LangChain provides a convenient way to load documents into your applications using the DocumentLoader class. This class allows you to load documents from various sources, such as text files, directories, or URLs. In this comprehensive guide, we will explore how to use the DocumentLoader class to load documents for your vector database applications.
Prerequisites
- LangChain: Ensure you have LangChain installed on your system.
- Python: Install Python and the necessary libraries (e.g.,
langchain,requests).
Creating a Document Loader
Import Necessary Libraries:
Python
from langchain.document_loaders import DirectoryLoader, TextLoader
Create a Document Loader:
Python
loader = DirectoryLoader(“path/to/your/documents”, glob=”*.txt”)
Loading Documents
Load Documents:
Python
documents = loader.load()
Example
Python
from langchain.document_loaders import DirectoryLoader
loader = DirectoryLoader("data/documents")
documents = loader.load()
for document in documents:
print(document.page_content)
Customizing Document Loaders
LangChain provides several built-in document loaders, including:
- DirectoryLoader: Loads documents from a directory.
- TextLoader: Loads text from a single file.
- CSVLoader: Loads CSV files.
- PDFLoader: Loads PDF files.
- DocxLoader: Loads Word documents.
You can also create custom document loaders to handle specific file formats or data sources.
The LangChain DocumentLoader class simplifies the process of loading documents into your applications. By understanding the different document loaders available and customizing them to your needs, you can efficiently load data for your vector database applications.
