You can use self-hosted models from the NVIDIA API catalog or models deployed on NVIDIA NIM.

Prerequisites

You need an active NVIDIA API key. For details on how to obtain it, see NVIDIA documentation.

Use NVIDIA Models

First, connect deepset Cloud to NVIDIA through the Connections page:

Click your initials in the top right corner and select Connections.
Click Connect next to the provider.
Enter your user access token and submit it.

Then, add a component that uses a model hosted on NVIDIA to your pipeline. Here are the components by the model type they use:

Embedding models:
- NvidiaTextEmbedder: Uses an embedding model to calculate vector representations of text. Often used in query pipelines to embed the query string and send it to an embedding retriever.
- NvidiaDocumentEmbedder: Uses an embedding model to calculate embeddings of documents. Often used in indexing pipelines to embed documents and send them to DocumentWriter.

ℹ️
Embedding Models in Query and Indexing Pipelines
The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.
This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

LLMs:
- NvidiaGenerator: Generates text using models from NVIDIA. Often used in RAG pipelines.

Usage Examples

This is a YAML example of how to use embedding models and an LLM hosted on NVIDIA in indexing and query pipelines (each in a separate tab):

components:
...
  
  query_embedder:
      type: haystack_integrations.components.embedders.nvidia.text_embedder.NvidiaTextEmbedder
      init_parameters: 
        api_url: "https://ai.api.nvidia.com/v1/retrieval/nvidia" # custom API URL for NVIDIA NIM.
        model: "NV-Embed-QA" # the model to use
        
    retriever:
      type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
      init_parameters: 
        document_store:
          init_parameters:
            use_ssl: True
            verify_certs: False
            http_auth:
              - "${OPENSEARCH_USER}"
              - "${OPENSEARCH_PASSWORD}"
          type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        top_k: 20 
  prompt_builder:
      type: haystack.components.builders.prompt_builder.PromptBuilder
      init_parameters:
        template: |-
          You are a technical expert.
          You answer questions truthfully based on provided documents.
          For each document check whether it is related to the question.
          Only use documents that are related to the question to answer it.
          Ignore documents that are not related to the question.
          If the answer exists in several documents, summarize them.
          Only answer based on the documents provided. Don't make things up.
          If the documents can't answer the question or you are unsure say: 'The answer can't be found in the text'.
          These are the documents:
          {% for document in documents %}
          Document[{{ loop.index }}]:
          {{ document.content }}
          {% endfor %}
          Question: {{question}}
          Answer:

    generator:
      type: haystack_integrations.components.generators.nvidia.generator.NvidiaGenerator
      init_parameters:
          model: "meta/llama3-70b-instruct" # here, pass the name of the model to use
          api_url: "https://integrate.api.nvidia.com/v1"
          model_arguments:
            temperature: 0.2
            top_p: 0.7
            max_tokens: 1024
                 
    answer_builder:
      init_parameters: {}
      type: haystack.components.builders.answer_builder.AnswerBuilder
      ...

connections:
...
 - sender: query_embedder.embedding
   receiver: retriever.query_embedding
 - sender: retriever.documents
   receiver: prompt_builder.documents
 - sender: prompt_builder.prompt
   receiver: generator.prompt
 - sender: generator.replies
   receiver: answer_builder.replies
   ...

components:
  ...
     splitter:
      type: haystack.components.preprocessors.document_splitter.DocumentSplitter
      init_parameters:
        split_by: word
        split_length: 250
        split_overlap: 30

    document_embedder:
      type: haystack_integrations.components.embedders.nvidia.document_embedder.NvidiaDocumentEmbedder
      init_parameters:
        api_url: "https://ai.api.nvidia.com/v1/retrieval/nvidia" # A required custom NVIDIA API URL for NVIDIA NIM
        model: "NV-Embed-QA" # the model to use
    
    writer:
      type: haystack.components.writers.document_writer.DocumentWriter
      init_parameters:
        document_store:
          type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
          init_parameters:
            embedding_dim: 768
            similarity: cosine
        policy: OVERWRITE
        
connections:  # Defines how the components are connected
  ...
  - sender: splitter.documents
    receiver: document_embedder.documents
  - sender: document_embedder.documents
    receiver: writer.documents

This is how to connect the components of the query pipeline in Pipeline Builder:

Then the generator:

In indexing pipeline, the Document Embedder should use the same model as the Text Embedder in the query pipeline:

Prerequisites

Use NVIDIA Models

ℹ️Embedding Models in Query and Indexing Pipelines

Usage Examples

ℹ️
Embedding Models in Query and Indexing Pipelines