AI Agents with LangChain & Autonomous Workflows · レッスン

ドキュメントローダーを解説

PDF、ウェブページ、データベースなど多様なソースからデータを読み込み、LangChainで扱える形式に変換する方法を学びます。

「ドキュメントローダーを解説」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。

Data for Smarter LLMs

Large Language Models (LLMs) are powerful, but their knowledge is limited to their training data. To make AI agents truly useful, they often need access to fresh, external information.

Imagine an agent that needs to answer questions about today's news.
Or one that summarizes a specific document from your company's internal drive.

This is where data loading comes in.

What are Document Loaders?

Document Loaders are LangChain's way of bringing external data into your agent's workflow. They act as bridges, converting raw data from various sources into a standardized format that LLMs can understand.

Think of them as specialized data connectors. They handle the messy details of reading files, fetching web pages, or querying databases, and then present the data cleanly to LangChain.

The LangChain Document Object

When a loader fetches data, it wraps it into a Document object. This is LangChain's universal container for data.

Each Document typically has two main parts:

page_content: The actual text content extracted from the source (e.g., the text from a PDF page, a web article).
metadata: A dictionary containing extra information about the document (e.g., source URL, page number, file path, creation date).

Loading from Local Text Files

One of the simplest ways to load data is from a plain text file on your computer. LangChain provides the TextLoader for this purpose.

It takes a file path, reads the content, and creates a Document object. Let's see how it works by creating a temporary file and loading it.

TextLoader in Action

This example creates a temporary text file, writes some content, then uses TextLoader to read it and prints the result.

from langchain_community.document_loaders import TextLoader
import os
import tempfile

if __name__ == "__main__":
    # Create a temporary file
    with tempfile.NamedTemporaryFile(mode='w', delete=False, encoding='utf-8') as temp_file:
        temp_file.write("Hello CoddyKit!\nThis is a test document.")
        temp_file_path = temp_file.name

    print(f"Created temp file: {temp_file_path}")

    try:
        # Load the document using TextLoader
        loader = TextLoader(temp_file_path)
        documents = loader.load()

        # Print the content of the loaded document
        for doc in documents:
            print("--- Document Content ---")
            print(doc.page_content)
            print("--- Metadata ---")
            print(doc.metadata)
    finally:
        # Clean up the temporary file
        os.remove(temp_file_path)
        print(f"Cleaned up temp file: {temp_file_path}")

Web Content with WebBaseLoader

What if your data is on the internet? The WebBaseLoader is perfect for fetching content directly from web pages.

It's smart enough to extract the main text content, ignoring navigation, ads, and other irrelevant elements. You just provide the URL!

WebBaseLoader Example

Here's how to fetch content from a simple example web page. Note: This loader typically requires beautifulsoup4 and lxml to be installed.

from langchain_community.document_loaders import WebBaseLoader

if __name__ == "__main__":
    # Define the URL to load
    url = "https://www.google.com/search?q=hello"
    print(f"Loading content from: {url}")

    # Initialize the WebBaseLoader
    loader = WebBaseLoader(url)

    # Load the documents
    documents = loader.load()

    # Print the content of the first loaded document
    if documents:
        print("--- Extracted Content (first 200 chars) ---")
        print(documents[0].page_content[:200] + "...")
        print("--- Metadata ---")
        print(documents[0].metadata)
    else:
        print("No documents loaded.")

PDFs and Structured Documents

Loading data from PDFs is a common requirement. LangChain offers loaders like PyPDFLoader (which uses the pypdf library) to handle these.

These loaders are designed to extract text from complex formats, often preserving some structure. For PDFs, each page might become a separate Document object, with metadata indicating its page number.

There are also loaders for other formats like CSV, JSON, Markdown, and even specific data structures like Notion databases or Confluence pages.

Beyond Files: Database Loaders

Your data might live in databases. LangChain supports various database loaders to connect directly to your data sources:

SQL Database Loader: Connects to relational databases (e.g., PostgreSQL, MySQL) to fetch data based on queries.
MongoDB Loader: For NoSQL document databases.
Elasticsearch Loader: To pull data from Elasticsearch indices.

These loaders allow your agents to dynamically query and retrieve data from your existing data infrastructure.

Quick Check

Which of the following is NOT a primary purpose of a LangChain Document Loader?

Recap: Document Loaders

In this lesson, we explored LangChain's Document Loaders, essential tools for bringing external data into your AI agents.

Loaders convert diverse data sources (text files, web pages, PDFs, databases) into LangChain's universal Document object.
Each Document contains page_content and metadata.
We saw practical examples with TextLoader for local files and WebBaseLoader for web content.

Next, we'll learn how to handle large documents by splitting them into manageable chunks.

よくある質問

「ドキュメントローダーを解説」レッスンは無料ですか？

はい。「ドキュメントローダーを解説」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。

「ドキュメントローダーを解説」で何を学びますか？

PDF、ウェブページ、データベースなど多様なソースからデータを読み込み、LangChainで扱える形式に変換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1/4です。

「ドキュメントローダーを解説」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。

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