@memberjunction/ai-reranker

Two-stage retrieval reranking service for MemberJunction's AI agent framework. Provides centralized reranker management and LLM-based reranking capabilities for improving the relevance of vector search results, particularly agent memory notes. Supports configurable reranking models, observability integration, and agent-level configuration.

Architecture

graph TD
    subgraph "@memberjunction/ai-reranker"
        RS["RerankerService
Singleton Manager"]
        style RS fill:#2d8659,stroke:#1a5c3a,color:#fff

        LR["LLMReranker
LLM-Based Reranking"]
        style LR fill:#7c5295,stroke:#563a6b,color:#fff

        CT["Config Types
RerankerConfiguration"]
        style CT fill:#b8762f,stroke:#8a5722,color:#fff
    end

    subgraph "Two-Stage Retrieval"
        S1["Stage 1: Vector Search
Fast approximate retrieval"]
        style S1 fill:#2d6a9f,stroke:#1a4971,color:#fff

        S2["Stage 2: LLM Reranking
Precise relevance scoring"]
        style S2 fill:#7c5295,stroke:#563a6b,color:#fff

        S3["Final Results
Reranked by relevance"]
        style S3 fill:#2d8659,stroke:#1a5c3a,color:#fff
    end

    S1 -->|"Top-K candidates"| RS
    RS --> S2
    S2 -->|"Scored & sorted"| S3

    subgraph Dependencies
        AI["@memberjunction/ai
BaseReranker"]
        style AI fill:#2d6a9f,stroke:#1a4971,color:#fff

        AIP["@memberjunction/ai-prompts
AIPromptRunner"]
        style AIP fill:#2d6a9f,stroke:#1a4971,color:#fff

        AIE["@memberjunction/aiengine
NoteMatchResult"]
        style AIE fill:#2d6a9f,stroke:#1a4971,color:#fff
    end

    AI --> RS
    AIP --> LR
    AIE --> RS
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Installation

npm install @memberjunction/ai-reranker
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Key Exports

RerankerService (Singleton)

Centralized service for managing reranker instances and performing note reranking. Caches reranker instances by model for efficient reuse.

import { RerankerService, parseRerankerConfiguration } from '@memberjunction/ai-reranker';

// Parse configuration from agent entity
const config = parseRerankerConfiguration(agent.RerankerConfiguration);

if (config?.enabled) {
    const result = await RerankerService.Instance.rerankNotes(
        vectorSearchResults,   // NoteMatchResult[] from vector search
        userQuery,             // The user's query for relevance scoring
        config,                // RerankerConfiguration
        contextUser
    );

    if (result.success) {
        console.log(`Reranked ${result.notes.length} notes in ${result.durationMs}ms`);
    }
}
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LLMReranker

LLM-based reranking implementation that uses AI prompts to score document relevance. Extends BaseReranker from @memberjunction/ai.

import { createLLMReranker } from '@memberjunction/ai-reranker';

const reranker = createLLMReranker(modelId, contextUser);
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RerankerConfiguration

Configuration type stored as JSON on agent entities:

interface RerankerConfiguration {
    /** Whether reranking is enabled for this agent */
    enabled: boolean;
    /** AI model ID to use for reranking */
    modelId?: string;
    /** Maximum number of results to return after reranking */
    topK?: number;
    /** Minimum relevance score threshold (0-1) */
    minScore?: number;
}
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RerankServiceResult

interface RerankServiceResult {
    notes: NoteMatchResult[];     // Reranked notes sorted by relevance
    success: boolean;             // Whether reranking succeeded
    durationMs: number;           // Time taken in milliseconds
    usage?: {                     // Token usage for cost tracking
        promptTokens?: number;
        completionTokens?: number;
        cost?: number;
    };
    runStepID?: string;           // Created run step ID (if observability enabled)
}
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RerankObservabilityOptions

Integration with agent run step tracking:

const result = await RerankerService.Instance.rerankNotes(
    notes,
    query,
    config,
    contextUser,
    {
        agentRunID: runId,        // Link to agent run
        parentStepID: stepId,     // Parent step for hierarchy
        stepNumber: 3             // Step sequence number
    }
);
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How Two-Stage Retrieval Works

1. Stage 1 -- Vector Search: Fast approximate nearest-neighbor search using embeddings to retrieve a broad set of candidate notes (e.g., top 50)
2. Stage 2 -- LLM Reranking: An LLM evaluates each candidate note against the query and assigns a precise relevance score
3. Result: The top-K most relevant notes are returned, sorted by LLM-determined relevance

This approach combines the speed of vector search with the accuracy of LLM-based understanding, significantly improving retrieval quality for agent memory.

Interaction with Consolidation

The candidate corpus the reranker scores is the in-process vector store (AIEngine._noteVectorService). Since v5.30.x, that store is kept in sync with persisted Status='Active' (see @memberjunction/aiengine README), which has two consequences for reranking:

• Consolidated source notes drop out immediately. When MemoryManagerAgent's consolidation pipeline merges a cluster, source notes are revoked in the same operation that removes their vector entries. They stop appearing as rerank candidates without an MJAPI restart.
• Protection tier is metadata, not a rerank gate. ProtectionTier (Immutable / Protected / Standard / Ephemeral) lives on the note entity and flows through to the reranker as part of each candidate's data. The reranker itself does not filter or weight by tier; downstream consumers (MemoryManagerAgent's decay phase, retrieval policies layered on top of RerankServiceResult) are responsible for any tier-aware logic.

For the consolidation pipeline mechanics and protection-tier semantics, see @memberjunction/ai-agents.

Dependencies

• @memberjunction/ai -- BaseReranker, RerankParams, RerankResult
• @memberjunction/ai-prompts -- AIPromptRunner for LLM-based reranking
• @memberjunction/ai-core-plus -- Extended entity classes
• @memberjunction/aiengine -- AIEngine, NoteMatchResult
• @memberjunction/core -- MJ framework core
• @memberjunction/core-entities -- Generated entity classes
• @memberjunction/global -- Class factory