NVIDIA Embeddings

NVIDIA Embeddings turns text into a numeric fingerprint of its meaning using the models of NVIDIA’s AI service. With that fingerprint, NappAI can find the documents that say something similar to what you are looking for, even when they use different words, for example in a knowledge base.

You connect the component to a vector database, which uses it both to store your documents and to search them. Use it if your company works with NVIDIA.

When to use it

• Give an agent the knowledge in your manuals, contracts or FAQs by storing them in a vector database.
• Build an internal search that finds documents by their meaning, not only by exact words.
• Your company already has an account with NVIDIA’s AI service.

Configuration

Field	What it does
Credential (required)	The credential of the service NVIDIA API. Pick one with Choose an option or create one with Add new credential. See Credentials.
Model	The NVIDIA model that calculates the fingerprints: nvidia/nv-embed-v1 or snowflake/arctic-embed-I. Default: nvidia/nv-embed-v1.
NVIDIA Base URL	The address of the NVIDIA service. By default, NVIDIA’s public address; you do not need to change it.

Advanced options

Field	What it does
Model Temperature	A setting inherited from models that write text. Default: 0.1. You do not need to change it.

Output

• Embeddings: the model that calculates the fingerprints, ready to use. Connect it to the Embedding input of a vector database: Astra DB, Cassandra, Chroma DB, Couchbase, Elasticsearch, FAISS, Milvus, MongoDB Vector Store, PGVector, Pinecone, Qdrant, Redis, Supabase Vector Store, Upstash, Vectara, Weaviate Cloud (v4). Other components with an input of this type accept it too, such as Semantic Text Splitter or Text Embedder.

Use the same model to store and to search

Fingerprints from two different models cannot be compared with each other. Use the same component and the same model to store the documents and to search them. If you change the model, store the documents in the database again.

Example

A machinery manufacturer, Talleres Alcor, wants its assistant to answer using its maintenance manuals.

1. In a loading flow, split the documents into chunks with Recursive Character Text Splitter and pass them to Chroma DB with Operation set to Add.
2. Add NVIDIA Embeddings and connect its Embeddings output to the Embedding input of Chroma DB.
3. In the assistant flow, use the same Chroma DB collection with the same NVIDIA Embeddings component to find the chunks that answer each question.

Related components

• Chroma DB
• PGVector
• Text Embedder
• Embedding Similarity