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.
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.
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.
| GPU | Family | Memory | # of GPUs (min.) |
|---|---|---|---|
| H100 | SXM or PCIe | 80GB | 2 |
| A100 | SXM or PCIe | 80GB | 2 |
- Linux operating systems (Ubuntu 22.04 or later recommended)
- Docker
- Docker Compose
- CUDA Toolkit (NVIDIA Driver >=
535, CUDA >=12.2) - NVIDIA Container Toolkit
To get started using NVIDIA Ingest, you need to do a few things:
- Start supporting NIM microservices 🏗️
- Install the NVIDIA Ingest client dependencies in a Python environment 🐍
- Submit ingestion job(s) 📓
- Inspect and consume results 🔍
Optional:
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.
-
Git clone the repo:
git clone https://github.com/nvidia/nv-ingest -
Change directory to the cloned repo
cd nv-ingest. -
Generate API keys and authenticate with NGC with the
docker logincommand:
# 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.
- 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
- 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.
If you want to build from source, use docker compose up --build instead. This will build from your repo's code rather than from an already published container.
- When all services have fully started,
nvidia-smishould 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.
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.
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 pagesextract_images- uses PDFium to extract imagesextract_tables- uses YOLOX to find tables and charts. Uses PaddleOCR for table extraction, and Deplot and CACHED for chart extractionextract_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
from nv_ingest_client.primitives.tasks.table_extraction import TableExtractionTask
from nv_ingest_client.primitives.tasks.chart_extraction import ChartExtractionTask
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
)
table_data_extract = TableExtractionTask()
chart_data_extract = ChartExtractionTask()
job_spec.add_task(extract_task)
job_spec.add_task(table_data_extract)
job_spec.add_task(chart_data_extract)
# 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=7670You 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
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.
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.jsonYou 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-tkThen 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.jsonTip
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.
Beyond the relevant documentation, examples, and other links above, below is a description of contents in this repo's folders:
- .github: GitHub repo configuration files
- ci: scripts used to build the nv-ingest container and other packages
- client: docs and source code for the nv-ingest-cli utility
- config: various yaml files defining configuration for OTEL, Prometheus
- data: Sample PDFs provided for testing convenience
- docker: houses scripts used by the nv-ingest docker container
- docs: Various READMEs describing deployment, metadata schemas, auth and telemetry setup
- examples: Example notebooks, scripts, and longer form tutorial content
- helm: Documentation for deploying nv-ingest to a Kubernetes cluster via Helm chart
- skaffold: Skaffold configuration
- src: source code for the nv-ingest pipelines and service
- tests: unit tests for nv-ingest
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.
- Description: If set to
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>
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.