Integrate Morpheus Inference API with OpenAI Python SDK

Learn how to integrate the Morpheus Inference API with OpenAI’s official Python SDK. This guide covers basic chat completions, streaming responses, tool calling, and async operations.

Overview

The Morpheus Inference API is fully OpenAI-compatible. Simply point the official OpenAI Python SDK to the Morpheus base URL and start building.

Base URL: https://api.mor.org/api/v1

Prerequisites

Before you begin, ensure you have:

Python 3.8+ installed on your system
A Morpheus API key from app.mor.org
Basic knowledge of Python and async/await patterns
Familiarity with REST APIs

Create a Morpheus API Key

Visit app.mor.org and sign in to create your API key.

Navigate to the API Keys section
Click “Create API Key” and provide a name
Copy your API key immediately (it won’t be shown again)

Store your API key securely. Never commit it to version control or expose it in publicly accessible code.

Install the OpenAI Python SDK

Install the official OpenAI Python library:

pip install openai

Verify installation by running pip show openai to see the installed version.

Configure Environment Variables

Create a .env file in your project root or set environment variables:

.env

MORPHEUS_API_KEY=your_api_key_here

For better security, use environment variables instead of hardcoding API keys:

import os
from dotenv import load_dotenv

load_dotenv()

api_key = os.getenv("MORPHEUS_API_KEY")

Never commit your API key to version control. Add .env to your .gitignore file.

Basic Integration

Setting Up the Client

Configure the OpenAI client to use the Morpheus Inference API by setting a custom base_url:

setup.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

The only difference from using the standard OpenAI client is the base_url parameter. All other functionality remains the same.

Available Models

Query the available models using the Morpheus API:

list_models.py

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

# List all available models
models = client.models.list()
for model in models.data:
    print(f"Model: {model.id}")

Common Morpheus Models

Popular models available through Morpheus:

llama-3.3-70b:web - Meta’s Llama 3.3 with web search capabilities
llama-3.3-70b - Meta’s Llama 3.3 base model
qwen3-235b:web - Qwen 3 with web search capabilities
qwen3-235b - Qwen 3 base model

Model availability may vary based on provider availability in the Morpheus marketplace. The API automatically routes to the highest-rated provider for your selected model. The :web suffix indicates models optimized for web browsing tasks.

Text Generation

Basic Chat Completions

Use the chat.completions.create() method for standard, non-streaming text generation:

basic_chat.py

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    temperature=0.7,
    max_tokens=1024
)

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

Streaming Responses

For real-time output, enable streaming to receive tokens as they’re generated:

streaming_chat.py

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

stream = client.chat.completions.create(
    model="llama-3.3-70b:web",
    messages=[
        {"role": "user", "content": "Write a short story about artificial intelligence."}
    ],
    stream=True,
    temperature=0.8
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

print()  # New line after streaming completes

Streaming provides a better user experience by showing output immediately rather than waiting for the entire response.

Asynchronous Operations

Async Client Setup

Use the AsyncOpenAI client for concurrent operations and async/await patterns:

async_client.py

import asyncio
import os
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    )

    response = await client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[
            {"role": "user", "content": "What is the capital of France?"}
        ]
    )

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

asyncio.run(main())

Async Streaming

Combine async operations with streaming for efficient, concurrent request handling:

async_streaming.py

import asyncio
import os
from openai import AsyncOpenAI

async def stream_chat(client, prompt):
    stream = await client.chat.completions.create(
        model="llama-3.3-70b:web",
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )

    print(f"\nPrompt: {prompt}")
    print("Response: ", end="")
    
    async for chunk in stream:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="", flush=True)
    
    print("\n")

async def main():
    client = AsyncOpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    )

    # Process multiple streams concurrently
    await asyncio.gather(
        stream_chat(client, "Explain Python generators"),
        stream_chat(client, "What is machine learning?"),
        stream_chat(client, "Describe blockchain technology")
    )

asyncio.run(main())

Async operations are ideal for handling multiple concurrent requests efficiently, making your application more responsive.

Tool Calling

Enable your AI models to execute functions and interact with external systems through tool calling.

Defining Tools

Define tools using JSON schemas to specify available functions:

tools_definition.py

import json
import os
from openai import OpenAI

def get_weather(location: str, unit: str = "celsius") -> dict:
    """
    Get the current weather for a location.
    
    Args:
        location: City name or location
        unit: Temperature unit (celsius or fahrenheit)
    
    Returns:
        Weather information dictionary
    """
    # In a real application, call a weather API here
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "condition": "sunny"
    }

def calculate(expression: str) -> dict:
    """
    Evaluate a mathematical expression.
    
    Args:
        expression: Mathematical expression to evaluate
    
    Returns:
        Calculation result
    """
    try:
        result = eval(expression)
        return {"result": result, "expression": expression}
    except Exception as e:
        return {"error": str(e)}

# Define tool schemas
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name, e.g. San Francisco"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "calculate",
            "description": "Perform a mathematical calculation",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Mathematical expression to evaluate, e.g. '2 + 2'"
                    }
                },
                "required": ["expression"]
            }
        }
    }
]

# Map function names to implementations
available_functions = {
    "get_weather": get_weather,
    "calculate": calculate
}

Using Tools with Chat Completions

Integrate tools with chat completions to enable function calling:

tool_calling.py

import json
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

messages = [
    {"role": "user", "content": "What's the weather like in Tokyo and calculate 15 * 23"}
]

# Initial request with tools
response = client.chat.completions.create(
    model="llama-3.3-70b:web",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

response_message = response.choices[0].message
messages.append(response_message)

# Process tool calls
if response_message.tool_calls:
    for tool_call in response_message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        print(f"Calling function: {function_name}")
        print(f"Arguments: {function_args}")
        
        # Execute the function
        function_to_call = available_functions[function_name]
        function_response = function_to_call(**function_args)
        
        # Add function response to messages
        messages.append({
            "tool_call_id": tool_call.id,
            "role": "tool",
            "name": function_name,
            "content": json.dumps(function_response)
        })
    
    # Get final response with tool results
    final_response = client.chat.completions.create(
        model="llama-3.3-70b:web",
        messages=messages
    )
    
    print("\nFinal Response:")
    print(final_response.choices[0].message.content)
else:
    print(response_message.content)

Complete Tool Calling Example

Here’s a complete example with error handling and streaming:

complete_tool_example.py

import json
import os
from openai import OpenAI
from typing import Dict, Any, Callable

class ToolHandler:
    def __init__(self, client: OpenAI):
        self.client = client
        self.functions: Dict[str, Callable] = {}
        self.tools = []
    
    def register_function(self, func: Callable, schema: dict):
        """Register a function and its schema for tool calling."""
        self.functions[func.__name__] = func
        self.tools.append({
            "type": "function",
            "function": schema
        })
    
    def execute_tool_call(self, tool_call) -> dict:
        """Execute a single tool call."""
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        if function_name not in self.functions:
            return {"error": f"Function {function_name} not found"}
        
        try:
            result = self.functions[function_name](**function_args)
            return result
        except Exception as e:
            return {"error": str(e)}
    
    def chat_with_tools(self, messages: list, model: str = "llama-3.3-70b:web", 
                       max_iterations: int = 5) -> str:
        """
        Handle chat completions with automatic tool calling.
        
        Args:
            messages: List of message dictionaries
            model: Model to use
            max_iterations: Maximum number of tool calling iterations
        
        Returns:
            Final assistant response
        """
        for iteration in range(max_iterations):
            response = self.client.chat.completions.create(
                model=model,
                messages=messages,
                tools=self.tools if self.tools else None,
                tool_choice="auto" if self.tools else None
            )
            
            response_message = response.choices[0].message
            messages.append(response_message)
            
            # Check if we're done
            if not response_message.tool_calls:
                return response_message.content
            
            # Process tool calls
            for tool_call in response_message.tool_calls:
                print(f"[Tool Call {iteration + 1}] {tool_call.function.name}")
                
                result = self.execute_tool_call(tool_call)
                
                messages.append({
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": tool_call.function.name,
                    "content": json.dumps(result)
                })
        
        return "Max iterations reached without completion"

# Initialize client and handler
client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)
handler = ToolHandler(client)

# Define and register functions
def search_web(query: str) -> dict:
    """Search the web for information."""
    return {
        "query": query,
        "results": [
            {"title": "Example Result", "snippet": "This is a sample search result."}
        ]
    }

handler.register_function(search_web, {
    "name": "search_web",
    "description": "Search the web for current information",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query"
            }
        },
        "required": ["query"]
    }
})

# Use the handler
messages = [
    {"role": "user", "content": "Search for recent AI developments"}
]

response = handler.chat_with_tools(messages)
print(f"\nFinal Response:\n{response}")

Always provide clear, detailed descriptions for your tools and parameters. This helps the model understand when and how to use each function.

Advanced Configuration

Custom Timeouts and Retries

Configure timeouts and retry behavior for production applications:

config.py

import os
import httpx
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1",
    timeout=httpx.Timeout(
        connect=5.0,   # Connection timeout
        read=60.0,     # Read timeout
        write=10.0,    # Write timeout
        pool=60.0      # Pool timeout
    ),
    max_retries=3
)

# Override timeout for specific requests
response = client.with_options(timeout=30.0).chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Quick question"}]
)

Token Usage Tracking

Monitor token consumption and costs:

token_tracking.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "user", "content": "Explain neural networks"}
    ]
)

usage = response.usage
print(f"Prompt tokens: {usage.prompt_tokens}")
print(f"Completion tokens: {usage.completion_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Log usage to database or analytics
def log_usage(model: str, usage_data: dict):
    """Log token usage for monitoring."""
    print(f"Model: {model}")
    print(f"Usage: {usage_data}")
    # Add your logging logic here

log_usage(response.model, {
    "prompt_tokens": usage.prompt_tokens,
    "completion_tokens": usage.completion_tokens,
    "total_tokens": usage.total_tokens
})

Error Handling

Implement robust error handling for production deployments:

error_handling.py

import os
from openai import OpenAI, APIError, APITimeoutError, RateLimitError

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1",
    max_retries=2
)

def safe_chat_completion(messages: list, model: str = "llama-3.3-70b") -> str:
    """
    Make a chat completion with comprehensive error handling.
    
    Args:
        messages: List of message dictionaries
        model: Model to use
    
    Returns:
        Response text or error message
    """
    try:
        response = client.chat.completions.create(
            model=model,
            messages=messages,
            timeout=30.0
        )
        return response.choices[0].message.content
    
    except APITimeoutError:
        return "Request timed out. Please try again."
    
    except RateLimitError:
        return "Rate limit exceeded. Please wait before making more requests."
    
    except APIError as e:
        print(f"API Error: {e.status_code} - {e.message}")
        return f"An API error occurred: {e.message}"
    
    except Exception as e:
        print(f"Unexpected error: {str(e)}")
        return "An unexpected error occurred. Please try again."

# Use the safe function
messages = [
    {"role": "user", "content": "Tell me about Python decorators"}
]

result = safe_chat_completion(messages)
print(result)

Context Manager Pattern

Use context managers for automatic resource cleanup:

context_manager.py

import os
from openai import OpenAI

def process_queries(queries: list):
    """Process multiple queries with automatic cleanup."""
    with OpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    ) as client:
        for query in queries:
            response = client.chat.completions.create(
                model="llama-3.3-70b",
                messages=[{"role": "user", "content": query}]
            )
            print(f"Q: {query}")
            print(f"A: {response.choices[0].message.content}\n")
    
    # HTTP client is automatically closed here

queries = [
    "What is async/await in Python?",
    "Explain list comprehensions",
    "What are Python decorators?"
]

process_queries(queries)

Troubleshooting

Connection errors or timeouts

Cause: Network issues, firewall restrictions, or server unavailability.Solution:

Check your internet connection
Verify the base URL is correct: https://api.mor.org/api/v1
Increase timeout values for slower connections
Ensure your firewall allows HTTPS connections

client = OpenAI(
    api_key=os.getenv("MORPHEUS_API_KEY"),
    base_url="https://api.mor.org/api/v1",
    timeout=60.0,  # Increase timeout
    max_retries=3   # Enable retries
)

Authentication errors (401 Unauthorized)

Cause: Invalid or missing API key.Solution:

Verify your API key is correct
Ensure the API key is properly loaded from environment variables
Check that the key hasn’t been deleted from your Morpheus account

import os

# Debug API key loading
api_key = os.getenv("MORPHEUS_API_KEY")
print(f"API key loaded: {api_key is not None}")
print(f"API key length: {len(api_key) if api_key else 0}")

if not api_key:
    raise ValueError("MORPHEUS_API_KEY environment variable not set")

Tool calls fail or return unexpected results

Cause: Incorrect tool schema, missing function implementations, or model limitations.Solution:

Verify tool schemas match the JSON Schema specification
Ensure all required parameters are marked correctly
Provide detailed descriptions for tools and parameters
Test with different models (llama-3.3-70b often performs better)

# Good tool definition
{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a specific location. Use this when the user asks about weather conditions.",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City name, e.g., 'San Francisco' or 'Tokyo'"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "Temperature unit to use"
                }
            },
            "required": ["location"]
        }
    }
}

Streaming stops prematurely

Cause: Network interruption, timeout, or model completion.Solution:

Check the finish_reason in the response
Implement error handling for streams
Use appropriate timeout values

try:
    stream = client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[{"role": "user", "content": "Long task"}],
        stream=True,
        timeout=120.0  # Longer timeout for streaming
    )
    
    for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)
        
        # Check finish reason
        if chunk.choices[0].finish_reason:
            print(f"\nFinish reason: {chunk.choices[0].finish_reason}")
            
except Exception as e:
    print(f"Stream error: {str(e)}")

Model not found errors

Cause: Requested model is not available or misspelled.Solution:

List available models first
Use exact model names including suffixes (:web)
Check model availability in the marketplace

# List available models
models = client.models.list()
available_models = [model.id for model in models.data]
print("Available models:", available_models)

# Verify model exists before using
desired_model = "llama-3.3-70b:web"
if desired_model in available_models:
    response = client.chat.completions.create(
        model=desired_model,
        messages=[{"role": "user", "content": "Hello"}]
    )
else:
    print(f"Model {desired_model} not available. Using default.")

Async operations not working

Cause: Incorrect async/await usage or event loop issues.Solution:

Use AsyncOpenAI instead of OpenAI
Properly await all async operations
Run async functions with asyncio.run()

import asyncio
from openai import AsyncOpenAI

async def correct_async_usage():
    client = AsyncOpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    )
    
    # Await the response
    response = await client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[{"role": "user", "content": "Hello"}]
    )
    
    return response.choices[0].message.content

# Run the async function
result = asyncio.run(correct_async_usage())
print(result)

Best Practices

Use environment variables

Always store API keys in environment variables, never hardcode them in your source code.

Implement retry logic

Use the built-in max_retries parameter or implement custom retry logic for production applications.

Monitor token usage

Track token consumption to understand your application’s resource needs and optimize prompts.

Handle errors gracefully

Implement comprehensive error handling to provide good user experiences when API calls fail.

Use async for concurrency

Leverage AsyncOpenAI for applications that need to handle multiple concurrent requests.

Validate tool schemas

Test tool calling implementations thoroughly and provide clear descriptions for reliable function execution.

Example Applications

Command-Line Chat Application

A simple command-line chat interface:

cli_chat.py

import os
from openai import OpenAI

def main():
    client = OpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    )
    
    messages = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]
    
    print("Morpheus Chat (type 'quit' to exit)")
    print("-" * 50)
    
    while True:
        user_input = input("\nYou: ").strip()
        
        if user_input.lower() in ['quit', 'exit', 'q']:
            print("Goodbye!")
            break
        
        if not user_input:
            continue
        
        messages.append({"role": "user", "content": user_input})
        
        try:
            stream = client.chat.completions.create(
                model="llama-3.3-70b:web",
                messages=messages,
                stream=True,
                temperature=0.7
            )
            
            print("\nAssistant: ", end="", flush=True)
            full_response = ""
            
            for chunk in stream:
                if chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    print(content, end="", flush=True)
                    full_response += content
            
            print()
            messages.append({"role": "assistant", "content": full_response})
            
        except Exception as e:
            print(f"\nError: {str(e)}")

if __name__ == "__main__":
    main()

Batch Processing Script

Process multiple prompts efficiently:

batch_processor.py

import os
import asyncio
from openai import AsyncOpenAI
from typing import List, Dict

async def process_batch(prompts: List[str], model: str = "llama-3.3-70b") -> List[Dict]:
    """
    Process multiple prompts concurrently.
    
    Args:
        prompts: List of user prompts
        model: Model to use
    
    Returns:
        List of response dictionaries
    """
    client = AsyncOpenAI(
        api_key=os.getenv("MORPHEUS_API_KEY"),
        base_url="https://api.mor.org/api/v1"
    )
    
    async def process_single(prompt: str) -> Dict:
        try:
            response = await client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                timeout=30.0
            )
            
            return {
                "prompt": prompt,
                "response": response.choices[0].message.content,
                "tokens": response.usage.total_tokens,
                "success": True
            }
        except Exception as e:
            return {
                "prompt": prompt,
                "error": str(e),
                "success": False
            }
    
    # Process all prompts concurrently
    tasks = [process_single(prompt) for prompt in prompts]
    results = await asyncio.gather(*tasks)
    
    return results

# Example usage
async def main():
    prompts = [
        "What is Python?",
        "Explain machine learning",
        "What are REST APIs?",
        "Describe cloud computing",
        "What is Docker?"
    ]
    
    print(f"Processing {len(prompts)} prompts...")
    results = await process_batch(prompts)
    
    # Display results
    for i, result in enumerate(results, 1):
        print(f"\n{'=' * 60}")
        print(f"Prompt {i}: {result['prompt']}")
        print(f"{'=' * 60}")
        
        if result['success']:
            print(f"Response: {result['response']}")
            print(f"Tokens used: {result['tokens']}")
        else:
            print(f"Error: {result['error']}")

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

Next Steps

Explore Models

Browse all available models in the Morpheus marketplace and their capabilities.

OpenAI Python Docs

Explore the complete OpenAI Python SDK documentation for advanced features.

API Reference

Complete API documentation for all Morpheus Gateway endpoints and parameters.

Vercel AI SDK

Learn how to integrate Morpheus with Vercel’s AI SDK for frontend applications.

Summary

You’ve successfully integrated the Morpheus Inference API with OpenAI’s Python SDK! Key takeaways:

OpenAI Compatibility: Morpheus works seamlessly with the official OpenAI Python SDK by using a custom base_url

Flexible Deployment: Use synchronous or asynchronous clients based on your application needs

Streaming Support: Real-time streaming responses work identically to OpenAI’s API

Tool Calling: Define and execute custom functions with JSON schema-based tool definitions

The combination of Morpheus’s decentralized AI inference and the OpenAI Python SDK’s robust features enables you to build powerful AI applications without infrastructure costs or vendor lock-in.

​Integrate Morpheus Inference API with OpenAI Python SDK

​Overview

​Prerequisites

​Basic Integration

​Setting Up the Client

​Available Models

​Text Generation

​Basic Chat Completions

​Streaming Responses

​Asynchronous Operations

​Async Client Setup

​Async Streaming

​Tool Calling

​Defining Tools

​Using Tools with Chat Completions

​Complete Tool Calling Example

​Advanced Configuration

​Custom Timeouts and Retries

​Token Usage Tracking

​Error Handling

​Context Manager Pattern

​Troubleshooting

​Best Practices

Use environment variables

Implement retry logic

Monitor token usage

Handle errors gracefully

Use async for concurrency

Validate tool schemas

​Example Applications

​Command-Line Chat Application

​Batch Processing Script

​Next Steps

Explore Models

OpenAI Python Docs

API Reference

Vercel AI SDK

​Summary

Integrate Morpheus Inference API with OpenAI Python SDK

Overview

Prerequisites

Basic Integration

Setting Up the Client

Available Models

Text Generation

Basic Chat Completions

Streaming Responses

Asynchronous Operations

Async Client Setup

Async Streaming

Tool Calling

Defining Tools

Using Tools with Chat Completions

Complete Tool Calling Example

Advanced Configuration

Custom Timeouts and Retries

Token Usage Tracking

Error Handling

Context Manager Pattern

Troubleshooting

Best Practices

Example Applications

Command-Line Chat Application

Batch Processing Script

Next Steps

Summary