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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.
We’ve open-sourced zerank-1-small under an Apache 2.0 license, and it is also available through HuggingFace and Baseten.Our flagship model zerank-1 can be downloaded from HuggingFace under a non-commercial license. To use in a commercial setting, contact us at founders@zeroentropy.dev and we’ll get you a license ASAP!

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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)

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Ratelimiting

Each API key is limited to 2,000,000 UTF-8 bytes per minute. A reranker request consumes bytes based on the number of documents and the total length of the input. The formula is: 
Total bytes = 150 
+ len(query.encode("utf-8")) 
+ len(document.encode("utf-8"))
This is calculated per document, so the query is counted once for each document you pass in. For example, if you send a request with 10 documents, the total usage is: 
10 × len(query.encode("utf-8"))
+ ∑ len(document_i.encode("utf-8")) for i in 1…10
If you exceed the 2,000,000 bytes/minute limit:
  • Your requests will still be served.
  • However, they will be throttled to a high-throughput but high-latency mode.
  • You may experience latency of several seconds per request.
  • In this degraded mode, throughput can go up to 20,000,000 bytes per minute, but with reduced responsiveness.
To avoid throttling, keep your per-minute usage below the 2MB soft limit.

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Pricing

Our pricing is simple and transparent. We charge $0.025/1M tokens. If you use the reranker as a standalone API, we will invoice you based on your usage.