Do Document Loaders generate embeddings?

No. Document Loaders only load and normalize text. Embedding and indexing are separate steps.

Which loader is best for PDFs?

PyPDFLoader is the recommended and most reliable PDF loader in LangChain.

Get Started with LangChain Document Loaders: A Step-by-Step Guide (2025 Update)

Q: What is a LangChain Document Loader?

A Document Loader converts files, URLs, APIs, and external sources into LangChain Document objects so they can be used in RAG pipelines.

Name: Akira Sakamoto

Updated on 11/15/2025

LangChain has evolved rapidly since 2023. If you're exploring Retrieval-Augmented Generation (RAG), building chat-based applications, or integrating external knowledge into LLM pipelines, Document Loaders are now one of the most important components.

This guide gives you a clean, accurate, and modern understanding of how LangChain Document Loaders work (2025 version), how to use them properly, and how to build real-world applications on top of them.

What is LangChain?

LangChain is a framework designed to help developers build LLM-powered applications using tools like:

Document Loaders
Text Splitters
Vector Stores
Retrievers
Runnables & LCEL (LangChain Expression Language)

In modern LangChain (0.1–0.2+), the pipeline for building applications looks like this:

Load → Split → Embed → Store → Retrieve → Generate

Document Loaders handle the very first step:
getting real-world content into LLM-friendly “Document” objects.

What Are LangChain Document Loaders?

A LangChain Document has two fields:

{
  "page_content": "<raw text>",
  "metadata": {...}
}

Document Loaders convert external sources—files, URLs, APIs, PDFs, CSV, YouTube transcripts—into a list of Document objects.

Example: Load a `.txt` file

from langchain_community.document_loaders import TextLoader
 
loader = TextLoader("./data/sample.txt")
docs = loader.load()

Result:

{
    "page_content": "Welcome to LangChain!",
    "metadata": { "source": "./data/sample.txt" }
}

Types of Document Loaders in LangChain

LangChain provides dozens of loaders, but they fall into three main categories.

1. Transform Loaders (Local File Formats)

Load structured or unstructured files:

CSV Example (modern import)

from langchain_community.document_loaders import CSVLoader
 
loader = CSVLoader("./data/data.csv")
docs = loader.load()

Each CSV row becomes a Document.

Other transform loaders include:

PyPDFLoader
JSONLoader
Docx2txtLoader
UnstructuredFileLoader
PandasDataFrameLoader

2. Public Dataset or Web Service Loaders

These fetch text directly from online sources.

Wikipedia Example

from langchain_community.document_loaders import WikipediaLoader
 
loader = WikipediaLoader("Machine_learning")
docs = loader.load()

3. Proprietary / Authenticated Source Loaders

Used for internal services such as:

Company APIs
Internal CMS
SQL databases
SharePoint
Slack
Gmail

These require credentials and often custom loaders.

How Document Loaders Fit Into a Modern RAG Pipeline

Document Loaders only load raw text. They do NOT:

generate embeddings
create “chains”
produce “memory vectors”

(A common misconception.)

Correct pipeline:

Loader → Splitter → Embeddings → Vector Store → Retriever → LLM

Example with PDF:

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
 
loader = PyPDFLoader("file.pdf")
docs = loader.load()
 
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)
 
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(chunks, embeddings)

Use Cases for Modern LangChain Document Loaders

Example 1: Loading and Chunking Files for Indexing

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
 
loader = TextLoader("article.txt")
docs = loader.load()
 
splitter = RecursiveCharacterTextSplitter(
    chunk_size=800, chunk_overlap=150
)
chunks = splitter.split_documents(docs)

Example 2: Ingesting CSV for Data Understanding

from langchain_community.document_loaders import CSVLoader
 
loader = CSVLoader("data.csv")
docs = loader.load()
 
for doc in docs:
    print(doc.page_content)

Example 3: Loading YouTube Transcripts (2025-correct version)

from langchain_community.document_loaders import YoutubeLoader
 
loader = YoutubeLoader.from_youtube_url(
    "https://www.youtube.com/watch?v=O5nskjZ_GoI",
    add_video_info=True
)
docs = loader.load()

No manual tokenizer/models needed — LangChain handles text loading only.

Example 4: Pandas DataFrame → Documents

from langchain_community.document_loaders import DataFrameLoader
 
loader = DataFrameLoader(dataframe, page_content_column="text")
docs = loader.load()

Real-World Applications for LangChain Document Loaders

Below are three useful and modern examples.

Build a ChatGPT-Style PDF QA App (Modern RAG)

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
 
# 1. Load
pages = PyPDFLoader("./SpaceX.pdf").load()
 
# 2. Split
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, overlap=150)
chunks = splitter.split_documents(pages)
 
# 3. Embed & store
db = Chroma.from_documents(chunks, OpenAIEmbeddings())
 
# 4. Ask questions
retriever = db.as_retriever()
llm = ChatOpenAI(model="gpt-4.1")
 
from langchain.chains import RetrievalQA
qa = RetrievalQA.from_chain_type(llm, retriever=retriever)
 
qa.run("Summarize the mission in 3 bullet points.")

Build a YouTube Transcript QA App

loader = YoutubeLoader.from_youtube_url(url)
docs = loader.load()
 
chunks = RecursiveCharacterTextSplitter.from_tiktoken_encoder().split_documents(docs)
db = Chroma.from_documents(chunks, OpenAIEmbeddings())
 
qa.run("Explain the main argument of this video")

Build a Website QA Chatbot (via Sitemap)

from langchain_community.document_loaders.sitemap import SitemapLoader
 
loader = SitemapLoader("https://docs.chainstack.com/sitemap.xml")
docs = loader.load()

Run the same chunk → embed → retrieve → chat pipeline as PDF/YouTube.

Conclusion

LangChain Document Loaders are the foundation of any RAG workflow. They help you:

Load data from any source
Normalize text into consistent Document format
Build retrieval-ready datasets
Enable chat, summarization, and QA over your own content

With up-to-date loaders and correct LangChain 2025 patterns, you can build powerful AI applications on top of PDFs, websites, YouTube videos, CSVs, and more.

FAQ

What is a LangChain Document Loader? A Document Loader converts files, URLs, APIs, and other sources into LangChain Document objects for downstream use.

Do Document Loaders create embeddings or indexes? No. Loaders only load raw text. Embeddings and indexing are separate steps.

Which loader should I use for PDFs? PyPDFLoader is the most reliable choice for text extraction in LangChain.

How do YouTube loaders work? They fetch transcripts using YouTube’s API or community captions, returning a text transcript as a Document.