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Documentation Index

Fetch the complete documentation index at: https://docs.zeroentropy.dev/llms.txt

Use this file to discover all available pages before exploring further.

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Rerank an existing query

You can also rerank results that you’ve already retrieved using the /models/rerank endpoint or directly through the SDKs.
This is useful when you already have a list of candidate documents (from BM25, hybrid, or vector search) and want to reorder them by semantic relevance. 
from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()

# Example: reranking 5 retrieved snippets
query = "What is Retrieval Augmented Generation?"
documents = [
    "RAG combines retrieval with generation by conditioning the LLM on external documents.",
    "Retrieval-Augmented Generation is a machine learning technique introduced by Meta AI in 2020.",
    "It uses reinforcement learning to generate music sequences.",
    "RAG can improve factual accuracy by grounding answers in retrieved evidence.",
    "Transformers are a type of deep learning architecture."
]

response = zclient.models.rerank(
    model="zerank-2",
    query=query,
    documents=documents,
)

# Each document will include a score and be sorted by relevance
for doc in response.results:
    print(doc)
The reranker will return a sorted list of documents with confidence scores indicating their semantic relevance to the query. You can read more about available reranker models in the Models section.