Media [{media.source}]: {media.signed_url}")

if completed_run.message.code:
    for code in completed_run.message.code:
        print(f"Code [{code.language}]: {code.stdout}")

Assistants

# List
assistants = client.list_assistants()

# Create
assistant = client.create_assistant(
    name="My Assistant",
    instructions="You are a helpful assistant.",
    tools_config=None,
)

# Update
assistant = client.update_assistant(
    assistant_id=assistant.id,
    name="Updated Name",
    instructions="New instructions",
)

# Delete
client.delete_assistant(assistant.id)

Context Manager

with Client(api_key, base_url) as client:
    response = client.create_chat_completion(...)
# Session automatically closed

Error Handling

from pixigpt import APIError, is_auth_error, is_rate_limit_error

try:
    response = client.create_chat_completion(request)
except APIError as e:
    if is_auth_error(e):
        print("Invalid API key")
    elif is_rate_limit_error(e):
        print("Rate limit exceeded")
    else:
        print(f"API error: {e}")

Chain of Thought Reasoning

When enable_thinking=True (default), the server automatically extracts reasoning from <think> tags:

response = client.create_chat_completion(
    ChatCompletionRequest(
        messages=[
            Message(role="system", content="You are helpful"),
            Message(role="user", content="Explain quantum physics"),
        ],
        enable_thinking=True,  # Default: true
    )
)

# Main response (thinking tags removed by server)
print(response.choices[0].message.content)

# Reasoning content (automatically extracted by vLLM, provided in separate field)
if response.choices[0].reasoning_content:
    print(f"Chain of thought: {response.choices[0].reasoning_content}")

Note: reasoning_content is provided directly by the server (vLLM extracts it). Both content and reasoning_content are automatically trimmed of whitespace. When thinking is enabled and max_tokens < 3000, it's automatically bumped to 3000 (CoT needs space).

Examples

See examples/ directory:

• chat.py - Simple chat completion
• thread.py - Multi-turn conversation
• vision.py - Vision analysis and content moderation

# Install dev dependencies
pip install pixigpt[dev]

# Run examples
python examples/chat.py
python examples/vision.py

Testing

pip install pixigpt[dev]
pytest

Publishing to PyPI

# Update version in pyproject.toml
./publish.sh

License

MIT