chaingpt 1.1.3

Official Python SDK for ChainGPT API - Web3 AI Platform

ChainGPT SDK

A comprehensive Python SDK for interacting with the ChainGPT API ecosystem, providing access to AI-powered blockchain tools including LLM chat, NFT generation, smart contract development, auditing, and news services.

Features

• 🤖 LLM Service - AI chat with blockchain context and streaming support
• 🎨 NFT Service - AI-powered NFT generation, minting, and metadata management
• 📝 Smart Contract Service - AI-assisted smart contract generation with chat history
• 🔍 Auditor Service - Automated smart contract security auditing
• 📰 News Service - Curated blockchain and crypto news with advanced filtering
• 🔄 Streaming Support - Real-time streaming responses for chat and generation
• 📚 Chat History - Persistent conversation history across sessions
• ⚡ Async/Await - Built on modern async Python for optimal performance
• 🛡️ Error Handling - Comprehensive error mapping and retry logic
• 🔧 Easy Configuration - Environment variables or direct parameter setup

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Table of Contents

1. Installation
2. Configuration
3. Quick Start
4. Services
   • LLM Service
   • NFT Service
   • Smart Contract Service
   • Auditor Service
   • News Service
5. Advanced Usage
6. Error Handling
7. Examples
8. API Reference
9. Development

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Installation

Install via pip:

pip install chaingpt

Or add to your requirements.txt:

chaingpt>=1.1.3

Dependencies:

• httpx - Async HTTP client
• pydantic - Data validation and serialization
• colorama - For color logging
• python-dotenv - Environment variable management

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Configuration

Configure the SDK using environment variables or constructor parameters:

Variable	Default	Description
CHAINGPT_API_KEY	required	Your ChainGPT API key

Environment Variables

Create a .env file:

CHAINGPT_API_KEY=your_api_key_here

Direct Configuration

from chaingpt.client import ChainGPTClient

client = ChainGPTClient(api_key="your_api_key_here")

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Quick Start

import asyncio
import os
from chaingpt.client import ChainGPTClient
from chaingpt.models import LLMChatRequestModel
from chaingpt.types import ChatHistoryMode

async def main():
    # Initialize client
    client = ChainGPTClient(api_key=os.getenv("CHAINGPT_API_KEY"))
    
    try:
        # Simple chat
        request = LLMChatRequestModel(
            question="What is blockchain technology?",
            chatHistory=ChatHistoryMode.OFF
        )
        response = await client.llm.chat(request)
        print(f"AI: {response.data.bot}")
        
        # Streaming chat
        print("\nStreaming response:")
        async for chunk in client.llm.stream_chat(request):
            print(chunk.decode('utf-8'), end="", flush=True)
            
    finally:
        await client.close()

if __name__ == "__main__":
    asyncio.run(main())

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Services

LLM Service

AI-powered chat with blockchain context, custom tones, and conversation history.

from chaingpt.client import ChainGPTClient
from chaingpt.models import (
    LLMChatRequestModel, 
    ContextInjectionModel, 
    TokenInformationModel,
    SocialMediaUrlModel,
)
from chaingpt.types import AITone, PresetTone, ChatHistoryMode, BlockchainNetwork

# Basic chat
request = LLMChatRequestModel(
    question="Explain DeFi protocols",
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId="550e8400-e29b-41d4-a716-446655440000",  # Example UUID
)

# Buffered response
response = await client.llm.chat(request)
print(response.data.bot)

# Streaming response
async for chunk in client.llm.stream_chat(request):
    print(chunk.decode('utf-8'), end="")

Context Injection:

# Create token information
token_info = TokenInformationModel(
    tokenName="AwesomeToken",
    tokenSymbol="AWE",
    blockchain=[BlockchainNetwork.ETHEREUM, BlockchainNetwork.POLYGON],
)

# Create social media URLs
social_media = [
    SocialMediaUrlModel(name="twitter", url="https://twitter.com/awesometoken")
]

# Create context injection
context = ContextInjectionModel(
    companyName="Awesome Inc.",
    companyDescription="A company building next-gen DeFi tools.",
    cryptoToken=True,
    tokenInformation=token_info,
    socialMediaUrls=social_media,
    aiTone=AITone.PRE_SET_TONE,
    selectedTone=PresetTone.INFORMATIVE,
)

request = LLMChatRequestModel(
    question="Tell me about AwesomeToken",
    useCustomContext=True,
    contextInjection=context,
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId="550e8400-e29b-41d4-a716-446655440000",  # Example UUID
)

NFT Service

Complete NFT workflow from generation to minting with AI-powered image creation, including image-to-image generation.

from chaingpt.models import (
    GenerateImageRequestModel,
    GenerateNFTQueueRequestModel,
    EnhancePromptRequestModel,
    MintNFTRequestModel,
    TraitModel,
    TraitValueItemModel,
)
from chaingpt.types import NFTImageModel, ImageEnhanceOption

# 1. Enhance prompt (optional)
enhance_request = EnhancePromptRequestModel(prompt="a mystical dragon")
enhanced_response = await client.nft.enhance_prompt(enhance_request)
enhanced_prompt = enhanced_response.data.enhancedPrompt

# 2. Generate image synchronously
image_request = GenerateImageRequestModel(
    prompt=enhanced_prompt,
    model=NFTImageModel.VELOGEN,
    height=512,
    width=512,
    steps=3,
    enhance=ImageEnhanceOption.ORIGINAL,
    style="cinematic",
    traits=[
        TraitModel(
            trait_type="Background",
            value=[
                TraitValueItemModel(value="Heaven", ratio=20),
                TraitValueItemModel(value="Hell", ratio=60),
                TraitValueItemModel(value="garden", ratio=20),
            ],
        )
    ],
)
image_response = await client.nft.generate_image(image_request)

# Save the generated image
with open("generated_image.jpg", "wb") as f:
    f.write(bytes(image_response.data.data))

# 2b. Image-to-Image Generation (new feature)
# Generate variations of an existing image
image_to_image_request = GenerateImageRequestModel(
    prompt="A futuristic cyberpunk version of this character",
    model=NFTImageModel.NEBULA_FORGE_XL,
    height=1024,
    width=1024,
    image="https://example.com/reference-image.jpg",  # Valid image URL required
    isCharacterPreserve=True,  # Preserve character features
    enhance=ImageEnhanceOption.ENHANCE_1X,
)
# image_to_image_response = await client.nft.generate_image(image_to_image_request)

# 3. Queue NFT generation for minting
nft_request = GenerateNFTQueueRequestModel(
    walletAddress="0x000000000000000000000000000000000000dEaD",
    prompt="A serene alien landscape with two moons",
    model=NFTImageModel.NEBULA_FORGE_XL,
    height=1024,
    width=1024,
    steps=25,
    enhance=ImageEnhanceOption.ENHANCE_1X,
    chainId=56,  # BSC Mainnet
    amount=1,
    style="cinematic",
    traits=[
        TraitModel(
            trait_type="Background",
            value=[
                TraitValueItemModel(value="Heaven", ratio=20),
                TraitValueItemModel(value="Hell", ratio=60),
                TraitValueItemModel(value="garden", ratio=20),
            ],
        )
    ],
)
queue_response = await client.nft.generate_nft_queue(nft_request)

# 4. Track progress
progress = await client.nft.get_progress(queue_response.data.collectionId)
print(f"Generated: {progress.data.generated}, Progress: {progress.data.progress}%")

# Wait for completion
while not progress.data.generated:
    await asyncio.sleep(10)
    progress = await client.nft.get_progress(queue_response.data.collectionId)
    print(f"Generated: {progress.data.generated}, Progress: {progress.data.progress}%")

# 5. Get mint metadata (after generation completes)
if progress.data.generated:
    mint_metadata_request = MintNFTRequestModel(
        collectionId=queue_response.data.collectionId,
        name="Serene Alien Landscape #1",
        description="An AI-generated artwork of an alien world.",
        symbol="ALIEN",
        ids=[1],
    )
    mint_metadata_response = await client.nft.mint_nft_metadata(mint_metadata_request)
    print(f"Image URI: {mint_metadata_response.data.image or 'Not provided'}")

# 6. Get supported chains
chains_response = await client.nft.get_chains(test_net=True)
for chain in chains_response.data.chains:
    print(f"Chain ID: {chain.chainId}, Name: {chain.chainName}")

# 7. Get contract ABI
abi_response = await client.nft.get_abi()
print(f"Contract ABI: {abi_response.data}")

New Features:

• Image-to-Image Generation: Create variations of existing images by providing a reference image URL
• Character Preservation: Maintain character features while applying new styles or settings
• Enhanced Traits System: More flexible trait ratios and combinations
• Updated Chain Support: Support for the latest blockchain networks

Smart Contract Service

AI-assisted smart contract generation with conversation history.

from chaingpt.models import SmartContractGeneratorRequestModel
from chaingpt.types import ChatHistoryMode

request = SmartContractGeneratorRequestModel(
    question="Create an ERC20 token with burn functionality",
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId="550e8400-e29b-41d4-a716-446655440000",  # Example UUID
)

# Generate contract (buffered)
response = await client.smart_contract.generate_contract(request)
print(f"Status: {response.statusCode}")
print(f"User Prompt: {response.data.user}")
print(f"Generated Contract: {response.data.bot}")

# Generate contract (streaming)
async for chunk in client.smart_contract.stream_contract(request):
    print(chunk.decode('utf-8'), end="")

# Get chat history
history = await client.smart_contract.get_chat_history(
    limit=10,
    sort_order="desc"
)
print(f"Total entries: {history.data.count}")
for entry in history.data.rows:
    print(f"ID: {entry.id}, Question: {entry.question[:50]}...")
    print(f"Bot: {entry.chatBot.name}, User: {entry.user.email}")
    print(f"Created: {entry.createdAt}")

Auditor Service

Automated smart contract security auditing with detailed vulnerability reports.

from chaingpt.models import SmartContractAuditRequestModel

contract_code = """
pragma solidity ^0.8.0;
contract UnsafeBank {
    mapping(address => uint) public balances;
    
    function deposit() public payable {
        balances[msg.sender] += msg.value;
    }
    
    function withdraw(uint amount) public {
        // Vulnerability: Reentrancy possible
        (bool success, ) = msg.sender.call{value: amount}("");
        require(success, "Transfer failed.");
        balances[msg.sender] -= amount; // Balance updated after external call
    }
}
"""

audit_request = SmartContractAuditRequestModel(
    question=f"Please audit the following Solidity contract for security vulnerabilities:\n\n```solidity\n{contract_code}\n```",
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId="550e8400-e29b-41d4-a716-446655440000"  # Example UUID
)

# Get audit report
audit_response = await client.auditor.audit_contract(audit_request)
print(f"Status: {audit_response.status}")
print(f"Audit Report: {audit_response.data.bot}")

# Stream audit report
async for chunk in client.auditor.stream_audit(audit_request):
    print(chunk.decode('utf-8'), end="")

# Get audit history
history = await client.auditor.get_audit_history(
    sdk_unique_id="550e8400-e29b-41d4-a716-446655440000",
    limit=5
)
for entry in history.data.rows:
    print(f"Audit ID: {entry.id}, Created: {entry.createdAt}")
    print(f"Question: {entry.question[:100]}...")

News Service

Access curated blockchain and cryptocurrency news with advanced filtering.

# Get latest news
latest_news = await client.news.get_news(limit=10)
print(f"Found {latest_news.total} total articles")
for article in latest_news.data:
    print(f"{article.title} - {article.createdAt}")
    print(f"Category ID: {article.categoryId}, Token ID: {article.tokenId}")

# Filter by category and subcategory
# Category IDs: NFT=8, SubCategory IDs for Ethereum: 15, 39
nft_eth_news = await client.news.get_news(
    category_id=8,          # NFT category
    sub_category_id=[15, 39],  # Ethereum subcategories
    limit=5
)

# Filter by token and search query
bitcoin_news = await client.news.get_news(
    token_id=79,           # Bitcoin token ID
    search_query="halving",
    limit=5
)

# Filter by date
recent_news = await client.news.get_news(
    fetch_after="2024-01-01",
    sort_by="createdAt"
)

---

Advanced Usage

Session Management

# Use consistent session IDs for conversation continuity
session_id = "550e8400-e29b-41d4-a716-446655440000"  # Example UUID

request = LLMChatRequestModel(
    question="Start a new conversation about DeFi",
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId=session_id
)

# All subsequent requests with the same session_id will maintain context
follow_up = LLMChatRequestModel(
    question="Tell me more about the previous topic",
    chatHistory=ChatHistoryMode.ON,
    sdkUniqueId=session_id  # Same session
)

Error Handling with Retries

from chaingpt.exceptions import ChainGPTError

try:
    response = await client.llm.chat(request)
except ChainGPTError as e:
    print(f"API Error: {e}")
    # SDK automatically retries with exponential backoff
except Exception as e:
    print(f"Unexpected error: {e}")

Custom HTTP Configuration

import httpx
from chaingpt.client import ChainGPTClient

# Custom HTTP client configuration
custom_client = httpx.AsyncClient(
    timeout=60.0,
    limits=httpx.Limits(max_connections=10),
    headers={"User-Agent": "MyApp/1.0"}
)

client = ChainGPTClient(
    api_key="your_key",
    http_client=custom_client
)

Logging Configuration

from chaingpt.client import ChainGPTClient

# Setup debug logging
client = ChainGPTClient(api_key=API_KEY, debug=True)

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Error Handling

The SDK provides comprehensive error handling with specific exception types:

from chaingpt.exceptions import (
    ChainGPTError,
    AuthenticationError,
    ValidationError,
    RateLimitError,
    ServerError,
    TimeoutError,
    StreamingError,
    ConfigurationError
)

try:
    response = await client.llm.chat(request)
except AuthenticationError as e:
    print("Authentication failed - check your API key")
except RateLimitError as e:
    print("Rate limit exceeded - please wait before retrying")
except ValidationError as e:
    print(f"Invalid request parameters: {e}")
except ServerError as e:
    print("Server error - please try again later")
except TimeoutError as e:
    print("Request timed out - please try again")
except StreamingError as e:
    print(f"Streaming error: {e}")
except ChainGPTError as e:
    print(f"API error: {e}")

Common Error Codes:

• 400 - Bad Request (invalid parameters)
• 401 - Unauthorized (invalid API key)
• 429 - Rate Limited
• 500 - Internal Server Error

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Examples

Complete example scripts are available in the examples/ directory:

• example_llm_service.py - LLM chat with context injection
• example_nft_service.py - Complete NFT generation workflow
• example_smart_contract_service.py - Contract generation and history
• example_auditor_service.py - Contract auditing
• example_news_service.py - News filtering and retrieval

Run an example:

python examples/example_llm_service.py

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API Reference

Core Classes

• ChainGPTClient - Main SDK client (imported from chaingpt.client)
• LLMService - AI chat operations
• NFTService - NFT generation and minting
• SmartContractService - Contract generation
• AuditorService - Contract auditing
• NewsService - News retrieval

Request Models

All models are imported from chaingpt.models:

• LLMChatRequestModel - Chat requests
• GenerateImageRequestModel - Image generation
• GenerateNFTQueueRequestModel - NFT queue requests
• SmartContractGeneratorRequestModel - Contract generation
• SmartContractAuditRequestModel - Audit requests
• EnhancePromptRequestModel - Prompt enhancement
• MintNFTRequestModel - NFT minting
• ContextInjectionModel - Context injection for LLM
• TokenInformationModel - Token information
• SocialMediaUrlModel - Social media URLs
• TraitModel - NFT traits
• TraitValueItemModel - NFT trait values

Enums

All enums are imported from chaingpt.types:

• ChatHistoryMode - ON, OFF
• NFTImageModel - NEBULA_FORGE_XL, VELOGEN
• ImageEnhanceOption - ORIGINAL, ENHANCE_1X, ENHANCE_2X
• AITone - PRE_SET_TONE, CUSTOM_TONE
• PresetTone - PROFESSIONAL, CASUAL, INFORMATIVE
• BlockchainNetwork - Various blockchain networks

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Requirements

• Python 3.9+
• httpx>=0.28.1
• pydantic>=2.11.5
• colorama>=0.4.6
• python-dotenv>=1.1.0

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Development

Setup Development Environment

git clone https://github.com/ChainGPT-org/chaingpt-python.git
cd chaingpt-python
pip install -e ".[dev,test]"

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=chaingpt

# Run specific test categories
pytest -m unit
pytest -m integration
pytest -m asyncio

Code Quality

# Format code
black chaingpt/ tests/ examples/

# Type checking
mypy chaingpt/

# Linting
flake8 chaingpt/ tests/

Building and Publishing

# Build package
python -m build

# Check package
twine check dist/*

# Upload to PyPI (test)
twine upload --repository testpypi dist/*

# Upload to PyPI (production)
twine upload dist/*

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Commit your changes: git commit -m 'Add amazing feature'
5. Push to the branch: git push origin feature/amazing-feature
6. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add type hints to all functions
• Write comprehensive tests for new features
• Update documentation for any API changes
• Ensure all tests pass before submitting PR

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License

This project is licensed under the GPL-3.0-or-later License. See LICENSE for details.

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Support

• Documentation: https://docs.chaingpt.org
• API Reference: https://docs.chaingpt.org/dev-docs-b2b-saas-api-and-sdk/introduction-to-chaingpts-developer-tools
• Issues: GitHub Issues
• Discord: ChainGPT Community
• X(Twitter): @ChainGPT

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Built with ❤️ by the ChainGPT team