Rerankers

CohereReranker

<a id="coherereranker-api"></a>

class CohereReranker(model='rerank-english-v3.0', rank_by=None, limit=5, return_score=True, api_config=None)

Bases: BaseReranker

The CohereReranker class uses Cohere’s API to rerank documents based on an input query.

This reranker is designed to interact with Cohere’s /rerank API, requiring an API key for authentication. The key can be provided directly in the api_config dictionary or through the COHERE_API_KEY environment variable. User must obtain an API key from Cohere’s website (https://dashboard.cohere.com/). Additionally, the cohere python client must be installed with pip install cohere.

from redisvl.utils.rerank import CohereReranker

# set up the Cohere reranker with some configuration
reranker = CohereReranker(rank_by=["content"], limit=2)
# rerank raw search results based on user input/query
results = reranker.rank(
    query="your input query text here",
    docs=[
        {"content": "document 1"},
        {"content": "document 2"},
        {"content": "document 3"}
    ]
)

Initialize the CohereReranker with specified model, ranking criteria, and API configuration.

• Parameters:
  • model (str) – The identifier for the Cohere model used for reranking. Defaults to ‘rerank-english-v3.0’.
  • rank_by (Optional [ List [ str ] ]) – Optional list of keys specifying the attributes in the documents that should be considered for ranking. None means ranking will rely on the model’s default behavior.
  • limit (int) – The maximum number of results to return after reranking. Must be a positive integer.
  • return_score (bool) – Whether to return scores alongside the reranked results.
  • api_config (Optional [ Dict ] , optional) – Dictionary containing the API key. Defaults to None.
• Raises:
  • ImportError – If the cohere library is not installed.
  • ValueError – If the API key is not provided.

async arank(query, docs, **kwargs)

Rerank documents based on the provided query using the Cohere rerank API.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

• Parameters:
  • self (BaseModel) – The BaseModel instance.
  • context (Any) – The context.
• Return type: None

rank(query, docs, **kwargs)

Rerank documents based on the provided query using the Cohere rerank API.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

HFCrossEncoderReranker

<a id="hfcrossencoderreranker-api"></a>

class HFCrossEncoderReranker(model='cross-encoder/ms-marco-MiniLM-L-6-v2', limit=3, return_score=True, *, rank_by=None)

Bases: BaseReranker

The HFCrossEncoderReranker class uses a cross-encoder models from Hugging Face to rerank documents based on an input query.

This reranker loads a cross-encoder model using the CrossEncoder class from the sentence_transformers library. It requires the sentence_transformers library to be installed.

from redisvl.utils.rerank import HFCrossEncoderReranker

# set up the HFCrossEncoderReranker with a specific model
reranker = HFCrossEncoderReranker(model_name="cross-encoder/ms-marco-MiniLM-L-6-v2", limit=3)
# rerank raw search results based on user input/query
results = reranker.rank(
    query="your input query text here",
    docs=[
        {"content": "document 1"},
        {"content": "document 2"},
        {"content": "document 3"}
    ]
)

Initialize the HFCrossEncoderReranker with a specified model and ranking criteria.

• Parameters:
  • model (str) – The name or path of the cross-encoder model to use for reranking. Defaults to ‘cross-encoder/ms-marco-MiniLM-L-6-v2’.
  • limit (int) – The maximum number of results to return after reranking. Must be a positive integer.
  • return_score (bool) – Whether to return scores alongside the reranked results.
  • rank_by (List [ str ] | None)

async arank(query, docs, **kwargs)

Asynchronously rerank documents based on the provided query using the loaded cross-encoder model.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

• Parameters:
  • self (BaseModel) – The BaseModel instance.
  • context (Any) – The context.
• Return type: None

rank(query, docs, **kwargs)

Rerank documents based on the provided query using the loaded cross-encoder model.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

VoyageAIReranker

<a id="voyageaireranker-api"></a>

class VoyageAIReranker(model, rank_by=None, limit=5, return_score=True, api_config=None)

Bases: BaseReranker

The VoyageAIReranker class uses VoyageAI’s API to rerank documents based on an input query.

This reranker is designed to interact with VoyageAI’s /rerank API, requiring an API key for authentication. The key can be provided directly in the api_config dictionary or through the VOYAGE_API_KEY environment variable. User must obtain an API key from VoyageAI’s website (https://dash.voyageai.com/). Additionally, the voyageai python client must be installed with pip install voyageai.

from redisvl.utils.rerank import VoyageAIReranker

# set up the VoyageAI reranker with some configuration
reranker = VoyageAIReranker(rank_by=["content"], limit=2)
# rerank raw search results based on user input/query
results = reranker.rank(
    query="your input query text here",
    docs=[
        {"content": "document 1"},
        {"content": "document 2"},
        {"content": "document 3"}
    ]
)

Initialize the VoyageAIReranker with specified model, ranking criteria, and API configuration.

• Parameters:
  • model (str) – The identifier for the VoyageAI model used for reranking.
  • rank_by (Optional [ List [ str ] ]) – Optional list of keys specifying the attributes in the documents that should be considered for ranking. None means ranking will rely on the model’s default behavior.
  • limit (int) – The maximum number of results to return after reranking. Must be a positive integer.
  • return_score (bool) – Whether to return scores alongside the reranked results.
  • api_config (Optional [ Dict ] , optional) – Dictionary containing the API key. Defaults to None.
• Raises:
  • ImportError – If the voyageai library is not installed.
  • ValueError – If the API key is not provided.

async arank(query, docs, **kwargs)

Rerank documents based on the provided query using the VoyageAI rerank API.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

• Parameters:
  • self (BaseModel) – The BaseModel instance.
  • context (Any) – The context.
• Return type: None

rank(query, docs, **kwargs)

Rerank documents based on the provided query using the VoyageAI rerank API.

This method processes the user’s query and the provided documents to rerank them in a manner that is potentially more relevant to the query’s context.

• Parameters:
  • query (str) – The user’s search query.
  • docs (Union [ List [ Dict [ str , Any ] ] , List [ str ] ]) – The list of documents to be ranked, either as dictionaries or strings.
• Returns: The reranked list of documents and optionally associated scores.
• Return type: Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].