LangGraph is a powerful framework for building reliable and controllable applications with Large Language Models (LLMs). Two key concepts in LangGraph are "tools" and "functions", which allow developers to create structured workflows and give LLMs the ability to interact with external systems. In this post, we'll explore how to use tools and functions in LangGraph, with examples from the official documentation.
What are Tools and Functions in LangGraph?
In LangGraph, tools and functions are mechanisms that allow LLMs to perform actions or retrieve information beyond their training data. They serve as the interface between the LLM and external systems or APIs.
- Tools: These are predefined actions that an LLM can choose to execute. Tools often represent API calls, database queries, or other external operations.
- Functions: In the context of LangGraph, functions are similar to tools but are typically used to represent more complex operations or to group related tools together.
Using Tools in LangGraph
Tools are a fundamental part of building agent-like behaviors in LangGraph. Here's an example of how to define and use a tool:
from langchain_core.tools import tool
@tool
def search_flights(departure_airport: str, arrival_airport: str, date: str) -> list:
"""Search for flights between two airports on a specific date."""
# Implementation details...
return flight_results
# Binding the tool to an LLM
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
llm_with_tools = llm.bind_tools([search_flights])
In this example, we define a search_flights tool that can be used by the LLM to find flight information. The tool is then bound to the LLM, allowing it to be called when needed.
Functions in LangGraph
Functions in LangGraph are often used to create more complex behaviors or to group related tools. Here's an example of a function that combines multiple tools:
from pydantic import BaseModel, Field
class BookTravel(BaseModel):
"""Book a complete travel itinerary."""
departure_city: str = Field(description="The city to depart from")
arrival_city: str = Field(description="The destination city")
departure_date: str = Field(description="The date of departure")
return_date: str = Field(description="The date of return")
def book_travel(departure_city: str, arrival_city: str, departure_date: str, return_date: str) -> str:
# Implementation using multiple tools (search_flights, book_hotel, etc.)
return "Travel itinerary booked successfully"
# Binding the function to an LLM
llm_with_functions = llm.bind_tools([BookTravel])
This book_travel function combines multiple tools to create a complete travel booking experience.
Integrating Tools and Functions in LangGraph Workflows
LangGraph allows you to create complex workflows by combining tools and functions with graph-based control flows. Here's a simplified example of how this might look:
from langgraph.graph import StateGraph, END
# Define the graph state
class State(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
# Create the graph
builder = StateGraph(State)
# Add nodes for different steps in the workflow
builder.add_node("search_flights", tool_node(search_flights))
builder.add_node("book_hotel", tool_node(book_hotel))
builder.add_node("plan_itinerary", Assistant(llm_with_functions))
# Define edges to connect the nodes
builder.add_edge("search_flights", "book_hotel")
builder.add_edge("book_hotel", "plan_itinerary")
builder.add_edge("plan_itinerary", END)
# Compile the graph
graph = builder.compile()
This example demonstrates how tools and functions can be integrated into a LangGraph workflow, allowing for a structured and controllable application flow.
Benefits of Using Tools and Functions in LangGraph
- Controllability: Developers can define specific actions that LLMs can take, ensuring the application behaves as intended.
- Extensibility: New capabilities can be easily added by creating new tools or functions.
- Reliability: By structuring interactions through tools and functions, applications become more predictable and easier to debug.
- Human-in-the-Loop: LangGraph's design allows for easy integration of human oversight and intervention when needed.
Conclusion
Tools and functions in LangGraph provide a powerful way to create structured, reliable, and extensible LLM applications. By leveraging these concepts, developers can build complex workflows that combine the strengths of LLMs with the specificity and control of traditional programming paradigms.
For more information and detailed examples, check out the official LangGraph documentation:
- LangGraph Python: https://langchain-ai.github.io/langgraph/
- LangGraph JavaScript: https://langchain-ai.github.io/langgraphjs/
By mastering tools and functions in LangGraph, you'll be well-equipped to build sophisticated AI applications that harness the power of LLMs while maintaining the control and reliability needed for production systems.
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