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LlamaParse

llama-index-integrations/readers/llama-index-readers-llama-parse/README.md

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LlamaParse

NOTE: This package has been deprecated and is no longer maintained. Please use the llama-cloud package instead.

LlamaParse is an API created by LlamaIndex to efficiently parse and represent files for efficient retrieval and context augmentation using LlamaIndex frameworks.

LlamaParse directly integrates with LlamaIndex.

Currently available for free. Try it out today!

Getting Started

First, login and get an api-key from https://cloud.llamaindex.ai.

Then, make sure you have the latest LlamaIndex version installed.

pip uninstall llama-index  # run this if upgrading from v0.9.x or older
pip install -U llama-index --upgrade --no-cache-dir --force-reinstall

Lastly, install the package:

pip install llama-parse

Now you can run the following to parse your first PDF file:

python
import nest_asyncio

nest_asyncio.apply()

from llama_parse import LlamaParse

parser = LlamaParse(
    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
    result_type="markdown",  # "markdown" and "text" are available
    verbose=True,
)

# sync
documents = parser.load_data("./my_file.pdf")

# sync batch
documents = parser.load_data(["./my_file1.pdf", "./my_file2.pdf"])

# async
documents = await parser.aload_data("./my_file.pdf")

# async batch
documents = await parser.aload_data(["./my_file1.pdf", "./my_file2.pdf"])

Using with SimpleDirectoryReader

You can also integrate the parser as the default PDF loader in SimpleDirectoryReader:

python
import nest_asyncio

nest_asyncio.apply()

from llama_parse import LlamaParse
from llama_index.core import SimpleDirectoryReader

parser = LlamaParse(
    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
    result_type="markdown",  # "markdown" and "text" are available
    verbose=True,
)

file_extractor = {".pdf": parser}
documents = SimpleDirectoryReader(
    "./data", file_extractor=file_extractor
).load_data()

Full documentation for SimpleDirectoryReader can be found on the LlamaIndex Documentation.

Examples

Several end-to-end indexing examples can be found in the examples folder

Terms of Service

See the Terms of Service Here.