LangChain / RAG / Vector DBs · レッスン

多様なドキュメント形式の読み込み

PDF、Webページ、データベースなどに対応するLangChainのドキュメントローダーを使い、RAGシステム用のコンテンツを抽出します。

「多様なドキュメント形式の読み込み」はCoddyKit上の無料LangChain / RAG / Vector DBs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LangChain / RAG / Vector DBs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。 LangChain / RAG / Vector DBs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。

Document Loaders: The First Step

Welcome to the world of LangChain! Before an LLM can answer questions about your data, it needs to 'read' it. This is where Document Loaders come in.

Document loaders are tools that help LangChain ingest data from various sources like text files, PDFs, web pages, or databases. They convert raw data into a standardized format called Document objects.

Loading Simple Text Files

The simplest way to load data is from a plain text file. LangChain's TextLoader is perfect for this. It reads the content and wraps it into a Document object.

Try running this example to see how it works:

import os
from langchain_community.document_loaders import TextLoader

# Create a dummy text file for demonstration
file_content = "Hello CoddyKit learners!\nThis is a sample text file."
file_path = "sample.txt"
with open(file_path, "w") as f:
    f.write(file_content)

# Initialize the TextLoader with the file path
loader = TextLoader(file_path)

# Load the documents
documents = loader.load()

# Print the content of the first document
if documents:
    print(f"Loaded content:\n{documents[0].page_content}")
    print(f"Source: {documents[0].metadata.get('source')}")

# Clean up the dummy file
os.remove(file_path)

Understanding Document Objects

When a loader processes data, it creates one or more Document objects. These objects are fundamental in LangChain.

page_content: This is the main text extracted from your source.
metadata: A dictionary containing additional information like the source file path, page number (for PDFs), URL (for web pages), etc. This metadata is super useful for tracking and filtering!

Loaders standardize diverse data into this consistent format.

Loading PDF Documents

PDFs are common sources of information. LangChain provides the PyPDFLoader to extract text from PDF files. It uses the pypdf library under the hood.

Each page of a PDF typically becomes a separate Document object, with metadata indicating the page number.

from langchain_community.document_loaders import PyPDFLoader

# To use PyPDFLoader, you'd typically have a PDF file.
# For example, let's imagine 'my_report.pdf' exists.
# loader = PyPDFLoader("my_report.pdf")
# documents = loader.load()

print("PyPDFLoader is used to load text from PDF files.")
print("It often creates one Document per PDF page.")
print("Requires 'pypdf' library (pip install pypdf).")

Extracting Web Page Content

Need to get content from a website? The WebBaseLoader is your friend! It can fetch HTML content from URLs and extract the main text.

This is extremely useful for RAG systems that need to query up-to-date information from the internet.

from langchain_community.document_loaders import WebBaseLoader

# To use WebBaseLoader, you provide a list of URLs.
# loader = WebBaseLoader(["https://www.example.com"])
# documents = loader.load()

print("WebBaseLoader fetches content from specified URLs.")
print("It's great for pulling information from websites.")
print("Requires 'bs4' and 'requests' (pip install beautifulsoup4 requests).")

Loading from Directories

What if you have many files in a folder? The DirectoryLoader can process an entire directory of documents at once!

You can specify a glob pattern to filter for specific file types (e.g., *.txt, *.md). It can also use a specific loader for the files it finds.

import os
import shutil
from langchain_community.document_loaders import DirectoryLoader, TextLoader

# Create a dummy directory and files
dir_path = "temp_docs"
os.makedirs(dir_path, exist_ok=True)
with open(os.path.join(dir_path, "doc1.txt"), "w") as f:
    f.write("Content of document 1.")
with open(os.path.join(dir_path, "doc2.txt"), "w") as f:
    f.write("Content of document 2.")

# Initialize DirectoryLoader for .txt files
loader = DirectoryLoader(
    dir_path, glob="*.txt", loader_cls=TextLoader
)

# Load documents from the directory
documents = loader.load()

print(f"Found {len(documents)} documents in '{dir_path}'.")
for i, doc in enumerate(documents):
    print(f"Doc {i+1}: {doc.page_content}")

# Clean up the dummy directory
shutil.rmtree(dir_path)

Database and API Loaders

LangChain isn't just for files! It also offers loaders for various databases and APIs:

SQL Databases: Load data directly from tables using SQLDatabaseLoader.
NoSQL Databases: Load from MongoDB, Cassandra, and more.
APIs: Fetch data from REST APIs, Notion, Jira, Confluence, etc.

These loaders allow you to integrate live, structured data into your RAG pipeline, keeping your LLM's knowledge up-to-date.

Choose the Right Loader

You're building a RAG system and need to ingest data from three sources: a local folder with multiple text files, a PDF user manual, and a company's public documentation website. Which LangChain loaders would be most appropriate for each source?

Recap: Loading Diverse Data

In this lesson, you learned about the crucial role of Document Loaders in LangChain. These tools are the first step in bringing your data into an LLM-powered application.

We explored loaders for text files, PDFs, and web pages.
You saw how Document objects standardize data with page_content and metadata.
We also touched upon loading from directories, databases, and other diverse sources.

Mastering document loading is key to building powerful RAG systems that can access and understand a wide range of information!

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