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Parallel Function Calling: MegaLLM supports parallel function execution for improved performance.

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How It Works

1

Define Available Tools

Specify functions the AI can use with JSON Schema descriptions.
2

AI Decides When to Call

The model determines when and how to use tools based on context.
3

Execute and Return Results

Your application executes the function and returns results to the AI.
4

AI Processes Results

The AI incorporates function results into its final response.

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Basic Example

  • Python
  • JavaScript
  • TypeScript
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

# Define your functions
def get_weather(location: str, unit: str = "celsius"):
    """Get the current weather for a location"""
    # Simulate API call
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "condition": "sunny"
    }

# Define tools for the AI
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 and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Send message with tools
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "What's the weather in London?"}
    ],
    tools=tools,
    tool_choice="auto"  # Let the model decide
)

# Check if the model wants to call a function
message = response.choices[0].message

if message.tool_calls:
    # Execute the function
    for tool_call in message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        if function_name == "get_weather":
            result = get_weather(**function_args)

            # Send the result back to the model
            follow_up = client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "user", "content": "What's the weather in London?"},
                    message,  # Include the assistant's tool call
                    {
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": json.dumps(result)
                    }
                ]
            )

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

​
Parallel Function Calling

MegaLLM supports calling multiple functions in parallel for better performance: 
# The AI can call multiple functions at once
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Compare the weather in London, Paris, and Tokyo"}
    ],
    tools=tools
)

# Process multiple tool calls in parallel
if response.choices[0].message.tool_calls:
    import asyncio

    async def execute_tool(tool_call):
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        # Execute function asynchronously
        result = await async_function_registry[function_name](**function_args)

        return {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        }

    # Execute all functions in parallel
    tool_results = await asyncio.gather(
        *[execute_tool(tc) for tc in response.choices[0].message.tool_calls]
    )

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Advanced Patterns

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Function Chaining

Build complex workflows by chaining function calls: 
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_products",
            "description": "Search for products",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "category": {"type": "string"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "check_inventory",
            "description": "Check product inventory",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"}
                },
                "required": ["product_id"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "place_order",
            "description": "Place an order for a product",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"},
                    "quantity": {"type": "integer"}
                },
                "required": ["product_id", "quantity"]
            }
        }
    }
]

# The AI will chain these functions to complete complex tasks
messages = [
    {"role": "user", "content": "Find and order 2 blue t-shirts if they're in stock"}
]

# The AI might:
# 1. Call search_products(query="blue t-shirt")
# 2. Call check_inventory(product_id="...")
# 3. Call place_order(product_id="...", quantity=2)

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Tool Choice Control

Control when and how the AI uses tools: 
# Force a specific function
response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    tool_choice={
        "type": "function",
        "function": {"name": "get_weather"}
    }
)

# Let the AI decide (default)
response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

# Prevent function calling
response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    tool_choice="none"
)

# Force some function call (any)
response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    tool_choice="required"
)

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Error Handling

Properly handle function execution errors: 
def safe_function_call(function_name, arguments):
    try:
        result = function_registry[function_name](**arguments)
        return {"success": True, "result": result}
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "error_type": type(e).__name__
        }

# In your tool response
for tool_call in message.tool_calls:
    function_name = tool_call.function.name
    function_args = json.loads(tool_call.function.arguments)

    result = safe_function_call(function_name, function_args)

    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps(result)
    })

# The AI will handle the error gracefully

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Real-World Examples

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Database Query Assistant

tools = [
    {
        "type": "function",
        "function": {
            "name": "execute_sql",
            "description": "Execute a SQL query on the database",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "SQL query to execute"
                    },
                    "database": {
                        "type": "string",
                        "enum": ["users", "products", "orders"],
                        "description": "Target database"
                    }
                },
                "required": ["query", "database"]
            }
        }
    }
]

# User: "How many orders were placed last month?"
# AI generates: execute_sql(
#   query="SELECT COUNT(*) FROM orders WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 MONTH)",
#   database="orders"
# )

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API Integration Agent

tools = [
    {
        "type": "function",
        "function": {
            "name": "call_api",
            "description": "Make an HTTP API call",
            "parameters": {
                "type": "object",
                "properties": {
                    "method": {
                        "type": "string",
                        "enum": ["GET", "POST", "PUT", "DELETE"]
                    },
                    "url": {"type": "string"},
                    "headers": {"type": "object"},
                    "body": {"type": "object"}
                },
                "required": ["method", "url"]
            }
        }
    }
]

# The AI can now interact with any API

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File System Operations

tools = [
    {
        "type": "function",
        "function": {
            "name": "read_file",
            "description": "Read contents of a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"}
                },
                "required": ["path"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "write_file",
            "description": "Write content to a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"},
                    "content": {"type": "string"},
                    "append": {"type": "boolean", "default": false}
                },
                "required": ["path", "content"]
            }
        }
    }
]

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Best Practices

Validation: Always validate function arguments before execution to prevent errors.

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1. Clear Function Descriptions

# Good
"description": "Get current weather data including temperature, humidity, and conditions for a specific location"

# Bad
"description": "Weather function"

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2. Type Safety

// Define types for all function parameters
interface FunctionParams {
  [key: string]: unknown;
}

// Validate before execution
function validateParams<T extends FunctionParams>(
  params: unknown,
  schema: JSONSchema
): params is T {
  // Implement JSON Schema validation
  return ajv.validate(schema, params);
}

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3. Rate Limiting

from functools import wraps
import time

def rate_limit(calls_per_second=1):
    min_interval = 1.0 / calls_per_second
    last_called = [0.0]

    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            left_to_wait = min_interval - elapsed
            if left_to_wait > 0:
                time.sleep(left_to_wait)
            ret = func(*args, **kwargs)
            last_called[0] = time.time()
            return ret
        return wrapper
    return decorator

@rate_limit(calls_per_second=10)
def call_external_api():
    # Your API call
    pass

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Streaming with Functions

Combine streaming with function calling: 
stream = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    stream=True
)

function_call = None
for chunk in stream:
    delta = chunk.choices[0].delta

    # Check for tool calls in stream
    if delta.tool_calls:
        if function_call is None:
            function_call = {
                "id": "",
                "name": "",
                "arguments": ""
            }

        tool_call = delta.tool_calls[0]
        if tool_call.id:
            function_call["id"] = tool_call.id
        if tool_call.function.name:
            function_call["name"] = tool_call.function.name
        if tool_call.function.arguments:
            function_call["arguments"] += tool_call.function.arguments

    # When function call is complete
    if function_call and chunk.choices[0].finish_reason == "tool_calls":
        # Execute function
        result = execute_function(function_call)
        # Continue conversation with result

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Next Steps