Reranking Sentences for Improved Semantic Search

When working with search systems, getting the most relevant results is crucial — yet traditional vector search methods don’t always guarantee this. That’s where reranking comes into play, and with Langformers, implementing reranking has never been easier.

In this article, we’ll discuss:

• What reranking is?
• Why it’s needed alongside vector search?
• How Langformers makes reranking simple and effective?
• A step-by-step example to get you started

What is Reranking?

Reranking is the process of reordering a list of retrieved documents, sentences, or texts based on their relevance to a given query. Rather than relying solely on the initial retrieval method (like vector search), reranking applies an additional layer of intelligence, ensuring that the most useful results appear first.

In short:

Vector search finds documents, but reranking orders them for maximum relevance.

Why Do You Need Reranking?

Let’s look at a simple example.

Suppose a user asks:

"Where is Mount Everest?"

And your database contains:

1. "Mount Everest is the highest mountain in the world."
2. "Mount Everest is in Nepal."
3. "Where is Mount Everest?"

A basic vector search might consider the third document the best match because it closely resembles the query’s wording — that's what embedding-based similarity measures (like cosine similarity) pick up on.

But there’s a problem:
The third document merely repeats the question without answering it.

The right answer?
Clearly, "Mount Everest is in Nepal." — which tells the user what they want to know.

This is where reranking steps in — refining the initial results to prioritize the most relevant, informative answers.

Vector Search + Reranking = Smarter Search

In a modern retrieval system:

• Vector search brings back potentially relevant documents quickly based on semantic similarity.
• Reranking then re-evaluates those documents against the query using more sophisticated models (like cross-encoders) to surface the truly best matches.

Result?
More accurate, helpful search experiences for your users!

Reranking with Langformers

First, make sure you have Langformers installed in your environment. If not, install it using pip:

pip install -U langformers

Create a Reranker

from langformers import tasks

reranker = tasks.create_reranker(
    model_type="cross_encoder",
    model_name="cross-encoder/ms-marco-MiniLM-L-6-v2"
)

• model_type specifies the reranker model type. Currently, cross_encoder is supported.
• model_name is a Hugging Face model optimized for reranking tasks.

Define Your Query and Documents

query = "Where is the Mount Everest?"

documents = [
    "Mount Everest is the highest mountain in the world.",
    "Mount Everest is in Nepal.",
    "Where is the Mount Everest?"
]

Rank the Documents

reranked_docs = reranker.rank(query=query, documents=documents)
print(reranked_docs)

After reranking, the documents are reordered — with the most relevant appearing first.

Many others cross encoders are available on Hugging Face. If you want detailed information on cross encoders, refer to this page by Sentence Transformers.

View official documentation here: https://langformers.com/reranker.html