NvidiaRanker

A component for ranking documents using ranking models provided by

Basic Information

• Type: haystack_integrations.nvidia.src.haystack_integrations.components.rankers.nvidia.ranker.NvidiaRanker

Inputs

Parameter	Type	Default	Description
query	str		The query to rank the documents against.
documents	List[Document]		The list of documents to rank.
top_k	Optional[int]	None	The number of documents to return.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A dictionary containing the ranked documents.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

A component for ranking documents using ranking models provided by NVIDIA NIMs.

Usage example:

from haystack_integrations.components.rankers.nvidia import NvidiaRanker
from haystack import Document
from haystack.utils import Secret

ranker = NvidiaRanker(
    model="nvidia/nv-rerankqa-mistral-4b-v3",
    api_key=Secret.from_env_var("NVIDIA_API_KEY"),
)
ranker.warm_up()

query = "What is the capital of Germany?"
documents = [
    Document(content="Berlin is the capital of Germany."),
    Document(content="The capital of Germany is Berlin."),
    Document(content="Germany's capital is Berlin."),
]

result = ranker.run(query, documents, top_k=2)
print(result["documents"])

Usage Example

components:
  NvidiaRanker:
    type: nvidia.src.haystack_integrations.components.rankers.nvidia.ranker.NvidiaRanker
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	Optional[str]	None	Ranking model to use.
truncate	Optional[Union[RankerTruncateMode, str]]	None	Truncation strategy to use. Can be "NONE", "END", or RankerTruncateMode. Defaults to NIM's default.
api_key	Optional[Secret]	Secret.from_env_var('NVIDIA_API_KEY')	API key for the NVIDIA NIM.
api_url	str	os.getenv('NVIDIA_API_URL', DEFAULT_API_URL)	Custom API URL for the NVIDIA NIM.
top_k	int	5	Number of documents to return.
query_prefix	str		A string to add at the beginning of the query text before ranking. Use it to prepend the text with an instruction, as required by reranking models like bge.
document_prefix	str		A string to add at the beginning of each document before ranking. You can use it to prepend the document with an instruction, as required by embedding models like bge.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed with the document.
embedding_separator	str	\n	Separator to concatenate metadata fields to the document.
timeout	Optional[float]	None	Timeout for request calls, if not set it is inferred from the NVIDIA_TIMEOUT environment variable or set to 60 by default.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
query	str		The query to rank the documents against.
documents	List[Document]		The list of documents to rank.
top_k	Optional[int]	None	The number of documents to return.