NVIDIA-Ingest: Multi-modal data extraction

NVIDIA-Ingest is a scalable, performance-oriented document content and metadata extraction microservice. Including support for parsing PDFs, Word and PowerPoint documents, it uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images for use in downstream generative applications.

NVIDIA Ingest enables parallelization of the process of splitting documents into pages where contents are classified (as tables, charts, images, text), extracted into discrete content, and further contextualized via optical character recognition (OCR) into a well defined JSON schema. From there, NVIDIA Ingest can optionally manage computation of embeddings for the extracted content, and also optionally manage storing into a vector database Milvus.

Table of Contents

1. Introduction
2. Prerequisites
3. Quickstart
4. Repo Structure
5. Notices

Introduction

What NVIDIA-Ingest is ✔️

A microservice that:

• Accepts a JSON Job description, containing a document payload, and a set of ingestion tasks to perform on that payload.
• Allows the results of a Job to be retrieved; the result is a JSON dictionary containing a list of Metadata describing objects extracted from the base document, as well as processing annotations and timing/trace data.
• Supports PDF, Docx, pptx, and images.
• Supports multiple methods of extraction for each document type in order to balance trade-offs between throughput and accuracy. For example, for PDF documents we support extraction via pdfium, Unstructured.io, and Adobe Content Extraction Services.
• Supports various types of pre and post processing operations, including text splitting and chunking; transform, and filtering; embedding generation, and image offloading to storage.

What NVIDIA-Ingest is not ✖️

A service that:

• Runs a static pipeline or fixed set of operations on every submitted document.
• Acts as a wrapper for any specific document parsing library.

Prerequisites

Hardware

GPU	Family	Memory	# of GPUs (min.)
H100	SXM/NVLink or PCIe	80GB	2
A100	SXM/NVLink or PCIe	80GB	2

Software

• Linux operating systems (Ubuntu 22.04 or later recommended)
• Docker
• Docker Compose
• CUDA Toolkit (NVIDIA Driver >= 535, CUDA >= 12.2)
• NVIDIA Container Toolkit

Quickstart

To get started using NVIDIA Ingest, you need to do a few things:

1. Start supporting NIM microservices 🏗️
2. Install the NVIDIA Ingest client dependencies in a Python environment 🐍
3. Submit ingestion job(s) 📓
4. Inspect and consume results 🔍

Step 1: Starting containers

This example demonstrates how to use the provided docker-compose.yaml to start all needed services with a few commands.

Important

NIM containers on their first startup can take 10-15 minutes to pull and fully load models.

If preferred, you can also start services one by one, or run on Kubernetes via our Helm chart. Also of note are additional environment variables you may wish to configure.

1. Git clone the repo: git clone https://github.com/nvidia/nv-ingest

2. Change directory to the cloned repo cd nv-ingest.

3. Generate API keys and authenticate with NGC with the docker login command:

# This is required to access pre-built containers and NIM microservices
$ docker login nvcr.io
Username: $oauthtoken
Password: <Your Key>

Note

during the early access (EA) phase, your API key must be created as a member of nemo-microservice / ea-participants which you may join by applying for early access here: https://developer.nvidia.com/nemo-microservices-early-access/join. When approved, switch your profile to this org / team, then the key you generate will have access to the resources outlined below.

4. Create a .env file containing your NGC API key, and the following paths:

# Container images must access resources from NGC. 
NGC_API_KEY=...
DATASET_ROOT=<PATH_TO_THIS_REPO>/data
NV_INGEST_ROOT=<PATH_TO_THIS_REPO>

Note

As configured by default in docker-compose.yaml, the DePlot NIM is on a dedicated GPU. All other NIMs and the nv-ingest container itself share a second. This is to avoid DePlot and other NIMs competing for VRAM on the same device.

Change the CUDA_VISIBLE_DEVICES pinnings as desired for your system within docker-compose.yaml.

Important

Make sure NVIDIA is set as your default container runtime before running the docker compose command with the command: sudo nvidia-ctk runtime configure --runtime=docker --set-as-default

5. Start all services: docker compose up

Tip

By default we have configured log levels to be verbose.

It's possible to observe service startup proceeding: you will notice many log messages. Disable verbose logging by configuring NIM_TRITON_LOG_VERBOSE=0 for each NIM in docker-compose.yaml.

6. When all services have fully started, nvidia-smi should show processes like the following:

# If it's taking > 1m for `nvidia-smi` to return, it's likely the bus is still busy setting up the models.
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A   1352957      C   tritonserver                                762MiB |
|    1   N/A  N/A   1322081      C   /opt/nim/llm/.venv/bin/python3            63916MiB |
|    2   N/A  N/A   1355175      C   tritonserver                                478MiB |
|    2   N/A  N/A   1367569      C   ...s/python/triton_python_backend_stub       12MiB |
|    3   N/A  N/A   1321841      C   python                                      414MiB |
|    3   N/A  N/A   1352331      C   tritonserver                                478MiB |
|    3   N/A  N/A   1355929      C   ...s/python/triton_python_backend_stub      424MiB |
|    3   N/A  N/A   1373202      C   tritonserver                                414MiB |
+---------------------------------------------------------------------------------------+

Observe the started containers with docker ps:

CONTAINER ID   IMAGE                                                                      COMMAND                  CREATED          STATUS                    PORTS                                                                                                                                                                                                                                                                                NAMES
0f2f86615ea5   nvcr.io/ohlfw0olaadg/ea-participants/nv-ingest:24.10                       "/opt/conda/bin/tini…"   35 seconds ago   Up 33 seconds             0.0.0.0:7670->7670/tcp, :::7670->7670/tcp                                                                                                                                                                                                                                            nv-ingest-nv-ingest-ms-runtime-1
de44122c6ddc   otel/opentelemetry-collector-contrib:0.91.0                                "/otelcol-contrib --…"   14 hours ago     Up 24 seconds             0.0.0.0:4317-4318->4317-4318/tcp, :::4317-4318->4317-4318/tcp, 0.0.0.0:8888-8889->8888-8889/tcp, :::8888-8889->8888-8889/tcp, 0.0.0.0:13133->13133/tcp, :::13133->13133/tcp, 55678/tcp, 0.0.0.0:32849->9411/tcp, :::32848->9411/tcp, 0.0.0.0:55680->55679/tcp, :::55680->55679/tcp   nv-ingest-otel-collector-1
02c9ab8c6901   nvcr.io/ohlfw0olaadg/ea-participants/cached:0.2.0                          "/opt/nvidia/nvidia_…"   14 hours ago     Up 24 seconds             0.0.0.0:8006->8000/tcp, :::8006->8000/tcp, 0.0.0.0:8007->8001/tcp, :::8007->8001/tcp, 0.0.0.0:8008->8002/tcp, :::8008->8002/tcp                                                                                                                                                      nv-ingest-cached-1
d49369334398   nvcr.io/nim/nvidia/nv-embedqa-e5-v5:1.1.0                                  "/opt/nvidia/nvidia_…"   14 hours ago     Up 33 seconds             0.0.0.0:8012->8000/tcp, :::8012->8000/tcp, 0.0.0.0:8013->8001/tcp, :::8013->8001/tcp, 0.0.0.0:8014->8002/tcp, :::8014->8002/tcp                                                                                                                                                      nv-ingest-embedding-1
508715a24998   nvcr.io/ohlfw0olaadg/ea-participants/nv-yolox-structured-images-v1:0.2.0   "/opt/nvidia/nvidia_…"   14 hours ago     Up 33 seconds             0.0.0.0:8000-8002->8000-8002/tcp, :::8000-8002->8000-8002/tcp                                                                                                                                                                                                                        nv-ingest-yolox-1
5b7a174a0a85   nvcr.io/ohlfw0olaadg/ea-participants/deplot:1.0.0                          "/opt/nvidia/nvidia_…"   14 hours ago     Up 33 seconds             0.0.0.0:8003->8000/tcp, :::8003->8000/tcp, 0.0.0.0:8004->8001/tcp, :::8004->8001/tcp, 0.0.0.0:8005->8002/tcp, :::8005->8002/tcp                                                                                                                                                      nv-ingest-deplot-1
430045f98c02   nvcr.io/ohlfw0olaadg/ea-participants/paddleocr:0.2.0                       "/opt/nvidia/nvidia_…"   14 hours ago     Up 24 seconds             0.0.0.0:8009->8000/tcp, :::8009->8000/tcp, 0.0.0.0:8010->8001/tcp, :::8010->8001/tcp, 0.0.0.0:8011->8002/tcp, :::8011->8002/tcp                                                                                                                                                      nv-ingest-paddle-1
8e587b45821b   grafana/grafana                                                            "/run.sh"                14 hours ago     Up 33 seconds             0.0.0.0:3000->3000/tcp, :::3000->3000/tcp                                                                                                                                                                                                                                            grafana-service
aa2c0ec387e2   redis/redis-stack                                                          "/entrypoint.sh"         14 hours ago     Up 33 seconds             0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 8001/tcp                                                                                                                                                                                                                                  nv-ingest-redis-1
bda9a2a9c8b5   openzipkin/zipkin                                                          "start-zipkin"           14 hours ago     Up 33 seconds (healthy)   9410/tcp, 0.0.0.0:9411->9411/tcp, :::9411->9411/tcp                                                                                                                                                                                                                                  nv-ingest-zipkin-1
ac27e5297d57   prom/prometheus:latest                                                     "/bin/prometheus --w…"   14 hours ago     Up 33 seconds             0.0.0.0:9090->9090/tcp, :::9090->9090/tcp                                                                                                                                                                                                                                            nv-ingest-prometheus-1

Tip

nv-ingest is in Early Access mode, meaning the codebase gets frequent updates. To build an updated nv-ingest service container with the latest changes you can:

docker compose build

After the image is built, run docker compose up per item 5 above.

Step 2: Installing Python dependencies

To interact with the nv-ingest service, you can do so from the host, or by docker exec-ing into the nv-ingest container.

To interact from the host, you'll need a Python environment and install the client dependencies:

# conda not required, but makes it easy to create a fresh python environment
conda create --name nv-ingest-dev python=3.10
conda activate nv-ingest-dev
cd client
pip install -r ./requirements.txt
pip install .

Note

Interacting from the host depends on the appropriate port being exposed from the nv-ingest container to the host as defined in docker-compose.yaml.

If you prefer, you can disable exposing that port, and interact with the nv-ingest service directly from within its container.

To interact within the container:

docker exec -it nv-ingest-nv-ingest-ms-runtime-1 bash

You'll be in the /workspace directory, which has DATASET_ROOT from the .env file mounted at ./data. The pre-activated morpheus conda environment has all the python client libraries pre-installed:

(morpheus) root@aba77e2a4bde:/workspace#

From the bash prompt above, you can run nv-ingest-cli and Python examples described below.

Step 3: Ingesting Documents

You can submit jobs programmatically in Python or via the nv-ingest-cli tool.

In the below examples, we are doing text, chart, table, and image extraction:

• extract_text, - uses PDFium to find and extract text from pages
• extract_images - uses PDFium to extract images
• extract_tables - uses YOLOX to find tables and charts. Uses PaddleOCR for table extraction, and Deplot and CACHED for chart extraction
• extract_charts - (optional) enables or disables the use of Deplot and CACHED for chart extraction.

Important

extract_tables controls extraction for both tables and charts. You can optionally disable chart extraction by setting extract_charts to false.

In Python (you can find more documentation and examples here):

import logging, time

from nv_ingest_client.client import NvIngestClient
from nv_ingest_client.primitives import JobSpec
from nv_ingest_client.primitives.tasks import ExtractTask
from nv_ingest_client.primitives.tasks import SplitTask
from nv_ingest_client.util.file_processing.extract import extract_file_content

logger = logging.getLogger("nv_ingest_client")

file_name = "data/multimodal_test.pdf"
file_content, file_type = extract_file_content(file_name)

# A JobSpec is an object that defines a document and how it should
# be processed by the nv-ingest service.
job_spec = JobSpec(
  document_type=file_type,
  payload=file_content,
  source_id=file_name,
  source_name=file_name,
  extended_options=
    {
      "tracing_options":
      {
        "trace": True,
        "ts_send": time.time_ns()
      }
    }
)

# configure desired extraction modes here. Multiple extraction
# methods can be defined for a single JobSpec
extract_task = ExtractTask(
  document_type=file_type,
  extract_text=True,
  extract_images=True,
  extract_tables=True
)

job_spec.add_task(extract_task)

# Create the client and inform it about the JobSpec we want to process.
client = NvIngestClient(
  message_client_hostname="localhost", # Host where nv-ingest-ms-runtime is running
  message_client_port=7670 # REST port, defaults to 7670
)
job_id = client.add_job(job_spec)
client.submit_job(job_id, "morpheus_task_queue")
result = client.fetch_job_result(job_id, timeout=60)
print(f"Got {len(result)} results")

Using the the nv-ingest-cli (you can find more nv-ingest-cli examples here):

nv-ingest-cli \
  --doc ./data/multimodal_test.pdf \
  --output_directory ./processed_docs \
  --task='extract:{"document_type": "pdf", "extract_method": "pdfium", "extract_tables": "true", "extract_images": "true"}' \
  --client_host=localhost \
  --client_port=7670

You should notice output indicating document processing status, followed by a breakdown of time spent during job execution:

INFO:nv_ingest_client.nv_ingest_cli:Processing 1 documents.
INFO:nv_ingest_client.nv_ingest_cli:Output will be written to: ./processed_docs
Processing files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.47s/file, pages_per_sec=0.29]
INFO:nv_ingest_client.cli.util.processing:dedup_images: Avg: 1.02 ms, Median: 1.02 ms, Total Time: 1.02 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:dedup_images_channel_in: Avg: 1.44 ms, Median: 1.44 ms, Total Time: 1.44 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:docx_content_extractor: Avg: 0.66 ms, Median: 0.66 ms, Total Time: 0.66 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:docx_content_extractor_channel_in: Avg: 1.09 ms, Median: 1.09 ms, Total Time: 1.09 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:filter_images: Avg: 0.84 ms, Median: 0.84 ms, Total Time: 0.84 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:filter_images_channel_in: Avg: 7.75 ms, Median: 7.75 ms, Total Time: 7.75 ms, Total % of Trace Computation: 0.07%
INFO:nv_ingest_client.cli.util.processing:job_counter: Avg: 2.13 ms, Median: 2.13 ms, Total Time: 2.13 ms, Total % of Trace Computation: 0.02%
INFO:nv_ingest_client.cli.util.processing:job_counter_channel_in: Avg: 2.05 ms, Median: 2.05 ms, Total Time: 2.05 ms, Total % of Trace Computation: 0.02%
INFO:nv_ingest_client.cli.util.processing:metadata_injection: Avg: 14.48 ms, Median: 14.48 ms, Total Time: 14.48 ms, Total % of Trace Computation: 0.14%
INFO:nv_ingest_client.cli.util.processing:metadata_injection_channel_in: Avg: 0.22 ms, Median: 0.22 ms, Total Time: 0.22 ms, Total % of Trace Computation: 0.00%
INFO:nv_ingest_client.cli.util.processing:pdf_content_extractor: Avg: 10332.97 ms, Median: 10332.97 ms, Total Time: 10332.97 ms, Total % of Trace Computation: 99.45%
INFO:nv_ingest_client.cli.util.processing:pdf_content_extractor_channel_in: Avg: 0.44 ms, Median: 0.44 ms, Total Time: 0.44 ms, Total % of Trace Computation: 0.00%
INFO:nv_ingest_client.cli.util.processing:pptx_content_extractor: Avg: 1.19 ms, Median: 1.19 ms, Total Time: 1.19 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:pptx_content_extractor_channel_in: Avg: 0.98 ms, Median: 0.98 ms, Total Time: 0.98 ms, Total % of Trace Computation: 0.01%
INFO:nv_ingest_client.cli.util.processing:redis_source_network_in: Avg: 12.27 ms, Median: 12.27 ms, Total Time: 12.27 ms, Total % of Trace Computation: 0.12%
INFO:nv_ingest_client.cli.util.processing:redis_task_sink_channel_in: Avg: 2.16 ms, Median: 2.16 ms, Total Time: 2.16 ms, Total % of Trace Computation: 0.02%
INFO:nv_ingest_client.cli.util.processing:redis_task_source: Avg: 8.00 ms, Median: 8.00 ms, Total Time: 8.00 ms, Total % of Trace Computation: 0.08%
INFO:nv_ingest_client.cli.util.processing:Unresolved time: 82.82 ms, Percent of Total Elapsed: 0.79%
INFO:nv_ingest_client.cli.util.processing:Processed 1 files in 10.47 seconds.
INFO:nv_ingest_client.cli.util.processing:Total pages processed: 3
INFO:nv_ingest_client.cli.util.processing:Throughput (Pages/sec): 0.29
INFO:nv_ingest_client.cli.util.processing:Throughput (Files/sec): 0.10

Step 4: Inspecting and Consuming Results

After the ingestion steps above have completed, you should be able to find text and image subfolders inside your processed docs folder. Each will contain JSON formatted extracted content and metadata.

When processing has completed, you'll have separate result files for text and image data:

ls -R processed_docs/

processed_docs/:
image  structured  text

processed_docs/image:
multimodal_test.pdf.metadata.json

processed_docs/structured:
multimodal_test.pdf.metadata.json

processed_docs/text:
multimodal_test.pdf.metadata.json

You can view the full JSON extracts and the metadata definitions here.

We also provide a script for inspecting extracted images

First, install tkinter by running the following commands depending on your OS.

• For Ubuntu/Debian Linux:

sudo apt-get update
sudo apt-get install python3-tk

• For Fedora/RHEL Linux:

sudo dnf install python3-tkinter

• For macOS using Homebrew:

brew install python-tk

Then run the following command to execute the script for inspecting the extracted image:

python src/util/image_viewer.py --file_path ./processed_docs/image/multimodal_test.pdf.metadata.json

Tip

Beyond inspecting the results, you can read them into things like llama-index or langchain retrieval pipelines.

Please also checkout our demo using a retrieval pipeline on build.nvidia.com to query over document content pre-extracted w/ NVIDIA Ingest.

Repo Structure

Beyond the relevant documentation, examples, and other links above, below is a description of contents in this repo's folders:

1. .github: GitHub repo configuration files
2. ci: scripts used to build the nv-ingest container and other packages
3. client: docs and source code for the nv-ingest-cli utility
4. config: various yaml files defining configuration for OTEL, Prometheus
5. data: Sample PDFs provided for testing convenience
6. docker: houses scripts used by the nv-ingest docker container
7. docs: Various READMEs describing deployment, metadata schemas, auth and telemetry setup
8. examples: Example notebooks, scripts, and longer form tutorial content
9. helm: Documentation for deploying nv-ingest to a Kubernetes cluster via Helm chart
10. skaffold: Skaffold configuration
11. src: source code for the nv-ingest pipelines and service
12. tests: unit tests for nv-ingest

Notices

Third Party License Notice:

If configured to do so, this project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use:

https://pypi.org/project/pdfservices-sdk/

• INSTALL_ADOBE_SDK:
  • Description: If set to true, the Adobe SDK will be installed in the container at launch time. This is required if you want to use the Adobe extraction service for PDF decomposition. Please review the license agreement for the pdfservices-sdk before enabling this option.

Contributing

We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license.

Any contribution which contains commits that are not Signed-Off will not be accepted.

To sign off on a commit you simply use the --signoff (or -s) option when committing your changes:

$ git commit -s -m "Add cool feature."

This will append the following to your commit message:

Signed-off-by: Your Name <your@email.com>

Full text of the DCO:

  Developer Certificate of Origin
  Version 1.1

  Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
  1 Letterman Drive
  Suite D4700
  San Francisco, CA, 94129

  Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

  Developer's Certificate of Origin 1.1

  By making a contribution to this project, I certify that:

  (a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or

  (b) The contribution is based upon previous work that, to the best of my knowledge, is covered under an appropriate open source license and I have the right under that license to submit that work with modifications, whether created in whole or in part by me, under the same open source license (unless I am permitted to submit under a different license), as indicated in the file; or

  (c) The contribution was provided directly to me by some other person who certified (a), (b) or (c) and I have not modified it.

  (d) I understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information I submit with it, including my sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open source license(s) involved.