
Name	NvidiaTextEmbedder
Source	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory input variables	“text”: A string
Output variables	“embedding”: A list of float numbers (vectors) “meta”: A dictionary of metadata strings

Overview

NvidiaTextEmbedder embeds a simple string (such as a query) into a vector.

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

To embed a list of documents, use the NvidiaDocumentEmbedder, which enriches the document with the computed embedding, also known as vector.

Usage

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

pip install nvidia-haystack

You can use the NvidiaTextEmbedder 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 NvidiaTextEmbedder 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 NvidiaTextEmbedder

embedder = NvidiaTextEmbedder(
    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 NvidiaTextEmbedder

embedder = NvidiaTextEmbedder(
    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])