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Documentation Index

Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt

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

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Overview

The model is optimized for precision in RAG reranking tasks It assigns a relevance score from 0 to 1 for each query-document pair, with higher scores indicating a stronger match. To maintain accuracy, we’ve set the model’s maximum context length to 512 tokens — an optimal limit for preserving ranking quality in reranking tasks.

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Installation

pip install -U pinecone

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Rerank

from pinecone import Pinecone

pc = Pinecone("API-KEY")

query = "Tell me about Apple's products"
results = pc.inference.rerank(
    model="pinecone-rerank-v0",
    query=query,
    documents=[
"Apple is a popular fruit known for its sweetness and crisp texture.",	
"Apple is known for its innovative products like the iPhone.",
"Many people enjoy eating apples as a healthy snack.",
"Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.",
"An apple a day keeps the doctor away, as the saying goes.",
    ],
    top_n=3,
    return_documents=True,
    parameters= {
        "truncate": "END"
    }
)

print(query)
for r in results.data:
  print(r.score, r.document.text)
Lorem Ipsum