langchain-iointelligence 0.4.0

LangChain integration for io Intelligence LLM API with OpenAI compatibility

langchain-iointelligence

📦 Available on PyPI - Install with pip install langchain-iointelligence

📋 Feature Support Matrix

Feature	Status	Notes
✅ Chat Model	Fully Supported	Message-based interface with system/user/assistant roles
✅ Text LLM	Fully Supported	Traditional prompt-response interface
✅ Sync Generation	Fully Supported	Standard text generation with token usage tracking
✅ Error Handling	Production Ready	Comprehensive error classification and retry logic
✅ Token Usage	Fully Supported	response.usage_metadata access (0.2.0+)
✅ Vision/Multimodal	Supported	Image input via vision_message() on vision models (0.3.0+)
✅ Function/Tool Calling	Supported	bind_tools() with tool-call parsing + streaming (0.4.0+)
✅ Structured Output	Supported	with_structured_output() — function calling / json_schema / json_mode (0.4.0+)
⚠️ Streaming	Basic Support	SSE token + tool-call chunks; usage-at-end coming soon
❌ Embeddings	Not Supported	Use dedicated embedding providers

Note: Non-core message roles default to user. Usage metadata always includes all required fields (input_tokens, output_tokens, total_tokens) with defaults of 0 when data unavailable.

LangChain-compatible wrapper for io Intelligence LLM API with OpenAI-compatible interface. Features both traditional Text LLM and modern Chat Model interfaces with comprehensive error handling, token usage tracking, and streaming support.

🚀 Key Features

• 🔄 Dual Interface Support: Both IOIntelligenceLLM (text-based) and IOIntelligenceChatModel (message-based)
• ⚡ OpenAI-Compatible Interface: Drop-in replacement with identical parameters and behavior
• 📡 Streaming Support: Token-by-token streaming (usage-at-end coming soon)
• 🛡️ Production-Grade Reliability: Automatic retries, detailed error classification, and robust fallbacks
• 🔀 Runtime Provider Switching: Easy switching between OpenAI, Anthropic, and io Intelligence
• 📊 LangChain Token Tracking: Standard usage_metadata with input_tokens/output_tokens/total_tokens
• 🖼️ Vision / Multimodal: Image input on vision models via the vision_message() helper (URLs, local files, bytes)
• 🎛️ Modern LangChain Integration: Full support for prompt | llm | parser chains

⚙️ Installation

pip install langchain-iointelligence

🔐 Quick Setup

Create a .env file:

IO_API_KEY=your_api_key_here
IO_API_URL=https://api.intelligence.io.solutions/api/v1/chat/completions

🎯 Quick Start

Modern Chat Model (Recommended)

from langchain_iointelligence import IOIntelligenceChat
from langchain_core.messages import HumanMessage

# Initialize with ChatGPT-compatible parameters
chat = IOIntelligenceChat(
    model="meta-llama/Llama-3.3-70B-Instruct",
    temperature=0.7,
    max_tokens=1000,
    timeout=30,
    max_retries=3
)

# Direct usage
messages = [HumanMessage(content="Explain quantum computing")]
response = chat.invoke(messages)
print(response.content)  # AIMessage content
print(response.usage_metadata)  # Token usage: input_tokens, output_tokens, total_tokens

Streaming Responses

from langchain_iointelligence import IOIntelligenceChat

chat = IOIntelligenceChat(streaming=True)
messages = [HumanMessage(content="Write a story about AI")]

# Real streaming with server-sent events
for chunk in chat.stream(messages):
    print(chunk.content, end="", flush=True)

Model Discovery

from langchain_iointelligence import list_available_models, is_model_available

# List all available models
models = list_available_models()
print("Available models:", models)

# Check if specific model exists
if is_model_available("meta-llama/Llama-3.3-70B-Instruct"):
    print("Model is available!")

Vision / Multimodal (Image Input) 🖼️

io Intelligence offers OpenAI-compatible vision models that accept images alongside text, through the same chat endpoint. Use the vision_message() helper to attach images from remote URLs, local files, or raw bytes — base64 data URLs are built automatically.

from langchain_iointelligence import (
    IOIntelligenceChatModel,
    vision_message,
    VISION_MODELS,
    DEFAULT_VISION_MODEL,
)

# Pick a vision-capable model (see VISION_MODELS for the full list)
chat = IOIntelligenceChatModel(model=DEFAULT_VISION_MODEL)

# 1) Remote image URL
message = vision_message(
    "What is in this image?",
    "https://example.com/photo.jpg",
)
print(chat.invoke([message]).content)

# 2) Local file (auto base64-encoded into a data URL)
message = vision_message("Describe this diagram", "./diagram.png")

# 3) Raw bytes
message = vision_message("What's shown here?", image_bytes)

# 4) Multiple images (up to 10) + optional detail hint
message = vision_message(
    "Compare these two products",
    ["./a.jpg", "https://example.com/b.png"],
    detail="high",
)

Available vision models (always confirm with list_available_models()):

Model	Provider
meta-llama/Llama-3.2-90B-Vision-Instruct (default)	Meta
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	Meta
Qwen/Qwen2.5-VL-32B-Instruct	Qwen
Qwen/Qwen2-VL-7B-Instruct	Qwen

Limits: up to 10 images per request, min 512×512, max 20 MB each. Remote image URLs must be publicly accessible.

You can also build content blocks manually with image_content_block() or encode a file yourself with encode_image_to_data_url(). Any LangChain-standard multimodal HumanMessage (a list of text / image_url content blocks) is passed through unchanged, so existing OpenAI-style vision code works as-is.

Tool / Function Calling 🛠️

Bind tools with bind_tools(); tool calls are parsed into the standard AIMessage.tool_calls, so the model drops straight into LangGraph / agent workflows. Works with @tool functions, Pydantic models, BaseTool instances, plain functions, or raw OpenAI tool dicts.

from langchain_core.tools import tool
from langchain_iointelligence import IOIntelligenceChatModel

@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"It's sunny in {location}."

chat = IOIntelligenceChatModel(model="meta-llama/Llama-3.3-70B-Instruct")
llm_with_tools = chat.bind_tools([get_weather])

ai_msg = llm_with_tools.invoke("What's the weather in Tokyo?")
for call in ai_msg.tool_calls:
    print(call["name"], call["args"])   # get_weather {'location': 'Tokyo'}

tool_choice accepts "auto" / "none" / "required" (or True), a specific tool name, or an explicit OpenAI tool_choice dict. Multi-turn tool conversations (assistant tool_calls → ToolMessage results) round-trip correctly, and tool-call deltas are also surfaced when streaming.

Structured Output 🧱

with_structured_output() returns a runnable that parses the response into your schema (a Pydantic model, TypedDict, or JSON-schema dict).

from pydantic import BaseModel, Field
from langchain_iointelligence import IOIntelligenceChatModel

class Person(BaseModel):
    name: str = Field(..., description="Full name")
    age: int = Field(..., description="Age in years")

chat = IOIntelligenceChatModel(model="meta-llama/Llama-3.3-70B-Instruct")
structured = chat.with_structured_output(Person)          # default: function_calling

person = structured.invoke("Jane Doe is 32 years old.")
print(person.name, person.age)                            # Jane Doe 32

Choose how structure is enforced via method:

• "function_calling" (default) — forces a tool call shaped like the schema.
• "json_schema" — uses the API's response_format json_schema (strict).
• "json_mode" — requests a generic JSON object (describe the schema in your prompt).

Pass include_raw=True to get {"raw", "parsed", "parsing_error"} instead of raising on a parse failure.

🔗 Advanced LangChain Integration

Modern Chain with Full Pipeline

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_iointelligence import IOIntelligenceChat

# Complete modern pipeline
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert technical writer."),
    ("human", "Explain {topic} in {style} style, max {words} words.")
])

chat = IOIntelligenceChat(
    model="meta-llama/Llama-3.3-70B-Instruct",
    temperature=0.3
)

parser = StrOutputParser()

# Modern chain: prompt | chat | parser
chain = prompt | chat | parser

result = chain.invoke({
    "topic": "machine learning",
    "style": "beginner-friendly", 
    "words": "200"
})
print(result)

Runtime Provider Switching

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic  
from langchain_iointelligence import IOIntelligenceChat

# Configure multiple providers
providers = {
    "openai": ChatOpenAI(model="gpt-4"),
    "anthropic": ChatAnthropic(model="claude-3-sonnet-20240229"),
    "iointelligence": IOIntelligenceChat(model="meta-llama/Llama-3.3-70B-Instruct")
}

# Runtime switching via configuration
configurable_chat = providers["openai"].configurable_alternatives(
    ConfigurableField(id="provider"),
    default_key="openai",
    **providers
)

# Switch provider at runtime
response = configurable_chat.invoke(
    "Hello world!",
    config={"configurable": {"provider": "iointelligence"}}
)

Production Fallback Configuration

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_iointelligence import IOIntelligenceChat

# Multi-tier fallback system
primary = IOIntelligenceChat(
    model="meta-llama/Llama-3.3-70B-Instruct",
    timeout=10,
    max_retries=2
)

secondary = ChatOpenAI(model="gpt-4", timeout=15)
tertiary = ChatAnthropic(model="claude-3-sonnet-20240229")

# Automatic fallback chain
reliable_chat = primary.with_fallbacks([secondary, tertiary])

# Will automatically try alternatives on failure
response = reliable_chat.invoke("Complex analysis request")

Error Handling and Monitoring

from langchain_iointelligence import (
    IOIntelligenceChat,
    IOIntelligenceRateLimitError,
    IOIntelligenceServerError,
    IOIntelligenceAuthenticationError
)

chat = IOIntelligenceChat()

try:
    response = chat.invoke("Generate report")
    print(f"Success! Used {response.usage_metadata['total_tokens']} tokens")
    
except IOIntelligenceRateLimitError as e:
    print(f"Rate limited: {e}. Retry after: {e.retry_after}")
    
except IOIntelligenceServerError as e:
    print(f"Server error {e.status_code}: {e}")
    
except IOIntelligenceAuthenticationError:
    print("Invalid API key - check your credentials")

🛠️ Configuration Options

Complete Parameter Reference

from langchain_iointelligence import IOIntelligenceChat

chat = IOIntelligenceChat(
    # API Configuration
    api_key="your_key",                           # or use IO_API_KEY env var  
    api_url="https://api.example.com/v1/chat",    # or use IO_API_URL env var
    
    # Model Parameters (ChatGPT Compatible)
    model="meta-llama/Llama-3.3-70B-Instruct",   # Model identifier
    temperature=0.7,                              # Creativity (0.0-2.0)
    max_tokens=2000,                              # Response length limit
    
    # Reliability & Performance  
    timeout=30,                                   # Request timeout (seconds)
    max_retries=3,                                # Retry attempts
    retry_delay=1.0,                              # Initial retry delay
    streaming=True,                               # Enable real streaming
)

Available Models

Check the latest models dynamically:

from langchain_iointelligence import IOIntelligenceUtils

utils = IOIntelligenceUtils()
models = utils.list_models()

for model in models:
    print(f"Model: {model['id']}")
    if 'description' in model:
        print(f"  Description: {model['description']}")

# Get recommended models
recommended = utils.get_recommended_models()
print("Recommended models:", recommended)

Common models include:

• meta-llama/Llama-3.3-70B-Instruct (default, balanced performance)
• deepseek-ai/DeepSeek-R1-0528 (reasoning)
• Qwen/Qwen3-235B-A22B-Thinking-2507 (high capability)

Vision-capable models (for image input — see the Vision section):

• meta-llama/Llama-3.2-90B-Vision-Instruct
• meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
• Qwen/Qwen2.5-VL-32B-Instruct
• Qwen/Qwen2-VL-7B-Instruct

🔌 API Compatibility

Full OpenAI ChatCompletion API compatibility:

{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "messages": [{"role": "user", "content": "Hello"}],
  "temperature": 0.7,
  "max_tokens": 1000,
  "stream": true,
  "stop": ["END"]
}

Also supports legacy Text Completion format:

{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "prompt": "Hello",
  "temperature": 0.7,
  "max_tokens": 1000
}

🔍 Migration Guides

From OpenAI ChatGPT

# Before (OpenAI)
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(
    model="gpt-4",
    temperature=0.7,
    max_tokens=1000,
    timeout=30
)

# After (io Intelligence) - Same parameters!
from langchain_iointelligence import IOIntelligenceChat
chat = IOIntelligenceChat(
    model="meta-llama/Llama-3.3-70B-Instruct",
    temperature=0.7,
    max_tokens=1000, 
    timeout=30
)

From Anthropic Claude

# Before (Anthropic)
from langchain_anthropic import ChatAnthropic
chat = ChatAnthropic(model="claude-3-sonnet-20240229")

# After (io Intelligence)
from langchain_iointelligence import IOIntelligenceChat
chat = IOIntelligenceChat(model="meta-llama/Llama-3.3-70B-Instruct")

# Same interface - no code changes needed!
response = chat.invoke([HumanMessage(content="Hello")])

✅ Testing

# Install test dependencies
pip install pytest pytest-cov

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=langchain_iointelligence --cov-report=html

# Test specific functionality
pytest tests/test_chat.py::TestIOIntelligenceChatModel::test_streaming -v

🚀 Advanced Examples

Batch Processing with Rate Limit Handling

import asyncio
from langchain_iointelligence import IOIntelligenceChat, IOIntelligenceRateLimitError

async def process_batch(prompts, chat):
    results = []
    for prompt in prompts:
        try:
            result = await chat.ainvoke(prompt)
            results.append(result)
        except IOIntelligenceRateLimitError:
            await asyncio.sleep(60)  # Wait for rate limit reset
            result = await chat.ainvoke(prompt)  # Retry
            results.append(result)
    return results

Custom Retry Logic

from langchain_iointelligence import IOIntelligenceChat

# Custom retry configuration
chat = IOIntelligenceChat(
    max_retries=5,
    retry_delay=2.0,  # Start with 2 second delays
    timeout=60        # Longer timeout for complex requests
)

Model Performance Comparison

from langchain_iointelligence import IOIntelligenceChat

models = [
    "meta-llama/Llama-3.3-70B-Instruct",
    "meta-llama/Llama-3.1-405B-Instruct",
    "meta-llama/Llama-3.1-70B-Instruct"
]

prompt = "Solve this math problem: 2x + 5 = 15"

for model in models:
    chat = IOIntelligenceChat(model=model)
    response = chat.invoke(prompt)
    print(f"Model {model}: {response.content}")
    print(f"Tokens used: {response.usage_metadata}")

🔧 Development

# Clone repository
git clone https://github.com/soh7410/langchain-iointelligence.git
cd langchain-iointelligence

# Install in development mode
pip install -e ".[dev]"

# Setup environment
cp .env.example .env
# Edit .env with your API credentials

# Code formatting & linting
black langchain_iointelligence/ tests/
mypy langchain_iointelligence/
flake8 langchain_iointelligence/

# Run tests
pytest tests/ -v

📊 Performance & Monitoring

Usage Tracking

chat = IOIntelligenceChat()
response = chat.invoke("Analyze market trends")

# Access detailed usage information
usage = response.usage_metadata
print(f"Prompt tokens: {usage.get('prompt_tokens')}")
print(f"Completion tokens: {usage.get('completion_tokens')}")
print(f"Total tokens: {usage.get('total_tokens')}")
print(f"Model used: {response.response_metadata.get('model')}")

Response Timing

import time

start_time = time.time()
response = chat.invoke("Complex reasoning task")
end_time = time.time()

print(f"Response time: {end_time - start_time:.2f} seconds")
print(f"Tokens per second: {response.usage_metadata['total_tokens'] / (end_time - start_time):.1f}")

🛡️ Production Best Practices

1. Always use environment variables for API keys
2. Implement proper fallback chains for reliability
3. Monitor token usage to control costs
4. Use streaming for better user experience
5. Handle rate limits gracefully with exponential backoff
6. Validate models before deployment

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Run tests (pytest tests/ -v)
4. Commit changes (git commit -m 'Add amazing feature')
5. Push to branch (git push origin feature/amazing-feature)
6. Create a Pull Request

📧 Support

• Issues: GitHub Issues
• Documentation: GitHub Wiki
• Examples: See examples/ directory

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🎯 Ready for production use with complete ChatGPT API compatibility and modern LangChain integration!