"""
Pattern 021: Function Calling
Description:
The Function Calling pattern enables LLMs to invoke structured functions with
type-safe parameters. Unlike general tool use, function calling emphasizes strong
typing, structured schemas, and direct integration with code functions. The LLM
analyzes user intent, selects appropriate functions, extracts typed parameters,
and invokes functions with validated inputs.
Components:
- Function Schema: Structured definitions with parameter types
- Parameter Validation: Type checking and constraint enforcement
- Function Binding: Direct mapping to callable functions
- Response Formatting: Structured outputs from function calls
- Multi-turn Handling: Multiple function calls in sequence
Use Cases:
- Database queries with typed parameters
- API calls with strict schemas
- System operations requiring validation
- Structured data processing
- Multi-step function orchestration
LangChain Implementation:
Uses LangChain's function calling with OpenAI's function calling API,
implements type-safe parameter extraction, and handles multi-turn conversations.
Key Features:
- Type-safe parameter extraction
- JSON schema validation
- Multiple function selection
- Error handling and retries
- Conversation context preservation
"""
import os
from typing import List, Dict, Any, Optional, Union, Callable
from dataclasses import dataclass, field
from enum import Enum
import json
from datetime import datetime
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
load_dotenv()
class ParameterType(Enum):
"""Types of function parameters."""
STRING = "string"
INTEGER = "integer"
NUMBER = "number"
BOOLEAN = "boolean"
ARRAY = "array"
OBJECT = "object"
@dataclass
class FunctionParameter:
"""Definition of a function parameter."""
name: str
type: ParameterType
description: str
required: bool = True
enum: Optional[List[Any]] = None
default: Optional[Any] = None
def to_schema(self) -> Dict[str, Any]:
"""Convert to JSON schema format."""
schema = {
"type": self.type.value,
"description": self.description
}
if self.enum:
schema["enum"] = self.enum
return schema
@dataclass
class FunctionDefinition:
"""Complete function definition with schema."""
name: str
description: str
parameters: List[FunctionParameter]
function: Callable
returns: str = "object"
def to_openai_schema(self) -> Dict[str, Any]:
"""Convert to OpenAI function calling schema."""
required_params = [p.name for p in self.parameters if p.required]
properties = {
param.name: param.to_schema()
for param in self.parameters
}
return {
"name": self.name,
"description": self.description,
"parameters": {
"type": "object",
"properties": properties,
"required": required_params
}
}
def validate_parameters(self, params: Dict[str, Any]) -> tuple[bool, Optional[str]]:
"""Validate parameters against schema."""
# Check required parameters
for param in self.parameters:
if param.required and param.name not in params:
return False, f"Missing required parameter: {param.name}"
# Check types (basic validation)
for param in self.parameters:
if param.name in params:
value = params[param.name]
# Type checking
if param.type == ParameterType.STRING and not isinstance(value, str):
return False, f"Parameter {param.name} must be string"
elif param.type == ParameterType.INTEGER and not isinstance(value, int):
return False, f"Parameter {param.name} must be integer"
elif param.type == ParameterType.NUMBER and not isinstance(value, (int, float)):
return False, f"Parameter {param.name} must be number"
elif param.type == ParameterType.BOOLEAN and not isinstance(value, bool):
return False, f"Parameter {param.name} must be boolean"
elif param.type == ParameterType.ARRAY and not isinstance(value, list):
return False, f"Parameter {param.name} must be array"
# Enum validation
if param.enum and value not in param.enum:
return False, f"Parameter {param.name} must be one of {param.enum}"
return True, None
@dataclass
class FunctionCall:
"""Record of a function call."""
function_name: str
arguments: Dict[str, Any]
result: Any
success: bool
timestamp: datetime = field(default_factory=datetime.now)
error: Optional[str] = None
class FunctionRegistry:
"""
Registry of callable functions with schemas.
"""
def __init__(self):
"""Initialize function registry."""
self.functions: Dict[str, FunctionDefinition] = {}
self._register_builtin_functions()
def _register_builtin_functions(self):
"""Register built-in functions."""
# Get user info function
self.register_function(FunctionDefinition(
name="get_user_info",
description="Retrieves information about a user by their ID",
parameters=[
FunctionParameter("user_id", ParameterType.INTEGER, "The user's ID"),
FunctionParameter("fields", ParameterType.ARRAY, "Fields to retrieve", required=False)
],
function=self._get_user_info
))
# Create order function
self.register_function(FunctionDefinition(
name="create_order",
description="Creates a new order in the system",
parameters=[
FunctionParameter("product_id", ParameterType.STRING, "Product identifier"),
FunctionParameter("quantity", ParameterType.INTEGER, "Number of items"),
FunctionParameter("priority", ParameterType.STRING, "Order priority",
enum=["low", "normal", "high", "urgent"])
],
function=self._create_order
))
# Search database function
self.register_function(FunctionDefinition(
name="search_database",
description="Searches the database with filters",
parameters=[
FunctionParameter("table", ParameterType.STRING, "Table name"),
FunctionParameter("query", ParameterType.STRING, "Search query"),
FunctionParameter("limit", ParameterType.INTEGER, "Max results", required=False, default=10)
],
function=self._search_database
))
# Send notification function
self.register_function(FunctionDefinition(
name="send_notification",
description="Sends a notification to a user",
parameters=[
FunctionParameter("user_id", ParameterType.INTEGER, "Recipient user ID"),
FunctionParameter("message", ParameterType.STRING, "Notification message"),
FunctionParameter("urgent", ParameterType.BOOLEAN, "Is urgent", required=False, default=False)
],
function=self._send_notification
))
# Calculate price function
self.register_function(FunctionDefinition(
name="calculate_price",
description="Calculates total price with tax and discount",
parameters=[
FunctionParameter("base_price", ParameterType.NUMBER, "Base price"),
FunctionParameter("tax_rate", ParameterType.NUMBER, "Tax rate (0-1)", required=False, default=0.1),
FunctionParameter("discount", ParameterType.NUMBER, "Discount amount", required=False, default=0)
],
function=self._calculate_price
))
def register_function(self, function_def: FunctionDefinition):
"""Register a new function."""
self.functions[function_def.name] = function_def
def get_function(self, name: str) -> Optional[FunctionDefinition]:
"""Get function by name."""
return self.functions.get(name)
def get_all_schemas(self) -> List[Dict[str, Any]]:
"""Get all function schemas in OpenAI format."""
return [func.to_openai_schema() for func in self.functions.values()]
# Built-in function implementations (mock)
def _get_user_info(self, user_id: int, fields: Optional[List[str]] = None) -> Dict[str, Any]:
"""Mock user info retrieval."""
users = {
1: {"name": "Alice", "email": "alice@example.com", "age": 30, "status": "active"},
2: {"name": "Bob", "email": "bob@example.com", "age": 25, "status": "active"},
3: {"name": "Charlie", "email": "charlie@example.com", "age": 35, "status": "inactive"}
}
user = users.get(user_id, {"error": "User not found"})
if fields and "error" not in user:
user = {k: v for k, v in user.items() if k in fields}
return user
def _create_order(self, product_id: str, quantity: int, priority: str) -> Dict[str, Any]:
"""Mock order creation."""
import random
return {
"order_id": random.randint(1000, 9999),
"product_id": product_id,
"quantity": quantity,
"priority": priority,
"status": "pending",
"created_at": datetime.now().isoformat()
}
def _search_database(self, table: str, query: str, limit: int = 10) -> Dict[str, Any]:
"""Mock database search."""
mock_results = {
"products": [
{"id": "P001", "name": "Laptop", "price": 999.99},
{"id": "P002", "name": "Mouse", "price": 29.99}
],
"users": [
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"}
]
}
results = mock_results.get(table, [])
return {
"table": table,
"query": query,
"results": results[:limit],
"total": len(results)
}
def _send_notification(self, user_id: int, message: str, urgent: bool = False) -> Dict[str, Any]:
"""Mock notification sending."""
return {
"notification_id": f"N{user_id}{datetime.now().microsecond}",
"user_id": user_id,
"message": message,
"urgent": urgent,
"sent": True,
"timestamp": datetime.now().isoformat()
}
def _calculate_price(self, base_price: float, tax_rate: float = 0.1, discount: float = 0) -> Dict[str, Any]:
"""Calculate total price."""
tax = base_price * tax_rate
total = base_price + tax - discount
return {
"base_price": base_price,
"tax": tax,
"discount": discount,
"total": total
}
class FunctionCallingAgent:
"""
Agent that uses function calling to accomplish tasks.
"""
def __init__(
self,
function_registry: FunctionRegistry,
model: str = "gpt-3.5-turbo"
):
"""
Initialize function calling agent.
Args:
function_registry: Registry of available functions
model: LLM model to use
"""
self.function_registry = function_registry
self.llm = ChatOpenAI(model=model, temperature=0.1)
self.call_history: List[FunctionCall] = []
def select_and_extract(
self,
user_request: str,
context: Optional[str] = None
) -> List[Dict[str, Any]]:
"""
Select functions and extract parameters from user request.
Args:
user_request: User's request
context: Optional context from previous turns
Returns:
List of function calls to make
"""
# Get all function schemas
schemas = self.function_registry.get_all_schemas()
schemas_text = json.dumps(schemas, indent=2)
prompt = ChatPromptTemplate.from_messages([
("system", """You are a function calling expert. Analyze user requests and
determine which functions to call with what parameters.
Available functions:
{schemas}
Respond with a JSON array of function calls. Each call should have:
- "function": function name
- "arguments": object with parameter values
If multiple functions are needed, include them all in the array.
If no function is needed, respond with an empty array []."""),
("user", "{request}")
])
chain = prompt | self.llm | StrOutputParser()
full_request = user_request
if context:
full_request = f"Context: {context}\n\nRequest: {user_request}"
response = chain.invoke({
"schemas": schemas_text,
"request": full_request
})
# Parse function calls
try:
json_start = response.find("[")
json_end = response.rfind("]") + 1
if json_start != -1 and json_end > json_start:
json_str = response[json_start:json_end]
function_calls = json.loads(json_str)
return function_calls if isinstance(function_calls, list) else []
else:
return []
except Exception as e:
print(f"Error parsing function calls: {e}")
return []
def execute_function(
self,
function_name: str,
arguments: Dict[str, Any]
) -> FunctionCall:
"""
Execute a function with given arguments.
Args:
function_name: Name of function to call
arguments: Arguments for the function
Returns:
FunctionCall record
"""
func_def = self.function_registry.get_function(function_name)
if func_def is None:
call = FunctionCall(
function_name=function_name,
arguments=arguments,
result=None,
success=False,
error=f"Function '{function_name}' not found"
)
self.call_history.append(call)
return call
# Validate parameters
valid, error = func_def.validate_parameters(arguments)
if not valid:
call = FunctionCall(
function_name=function_name,
arguments=arguments,
result=None,
success=False,
error=error
)
self.call_history.append(call)
return call
# Execute function
try:
result = func_def.function(**arguments)
call = FunctionCall(
function_name=function_name,
arguments=arguments,
result=result,
success=True
)
except Exception as e:
call = FunctionCall(
function_name=function_name,
arguments=arguments,
result=None,
success=False,
error=str(e)
)
self.call_history.append(call)
return call
def process_request(
self,
user_request: str,
context: Optional[str] = None
) -> Dict[str, Any]:
"""
Process user request with function calling.
Args:
user_request: User's request
context: Optional context
Returns:
Results including function calls and response
"""
print(f"\n[Agent] Processing request: {user_request}\n")
# Select and extract function calls
print("[Agent] Analyzing request and selecting functions...")
function_calls = self.select_and_extract(user_request, context)
if not function_calls:
print("[Agent] No function calls needed\n")
return {
"request": user_request,
"function_calls": [],
"response": "No functions needed for this request"
}
print(f"[Agent] Planning {len(function_calls)} function call(s)\n")
# Execute function calls
results = []
for fc in function_calls:
function_name = fc.get("function", "")
arguments = fc.get("arguments", {})
print(f"[Agent] Calling: {function_name}")
print(f"[Agent] Arguments: {json.dumps(arguments, indent=2)}")
call = self.execute_function(function_name, arguments)
results.append(call)
if call.success:
print(f"[Agent] Success: {json.dumps(call.result, indent=2)}\n")
else:
print(f"[Agent] Error: {call.error}\n")
# Generate final response
response = self._generate_response(user_request, results)
return {
"request": user_request,
"function_calls": results,
"response": response
}
def _generate_response(
self,
user_request: str,
function_calls: List[FunctionCall]
) -> str:
"""Generate natural language response from function results."""
results_text = "\n".join([
f"Function: {call.function_name}\n"
f"Result: {json.dumps(call.result, indent=2) if call.success else f'Error: {call.error}'}"
for call in function_calls
])
prompt = ChatPromptTemplate.from_messages([
("system", "You generate natural responses from function call results."),
("user", """User Request: {request}
Function Results:
{results}
Provide a natural, helpful response incorporating these results.""")
])
chain = prompt | self.llm | StrOutputParser()
response = chain.invoke({
"request": user_request,
"results": results_text
})
return response.strip()
def demonstrate_function_calling():
"""Demonstrate the Function Calling pattern."""
print("=" * 80)
print("FUNCTION CALLING PATTERN DEMONSTRATION")
print("=" * 80)
# Initialize registry and agent
registry = FunctionRegistry()
agent = FunctionCallingAgent(registry)
# Show available functions
print("\n" + "=" * 80)
print("AVAILABLE FUNCTIONS")
print("=" * 80)
schemas = registry.get_all_schemas()
for schema in schemas:
print(f"\n{schema['name']}:")
print(f" Description: {schema['description']}")
print(f" Parameters: {list(schema['parameters']['properties'].keys())}")
# Test 1: Single function call
print("\n" + "=" * 80)
print("TEST 1: Single Function Call")
print("=" * 80)
request1 = "Get information about user ID 1"
result1 = agent.process_request(request1)
print("=" * 80)
print("FINAL RESPONSE:")
print("=" * 80)
print(result1["response"])
# Test 2: Multiple function calls
print("\n" + "=" * 80)
print("TEST 2: Multiple Function Calls")
print("=" * 80)
request2 = "Create an order for product P001 with quantity 5 and high priority, then send an urgent notification to user 1 about the order"
result2 = agent.process_request(request2)
print("=" * 80)
print("FINAL RESPONSE:")
print("=" * 80)
print(result2["response"])
# Test 3: Function with optional parameters
print("\n" + "=" * 80)
print("TEST 3: Optional Parameters")
print("=" * 80)
request3 = "Calculate the price for a $100 item with 15% tax and a $10 discount"
result3 = agent.process_request(request3)
print("=" * 80)
print("FINAL RESPONSE:")
print("=" * 80)
print(result3["response"])
# Test 4: Database search
print("\n" + "=" * 80)
print("TEST 4: Database Query")
print("=" * 80)
request4 = "Search the products table for laptops, limit to 5 results"
result4 = agent.process_request(request4)
print("=" * 80)
print("FINAL RESPONSE:")
print("=" * 80)
print(result4["response"])
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print(f"""
Total requests processed: 4
Total function calls made: {len(agent.call_history)}
Success rate: {sum(1 for call in agent.call_history if call.success) / len(agent.call_history) * 100:.0f}%
Function Call Breakdown:
""")
for call in agent.call_history:
status = "✓" if call.success else "✗"
print(f" {status} {call.function_name}({list(call.arguments.keys())})")
print("""
The Function Calling pattern demonstrates:
1. **Type-Safe Invocation**: Strong parameter typing and validation
2. **Schema-Driven**: JSON schemas define function contracts
3. **Multi-Function**: Can orchestrate multiple function calls
4. **Parameter Extraction**: LLM extracts typed parameters from text
5. **Error Handling**: Validates inputs before execution
Key Benefits:
- **Type Safety**: Prevents invalid function calls
- **Clear Contracts**: Well-defined function interfaces
- **Composable**: Multiple functions work together
- **Reliable**: Validation before execution
- **Structured**: Predictable input/output formats
Use Cases:
- Database operations with typed queries
- API integrations with strict schemas
- System commands requiring validation
- Multi-step workflows with dependencies
- Enterprise integrations
This pattern is essential for production agentic systems that need
reliable, type-safe interactions with external systems and APIs.
""")
if __name__ == "__main__":
demonstrate_function_calling()
Agentic Design Pattern: Function Calling
Pattern 021: Function Calling – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Function Calling pattern implementation.
Pattern Description
Pattern 021: Function Calling
Description: The Function Calling pattern enables LLMs to invoke structured functions with type-safe parameters. Unlike general tool use, function calling emphasizes strong typing, structured schemas, and direct integration with code functions. The LLM analyzes user intent, selects appropriate functions, extracts typed parameters, and invokes functions with validated inputs.
Components: – Function Schema: Structured definitions with parameter types – Parameter Validation: Type checking and constraint enforcement – Function Binding: Direct mapping to callable functions – Response Formatting: Structured outputs from function calls – Multi-turn Handling: Multiple function calls in sequence
Use Cases: – Database queries with typed parameters – API calls with strict schemas – System operations requiring validation – Structured data processing – Multi-step function orchestration
LangChain Implementation: Uses LangChain’s function calling with OpenAI’s function calling API, implements type-safe parameter extraction, and handles multi-turn conversations.
Key Features: – Type-safe parameter extraction – JSON schema validation – Multiple function selection – Error handling and retries – Conversation context preservation
File Statistics
- Total Lines: 663
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 021: Function Calling
Description:
The Function Calling pattern enables LLMs to invoke structured functions with
type-safe parameters. Unlike general tool use, function calling emphasizes strong
typing, structured schemas, and direct integration with code functions. The LLM
analyzes user intent, selects appropriate functions, extracts typed parameters,
and invokes functions with validated inputs.
Components:
- Function Schema: Structured definitions with parameter types
- Parameter Validation: Type checking and constraint enforcement
- Function Binding: Direct mapping to callable functions
- Response Formatting: Structured outputs from function calls
- Multi-turn Handling: Multiple function calls in sequence
Use Cases:
- Database queries with typed parameters
- API calls with strict schemas
- System operations requiring validation
- Structured data processing
- Multi-step function orchestration
LangChain Implementation:
Uses LangChain's function calling with OpenAI's function calling API,
implements type-safe parameter extraction, and handles multi-turn conversations.
Key Features:
- Type-safe parameter extraction
- JSON schema validation
- Multiple function selection
- Error handling and retries
- Conversation context preservation
"""
Purpose: This module implements the Function Calling pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import json
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import random
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional
import typing.Union
Purpose: Core Python functionality:
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– json: JSON serialization/deserialization
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– random: Random number generation
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Union: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: ParameterType
Lines 52-59
Inherits from: Enum
Purpose:
Types of function parameters.
Class: FunctionParameter
Lines 63-80
Purpose:
Definition of a function parameter.
Methods:
to_schema(self)
Lines 72-80
Purpose:
Convert to JSON schema format.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D622310>
Class: FunctionDefinition
Lines 84-139
Purpose:
Complete function definition with schema.
Methods:
to_openai_schema(self)
Lines 92-109
Purpose:
Convert to OpenAI function calling schema.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D70EF90>
validate_parameters(self, params)
Lines 111-139
Purpose:
Validate parameters against schema.
Parameters:
– params: Params
Returns: <ast.Subscript object at 0x000001A06D6EA090>
Class: FunctionCall
Lines 143-150
Purpose:
Record of a function call.
Class: FunctionRegistry
Lines 153-309
Purpose:
Registry of callable functions with schemas.
Methods:
__init__(self)
Lines 158-161
Purpose:
Initialize function registry.
Parameters:
_register_builtin_functions(self)
Lines 163-224
Purpose:
Register built-in functions.
Parameters:
register_function(self, function_def)
Lines 226-228
Purpose:
Register a new function.
Parameters:
– function_def: Function def
get_function(self, name)
Lines 230-232
Purpose:
Get function by name.
Parameters:
– name: Name
Returns: <ast.Subscript object at 0x000001A06D15A090>
get_all_schemas(self)
Lines 234-236
Purpose:
Get all function schemas in OpenAI format.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D1588D0>
_get_user_info(self, user_id, fields)
Lines 240-253
Purpose:
Mock user info retrieval.
Parameters:
– user_id: User id
– fields: Fields
Returns: <ast.Subscript object at 0x000001A06D701B90>
_create_order(self, product_id, quantity, priority)
Lines 255-265
Purpose:
Mock order creation.
Parameters:
– product_id: Product id
– quantity: Quantity
– priority: Priority
Returns: <ast.Subscript object at 0x000001A06D6F7810>
_search_database(self, table, query, limit)
Lines 267-286
Purpose:
Mock database search.
Parameters:
– table: Table
– query: Input query or question
– limit: Limit
Returns: <ast.Subscript object at 0x000001A06D15FC50>
_send_notification(self, user_id, message, urgent)
Lines 288-297
Purpose:
Mock notification sending.
Parameters:
– user_id: User id
– message: Message
– urgent: Urgent
Returns: <ast.Subscript object at 0x000001A06D15E950>
_calculate_price(self, base_price, tax_rate, discount)
Lines 299-309
Purpose:
Calculate total price.
Parameters:
– base_price: Base price
– tax_rate: Tax rate
– discount: Discount
Returns: <ast.Subscript object at 0x000001A06D676D10>
Class: FunctionCallingAgent
Lines 312-541
Purpose:
Agent that uses function calling to accomplish tasks.
Methods:
__init__(self, function_registry, model)
Lines 317-331
Purpose:
Initialize function calling agent.
Args:
function_registry: Registry of available functions
model: LLM model to use
Parameters:
– function_registry: Function registry
– model: Language model instance
select_and_extract(self, user_request, context)
Lines 333-391
Purpose:
Select functions and extract parameters from user request.
Args:
user_request: User's request
context: Optional context from previous turns
Returns:
List of function calls to make
Parameters:
– user_request: User request
– context: Context
Returns: <ast.Subscript object at 0x000001A06D73B6D0>
execute_function(self, function_name, arguments)
Lines 393-453
Purpose:
Execute a function with given arguments.
Args:
function_name: Name of function to call
arguments: Arguments for the function
Returns:
FunctionCall record
Parameters:
– function_name: Function name
– arguments: Arguments
Returns: FunctionCall
process_request(self, user_request, context)
Lines 455-510
Purpose:
Process user request with function calling.
Args:
user_request: User's request
context: Optional context
Returns:
Results including function calls and response
Parameters:
– user_request: User request
– context: Context
Returns: <ast.Subscript object at 0x000001A06D178AD0>
_generate_response(self, user_request, function_calls)
Lines 512-541
Purpose:
Generate natural language response from function results.
Parameters:
– user_request: User request
– function_calls: Function calls
Returns: str
Functions
demonstrate_function_calling()
Lines 544-658
Purpose:
Demonstrate the Function Calling pattern.
Key Concepts Summary
Pattern Overview
The Function Calling pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 020
- Pattern 022
- Other Tool Use Patterns (020-025)
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Function Calling pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 021_function_calling.py
Total Lines: 663
Pattern Category: AI Agent Design Pattern #021
Generated from actual implementation code
For educational and reference purposes
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