rerank()

The rerank() function provides advanced reranking capabilities for vector search results by combining semantic relevance, vector similarity, and position-based scoring.

function rerank(
  results: QueryResult[],
  query: string,
  model: MastraLanguageModel,
  options: RerankerFunctionOptions,
): Promise<RerankResult[]>

Usage example

import { ModelRouterLanguageModel } from '@mastra/core/llm'
import { rerank } from '@mastra/rag'

const model = new ModelRouterLanguageModel('openai/gpt-5.6-sol')

const rerankedResults = await rerank(vectorSearchResults, 'How do I deploy to production?', model, {
  weights: {
    semantic: 0.5,
    vector: 0.3,
    position: 0.2,
  },
  topK: 3,
})

Parameters

results:

QueryResult[]

The vector search results to rerank

query:

string

The search query text used to evaluate relevance

model:

MastraLanguageModel

The language Model to use for reranking

options:

RerankerFunctionOptions

Options for the reranking model

RerankerFunctionOptions

weights?:

WeightConfig

Weights for different scoring components (must add up to 1)

number

semantic?:

number (default: 0.4)

Weight for semantic relevance

number

vector?:

number (default: 0.4)

Weight for vector similarity

number

position?:

number (default: 0.2)

Weight for position-based scoring

queryEmbedding?:

number[]

Embedding of the query

topK?:

number

Number of top results to return

The rerank function accepts any LanguageModel from the Vercel AI SDK. When using the Cohere model rerank-v3.5, it will automatically use Cohere's reranking capabilities.

备注

For semantic scoring to work properly during re-ranking, each result must include the text content in its metadata.text field.

Returns

The function returns an array of RerankResult objects:

result:

QueryResult

The original query result

score:

number

Combined reranking score. Usually between 0 and 1, but query-based adjustments can raise it slightly above 1.

details:

ScoringDetails

Detailed scoring information

ScoringDetails

semantic:

number

Semantic relevance score (0-1)

vector:

number

Vector similarity score (0-1)

position:

number

Position-based score (0-1)

queryAnalysis?:

object

Query analysis details

number

magnitude:

Magnitude of the query

number[]

dominantFeatures:

Dominant features of the query

Related

• createVectorQueryTool