TransformersSimilarityRanker
Use this component to rank Documents based on their similarity to the query. The TransformersSimilarityRanker
is a powerful, model-based Ranker that uses a cross-encoder model to produce Document and query embeddings.
Name | TransformersSimilarityRanker |
Folder Path | /rankers/ |
Position in a Pipeline | In a query Pipeline, after a component that returns a list of Documents such as a Retriever. |
Inputs | “documents”: List of Document objects ”query”: A query string |
Outputs | “documents”: List of Document objects |
Overview
TransformersSimilarityRanker
ranks Documents based on how similar they are to the query. It uses a pre-trained cross-encoder model from the Hugging Face Hub to embed both the query and the Documents. It then compares the embeddings to determine how similar they are. The result is a list of Document objects in ranked order, with the Documents most similar to the query appearing first.
TransformersSimilarityRanker
is most useful in query pipelines, such as a retrieval-augmented generation (RAG) pipeline or a document search pipeline, to ensure the retrieved Documents are ordered by relevance. You can use it after a Retriever (such as the InMemoryEmbeddingRetriever
) to improve the search results. When using TransformersSimilarityRanker
with a Retriever, consider setting the Retriever's top_k
to a small number. This way the Ranker will have fewer Documents to process which can help make your pipeline faster.
By default, this component uses the cross-encoder/ms-marco-MiniLM-L-6-v2
model, but it's flexible. You can switch to a different model by adjusting the model
parameter when initializing the Ranker. For details on different initialization settings, check out the API reference for this component.
Authorization
The component uses a HF_API_TOKEN
environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with token
– see code examples below.
ranker = TransformersSimilarityRanker(token=Secret.from_token("<your-api-key>"))
Usage
On its own
You can use TransformersSimilarityRanker
outside of a pipeline to order Documents based on your query.
This example uses the TransformersSimilarityRanker
to rank two simple Documents. To run the Ranker, pass a query, provide the Documents, and set the number of Documents to return in the top_k
parameter.
from haystack import Document
from haystack.components.rankers import TransformersSimilarityRanker
docs = [Document(content="Paris"), Document(content="Berlin")]
ranker = TransformersSimilarityRanker()
ranker.warm_up()
ranker.run(query="City in France", documents=docs, top_k=1)
In a pipeline
TransformersSimilarityRanker
is most efficient in query pipelines when used after a Retriever.
Below is an example of a pipeline that retrieves Documents from an InMemoryDocumentStore
based on keyword search (using InMemoryBM25Retriever
). It then uses the TransformersSimilarityRanker
to rank the retrieved Documents according to their similarity to the query. The pipeline uses the default settings of the Ranker.
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.rankers import TransformersSimilarityRanker
docs = [Document(content="Paris is in France"),
Document(content="Berlin is in Germany"),
Document(content="Lyon is in France")]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)
retriever = InMemoryBM25Retriever(document_store = document_store)
ranker = TransformersSimilarityRanker()
ranker.warm_up()
document_ranker_pipeline = Pipeline()
document_ranker_pipeline.add_component(instance=retriever, name="retriever")
document_ranker_pipeline.add_component(instance=ranker, name="ranker")
document_ranker_pipeline.connect("retriever.documents", "ranker.documents")
query = "Cities in France"
document_ranker_pipeline.run(data={"retriever": {"query": query, "top_k": 3},
"ranker": {"query": query, "top_k": 2}})
Ranker
top_k
In the example above, the
top_k
values for the Retriever and the Ranker are different. The Retriever'stop_k
specifies how many Documents it returns. The Ranker then orders these Documents.You can set the same or a smaller
top_k
value for the Ranker. The Ranker'stop_k
is the number of Documents it returns (if it's the last component in the pipeline) or forwards to the next component. In the pipeline example above, the Ranker is the last component, so the output you get when you run the pipeline are the top two Documents, as per the Ranker'stop_k
.Adjusting the
top_k
values can help you optimize performance. In this case, a smallertop_k
value of the Retriever means fewer Documents to process for the Ranker, which can speed up the pipeline.
Updated 7 months ago