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POST
/
rerank
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

result = pc.inference.rerank(
    model="bge-reranker-v2-m3",
    query="The tech company Apple is known for its innovative products like the iPhone.",
    documents=[
        {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
        {"id": "vec2", "text": "Many people enjoy eating apples as a healthy snack."},
        {"id": "vec3", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {"id": "vec4", "text": "An apple a day keeps the doctor away, as the saying goes."},
    ],
    top_n=4,
    return_documents=True,
    parameters={
        "truncate": "END"
    }
)

print(result)

RerankResult(
  model='bge-reranker-v2-m3',
  data=[
    {"index": 2, "score": 0.48357219,
      "document": {"id": "vec3", "text": "Apple Inc. has re..."}},
    {"index": 0, "score": 0.048405956,
      "document": {"id": "vec1", "text": "Apple is a popula..."}},
    {"index": 3, "score": 0.007846239,
      "document": {"id": "vec4", "text": "An apple a day ke..."}},
    {"index": 1, "score": 0.0006563728,
      "document": {"id": "vec2", "text": "Many people enjoy..."}}
  ],
  usage={'rerank_units': 1}
)

from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

result = pc.inference.rerank(
    model="bge-reranker-v2-m3",
    query="The tech company Apple is known for its innovative products like the iPhone.",
    documents=[
        {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
        {"id": "vec2", "text": "Many people enjoy eating apples as a healthy snack."},
        {"id": "vec3", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {"id": "vec4", "text": "An apple a day keeps the doctor away, as the saying goes."},
    ],
    top_n=4,
    return_documents=True,
    parameters={
        "truncate": "END"
    }
)

print(result)

RerankResult(
  model='bge-reranker-v2-m3',
  data=[
    {"index": 2, "score": 0.48357219,
      "document": {"id": "vec3", "text": "Apple Inc. has re..."}},
    {"index": 0, "score": 0.048405956,
      "document": {"id": "vec1", "text": "Apple is a popula..."}},
    {"index": 3, "score": 0.007846239,
      "document": {"id": "vec4", "text": "An apple a day ke..."}},
    {"index": 1, "score": 0.0006563728,
      "document": {"id": "vec2", "text": "Many people enjoy..."}}
  ],
  usage={'rerank_units': 1}
)

Authorizations

​
Api-Key
string
header
required

An API Key is required to call Pinecone APIs. Get yours from the console.

Body

application/json

Rerank documents for the given query

​
model
string
required

The model to use for reranking.

Example:

"bge-reranker-v2-m3"

​
query
string
required

The query to rerank documents against.

Example:

"What is the capital of France?"

​
documents
object[]
required

The documents to rerank.

​
top_n
integer

The number of results to return sorted by relevance. Defaults to the number of inputs.

Example:

5

​
return_documents
boolean
default:true

Whether to return the documents in the response.

Example:

true

​
rank_fields
string[]

The field(s) to consider for reranking. If not provided, the default is ["text"].

The number of fields supported is model-specific.

​
parameters
object

Additional model-specific parameters for the reranker.

Response

OK

The result of a reranking request.

​
model
string
required

The model used to rerank documents.

Example:

"bge-reranker-v2-m3"

​
data
object[]
required

The reranked documents.

​
usage
object
required

Usage statistics for the model inference.