Models

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Rerankers

Rerankers are cross-encoder neural networks that can boost the accuracy of any search system, you can read more about what rerankers are and when they are most useful in this blog post.

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zerank-1 and zerank-1-small

zerank-1 and zerank-1-small are reranker models developed by ZeroEntropy. zerank-1 is our flagship state-of-the-art reranker, you can read more about its performance and cost in this blog post. Both these models can be called using:

• Using the models/rerank API endpoint, which is callable via the Python and Node SDKs.
• By passing in the reranker query parameter into top-snippets
• Downloading from our HuggingFace and self-hosting the models.

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Using the ZeroEntropy SDK

# Create an API Key at https://dashboard.zeroentropy.dev
# pip install zeroentropy
from zeroentropy import ZeroEntropy

# Initialize the ZeroEntropy client (reads ZEROENTROPY_API_KEY from env)
zclient = ZeroEntropy()

response = zclient.models.rerank(
    model="zerank-1",
    query="What is 2+2?",
    documents=[
        "4",
        "The answer is definitely 1 million.",
    ],
)
print(response.model_dump_json(indent=4))

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Using top-snippets

When querying for /top-snippets from a ZeroEntropy collection, you can easily apply the reranker and get a significantly better ranking. Scores from a reranker are deterministic and more readily interpretable, which is another benefit over just hybrid search.

from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()

# Assuming you have already added documents to the collection "pdfs"
response = zclient.queries.top_snippets(
    collection_name="pdfs",
    query="What is Retrieval Augemented Generation?",
    k=10,
    reranker="zerank-1", # All K results will be reranked using our reranker.
)

print(response.results)