Rerank results ↗

Summary: Improve the quality of results with reranking.

Original Documentation

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.

Improve the quality of results with reranking.

Reranking is used as part of a two-stage vector retrieval process to improve the quality of results. You first query an index for a given number of relevant results, and then you send the query and results to a reranking model. The reranking model scores the results based on their semantic relevance to the query and returns a new, more accurate ranking. This approach is one of the simplest methods for improving quality in retrieval augmented generation (RAG) pipelines.

Pinecone provides hosted reranking models so it’s easy to manage two-stage vector retrieval on a single platform. You can use a hosted model to rerank results as an integrated part of a query, or you can use a hosted model or external model to rerank results as a standalone operation.

Integrated reranking#

To rerank initial results as an integrated part of a query, without any extra steps, use the search operation with the rerank parameter, including the hosted reranking model you want to use, the number of reranked results to return, and the fields to use for reranking, if different than the main query.

For example, the following code searches for the 3 records most semantically related to a query text and uses the hosted bge-reranker-v2-m3 model to rerank the results and return only the 2 most relevant documents:

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

ranked_results = index.search(
    namespace="example-namespace", 
    query={
        "inputs": {"text": "Disease prevention"}, 
        "top_k": 4
    },
    rerank={
        "model": "bge-reranker-v2-m3",
        "top_n": 2,
        "rank_fields": ["chunk_text"]
    },
    fields=["category", "chunk_text"]
)

print(ranked_results)

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: "YOUR_API_KEY" })

// To get the unique host for an index, 
// see https://docs.pinecone.io/guides/manage-data/target-an-index
const namespace = pc.index("INDEX_NAME", "INDEX_HOST").namespace("example-namespace");

const response = await namespace.searchRecords({
  query: {
    topK: 2,
    inputs: { text: 'Disease prevention' },
  },
  fields: ['chunk_text', 'category'],
  rerank: {
    model: 'bge-reranker-v2-m3',
    rankFields: ['chunk_text'],
    topN: 2,
  },
});

console.log(response);

import io.pinecone.clients.Index;
import io.pinecone.configs.PineconeConfig;
import io.pinecone.configs.PineconeConnection;
import org.openapitools.db_data.client.ApiException;
import org.openapitools.db_data.client.model.SearchRecordsRequestRerank;
import org.openapitools.db_data.client.model.SearchRecordsResponse;

import java.util.*;

public class SearchText {
    public static void main(String[] args) throws ApiException {
        PineconeConfig config = new PineconeConfig("YOUR_API_KEY");
        // To get the unique host for an index, 
        // see https://docs.pinecone.io/guides/manage-data/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);

        Index index = new Index(config, connection, "integrated-dense-java");

        String query = "Disease prevention";
        List<String> fields = new ArrayList<>();
        fields.add("category");
        fields.add("chunk_text");

        List<String>rankFields = new ArrayList<>();
        rankFields.add("chunk_text");
        SearchRecordsRequestRerank rerank = new SearchRecordsRequestRerank()
                .query(query)
                .model("bge-reranker-v2-m3")
                .topN(2)
                .rankFields(rankFields);

        SearchRecordsResponse recordsResponseReranked = index.searchRecordsByText(query,  "example-namespace", fields,4, null, rerank);

        System.out.println(recordsResponseReranked);
    }
}

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/pinecone"
)

func prettifyStruct(obj interface{}) string {
  	bytes, _ := json.MarshalIndent(obj, "", "  ")
    return string(bytes)
}

func main() {
    ctx := context.Background()

    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

    // To get the unique host for an index, 
    // see https://docs.pinecone.io/guides/manage-data/target-an-index
    idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "INDEX_HOST", Namespace: "example-namespace"})
    if err != nil {
        log.Fatalf("Failed to create IndexConnection for Host: %v", err)
    } 

    topN := int32(2)
    res, err := idxConnection.SearchRecords(ctx, &pinecone.SearchRecordsRequest{
        Query: pinecone.SearchRecordsQuery{
            TopK: 3,
            Inputs: &map[string]interface{}{
                "text": "Disease prevention",
            },
        },
        Rerank: &pinecone.SearchRecordsRerank{
            Model:      "bge-reranker-v2-m3",
            TopN:       &topN,
            RankFields: []string{"chunk_text"},
        },
        Fields: &[]string{"chunk_text", "category"},
    })
    if err != nil {
        log.Fatalf("Failed to search records: %v", err)
    }
    fmt.Printf(prettifyStruct(res))
}

using Pinecone;

var pinecone = new PineconeClient("YOUR_API_KEY");

// To get the unique host for an index, 
// see https://docs.pinecone.io/guides/manage-data/target-an-index
var index = pinecone.Index(host: "INDEX_HOST");

var response = await index.SearchRecordsAsync(
    "example-namespace",
    new SearchRecordsRequest
    {
        Query = new SearchRecordsRequestQuery
        {
            TopK = 4,
            Inputs = new Dictionary<string, object?> { { "text", "Disease prevention" } },
        },
        Fields = ["category", "chunk_text"],
        Rerank = new SearchRecordsRequestRerank
        {
            Model = "bge-reranker-v2-m3",
            TopN = 2,
            RankFields = ["chunk_text"],
        }
    }
);

Console.WriteLine(response);

INDEX_HOST="INDEX_HOST"
NAMESPACE="YOUR_NAMESPACE"
PINECONE_API_KEY="YOUR_API_KEY"

curl "https://$INDEX_HOST/records/namespaces/$NAMESPACE/search" \
  -H "Accept: application/json" \
  -H "Content-Type: application/json" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "X-Pinecone-Api-Version: unstable" \
  -d '{
        "query": {
            "inputs": {"text": "Disease prevention"},
            "top_k": 4
        },
        "rerank": {
            "model": "bge-reranker-v2-m3",
            "top_n": 2,
            "rank_fields": ["chunk_text"]
        },
        "fields": ["category", "chunk_text"]
     }'

The response looks as follows. For each hit, the _score represents the relevance of a document to the query, normalized between 0 and 1, with scores closer to 1 indicating higher relevance.

{'result': {'hits': [{'_id': 'rec3',
                      '_score': 0.004399413242936134,
                      'fields': {'category': 'immune system',
                                 'chunk_text': 'Rich in vitamin C and other '
                                                'antioxidants, apples '
                                                'contribute to immune health '
                                                'and may reduce the risk of '
                                                'chronic diseases.'}},
                     {'_id': 'rec4',
                      '_score': 0.0029235430993139744,
                      'fields': {'category': 'endocrine system',
                                 'chunk_text': 'The high fiber content in '
                                                'apples can also help regulate '
                                                'blood sugar levels, making '
                                                'them a favorable snack for '
                                                'people with diabetes.'}}]},
 'usage': {'embed_total_tokens': 8, 'read_units': 6, 'rerank_units': 1}}

{
  result: { 
    hits: [ 
      {
        _id: 'rec3',
        _score: 0.004399413242936134,
        fields: {
          category: 'immune system',
          chunk_text: 'Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases.'
        }
      },
      {
        _id: 'rec4',
        _score: 0.0029235430993139744,
        fields: {
          category: 'endocrine system',
          chunk_text: 'The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes.'
        }
      }
    ]
  },
  usage: { 
    readUnits: 6, 
    embedTotalTokens: 8,
    rerankUnits: 1 
  }
}

class SearchRecordsResponse {
    result: class SearchRecordsResponseResult {
        hits: [class Hit {
            id: rec3
            score: 0.004399413242936134
            fields: {category=immune system, chunk_text=Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases.}
            additionalProperties: null
        }, class Hit {
            id: rec4
            score: 0.0029235430993139744
            fields: {category=endocrine system, chunk_text=The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes.}
            additionalProperties: null
        }]
        additionalProperties: null
    }
    usage: class SearchUsage {
        readUnits: 6
        embedTotalTokens: 13
        rerankUnits: 1
        additionalProperties: null
    }
    additionalProperties: null
}

{
  "result": {
    "hits": [
      {
        "_id": "rec3",
        "_score": 0.13683891,
        "fields": {
          "category": "immune system",
          "chunk_text": "Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases."
        }
      },
      {
        "_id": "rec4",
        "_score": 0.0029235430993139744,
        "fields": {
          "category": "endocrine system",
          "chunk_text": "The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes."
        }
      }
    ]
  },
  "usage": {
    "read_units": 6,
    "embed_total_tokens": 8,
    "rerank_units": 1
  }
}

{
  "result": {
    "hits": [
      {
        "_id": "rec3",
        "_score": 0.004399413242936134,
        "fields": {
          "category": "immune system",
          "chunk_text": "Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases."
        }
      },
      {
        "_id": "rec4",
        "_score": 0.0029121784027665854,
        "fields": {
          "category": "endocrine system",
          "chunk_text": "The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes."
        }
      }
    ]
  },
  "usage": {
    "read_units": 6,
    "embed_total_tokens": 8,
    "rerank_units": 1
  }
}

{
    "result": {
        "hits": [
            {
                "_id": "rec3",
                "_score": 0.004433765076100826,
                "fields": {
                    "category": "immune system",
                    "chunk_text": "Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases."
                }
            },
            {
                "_id": "rec4",
                "_score": 0.0029121784027665854,
                "fields": {
                    "category": "endocrine system",
                    "chunk_text": "The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes."
                }
            }
        ]
    },
    "usage": {
        "embed_total_tokens": 8,
        "read_units": 6,
        "rerank_units": 1
    }
}

Standalone reranking#

To rerank initial results as a standalone operation, use the rerank operation with the hosted reranking model you want to use, the query results and the query, the number of ranked results to return, the field to use for reranking, and any other model-specific parameters.

For example, the following code uses the hosted bge-reranker-v2-m3 model to rerank the values of the documents.chunk_text fields based on their relevance to the query and return only the 2 most relevant documents, along with their score:

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

ranked_results = pc.inference.rerank(
    model="bge-reranker-v2-m3",
    query="What is AAPL's outlook, considering both product launches and market conditions?",
    documents=[
        {"id": "vec2", "chunk_text": "Analysts suggest that AAPL'\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market."},
        {"id": "vec3", "chunk_text": "AAPL'\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production."},
        {"id": "vec1", "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones."},
    ],
    top_n=2,
    rank_fields=["chunk_text"],
    return_documents=True,
    parameters={
        "truncate": "END"
    }
)

print(ranked_results)

import { Pinecone } from '@pinecone-database/pinecone';

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

const rerankingModel = 'bge-reranker-v2-m3';

const query = "What is AAPL's outlook, considering both product launches and market conditions?";

const documents = [
  { id: 'vec2', chunk_text: "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market." },
  { id: 'vec3', chunk_text: "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production." },
  { id: 'vec1', chunk_text: "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones." },
];

const rerankOptions = {
  topN: 2,
  rankFields: ['chunk_text'],
  returnDocuments: true,
  parameters: {
    truncate: 'END'
  }, 
};

const rankedResults = await pc.inference.rerank(
  rerankingModel,
  query,
  documents,
  rerankOptions
);

console.log(rankedResults);

import io.pinecone.clients.Inference;
import io.pinecone.clients.Pinecone;
import org.openapitools.inference.client.model.RerankResult;
import org.openapitools.inference.client.ApiException;

import java.util.*;

public class RerankExample {
    public static void main(String[] args) throws ApiException {
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        Inference inference = pc.getInferenceClient();

        // The model to use for reranking
        String model = "bge-reranker-v2-m3";

        // The query to rerank documents against
        String query = "What is AAPL's outlook, considering both product launches and market conditions?";

        // Add the documents to rerank
        List<Map<String, Object>> documents = new ArrayList<>();
        Map<String, Object> doc1 = new HashMap<>();
        doc1.put("id", "vec2");
        doc1.put("chunk_text", "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market.");
        documents.add(doc1);

        Map<String, Object> doc2 = new HashMap<>();
        doc2.put("id", "vec3");
        doc2.put("chunk_text", "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production");
        documents.add(doc2);

        Map<String, Object> doc3 = new HashMap<>();
        doc3.put("id", "vec1");
        doc3.put("chunk_text", "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.");
        documents.add(doc3);

        // The fields to rank the documents by. If not provided, the default is "text"
        List<String> rankFields = Arrays.asList("chunk_text");

        // The number of results to return sorted by relevance. Defaults to the number of inputs
        int topN = 2;

        // Whether to return the documents in the response
        boolean returnDocuments = true;

        // Additional model-specific parameters for the reranker
        Map<String, Object> parameters = new HashMap<>();
        parameters.put("truncate", "END");

        // Send ranking request
        RerankResult result = inference.rerank(model, query, documents, rankFields, topN, returnDocuments, parameters);

        // Get ranked data
        System.out.println(result.getData());
    }
}

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"

	"github.com/pinecone-io/go-pinecone/v4/pinecone"
)

func prettifyStruct(obj interface{}) string {
	bytes, _ := json.MarshalIndent(obj, "", "  ")
	return string(bytes)
}

func main() {
	ctx := context.Background()

	pc, err := pinecone.NewClient(pinecone.NewClientParams{
		ApiKey: "YOUR_API_KEY",
	})
	if err != nil {
		log.Fatalf("Failed to create Client: %v", err)
	}

	rerankModel := "bge-reranker-v2-m3"
	topN := 2
	returnDocuments := true
	documents := []pinecone.Document{
		{"id": "vec2", "chunk_text": "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market."},
		{"id": "vec3", "chunk_text": "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production."},
		{"id": "vec1", "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones."},
	}

	ranking, err := pc.Inference.Rerank(ctx, &pinecone.RerankRequest{
		Model:           rerankModel,
		Query:           "What is AAPL's outlook, considering both product launches and market conditions?",
		ReturnDocuments: &returnDocuments,
		TopN:            &topN,
		RankFields:      &[]string{"chunk_text"},
		Documents:       documents,
	})
	if err != nil {
		log.Fatalf("Failed to rerank: %v", err)
	}
	fmt.Printf(prettifyStruct(ranking))
}

using Pinecone;

var pinecone = new PineconeClient("YOUR_API_KEY");

// Add the documents to rerank
var documents = new List<Dictionary<string, object?>>
{
    new()
    {
        ["id"] = "vec2",
        ["chunk_text"] = "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market."
    },
    new()
    {
        ["id"] = "vec3",
        ["chunk_text"] = "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production."
    },
    new()
    {
        ["id"] = "vec1",
        ["chunk_text"] = "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones."
    }
};

// The fields to rank the documents by. If not provided, the default is "text"
var rankFields = new List<string> { "chunk_text" };

// Additional model-specific parameters for the reranker
var parameters = new Dictionary<string, object>
{
    ["truncate"] = "END"
};

// Send ranking request
var result = await pinecone.Inference.RerankAsync(
    new RerankRequest
    {
        Model = "bge-reranker-v2-m3",
        Query = "What is AAPL's outlook, considering both product launches and market conditions?",
        Documents = documents,
        RankFields = rankFields,
        TopN = 2,
        ReturnDocuments = true,
        Parameters = parameters
    });

Console.WriteLine(result);

PINECONE_API_KEY="YOUR_API_KEY"

curl https://api.pinecone.io/rerank \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "X-Pinecone-Api-Version: 2025-10" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -d '{
  "model": "bge-reranker-v2-m3",
  "query": "What is AAPL'\''s outlook, considering both product launches and market conditions?",
  "documents": [
    {"id": "vec2", "chunk_text": "Analysts suggest that AAPL'\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market."},
    {"id": "vec3", "chunk_text": "AAPL'\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production."},
    {"id": "vec1", "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones."}
  ],
  "top_n": 2,
  "rank_fields": ["chunk_text"],
  "return_documents": true,
  "parameters": {
    "truncate": "END"
  }
}'

The response looks as follows. For each hit, the _score represents the relevance of a document to the query, normalized between 0 and 1, with scores closer to 1 indicating higher relevance.

RerankResult(
  model='bge-reranker-v2-m3',
  data=[{
    index=0,
    score=0.004166256,
    document={
        id='vec2',
        chunk_text="Analysts suggest that AAPL'''s upcoming Q4 product launch event might solidify its position in the premium smartphone market."
    }
  },{
    index=2,
    score=0.0011513996,
    document={
        id='vec1',
        chunk_text='AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.'
    }
  }],
  usage={'rerank_units': 1}
)

{
  model: 'bge-reranker-v2-m3',
  data: [
    { index: 0, score: 0.004166256, document: [id: 'vec2', chunk_text: "Analysts suggest that AAPL'''s upcoming Q4 product launch event might solidify its position in the premium smartphone market."] },
    { index: 2, score: 0.0011513996, document: [id: 'vec1', chunk_text: 'AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.'] }
  ],
  usage: { rerankUnits: 1 }
}

[class RankedDocument {
    index: 0
    score: 0.0063143647
    document: {id=vec2, chunk_text=Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market.}
    additionalProperties: null
}, class RankedDocument {
    index: 2
    score: 0.0011513996
    document: {id=vec1, chunk_text=AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.}
    additionalProperties: null
}]

{
  "data": [
    {
      "document": {
        "id": "vec2",
        "chunk_text": "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market."
      },
      "index": 0,
      "score": 0.0063143647
    },
    {
      "document": {
        "id": "vec1",
        "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones."
      },
      "index": 2,
      "score": 0.0011513996
    }
  ],
  "model": "bge-reranker-v2-m3",
  "usage": {
    "rerank_units": 1
  }
}

{
  "model": "bge-reranker-v2-m3",
  "data": [
    {
      "index": 0,
      "score": 0.006289902,
      "document": {
        "chunk_text": "Analysts suggest that AAPL\u0027s upcoming Q4 product launch event might solidify its position in the premium smartphone market.",
        "id": "vec2"
      }
    },
    {
      "index": 3,
      "score": 0.0011513996,
      "document": {
        "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.",
        "id": "vec1"
      }
    }
  ],
  "usage": {
    "rerank_units": 1
  }
}

{
    "model": "bge-reranker-v2-m3",
    "data": [
        {
            "index": 0,
            "document": {
                "chunk_text": "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market.",
                "id": "vec2"
            },
            "score": 0.007606672
        },
        {
            "index": 3,
            "document": {
                "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.",
                "id": "vec1"
            },
            "score": 0.0013406205
        }
    ],
    "usage": {
        "rerank_units": 1
    }
}

Reranking models#

Pinecone hosts several reranking models so it’s easy to manage two-stage vector retrieval on a single platform. You can use a hosted model to rerank results as an integrated part of a query, or you can use a hosted model to rerank results as a standalone operation.

The following reranking models are hosted by Pinecone.

To understand how cost is calculated for reranking, see Reranking cost. To get model details via the API, see List models and Describe a model.

cohere-rerank-3.5 is Cohere’s leading reranking model, balancing performance and latency for a wide range of enterprise search applications.

Details

Modality: Text
Max tokens per query and document pair: 40,000
Max documents: 200

For rate limits, see Rerank requests per minute and Rerank request per month.

Parameters

The cohere-rerank-3.5 model supports the following parameters:

Parameter	Type	Required/Optional	Description
`max_chunks_per_doc`	integer	Optional	Long documents will be automatically truncated to the specified number of chunks. Accepted range: `1 - 3072`.
`rank_fields`	array of strings	Optional	The fields to use for reranking. The model reranks based on the order of the fields specified (e.g., `["field1", "field2", "field3"]`).	`["text"]`

[`bge-reranker-v2-m3`](/models/bge-reranker-v2-m3) is a high-performance, multilingual reranking model that works well on messy data and short queries expected to return medium-length passages of text (1-2 paragraphs).

Details

Modality: Text
Max tokens per query and document pair: 1024
Max documents: 100

For rate limits, see Rerank requests per minute and Rerank request per month.

Parameters

The bge-reranker-v2-m3 model supports the following parameters:

Parameter	Type	Required/Optional	Description	Default
`truncate`	string	Optional	How to handle inputs longer than those supported by the model. Accepted values: `END` or `NONE`. `END` truncates the input sequence at the input token limit. `NONE` returns an error when the input exceeds the input token limit.	`NONE`
`rank_fields`	array of strings	Optional	The field to use for reranking. The model supports only a single rerank field.	`["text"]`

pinecone-rerank-v0 is a state of the art reranking model that out-performs competitors on widely accepted benchmarks. It can handle chunks up to 512 tokens (1-2 paragraphs).

Details

Modality: Text
Max tokens per query and document pair: 512
Max documents: 100

For rate limits, see Rerank requests per minute and Rerank request per month.

Parameters

The pinecone-rerank-v0 model supports the following parameters:

Parameter	Type	Required/Optional	Description	Default
`truncate`	string	Optional	How to handle inputs longer than those supported by the model. Accepted values: `END` or `NONE`. `END` truncates the input sequence at the input token limit. `NONE` returns an error when the input exceeds the input token limit.	`END`
`rank_fields`	array of strings	Optional	The field to use for reranking. The model supports only a single rerank field.	`["text"]`

Link last verified June 7, 2026. View original ↗

Source: Pinecone Docs

Link last verified: 2026-02-26