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Quickstart Guide for NeMo Retriever Extraction (Library Mode)

For small-scale workloads, such as workloads of fewer than 100 documents, you can use library mode setup. Library mode depends on NIMs that are already self-hosted, or, by default, NIMs that are hosted on build.nvidia.com.

To get started using NeMo Retriever extraction in library mode, you need the following:

Step 1: Prepare Your Environment

Use the following procedure to prepare your environment.

  1. Run the following code to create your nvingest conda environment.

    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==2.5.4' 'pymilvus[bulk_writer, model]' milvus-lite nvidia-riva-client unstructured-client

    Tip

    Make sure your conda environment is activated. which python should indicate that you're using the conda provided python installation (not an OS provided python).

Make sure to set your NVIDIA_BUILD_API_KEY and NVIDIA_API_KEY. If you don't have one, you can get one on build.nvidia.com. 

#Note: these should be the same value
export NVIDIA_BUILD_API_KEY=nvapi-...
export NVIDIA_API_KEY=nvapi-...

Step 2: Ingest Documents

You can submit jobs programmatically by using Python.

Tip

For more Python examples, refer to NV-Ingest: Python Client Quick Start Guide.

If you have a very high number of CPUs and see the process hang without progress, we recommend using 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"
)

# Note: gpu_cagra accelerated indexing is not yet 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,
        # Slower, but maximally accurate, especially for PDFs with pages that are scanned images
        #extract_method="nemoretriever_parse",
        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]))

Note

To use library mode with nemoretriever_parse, uncomment extract_method="nemoretriever_parse" in the previous code. For more information, refer to Use Nemo Retriever Extraction with nemoretriever-parse.

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 ...

Step 3: Query Ingested Content

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.