NVIDIA-Ingest is a scalable, performance-oriented content and metadata extraction SDK for a variety of input formats. NV-Ingest includes support for parsing PDFs, text files, Microsoft Word and PowerPoint documents, plain images, and audio files. NV-Ingest uses specialized NVIDIA NIMs (self-hosted microservices, or hosted on build.nvidia.com) to find, contextualize, and extract text, tables, charts, and unstructured images that you can use in downstream generative applications.
Note
NVIDIA Ingest is also known as NV-Ingest and NeMo Retriever extraction.
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, such as Milvus.
The following diagram shows the Nemo Retriever extraction pipeline.
NV-Ingest is a library and microservice service that does the following:
- Accept a job specification that contains a document payload and a set of ingestion tasks to perform on that payload.
- Store the result of each job to retrieve later. The result is a dictionary that contains a list of metadata that describes the objects extracted from the base document, and processing annotations and timing/trace data.
- Support multiple methods of extraction for each document type to balance trade-offs between throughput and accuracy. For example, for .pdf documents nv-ingest supports extraction through pdfium, nemoretriever-parse, Unstructured.io, and Adobe Content Extraction Services.
- Support various types of before and after processing operations, including text splitting and chunking, transform and filtering, embedding generation, and image offloading to storage.
NV-Ingest supports the following file types:
pdfdocxpptxjpegpngsvgtifftxt
For more information, see the full NV Ingest documentation.
For production-level performance and scalability, we recommend that you deploy the pipeline and supporting NIMs by using Docker Compose or Kubernetes (helm charts). For more information, refer to prerequisites.
For small-scale workloads, such as workloads of fewer than 100 PDFs, you can use library mode setup. Library mode set up depends on NIMs that are already self-hosted, or, by default, NIMs that are hosted on build.nvidia.com.
Library mode deployment of nv-ingest requires:
- Linux operating systems (Ubuntu 22.04 or later recommended)
- Conda Python environment and package manager
- Python 3.10
Create a fresh Conda environment to install nv-ingest and dependencies.
conda create -y --name nvingest python=3.10 && \
conda activate nvingest && \
conda install -y -c rapidsai -c conda-forge -c nvidia nv_ingest=25.3.0 nv_ingest_client=25.3.0 nv_ingest_api=25.3.0 && \
pip install opencv-python llama-index-embeddings-nvidia pymilvus 'pymilvus[bulk_writer, model]' milvus-lite nvidia-riva-client unstructured-clientSet your NVIDIA_BUILD_API_KEY and NVIDIA_API_KEY. If you don't have a key, you can get one on build.nvidia.com. For instructions, refer to Generate Your NGC Keys.
#Note: these should be the same value
export NVIDIA_BUILD_API_KEY=nvapi-...
export NVIDIA_API_KEY=nvapi-...
You can submit jobs programmatically in Python.
To confirm that you have activated your Conda environment, run which python and confirm that you see nvingest in the result. You can do this before any python command that you run.
which python
/home/dev/miniforge3/envs/nvingest/bin/python
If you have a very high number of CPUs, and see the process hang without progress, we recommend that you use taskset to limit the number of CPUs visible to the process. Use the following code.
taskset -c 0-3 python your_ingestion_script.py
On a 4 CPU core low end laptop, the following code should take about 10 seconds.
import logging, os, time, sys
from nv_ingest.util.pipeline.pipeline_runners import start_pipeline_subprocess
from nv_ingest_client.client import Ingestor, NvIngestClient
from nv_ingest_client.message_clients.simple.simple_client import SimpleClient
from nv_ingest.util.pipeline.pipeline_runners import PipelineCreationSchema
from nv_ingest_client.util.process_json_files import ingest_json_results_to_blob
# Start the pipeline subprocess for library mode
config = PipelineCreationSchema()
pipeline_process = start_pipeline_subprocess(config)
# you can configure the subprocesses to log stderr to stdout for debugging purposes
#pipeline_process = start_pipeline_subprocess(config, stderr=sys.stderr, stdout=sys.stdout)
client = NvIngestClient(
message_client_allocator=SimpleClient,
message_client_port=7671,
message_client_hostname="localhost"
)
# gpu_cagra accelerated indexing is not available in milvus-lite
# Provide a filename for milvus_uri to use milvus-lite
milvus_uri = "milvus.db"
collection_name = "test"
sparse=False
# do content extraction from files
ingestor = (
Ingestor(client=client)
.files("data/multimodal_test.pdf")
.extract(
extract_text=True,
extract_tables=True,
extract_charts=True,
extract_images=True,
paddle_output_format="markdown",
extract_infographics=True,
#extract_method="nemoretriever_parse", #Slower, but maximally accurate, especially for PDFs with pages that are scanned images
text_depth="page"
).embed()
.vdb_upload(
collection_name=collection_name,
milvus_uri=milvus_uri,
sparse=sparse,
# for llama-3.2 embedder, use 1024 for e5-v5
dense_dim=2048
)
)
print("Starting ingestion..")
t0 = time.time()
results = ingestor.ingest(show_progress=True)
t1 = time.time()
print(f"Time taken: {t1-t0} seconds")
# results blob is directly inspectable
print(ingest_json_results_to_blob(results[0]))You can see the extracted text that represents the content of the ingested test document.
Starting ingestion..
Time taken: 9.243880033493042 seconds
TestingDocument
A sample document with headings and placeholder text
Introduction
This is a placeholder document that can be used for any purpose. It contains some
headings and some placeholder text to fill the space. The text is not important and contains
no real value, but it is useful for testing. Below, we will have some simple tables and charts
that we can use to confirm Ingest is working as expected.
Table 1
This table describes some animals, and some activities they might be doing in specific
locations.
Animal Activity Place
Gira@e Driving a car At the beach
Lion Putting on sunscreen At the park
Cat Jumping onto a laptop In a home o@ice
Dog Chasing a squirrel In the front yard
Chart 1
This chart shows some gadgets, and some very fictitious costs.
... document extract continues ...To query for relevant snippets of the ingested content, and use them with an LLM to generate answers, use the following code.
from openai import OpenAI
from nv_ingest_client.util.milvus import nvingest_retrieval
import os
milvus_uri = "milvus.db"
collection_name = "test"
sparse=False
queries = ["Which animal is responsible for the typos?"]
retrieved_docs = nvingest_retrieval(
queries,
collection_name,
milvus_uri=milvus_uri,
hybrid=sparse,
top_k=1,
)
# simple generation example
extract = retrieved_docs[0][0]["entity"]["text"]
client = OpenAI(
base_url = "https://integrate.api.nvidia.com/v1",
api_key = os.environ["NVIDIA_BUILD_API_KEY"]
)
prompt = f"Using the following content: {extract}\n\n Answer the user query: {queries[0]}"
print(f"Prompt: {prompt}")
completion = client.chat.completions.create(
model="nvidia/llama-3.1-nemotron-70b-instruct",
messages=[{"role":"user","content": prompt}],
)
response = completion.choices[0].message.content
print(f"Answer: {response}")Prompt: Using the following content: TestingDocument
A sample document with headings and placeholder text
Introduction
This is a placeholder document that can be used for any purpose. It contains some
headings and some placeholder text to fill the space. The text is not important and contains
no real value, but it is useful for testing. Below, we will have some simple tables and charts
that we can use to confirm Ingest is working as expected.
Table 1
This table describes some animals, and some activities they might be doing in specific
locations.
Animal Activity Place
Gira@e Driving a car At the beach
Lion Putting on sunscreen At the park
Cat Jumping onto a laptop In a home o@ice
Dog Chasing a squirrel In the front yard
Chart 1
This chart shows some gadgets, and some very fictitious costs.
Answer the user query: Which animal is responsible for the typos?
Answer: A clever query!
After carefully examining the provided content, I'd like to point out the potential "typos" (assuming you're referring to the unusual or intentionally incorrect text) and attempt to playfully "assign blame" to an animal based on the context:
1. **Gira@e** (instead of Giraffe) - **Animal blamed: Giraffe** (Table 1, first row)
* The "@" symbol in "Gira@e" suggests a possible typo or placeholder character, which we'll humorously attribute to the Giraffe's alleged carelessness.
2. **o@ice** (instead of Office) - **Animal blamed: Cat**
* The same "@" symbol appears in "o@ice", which is related to the Cat's activity in the same table. Perhaps the Cat was in a hurry while typing and introduced the error?
So, according to this whimsical analysis, both the **Giraffe** and the **Cat** are "responsible" for the typos, with the Giraffe possibly being the more egregious offender given the more blatant character substitution in its name.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.
The following is a description of the folders in the nv-ingest repository.
- .devcontainer — VSCode containers for local development
- .github — GitHub repo configuration files
- api — Core API logic shared across python modules
- ci — Scripts used to build the nv-ingest container and other packages
- client — Readme, examples, and source code for the nv-ingest-cli utility
- conda — Conda environment and packaging definitions
- config — Various .yaml files defining configuration for OTEL, Prometheus
- data — Sample PDFs for testing
- deploy — Brev.dev-hosted launchable
- docker — Scripts used by the nv-ingest docker container
- docs — Documentation for NV Ingest
- evaluation — Notebooks that demonstrate how to test recall accuracy
- examples — Notebooks, scripts, and 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
DOWNLOAD_LLAMA_TOKENIZER(Built With Llama)::- Description: The Split task uses the
meta-llama/Llama-3.2-1Btokenizer, which will be downloaded from HuggingFace at build time ifDOWNLOAD_LLAMA_TOKENIZERis set toTrue. Please review the license agreement for Llama 3.2 materials before using this. This is a gated model so you'll need to request access and setHF_ACCESS_TOKENto your HuggingFace access token in order to use it.
- Description: The Split task uses the
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