TXT Loader

Overview

This tutorial focuses on using LangChain’s TextLoader to efficiently load and process individual text files.

You’ll learn how to extract metadata and content, making it easier to prepare text data.

Table of Contents

Environment Setup

Set up the environment. You may refer to Environment Setup for more details.

[Note]

  • langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.

  • You can checkout the langchain-opentutorial for more details.

%%capture --no-stderr
%pip install langchain-opentutorial
# Install required packages
from langchain_opentutorial import package

package.install(
    [
        "langchain",
        "langchain_community",
        "chardet"
    ],
    verbose=False,
    upgrade=False,
)

TXT Loader

Let’s explore how to load files with the .txt extension using a loader.

from langchain_community.document_loaders import TextLoader

# Create a text loader
loader = TextLoader("data/appendix-keywords.txt", encoding="utf-8")

# Load the document
docs = loader.load()
print(f"Number of documents: {len(docs)}\n")
print("[Metadata]\n")
print(docs[0].metadata)
print("\n========= [Preview - First 500 Characters] =========\n")
print(docs[0].page_content[:500])
Number of documents: 1
    
    [Metadata]
    
    {'source': 'data/appendix-keywords.txt'}
    
    ========= [Preview - First 500 Characters] =========
    
    Semantic Search
    
    Definition: Semantic search is a search method that goes beyond simple keyword matching by understanding the meaning of the user’s query to return relevant results.
    Example: If a user searches for “planets in the solar system,” the system might return information about related planets such as “Jupiter” or “Mars.”
    Related Keywords: Natural Language Processing, Search Algorithms, Data Mining
    
    Embedding
    
    Definition: Embedding is the process of converting textual data, such as words

Automatic Encoding Detection with TextLoader

In this example, we explore several strategies for using the TextLoader class to efficiently load large batches of files from a directory with varying encodings.

To illustrate the problem, we’ll first attempt to load multiple text files with arbitrary encodings.

  • silent_errors: By passing the silent_errors parameter to the DirectoryLoader, you can skip files that cannot be loaded and continue the loading process without interruptions.

  • autodetect_encoding: Additionally, you can enable automatic encoding detection by passing the autodetect_encoding parameter to the loader class, allowing it to detect file encodings before failing.

from langchain_community.document_loaders import DirectoryLoader

path = "data/"

text_loader_kwargs = {"autodetect_encoding": True}

loader = DirectoryLoader(
    path,
    glob="**/*.txt",
    loader_cls=TextLoader,
    silent_errors=True,
    loader_kwargs=text_loader_kwargs,
)
docs = loader.load()

The data/appendix-keywords.txt file and its derivative files with similar names all have different encoding formats.

doc_sources = [doc.metadata["source"] for doc in docs]
doc_sources
['data/appendix-keywords-CP949.txt',
     'data/appendix-keywords-EUCKR.txt',
     'data/appendix-keywords.txt',
     'data/appendix-keywords-utf8.txt']
print("[Metadata]\n")
print(docs[0].metadata)
print("\n========= [Preview - First 500 Characters] =========\n")
print(docs[0].page_content[:500])
[Metadata]
    
    {'source': 'data/appendix-keywords-CP949.txt'}
    
    ========= [Preview - First 500 Characters] =========
    
    Semantic Search
    
    Definition: Semantic search is a search method that goes beyond simple keyword matching by understanding the meaning of the user¡¯s query to return relevant results.
    Example: If a user searches for ¡°planets in the solar system,¡± the system might return information about related planets such as ¡°Jupiter¡± or ¡°Mars.¡±
    Related Keywords: Natural Language Processing, Search Algorithms, Data Mining
    
    Embedding
    
    Definition: Embedding is the process of converting textual data, such a
print("[Metadata]\n")
print(docs[1].metadata)
print("\n========= [Preview - First 500 Characters] =========\n")
print(docs[1].page_content[:500])
[Metadata]
    
    {'source': 'data/appendix-keywords-EUCKR.txt'}
    
    ========= [Preview - First 500 Characters] =========
    
    Semantic Search
    
    Definition: Semantic search is a search method that goes beyond simple keyword matching by understanding the meaning of the user¡¯s query to return relevant results.
    Example: If a user searches for ¡°planets in the solar system,¡± the system might return information about related planets such as ¡°Jupiter¡± or ¡°Mars.¡±
    Related Keywords: Natural Language Processing, Search Algorithms, Data Mining
    
    Embedding
    
    Definition: Embedding is the process of converting textual data, such a
print("[Metadata]\n")
print(docs[3].metadata)
print("\n========= [Preview - First 500 Characters] =========\n")
print(docs[3].page_content[:500])
[Metadata]
    
    {'source': 'data/appendix-keywords-utf8.txt'}
    
    ========= [Preview - First 500 Characters] =========
    
    Semantic Search
    
    Definition: Semantic search is a search method that goes beyond simple keyword matching by understanding the meaning of the user’s query to return relevant results.
    Example: If a user searches for “planets in the solar system,” the system might return information about related planets such as “Jupiter” or “Mars.”
    Related Keywords: Natural Language Processing, Search Algorithms, Data Mining
    
    Embedding
    
    Definition: Embedding is the process of converting textual data, such as words
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