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.
Installation
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)