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SimpleDirectoryReader is the simplest way to load data from local files into LlamaIndex. For production use cases it's more likely that you'll want to use one of the many Readers available on LlamaHub, but SimpleDirectoryReader is a great way to get started.

Supported file types#

By default SimpleDirectoryReader will try to read any files it finds, treating them all as text. In addition to plain text, it explicitly supports the following file types, which are automatically detected based on file extension:

  • .csv - comma-separated values
  • .docx - Microsoft Word
  • .epub - EPUB ebook format
  • .hwp - Hangul Word Processor
  • .ipynb - Jupyter Notebook
  • .jpeg, .jpg - JPEG image
  • .mbox - MBOX email archive
  • .md - Markdown
  • .mp3, .mp4 - audio and video
  • .pdf - Portable Document Format
  • .png - Portable Network Graphics
  • .ppt, .pptm, .pptx - Microsoft PowerPoint

One file type you may be expecting to find here is JSON; for that we recommend you use our JSON Loader.


The most basic usage is to pass an input_dir and it will load all supported files in that directory:

from llama_index.core import SimpleDirectoryReader

reader = SimpleDirectoryReader(input_dir="path/to/directory")
documents = reader.load_data()

Documents can also be loaded with parallel processing if loading many files from a directory. Note that there are differences when using multiprocessing with Windows and Linux/MacOS machines, which is explained throughout the multiprocessing docs (e.g. see here). Ultimately, Windows users may see less or no performance gains whereas Linux/MacOS users would see these gains when loading the exact same set of files.

documents = reader.load_data(num_workers=4)

Reading from subdirectories#

By default, SimpleDirectoryReader will only read files in the top level of the directory. To read from subdirectories, set recursive=True:

SimpleDirectoryReader(input_dir="path/to/directory", recursive=True)

Iterating over files as they load#

You can also use the iter_data() method to iterate over and process files as they load

reader = SimpleDirectoryReader(input_dir="path/to/directory", recursive=True)
all_docs = []
for docs in reader.iter_data():
    # <do something with the documents per file>

Restricting the files loaded#

Instead of all files you can pass a list of file paths:

SimpleDirectoryReader(input_files=["path/to/file1", "path/to/file2"])

or you can pass a list of file paths to exclude using exclude:

    input_dir="path/to/directory", exclude=["path/to/file1", "path/to/file2"]

You can also set required_exts to a list of file extensions to only load files with those extensions:

    input_dir="path/to/directory", required_exts=[".pdf", ".docx"]

And you can set a maximum number of files to be loaded with num_files_limit:

SimpleDirectoryReader(input_dir="path/to/directory", num_files_limit=100)

Specifying file encoding#

SimpleDirectoryReader expects files to be utf-8 encoded but you can override this using the encoding parameter:

SimpleDirectoryReader(input_dir="path/to/directory", encoding="latin-1")

Extracting metadata#

You can specify a function that will read each file and extract metadata that gets attached to the resulting Document object for each file by passing the function as file_metadata:

def get_meta(file_path):
    return {"foo": "bar", "file_path": file_path}

SimpleDirectoryReader(input_dir="path/to/directory", file_metadata=get_meta)

The function should take a single argument, the file path, and return a dictionary of metadata.

Extending to other file types#

You can extend SimpleDirectoryReader to read other file types by passing a dictionary of file extensions to instances of BaseReader as file_extractor. A BaseReader should read the file and return a list of Documents. For example, to add custom support for .myfile files :

from llama_index.core import SimpleDirectoryReader
from llama_index.core.readers.base import BaseReader
from llama_index.core import Document

class MyFileReader(BaseReader):
    def load_data(self, file, extra_info=None):
        with open(file, "r") as f:
            text =
        # load_data returns a list of Document objects
        return [Document(text=text + "Foobar", extra_info=extra_info or {})]

reader = SimpleDirectoryReader(
    input_dir="./data", file_extractor={".myfile": MyFileReader()}

documents = reader.load_data()

Note that this mapping will override the default file extractors for the file types you specify, so you'll need to add them back in if you want to support them.

Support for External FileSystems#

As with other modules, the SimpleDirectoryReader takes an optional fs parameter that can be used to traverse remote filesystems.

This can be any filesystem object that is implemented by the fsspec protocol. The fsspec protocol has open-source implementations for a variety of remote filesystems including AWS S3, Azure Blob & DataLake, Google Drive, SFTP, and many others.

Here's an example that connects to S3:

from s3fs import S3FileSystem

s3_fs = S3FileSystem(key="...", secret="...")
bucket_name = "my-document-bucket"

reader = SimpleDirectoryReader(
    recursive=True,  # recursively searches all subdirectories

documents = reader.load_data()

A full example notebook can be found here.