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.
from pinecone import Pineconepc = 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)
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