> ## 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. # Rerank results > 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](#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. To run through this guide in your browser, see the [Rerank example notebook](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/pinecone-reranker.ipynb). ## Integrated reranking To rerank initial results as an integrated part of a query, without any extra steps, use the [`search`](/reference/api/latest/data-plane/search_records) operation with the `rerank` parameter, including the [hosted reranking model](#reranking-models) 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: ```python Python theme={null} 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) ``` ```javascript JavaScript theme={null} 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); ``` ```java Java theme={null} 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 fields = new ArrayList<>(); fields.add("category"); fields.add("chunk_text"); ListrankFields = 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); } } ``` ```go Go theme={null} 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)) } ``` ```csharp C# theme={null} 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 { { "text", "Disease prevention" } }, }, Fields = ["category", "chunk_text"], Rerank = new SearchRecordsRequestRerank { Model = "bge-reranker-v2-m3", TopN = 2, RankFields = ["chunk_text"], } } ); Console.WriteLine(response); ``` ```shell curl theme={null} 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. ```python Python theme={null} {'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}} ``` ```javascript JavaScript theme={null} { 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 } } ``` ```java Java theme={null} 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 } ``` ```go Go theme={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 } } ``` ```csharp C# theme={null} { "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 } } ``` ```json curl theme={null} { "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`](/reference/api/latest/inference/rerank) operation with the [hosted reranking model](#reranking-models) 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: ```python Python theme={null} 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) ``` ```javascript JavaScript theme={null} 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); ``` ```java Java theme={null} 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> documents = new ArrayList<>(); Map 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 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 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 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 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()); } } ``` ```go Go theme={null} 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)) } ``` ```csharp C# theme={null} using Pinecone; var pinecone = new PineconeClient("YOUR_API_KEY"); // Add the documents to rerank var documents = new List> { 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 { "chunk_text" }; // Additional model-specific parameters for the reranker var parameters = new Dictionary { ["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); ``` ```shell curl theme={null} 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. ```python Python theme={null} 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} ) ``` ```javascript JavaScript theme={null} { 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 } } ``` ```java Java theme={null} [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 }] ``` ```go Go theme={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 } } ``` ```csharp C# theme={null} { "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 } } ``` ```json curl theme={null} { "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 } } ``` ## Rerank results on the default field To [rerank search results](/reference/api/latest/inference/rerank), specify a [supported reranking model](/guides/search/rerank-results#reranking-models), and provide documents and a query as well as other model-specific parameters. By default, Pinecone expects the documents to be in the `documents.text` field. For example, the following request uses the `bge-reranker-v2-m3` reranking model to rerank the values of the `documents.text` field based on their relevance to the query, `"The tech company Apple is known for its innovative products like the iPhone."`. With `truncate` set to `"END"`, the input sequence (`query` + `document`) is truncated at the token limit (`1024`); to return an error instead, you'd set `truncate` to `"NONE"` or leave the parameter out. ```python Python theme={null} 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) ``` ```javascript JavaScript theme={null} import { Pinecone } from '@pinecone-database/pinecone'; const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' }); const rerankingModel = 'bge-reranker-v2-m3'; const query = 'The tech company Apple is known for its innovative products like the iPhone.'; const 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.' }, ]; const rerankOptions = { topN: 4, returnDocuments: true, parameters: { truncate: 'END' }, }; const response = await pc.inference.rerank( rerankingModel, query, documents, rerankOptions ); console.log(response); ``` ```java Java theme={null} 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 = "The tech company Apple is known for its innovative products like the iPhone."; // Add the documents to rerank List> documents = new ArrayList<>(); Map doc1 = new HashMap<>(); doc1.put("id", "vec1"); doc1.put("text", "Apple is a popular fruit known for its sweetness and crisp texture."); documents.add(doc1); Map doc2 = new HashMap<>(); doc2.put("id", "vec2"); doc2.put("text", "Many people enjoy eating apples as a healthy snack."); documents.add(doc2); Map doc3 = new HashMap<>(); doc3.put("id", "vec3"); doc3.put("text", "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."); documents.add(doc3); Map doc4 = new HashMap<>(); doc4.put("id", "vec4"); doc4.put("text", "An apple a day keeps the doctor away, as the saying goes."); documents.add(doc4); // The fields to rank the documents by. If not provided, the default is "text" List rankFields = Arrays.asList("text"); // The number of results to return sorted by relevance. Defaults to the number of inputs int topN = 4; // Whether to return the documents in the response boolean returnDocuments = true; // Additional model-specific parameters for the reranker Map 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()); } } ``` ```go Go theme={null} package main import ( "context" "fmt" "log" "github.com/pinecone-io/go-pinecone/v4/pinecone" ) 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 := 4 returnDocuments := true documents := []pinecone.Document{ {"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."}, } ranking, err := pc.Inference.Rerank(ctx, &pinecone.RerankRequest{ Model: rerankModel, Query: "The tech company Apple is known for its innovative products like the iPhone.", ReturnDocuments: &returnDocuments, TopN: &topN, RankFields: &[]string{"text"}, Documents: documents, }) if err != nil { log.Fatalf("Failed to rerank: %v", err) } fmt.Printf("Rerank result: %+v\n", ranking) } ``` ```csharp C# theme={null} using Pinecone; var pinecone = new PineconeClient("YOUR_API_KEY"); // The model to use for reranking var model = "bge-reranker-v2-m3"; // The query to rerank documents against var query = "The tech company Apple is known for its innovative products like the iPhone."; // Add the documents to rerank var documents = new List> { new() { ["id"] = "vec1", ["my_field"] = "Apple is a popular fruit known for its sweetness and crisp texture." }, new() { ["id"] = "vec2", ["my_field"] = "Many people enjoy eating apples as a healthy snack." }, new() { ["id"] = "vec3", ["my_field"] = "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces." }, new() { ["id"] = "vec4", ["my_field"] = "An apple a day keeps the doctor away, as the saying goes." } }; // The fields to rank the documents by. If not provided, the default is "text" var rankFields = new List { "my_field" }; // The number of results to return sorted by relevance. Defaults to the number of inputs int topN = 4; // Whether to return the documents in the response bool returnDocuments = true; // Additional model-specific parameters for the reranker var parameters = new Dictionary { ["truncate"] = "END" }; // Send ranking request var result = await pinecone.Inference.RerankAsync( new RerankRequest { Model = model, Query = query, Documents = documents, RankFields = rankFields, TopN = topN, ReturnDocuments = true, Parameters = parameters }); Console.WriteLine(result); ``` ```shell curl theme={null} 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": "The tech company Apple is known for its innovative products like the iPhone.", "return_documents": true, "top_n": 4, "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."} ], "parameters": { "truncate": "END" } }' ``` The returned object contains documents with relevance scores: Normalized between 0 and 1, the `score` represents the relevance of a passage to the query, with scores closer to 1 indicating higher relevance. ```python Python theme={null} 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} ) ``` ```javascript JavaScript theme={null} { model: 'bge-reranker-v2-m3', data: [ { index: 2, score: 0.48357219, document: [Object] }, { index: 0, score: 0.048405956, document: [Object] }, { index: 3, score: 0.007846239, document: [Object] }, { index: 1, score: 0.0006563728, document: [Object] } ], usage: { rerankUnits: 1 } } ``` ```java Java theme={null} [class RankedDocument { index: 2 score: 0.48357219 document: {id=vec3, text=Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.} additionalProperties: null }, class RankedDocument { index: 0 score: 0.048405956 document: {id=vec1, text=Apple is a popular fruit known for its sweetness and crisp texture.} additionalProperties: null }, class RankedDocument { index: 3 score: 0.007846239 document: {id=vec4, text=An apple a day keeps the doctor away, as the saying goes.} additionalProperties: null }, class RankedDocument { index: 1 score: 0.0006563728 document: {id=vec2, text=Many people enjoy eating apples as a healthy snack.} additionalProperties: null }] ``` ```go Go theme={null} Rerank result: { "data": [ { "document": { "id": "vec3", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces." }, "index": 2, "score": 0.48357219 }, { "document": { "id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture." }, "index": 0, "score": 0.048405956 }, { "document": { "id": "vec4", "text": "An apple a day keeps the doctor away, as the saying goes." }, "index": 3, "score": 0.007846239 }, { "document": { "id": "vec2", "text": "Many people enjoy eating apples as a healthy snack." }, "index": 1, "score": 0.0006563728 } ], "model": "bge-reranker-v2-m3", "usage": { "rerank_units": 1 } } ``` ```csharp C# theme={null} { "model": "bge-reranker-v2-m3", "data": [ { "index": 2, "score": 0.48357219, "document": { "id": "vec3", "my_field": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces." } }, { "index": 0, "score": 0.048405956, "document": { "id": "vec1", "my_field": "Apple is a popular fruit known for its sweetness and crisp texture." } }, { "index": 3, "score": 0.007846239, "document": { "id": "vec4", "my_field": "An apple a day keeps the doctor away, as the saying goes." } }, { "index": 1, "score": 0.0006563728, "document": { "id": "vec2", "my_field": "Many people enjoy eating apples as a healthy snack." } } ], "usage": { "rerank_units": 1 } } ``` ```JSON curl theme={null} { "data":[ { "index":2, "document":{ "id":"vec3", "text":"Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces." }, "score":0.47654688 }, { "index":0, "document":{ "id":"vec1", "text":"Apple is a popular fruit known for its sweetness and crisp texture." }, "score":0.047963805 }, { "index":3, "document":{ "id":"vec4", "text":"An apple a day keeps the doctor away, as the saying goes." }, "score":0.007587992 }, { "index":1, "document":{ "id":"vec2", "text":"Many people enjoy eating apples as a healthy snack." }, "score":0.0006491712 } ], "usage":{ "rerank_units":1 } } ``` ## Rerank results on a custom field To [rerank results](/reference/api/latest/inference/rerank) on a field other than `documents.text`, provide the `rank_fields` parameter to specify the fields on which to rerank. The [`bge-reranker-v2-m3`](#bge-reranker-v2-m3) and [`pinecone-rerank-v0`](#pinecone-rerank-v0) models support only a single rerank field. [`cohere-rerank-3.5`](#cohere-rerank-3-5) supports multiple rerank fields, ranked based on the order of the fields specified. For example, the following request reranks documents based on the values of the `documents.my_field` field: ```python Python theme={null} 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", "my_field": "Apple is a popular fruit known for its sweetness and crisp texture."}, {"id": "vec2", "my_field": "Many people enjoy eating apples as a healthy snack."}, {"id": "vec3", "my_field": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."}, {"id": "vec4", "my_field": "An apple a day keeps the doctor away, as the saying goes."}, ], rank_fields=["my_field"], top_n=4, return_documents=True, parameters={ "truncate": "END" } ) ``` ```javascript JavaScript theme={null} import { Pinecone } from '@pinecone-database/pinecone'; const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' }); const rerankingModel = 'bge-reranker-v2-m3'; const query = 'The tech company Apple is known for its innovative products like the iPhone.'; const documents = [ { id: 'vec1', my_field: 'Apple is a popular fruit known for its sweetness and crisp texture.' }, { id: 'vec2', my_field: 'Many people enjoy eating apples as a healthy snack.' }, { id: 'vec3', my_field: 'Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.' }, { id: 'vec4', my_field: 'An apple a day keeps the doctor away, as the saying goes.' }, ]; const rerankOptions = { rankFields: ['my_field'], topN: 4, returnDocuments: true, parameters: { truncate: "END" }, }; const response = await pc.inference.rerank( rerankingModel, query, documents, rerankOptions ); console.log(response); ``` ```java Java theme={null} 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 = "The tech company Apple is known for its innovative products like the iPhone."; // Add the documents to rerank List> documents = new ArrayList<>(); Map doc1 = new HashMap<>(); doc1.put("id", "vec1"); doc1.put("my_field", "Apple is a popular fruit known for its sweetness and crisp texture."); documents.add(doc1); Map doc2 = new HashMap<>(); doc2.put("id", "vec2"); doc2.put("my_field", "Many people enjoy eating apples as a healthy snack."); documents.add(doc2); Map doc3 = new HashMap<>(); doc3.put("id", "vec3"); doc3.put("my_field", "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."); documents.add(doc3); Map doc4 = new HashMap<>(); doc4.put("id", "vec4"); doc4.put("my_field", "An apple a day keeps the doctor away, as the saying goes."); documents.add(doc4); // The fields to rank the documents by. If not provided, the default is "text" List rankFields = Arrays.asList("my_field"); // 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 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()); } } ``` ```go Go theme={null} package main import ( "context" "fmt" "log" "github.com/pinecone-io/go-pinecone/v4/pinecone" ) 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 := 4 returnDocuments := true documents := []pinecone.Document{ {"id": "vec1", "my_field": "Apple is a popular fruit known for its sweetness and crisp texture."}, {"id": "vec2", "my_field": "Many people enjoy eating apples as a healthy snack."}, {"id": "vec3", "my_field": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."}, {"id": "vec4", "my_field": "An apple a day keeps the doctor away, as the saying goes."}, } ranking, err := pc.Inference.Rerank(ctx, &pinecone.RerankRequest{ Model: rerankModel, Query: "The tech company Apple is known for its innovative products like the iPhone.", ReturnDocuments: &returnDocuments, TopN: &topN, RankFields: &[]string{"my_field"}, Documents: documents, }) if err != nil { log.Fatalf("Failed to rerank: %v", err) } fmt.Printf("Rerank result: %+v\n", ranking) } ``` ```csharp C# theme={null} using Pinecone; var pinecone = new PineconeClient("YOUR_API_KEY"); // The model to use for reranking var model = "bge-reranker-v2-m3"; // The query to rerank documents against var query = "The tech company Apple is known for its innovative products like the iPhone."; // Add the documents to rerank var documents = new List> { new() { ["id"] = "vec1", ["my_field"] = "Apple is a popular fruit known for its sweetness and crisp texture." }, new() { ["id"] = "vec2", ["my_field"] = "Many people enjoy eating apples as a healthy snack." }, new() { ["id"] = "vec3", ["my_field"] = "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces." }, new() { ["id"] = "vec4", ["my_field"] = "An apple a day keeps the doctor away, as the saying goes." } }; // The fields to rank the documents by. If not provided, the default is "text" var rankFields = new List { "my_field" }; // 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 bool returnDocuments = true; // Additional model-specific parameters for the reranker var parameters = new Dictionary { ["truncate"] = "END" }; // Send ranking request var result = await pinecone.Inference.RerankAsync( new RerankRequest { Model = model, Query = query, Documents = documents, RankFields = rankFields, TopN = topN, ReturnDocuments = true, Parameters = parameters }); // Get ranked data var data = result.Data; Console.WriteLine(data); ``` ```shell curl theme={null} 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": "The tech company Apple is known for its innovative products like the iPhone.", "return_documents": true, "top_n": 4, "rank_fields": ["my_field"], "documents": [ {"id": "vec1", "my_field": "Apple is a popular fruit known for its sweetness and crisp texture."}, {"id": "vec2", "my_field": "Many people enjoy eating apples as a healthy snack."}, {"id": "vec3", "my_field": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."}, {"id": "vec4", "my_field": "An apple a day keeps the doctor away, as the saying goes."} ], "parameters": { "truncate": "END" } }' ``` ## 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](/guides/manage-cost/understanding-cost#reranking). To get model details via the API, see [List models](/reference/api/latest/inference/list_models) and [Describe a model](/reference/api/latest/inference/describe_model). [`cohere-rerank-3.5`](/models/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](/reference/api/database-limits#rerank-requests-per-minute-per-model) and [Rerank request per month](/reference/api/database-limits#rerank-requests-per-month-per-model). **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](/reference/api/database-limits#rerank-requests-per-minute-per-model) and [Rerank request per month](/reference/api/database-limits#rerank-requests-per-month-per-model). **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`](/models/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](/reference/api/database-limits#rerank-requests-per-minute-per-model) and [Rerank request per month](/reference/api/database-limits#rerank-requests-per-month-per-model). **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"]` |