0Pricing
AI Agents with LangChain & Autonomous Workflows · レッスン

LangChainチェーン入門

LangChainにおけるチェーンの概念と、LLMを使った複数ステップの処理を実現する仕組みを理解します。

「LangChainチェーン入門」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

What are LangChain Chains?

Welcome to LangChain Chains! Imagine you have a complex task for an AI, like writing a blog post or summarizing a long document. A single command to a Large Language Model (LLM) might not be enough.

This is where 'Chains' come in. They help you break down complex AI tasks into smaller, manageable steps, executed in a specific order. Think of them as a blueprint for AI workflows.

Why We Need Chains

An LLM is powerful, but for multi-step problems or interactions requiring specific formatting, you need more structure. Chains provide this structure by:

  • Enabling multi-step reasoning: Allowing the LLM to process information iteratively.
  • Connecting components: Linking LLMs with prompts, output parsers, or other tools.
  • Building structured workflows: Ensuring tasks are performed in a predefined sequence, making complex applications manageable.

Chains: A Sequential Flow

At its core, a chain is about sequential processing. The output from one step automatically becomes the input for the next step. It's like an assembly line for AI tasks.

This allows you to build sophisticated applications by combining different LangChain components and operations in a logical, step-by-step flow.

Key Components in Chains

Chains typically link together various LangChain components. The most common ones you'll encounter are:

  • LLMs: The 'brain' that generates text or responses.
  • Prompt Templates: Structured instructions that guide the LLM's behavior.
  • Output Parsers: Tools to format the LLM's raw text output into a more usable structure (e.g., JSON, lists).

For this lesson, we'll focus on LLMs and Prompt Templates.

The Simplest Chain: LLMChain

The most fundamental chain in LangChain is the LLMChain. It's designed to take an input, apply a PromptTemplate to format it, pass the formatted prompt to an LLM, and get a text output.

It's the basic building block for many more complex interactions and a great starting point for understanding chains.

Setting Up LLM & Prompt

Before we build an LLMChain, let's prepare our ingredients: an LLM and a Prompt Template. We'll use a simple prompt to ask the LLM to say something nice to a person by name.

Remember to replace 'YOUR_OPENAI_API_KEY' with your actual key or set it as an environment variable.

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate

# Set your API key (replace with your actual key or env var)
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# 1. Initialize the LLM (e.g., OpenAI's GPT-3.5)
llm = ChatOpenAI(temperature=0.7)

# 2. Define a Prompt Template
# '{name}' is the input variable for this prompt
prompt = PromptTemplate.from_template(
    "Hello, my name is {name}. Can you say something nice to me?"
)

print("LLM and Prompt Template are ready!")

Creating an LLMChain

Now, let's combine our LLM and Prompt Template into an LLMChain. This chain will take a 'name' as input, format it into the prompt, send it to the LLM, and return the LLM's response.

The LLMChain class from langchain.chains connects these two components seamlessly.

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain

# Set your API key (replace with your actual key or env var)
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# 1. Initialize the LLM
llm = ChatOpenAI(temperature=0.7)

# 2. Define a Prompt Template
prompt = PromptTemplate.from_template(
    "Hello, my name is {name}. Can you say something nice to me?"
)

# 3. Create the LLMChain
# The chain connects the prompt and the LLM
chain = LLMChain(llm=llm, prompt=prompt)

# 4. Invoke the chain with an input
# The input key 'name' must match the prompt variable
response = chain.invoke({"name": "Alice"})

print("Chain invoked successfully!")
print("Response:")
print(response["text"])

Understanding Chain Output

Notice that the output from chain.invoke() is a dictionary. For a basic LLMChain, it typically contains:

  • The input variables you provided (e.g., 'name').
  • 'text': The LLM's generated response based on the prompt.

This structured output makes it easy to extract the LLM's answer and use it in subsequent steps or display it to a user.

Benefits of LLMChain

Even though it's simple, the LLMChain offers significant benefits:

  • Encapsulation: It neatly packages the prompt and LLM logic together, making your code modular.
  • Readability: It makes your LLM interactions cleaner and easier to understand than raw API calls.
  • Foundation: It serves as the fundamental building block for constructing more complex multi-step chains and agents.

It helps organize your LLM interactions efficiently.

Quick Check

You've learned that LangChain Chains help organize multi-step operations. Based on our discussion, which of the following best describes the core purpose of an LLMChain?

Recap: Getting Chained Up!

Great job! In this lesson, you learned about:

  • The concept of Chains in LangChain for structuring multi-step AI tasks.
  • Why chains are essential for building complex, controlled workflows with LLMs.
  • The basic components that typically make up a chain (LLMs, Prompt Templates).
  • How to create and run an LLMChain, the simplest chain, by combining a PromptTemplate and an LLM.

Next, we'll explore how to combine multiple LLMChains into more powerful sequential workflows!

よくある質問

「LangChainチェーン入門」レッスンは無料ですか？

はい。「LangChainチェーン入門」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「LangChainチェーン入門」で何を学びますか？

LangChainにおけるチェーンの概念と、LLMを使った複数ステップの処理を実現する仕組みを理解します。 ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。

「LangChainチェーン入門」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. LangChainチェーン入門
  2. 逐次チェーンとシンプルチェーン
  3. チェーンロジックのカスタマイズ
  4. ルーティングと条件分岐チェーン
← AI Agents with LangChain & Autonomous Workflowsに戻る