
Name	NvidiaDocumentEmbedder
Folder Path	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia
Most common Position in a Pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory Input variables	“documents”: A list of documents
Output variables	“documents”: A list of documents (enriched with embeddings) ”meta”: A dictionary of metadata

Overview

NvidiaDocumentEmbedder enriches the metadata of documents with an embedding of their content.

It can be used with self-hosted models with NVIDIA NIM or models hosted on the NVIDIA API catalog.

To embed a string, use the NvidiaTextEmbedder.

Usage

To start using NvidiaDocumentEmbedder, first, install the nvidia-haystack package:

pip install nvidia-haystack

You can use the NvidiaDocumentEmbedder with all the embedder models available on the NVIDIA API catalog or using a model deployed with NVIDIA NIM. Follow the Deploying Text Embedding Models guide to learn how to deploy the model you want on your infrastructure.

On its own

To use LLMs from the NVIDIA API catalog, you need to specify the correct api_url and your API key. You can get your API key directly from the catalog website.

The NvidiaDocumentEmbedder needs an Nvidia API key to work. It uses the NVIDIA_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key, as in the following example.

from haystack.utils.auth import Secret
from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder

embedder = NvidiaDocumentEmbedder(
    model="NV-Embed-QA",
    api_url="https://ai.api.nvidia.com/v1/retrieval/nvidia",
    api_key=Secret.from_token("<your-api-key>"),
)
embedder.warm_up()

result = embedder.run("A transformer is a deep learning architecture")
print(result["embedding"])
print(result["meta"])

To use a locally deployed model, you need to set the api_url to your localhost and unset your api_key.

from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder

embedder = NvidiaDocumentEmbedder(
    model="NV-Embed-QA",
    api_url="http://0.0.0.0:9999/v1",
    api_key=None,
)
embedder.warm_up()

result = embedder.run("A transformer is a deep learning architecture")
print(result["embedding"])
print(result["meta"])

In a Pipeline

Here's an example of a RAG pipeline:

from haystack import Pipeline, Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.nvidia import NvidiaTextEmbedder, NvidiaDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", NvidiaDocumentEmbedder(
    model="NV-Embed-QA",
    api_url="https://ai.api.nvidia.com/v1/retrieval/nvidia",
    api_key=Secret.from_token("<your-api-key>"),
))
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", NvidiaTextEmbedder(
    model="NV-Embed-QA",
    api_url="https://ai.api.nvidia.com/v1/retrieval/nvidia",
    api_key=Secret.from_token("<your-api-key>"),
))
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])