In this post, you are going to learn how we can create this chatbot in LangGraph, Gemini Pro or any model you like, Custom Function and Streamlit that can respond to user's support requests

LangGraph is a library for building stateful “ multi-actor applications “ with LLM. LangChain Expression Language can be extended to allow multiple chains (or actors) to work together cyclically over multiple steps.One of the great values ​​of “LangChain” is that you can easily create custom chains.

We have provided LangChain Expression Language as a function for this purpose, but there was no easy way to introduce the cycle. LangGraph makes it easy to introduce cycles into LLM applications.

LangChain Expression Language is not suitable for describing cycles (loops), but by using LangGraph, it becomes possible to describe and introduce the cycles necessary for the agent.

Before we start! 🦸🏻‍♀️

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Before we dive into our application, we will create an ideal environment for the code to work. For this, we need to install the necessary Python libraries needed. Firstly, we will start by installing the libraries that support the model. For this, we will do a pip install of the below libraries.

pip install streamlit
pip install langchainhub    
pip install langgraph   
pip install langchain_google_genai 
pip install -U langchain-openai langchain  

Once installed we import Langchain, Langchain Google, langchain community, os, typing, langchain core, operator, langchian prebuilt, langGraph and streamlit

from langchain import hub
from langchain.agents import Tool, create_react_agent