AI Agents with LangChain & Autonomous Workflows · レッスン

LLMとLangChainの統合

さまざまなLLMプロバイダー（例：OpenAI、Hugging Face）をLangChainのエージェントに接続する方法を学びます。

レッスン 2/411 ステップ

「LLMとLangChainの統合」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2/4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。

Connect Your AI Brains

Imagine your AI agent as a chef. Sometimes they need a specific ingredient (an LLM) for a dish. LangChain lets your agent switch between different LLMs, like having a pantry full of options!

Why is this useful?

Flexibility: Use the best model for each task.
Cost: Optimize spending by using cheaper models for simple tasks.
Performance: Access cutting-edge models as they emerge.

LangChain's Unified Interface

LangChain acts as a universal adapter for Large Language Models (LLMs). Instead of learning a new way to interact with each LLM provider, you learn one LangChain way.

It provides a consistent interface, whether you're talking to OpenAI's GPT models or a model from Hugging Face. This makes your code cleaner and easier to manage.

Integrating OpenAI Models

OpenAI offers powerful LLMs like GPT-3.5 and GPT-4. To use them with LangChain, you'll need an OpenAI API key.

Remember to keep your API key secret! You'll typically set it as an environment variable to avoid hardcoding it in your code.

Before we start, install the necessary library:

pip install langchain-openai

OpenAI Chat Model in Action

Here's how to connect to an OpenAI chat model and get a response. We'll use the ChatOpenAI class, which is optimized for conversational interactions.

Try running this example (ensure OPENAI_API_KEY is set in your environment):

import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"

# Initialize the chat model
# model_name can be "gpt-3.5-turbo", "gpt-4", etc.
chat = ChatOpenAI(model_name="gpt-3.5-turbo")

# Invoke the model with a simple message
response = chat.invoke([
    HumanMessage(content="What is the capital of France?")
])

# Print the model's response
print(response.content)

Connecting Hugging Face Models

Hugging Face is a hub for open-source AI models. LangChain allows you to easily integrate models hosted on the Hugging Face Hub or even run local models.

You'll need a Hugging Face API token for models hosted on their Inference API. Get one from your Hugging Face profile settings.

Install the required library:

pip install langchain-huggingface

Hugging Face Hub in Action

Let's use a text generation model from the Hugging Face Hub. We'll use the HuggingFaceHub class, specifying a model repository ID.

Try running this example (ensure HUGGINGFACEHUB_API_TOKEN is set):

import os
from langchain_huggingface import HuggingFaceHub

# Set your Hugging Face API token as an environment variable
# os.environ["HUGGINGFACEHUB_API_TOKEN"] = "YOUR_HF_TOKEN_HERE"

# Initialize the Hugging Face Hub model
# repo_id refers to a model on the Hugging Face Hub (e.g., "google/flan-t5-large")
llm = HuggingFaceHub(
    repo_id="google/flan-t5-large",
    model_kwargs={"temperature": 0.5, "max_length": 64}
)

# Invoke the model with a simple prompt
response = llm.invoke("What is the capital of Germany?")

# Print the model's response
print(response)

Running Local HF Models

For advanced use cases, you might want to run Hugging Face models locally on your machine, especially if you have powerful hardware. LangChain supports this via the HuggingFacePipeline.

This requires installing the transformers library and often torch or tensorflow, and then loading a model directly. It gives you full control and privacy.

Beyond OpenAI & HF

LangChain's modular design means you're not limited to just OpenAI and Hugging Face!

You can integrate with many other LLM providers, including:

Google: (e.g., GooglePalm, VertexAI)
Anthropic: (e.g., ChatAnthropic for Claude models)
Cohere: (e.g., Cohere)

The pattern is very similar: install the relevant library, get an API key, and initialize the corresponding LangChain LLM class.

Selecting Your Perfect LLM

With so many options, how do you pick the right LLM?

Task Complexity: Simple tasks might use smaller, cheaper models.
Cost: API calls can add up. Compare pricing across providers.
Performance: Some models are better at specific tasks (e.g., code generation vs. creative writing).
Privacy: Local models offer maximum data privacy.
Latency: Response time can vary.

Experimentation is key to finding the best fit!

Integrate & Conquer!

You've learned how LangChain helps you connect to various LLM providers. Let's check your understanding.

対話的なレッスンで答える

Lesson Summary: LLM Integration

Great job! In this lesson, you learned how to connect different Large Language Models to your LangChain agents.

We explored how LangChain provides a unified interface for various LLMs.
You saw practical examples of integrating OpenAI and Hugging Face Hub models.
We touched upon other providers and discussed factors for choosing the right LLM for your needs.

Now your agents have access to a world of AI brains!

無料で開始

AI チューターと学ぶ AI Agents with LangChain & Autonomous Workflows — 無料

ブラウザでリアルコードを書いて実行し、24/7 の AI チューターから瞬時にサポートを受け、ウェブまたはアプリで続きから学習できます。

コース: 12
レッスン: 50

無料で学習を始めるコース全体を表示

よくある質問

「LLMとLangChainの統合」レッスンは無料ですか？

はい。「LLMとLangChainの統合」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。

「LLMとLangChainの統合」で何を学びますか？

さまざまなLLMプロバイダー（例：OpenAI、Hugging Face）をLangChainのエージェントに接続する方法を学びます。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2/4です。

「LLMとLangChainの統合」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

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