Exploring LangChain Chains & Agents: A Quick Overview

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In the LangChain framework, “Chains” represent predefined sequences of operations aimed at structuring complex processes into a more manageable and readable format. Chains involve a specific order of actions and are ideal for workflows with a fixed set of steps that remain constant. Conversely, “Agents” are dynamic components designed for decision-making, adaptability, and complex control flow within applications. In summary, while Chains are best suited for static, predefined sequences, agents are essential for tasks requiring flexibility, decision-making, and adaptive behavior in the LangChain framework.

Chains:

To begin, let’s consider an example of a simple sequential chain, where two simple chains are arranged in succession, functioning as an integrated pipeline. Initially, we provide input data into the first chain, which generates an output. This output is then passed to the second chain, culminating in the final result. The operation consistently follows a sequence, with the first chain executing first, followed by the second, maintaining a static, predetermined order.

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

from dotenv import load_dotenv, find_dotenv
import os
from langchain.chains import SimpleSequentialChain
from langchain.prompts.prompt import…