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retriever.ts
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/**
* Hybrid Retrieval System
* Combines vector search + BM25 full-text search with RRF fusion
*/
import type { MemoryEntry, MemoryStore, MemorySearchResult } from "./store.js";
import type { Embedder } from "./embedder.js";
import {
AccessTracker,
computeEffectiveHalfLife,
parseAccessMetadata,
} from "./access-tracker.js";
import { filterNoise } from "./noise-filter.js";
import type { DecayEngine, DecayableMemory } from "./decay-engine.js";
import type { TierManager } from "./tier-manager.js";
import {
getDecayableFromEntry,
isMemoryActiveAt,
parseSmartMetadata,
toLifecycleMemory,
} from "./smart-metadata.js";
import { TraceCollector, type RetrievalTrace } from "./retrieval-trace.js";
import { RetrievalStatsCollector } from "./retrieval-stats.js";
// ============================================================================
// Types & Configuration
// ============================================================================
export interface RetrievalConfig {
mode: "hybrid" | "vector";
vectorWeight: number;
bm25Weight: number;
minScore: number;
rerank: "cross-encoder" | "lightweight" | "none";
candidatePoolSize: number;
/** Recency boost half-life in days (default: 14). Set 0 to disable. */
recencyHalfLifeDays: number;
/** Max recency boost factor (default: 0.10) */
recencyWeight: number;
/** Filter noise from results (default: true) */
filterNoise: boolean;
/** Reranker API key (enables cross-encoder reranking) */
rerankApiKey?: string;
/** Reranker model (default: jina-reranker-v3) */
rerankModel?: string;
/** Reranker API endpoint (default: https://api.jina.ai/v1/rerank). */
rerankEndpoint?: string;
/** Reranker provider format. Determines request/response shape and auth header.
* - "jina" (default): Authorization: Bearer, string[] documents, results[].relevance_score
* - "siliconflow": same format as jina (alias, for clarity)
* - "voyage": Authorization: Bearer, string[] documents, data[].relevance_score
* - "pinecone": Api-Key header, {text}[] documents, data[].score
* - "tei": Authorization: Bearer, string[] texts, top-level [{ index, score }] */
rerankProvider?:
| "jina"
| "siliconflow"
| "voyage"
| "pinecone"
| "dashscope"
| "tei";
/** Rerank API timeout in milliseconds (default: 5000). Increase for local/CPU-based rerank servers. */
rerankTimeoutMs?: number;
/**
* Length normalization: penalize long entries that dominate via sheer keyword
* density. Formula: score *= 1 / (1 + log2(charLen / anchor)).
* anchor = reference length (default: 500 chars). Entries shorter than anchor
* get a slight boost; longer entries get penalized progressively.
* Set 0 to disable. (default: 300)
*/
lengthNormAnchor: number;
/**
* Hard cutoff after rerank: discard results below this score.
* Applied after all scoring stages (rerank, recency, importance, length norm).
* Higher = fewer but more relevant results. (default: 0.35)
*/
hardMinScore: number;
/**
* Time decay half-life in days. Entries older than this lose score.
* Different from recencyBoost (additive bonus for new entries):
* this is a multiplicative penalty for old entries.
* Formula: score *= 0.5 + 0.5 * exp(-ageDays / halfLife)
* At halfLife days: ~0.68x. At 2*halfLife: ~0.59x. At 4*halfLife: ~0.52x.
* Set 0 to disable. (default: 60)
*/
timeDecayHalfLifeDays: number;
/** Access reinforcement factor for time decay half-life extension.
* Higher = stronger reinforcement. 0 to disable. (default: 0.5) */
reinforcementFactor: number;
/** Maximum half-life multiplier from access reinforcement.
* Prevents frequently accessed memories from becoming immortal. (default: 3) */
maxHalfLifeMultiplier: number;
/** Tag prefixes for exact-match queries (default: ["proj", "env", "team", "scope"]).
* Queries containing these prefixes (e.g. "proj:AIF") will use BM25-only + mustContain
* to avoid semantic false positives from vector search. */
tagPrefixes: string[];
}
export interface RetrievalContext {
query: string;
limit: number;
scopeFilter?: string[];
category?: string;
/** Retrieval source: "manual" for user-triggered, "auto-recall" for system-initiated, "cli" for CLI commands. */
source?: "manual" | "auto-recall" | "cli";
}
export interface RetrievalResult extends MemorySearchResult {
sources: {
vector?: { score: number; rank: number };
bm25?: { score: number; rank: number };
fused?: { score: number };
reranked?: { score: number };
};
}
// ============================================================================
// Default Configuration
// ============================================================================
export const DEFAULT_RETRIEVAL_CONFIG: RetrievalConfig = {
mode: "hybrid",
vectorWeight: 0.7,
bm25Weight: 0.3,
minScore: 0.3,
rerank: "cross-encoder",
candidatePoolSize: 20,
recencyHalfLifeDays: 14,
recencyWeight: 0.1,
filterNoise: true,
rerankModel: "jina-reranker-v3",
rerankEndpoint: "https://api.jina.ai/v1/rerank",
rerankTimeoutMs: 5000,
lengthNormAnchor: 500,
hardMinScore: 0.35,
timeDecayHalfLifeDays: 60,
reinforcementFactor: 0.5,
maxHalfLifeMultiplier: 3,
tagPrefixes: ["proj", "env", "team", "scope"],
};
// ============================================================================
// Utility Functions
// ============================================================================
function clampInt(value: number, min: number, max: number): number {
if (!Number.isFinite(value)) return min;
return Math.min(max, Math.max(min, Math.floor(value)));
}
function clamp01(value: number, fallback: number): number {
if (!Number.isFinite(value)) return Number.isFinite(fallback) ? fallback : 0;
return Math.min(1, Math.max(0, value));
}
function clamp01WithFloor(value: number, floor: number): number {
const safeFloor = clamp01(floor, 0);
return Math.max(safeFloor, clamp01(value, safeFloor));
}
// ============================================================================
// Rerank Provider Adapters
// ============================================================================
type RerankProvider =
| "jina"
| "siliconflow"
| "voyage"
| "pinecone"
| "dashscope"
| "tei";
interface RerankItem {
index: number;
score: number;
}
/** Build provider-specific request headers and body */
function buildRerankRequest(
provider: RerankProvider,
apiKey: string,
model: string,
query: string,
candidates: string[],
topN: number,
): { headers: Record<string, string>; body: Record<string, unknown> } {
switch (provider) {
case "tei":
return {
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${apiKey}`,
},
body: {
query,
texts: candidates,
},
};
case "dashscope":
// DashScope wraps query+documents under `input` and does not use top_n.
// Endpoint: https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank
return {
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${apiKey}`,
},
body: {
model,
input: {
query,
documents: candidates,
},
},
};
case "pinecone":
return {
headers: {
"Content-Type": "application/json",
"Api-Key": apiKey,
"X-Pinecone-API-Version": "2024-10",
},
body: {
model,
query,
documents: candidates.map((text) => ({ text })),
top_n: topN,
rank_fields: ["text"],
},
};
case "voyage":
return {
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${apiKey}`,
},
body: {
model,
query,
documents: candidates,
// Voyage uses top_k (not top_n) to limit reranked outputs.
top_k: topN,
},
};
case "siliconflow":
case "jina":
default:
return {
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${apiKey}`,
},
body: {
model,
query,
documents: candidates,
top_n: topN,
},
};
}
}
/** Parse provider-specific response into unified format */
function parseRerankResponse(
provider: RerankProvider,
data: unknown,
): RerankItem[] | null {
const parseItems = (
items: unknown,
scoreKeys: Array<"score" | "relevance_score">,
): RerankItem[] | null => {
if (!Array.isArray(items)) return null;
const parsed: RerankItem[] = [];
for (const raw of items as Array<Record<string, unknown>>) {
const index =
typeof raw?.index === "number" ? raw.index : Number(raw?.index);
if (!Number.isFinite(index)) continue;
let score: number | null = null;
for (const key of scoreKeys) {
const value = raw?.[key];
const n = typeof value === "number" ? value : Number(value);
if (Number.isFinite(n)) {
score = n;
break;
}
}
if (score === null) continue;
parsed.push({ index, score });
}
return parsed.length > 0 ? parsed : null;
};
const objectData =
data && typeof data === "object" && !Array.isArray(data)
? (data as Record<string, unknown>)
: undefined;
switch (provider) {
case "tei":
return (
parseItems(data, ["score", "relevance_score"]) ??
parseItems(objectData?.results, ["score", "relevance_score"]) ??
parseItems(objectData?.data, ["score", "relevance_score"])
);
case "dashscope": {
// DashScope: { output: { results: [{ index, relevance_score }] } }
const output = objectData?.output as Record<string, unknown> | undefined;
if (output) {
return parseItems(output.results, ["relevance_score", "score"]);
}
// Fallback: try top-level results in case API format changes
return parseItems(objectData?.results, ["relevance_score", "score"]);
}
case "pinecone": {
// Pinecone: usually { data: [{ index, score, ... }] }
// Also tolerate results[] with score/relevance_score for robustness.
return (
parseItems(objectData?.data, ["score", "relevance_score"]) ??
parseItems(objectData?.results, ["score", "relevance_score"])
);
}
case "voyage": {
// Voyage: usually { data: [{ index, relevance_score }] }
// Also tolerate results[] for compatibility across gateways.
return (
parseItems(objectData?.data, ["relevance_score", "score"]) ??
parseItems(objectData?.results, ["relevance_score", "score"])
);
}
case "siliconflow":
case "jina":
default: {
// Jina / SiliconFlow: usually { results: [{ index, relevance_score }] }
// Also tolerate data[] for compatibility across gateways.
return (
parseItems(objectData?.results, ["relevance_score", "score"]) ??
parseItems(objectData?.data, ["relevance_score", "score"])
);
}
}
}
// Cosine similarity for reranking fallback
function cosineSimilarity(a: number[], b: number[]): number {
if (a.length !== b.length) {
throw new Error("Vector dimensions must match for cosine similarity");
}
let dotProduct = 0;
let normA = 0;
let normB = 0;
for (let i = 0; i < a.length; i++) {
dotProduct += a[i] * b[i];
normA += a[i] * a[i];
normB += b[i] * b[i];
}
const norm = Math.sqrt(normA) * Math.sqrt(normB);
return norm === 0 ? 0 : dotProduct / norm;
}
// ============================================================================
// Memory Retriever
// ============================================================================
export class MemoryRetriever {
private accessTracker: AccessTracker | null = null;
private tierManager: TierManager | null = null;
private _statsCollector: RetrievalStatsCollector | null = null;
constructor(
private store: MemoryStore,
private embedder: Embedder,
private config: RetrievalConfig = DEFAULT_RETRIEVAL_CONFIG,
private decayEngine: DecayEngine | null = null,
) { }
setAccessTracker(tracker: AccessTracker): void {
this.accessTracker = tracker;
}
/** Enable aggregate retrieval statistics collection. */
setStatsCollector(collector: RetrievalStatsCollector): void {
this._statsCollector = collector;
}
/** Get the stats collector (if set). */
getStatsCollector(): RetrievalStatsCollector | null {
return this._statsCollector;
}
private filterActiveResults<T extends MemorySearchResult>(results: T[]): T[] {
return results.filter((result) =>
isMemoryActiveAt(parseSmartMetadata(result.entry.metadata, result.entry)),
);
}
async retrieve(context: RetrievalContext): Promise<RetrievalResult[]> {
const { query, limit, scopeFilter, category, source } = context;
const safeLimit = clampInt(limit, 1, 20);
// Create trace only when stats collector is active (zero overhead otherwise)
const trace = this._statsCollector ? new TraceCollector() : undefined;
// Check if query contains tag prefixes -> use BM25-only + mustContain
const tagTokens = this.extractTagTokens(query);
let results: RetrievalResult[];
if (tagTokens.length > 0) {
results = await this.bm25OnlyRetrieval(
query, tagTokens, safeLimit, scopeFilter, category, trace,
);
} else if (this.config.mode === "vector" || !this.store.hasFtsSupport) {
results = await this.vectorOnlyRetrieval(
query, safeLimit, scopeFilter, category, trace,
);
} else {
results = await this.hybridRetrieval(
query, safeLimit, scopeFilter, category, trace,
);
}
// Feed completed trace to stats collector
if (trace && this._statsCollector) {
const mode = tagTokens.length > 0 ? "bm25"
: (this.config.mode === "vector" || !this.store.hasFtsSupport) ? "vector" : "hybrid";
const finalTrace = trace.finalize(query, mode);
this._statsCollector.recordQuery(finalTrace, source || "unknown");
}
// Record access for reinforcement (manual recall only)
if (this.accessTracker && source === "manual" && results.length > 0) {
this.accessTracker.recordAccess(results.map((r) => r.entry.id));
}
return results;
}
/**
* Retrieve with full trace, used by the memory_debug tool.
* Always collects a trace regardless of stats collector state.
*/
async retrieveWithTrace(
context: RetrievalContext,
): Promise<{ results: RetrievalResult[]; trace: RetrievalTrace }> {
const { query, limit, scopeFilter, category, source } = context;
const safeLimit = clampInt(limit, 1, 20);
const trace = new TraceCollector();
const tagTokens = this.extractTagTokens(query);
let results: RetrievalResult[];
if (tagTokens.length > 0) {
results = await this.bm25OnlyRetrieval(
query, tagTokens, safeLimit, scopeFilter, category, trace,
);
} else if (this.config.mode === "vector" || !this.store.hasFtsSupport) {
results = await this.vectorOnlyRetrieval(
query, safeLimit, scopeFilter, category, trace,
);
} else {
results = await this.hybridRetrieval(
query, safeLimit, scopeFilter, category, trace,
);
}
const mode = tagTokens.length > 0 ? "bm25"
: (this.config.mode === "vector" || !this.store.hasFtsSupport) ? "vector" : "hybrid";
const finalTrace = trace.finalize(query, mode);
if (this._statsCollector) {
this._statsCollector.recordQuery(finalTrace, source || "debug");
}
if (this.accessTracker && source === "manual" && results.length > 0) {
this.accessTracker.recordAccess(results.map((r) => r.entry.id));
}
return { results, trace: finalTrace };
}
private extractTagTokens(query: string): string[] {
if (!this.config.tagPrefixes?.length) return [];
const pattern = this.config.tagPrefixes.join("|");
const regex = new RegExp(`(?:${pattern}):[\\w-]+`, "gi");
const matches = query.match(regex);
return matches || [];
}
private async vectorOnlyRetrieval(
query: string,
limit: number,
scopeFilter?: string[],
category?: string,
trace?: TraceCollector,
): Promise<RetrievalResult[]> {
const queryVector = await this.embedder.embedQuery(query);
trace?.startStage("vector_search", []);
const results = await this.store.vectorSearch(
queryVector, limit, this.config.minScore, scopeFilter, { excludeInactive: true },
);
const filtered = category
? results.filter((r) => r.entry.category === category) : results;
const mapped = filtered.map(
(result, index) =>
({ ...result, sources: { vector: { score: result.score, rank: index + 1 } } }) as RetrievalResult,
);
if (trace) {
trace.endStage(mapped.map((r) => r.entry.id), mapped.map((r) => r.score));
}
let weighted: RetrievalResult[];
if (this.decayEngine) {
weighted = mapped;
} else {
trace?.startStage("recency_boost", mapped.map((r) => r.entry.id));
const boosted = this.applyRecencyBoost(mapped);
trace?.endStage(boosted.map((r) => r.entry.id), boosted.map((r) => r.score));
trace?.startStage("importance_weight", boosted.map((r) => r.entry.id));
weighted = this.applyImportanceWeight(boosted);
trace?.endStage(weighted.map((r) => r.entry.id), weighted.map((r) => r.score));
}
trace?.startStage("length_normalization", weighted.map((r) => r.entry.id));
const lengthNormalized = this.applyLengthNormalization(weighted);
trace?.endStage(lengthNormalized.map((r) => r.entry.id), lengthNormalized.map((r) => r.score));
trace?.startStage("hard_cutoff", lengthNormalized.map((r) => r.entry.id));
const hardFiltered = lengthNormalized.filter(r => r.score >= this.config.hardMinScore);
trace?.endStage(hardFiltered.map((r) => r.entry.id), hardFiltered.map((r) => r.score));
const decayStageName = this.decayEngine ? "decay_boost" : "time_decay";
trace?.startStage(decayStageName, hardFiltered.map((r) => r.entry.id));
const lifecycleRanked = this.decayEngine
? this.applyDecayBoost(hardFiltered)
: this.applyTimeDecay(hardFiltered);
trace?.endStage(lifecycleRanked.map((r) => r.entry.id), lifecycleRanked.map((r) => r.score));
trace?.startStage("noise_filter", lifecycleRanked.map((r) => r.entry.id));
const denoised = this.config.filterNoise
? filterNoise(lifecycleRanked, r => r.entry.text)
: lifecycleRanked;
trace?.endStage(denoised.map((r) => r.entry.id), denoised.map((r) => r.score));
trace?.startStage("mmr_diversity", denoised.map((r) => r.entry.id));
const deduplicated = this.applyMMRDiversity(denoised);
const finalResults = deduplicated.slice(0, limit);
trace?.endStage(finalResults.map((r) => r.entry.id), finalResults.map((r) => r.score));
return finalResults;
}
private async bm25OnlyRetrieval(
query: string,
tagTokens: string[],
limit: number,
scopeFilter?: string[],
category?: string,
trace?: TraceCollector,
): Promise<RetrievalResult[]> {
const candidatePoolSize = Math.max(this.config.candidatePoolSize, limit * 2);
trace?.startStage("bm25_search", []);
const bm25Results = await this.store.bm25Search(
query, candidatePoolSize, scopeFilter, { excludeInactive: true },
);
const categoryFiltered = category
? bm25Results.filter((r) => r.entry.category === category) : bm25Results;
const mustContainFiltered = categoryFiltered.filter((r) => {
const textLower = r.entry.text.toLowerCase();
return tagTokens.every((t) => textLower.includes(t.toLowerCase()));
});
const mapped = mustContainFiltered.map(
(result, index) =>
({ ...result, sources: { bm25: { score: result.score, rank: index + 1 } } }) as RetrievalResult,
);
trace?.endStage(mapped.map((r) => r.entry.id), mapped.map((r) => r.score));
let temporallyRanked: RetrievalResult[];
if (this.decayEngine) {
temporallyRanked = mapped;
} else {
trace?.startStage("recency_boost", mapped.map((r) => r.entry.id));
const boosted = this.applyRecencyBoost(mapped);
trace?.endStage(boosted.map((r) => r.entry.id), boosted.map((r) => r.score));
trace?.startStage("importance_weight", boosted.map((r) => r.entry.id));
temporallyRanked = this.applyImportanceWeight(boosted);
trace?.endStage(temporallyRanked.map((r) => r.entry.id), temporallyRanked.map((r) => r.score));
}
trace?.startStage("length_normalization", temporallyRanked.map((r) => r.entry.id));
const lengthNormalized = this.applyLengthNormalization(temporallyRanked);
trace?.endStage(lengthNormalized.map((r) => r.entry.id), lengthNormalized.map((r) => r.score));
trace?.startStage("hard_cutoff", lengthNormalized.map((r) => r.entry.id));
const hardFiltered = lengthNormalized.filter(r => r.score >= this.config.hardMinScore);
trace?.endStage(hardFiltered.map((r) => r.entry.id), hardFiltered.map((r) => r.score));
const decayStageName = this.decayEngine ? "decay_boost" : "time_decay";
trace?.startStage(decayStageName, hardFiltered.map((r) => r.entry.id));
const lifecycleRanked = this.decayEngine
? this.applyDecayBoost(hardFiltered) : this.applyTimeDecay(hardFiltered);
trace?.endStage(lifecycleRanked.map((r) => r.entry.id), lifecycleRanked.map((r) => r.score));
trace?.startStage("noise_filter", lifecycleRanked.map((r) => r.entry.id));
const denoised = this.config.filterNoise
? filterNoise(lifecycleRanked, r => r.entry.text) : lifecycleRanked;
trace?.endStage(denoised.map((r) => r.entry.id), denoised.map((r) => r.score));
trace?.startStage("mmr_diversity", denoised.map((r) => r.entry.id));
const deduplicated = this.applyMMRDiversity(denoised);
const finalResults = deduplicated.slice(0, limit);
trace?.endStage(finalResults.map((r) => r.entry.id), finalResults.map((r) => r.score));
return finalResults;
}
private async hybridRetrieval(
query: string,
limit: number,
scopeFilter?: string[],
category?: string,
trace?: TraceCollector,
): Promise<RetrievalResult[]> {
const candidatePoolSize = Math.max(this.config.candidatePoolSize, limit * 2);
const queryVector = await this.embedder.embedQuery(query);
// Run vector and BM25 searches in parallel.
// Trace as a single "parallel_search" stage since both run concurrently —
// splitting into separate sequential stages would misrepresent timing.
trace?.startStage("parallel_search", []);
const [vectorResults, bm25Results] = await Promise.all([
this.runVectorSearch(queryVector, candidatePoolSize, scopeFilter, category),
this.runBM25Search(query, candidatePoolSize, scopeFilter, category),
]);
if (trace) {
const allSearchIds = [
...new Set([...vectorResults.map((r) => r.entry.id), ...bm25Results.map((r) => r.entry.id)]),
];
const allScores = [...vectorResults.map((r) => r.score), ...bm25Results.map((r) => r.score)];
trace.endStage(allSearchIds, allScores);
}
// Fuse results using RRF
const allInputIds = [
...new Set([...vectorResults.map((r) => r.entry.id), ...bm25Results.map((r) => r.entry.id)]),
];
trace?.startStage("rrf_fusion", allInputIds);
const fusedResults = await this.fuseResults(vectorResults, bm25Results);
trace?.endStage(fusedResults.map((r) => r.entry.id), fusedResults.map((r) => r.score));
// Apply minimum score threshold
trace?.startStage("min_score_filter", fusedResults.map((r) => r.entry.id));
const filtered = fusedResults.filter((r) => r.score >= this.config.minScore);
trace?.endStage(filtered.map((r) => r.entry.id), filtered.map((r) => r.score));
// Rerank if enabled — only emit trace stage when rerank actually runs
let reranked: RetrievalResult[];
if (this.config.rerank !== "none") {
trace?.startStage("rerank", filtered.map((r) => r.entry.id));
reranked = await this.rerankResults(query, queryVector, filtered.slice(0, limit * 2));
trace?.endStage(reranked.map((r) => r.entry.id), reranked.map((r) => r.score));
} else {
reranked = filtered;
}
let temporallyRanked: RetrievalResult[];
if (this.decayEngine) {
temporallyRanked = reranked;
} else {
trace?.startStage("recency_boost", reranked.map((r) => r.entry.id));
const boosted = this.applyRecencyBoost(reranked);
trace?.endStage(boosted.map((r) => r.entry.id), boosted.map((r) => r.score));
trace?.startStage("importance_weight", boosted.map((r) => r.entry.id));
temporallyRanked = this.applyImportanceWeight(boosted);
trace?.endStage(temporallyRanked.map((r) => r.entry.id), temporallyRanked.map((r) => r.score));
}
trace?.startStage("length_normalization", temporallyRanked.map((r) => r.entry.id));
const lengthNormalized = this.applyLengthNormalization(temporallyRanked);
trace?.endStage(lengthNormalized.map((r) => r.entry.id), lengthNormalized.map((r) => r.score));
trace?.startStage("hard_cutoff", lengthNormalized.map((r) => r.entry.id));
const hardFiltered = lengthNormalized.filter(r => r.score >= this.config.hardMinScore);
trace?.endStage(hardFiltered.map((r) => r.entry.id), hardFiltered.map((r) => r.score));
const decayStageName = this.decayEngine ? "decay_boost" : "time_decay";
trace?.startStage(decayStageName, hardFiltered.map((r) => r.entry.id));
const lifecycleRanked = this.decayEngine
? this.applyDecayBoost(hardFiltered) : this.applyTimeDecay(hardFiltered);
trace?.endStage(lifecycleRanked.map((r) => r.entry.id), lifecycleRanked.map((r) => r.score));
trace?.startStage("noise_filter", lifecycleRanked.map((r) => r.entry.id));
const denoised = this.config.filterNoise
? filterNoise(lifecycleRanked, r => r.entry.text) : lifecycleRanked;
trace?.endStage(denoised.map((r) => r.entry.id), denoised.map((r) => r.score));
trace?.startStage("mmr_diversity", denoised.map((r) => r.entry.id));
const deduplicated = this.applyMMRDiversity(denoised);
const finalResults = deduplicated.slice(0, limit);
trace?.endStage(finalResults.map((r) => r.entry.id), finalResults.map((r) => r.score));
return finalResults;
}
private async runVectorSearch(
queryVector: number[],
limit: number,
scopeFilter?: string[],
category?: string,
): Promise<Array<MemorySearchResult & { rank: number }>> {
const results = await this.store.vectorSearch(
queryVector,
limit,
0.1,
scopeFilter,
{ excludeInactive: true },
);
// Filter by category if specified
const filtered = category
? results.filter((r) => r.entry.category === category)
: results;
return filtered.map((result, index) => ({
...result,
rank: index + 1,
}));
}
private async runBM25Search(
query: string,
limit: number,
scopeFilter?: string[],
category?: string,
): Promise<Array<MemorySearchResult & { rank: number }>> {
const results = await this.store.bm25Search(query, limit, scopeFilter, { excludeInactive: true });
// Filter by category if specified
const filtered = category
? results.filter((r) => r.entry.category === category)
: results;
return filtered.map((result, index) => ({
...result,
rank: index + 1,
}));
}
private async fuseResults(
vectorResults: Array<MemorySearchResult & { rank: number }>,
bm25Results: Array<MemorySearchResult & { rank: number }>,
): Promise<RetrievalResult[]> {
// Create maps for quick lookup
const vectorMap = new Map<string, MemorySearchResult & { rank: number }>();
const bm25Map = new Map<string, MemorySearchResult & { rank: number }>();
vectorResults.forEach((result) => {
vectorMap.set(result.entry.id, result);
});
bm25Results.forEach((result) => {
bm25Map.set(result.entry.id, result);
});
// Get all unique document IDs
const allIds = new Set([...vectorMap.keys(), ...bm25Map.keys()]);
// Calculate RRF scores
const fusedResults: RetrievalResult[] = [];
for (const id of allIds) {
const vectorResult = vectorMap.get(id);
const bm25Result = bm25Map.get(id);
// FIX(#15): BM25-only results may be "ghost" entries whose vector data was
// deleted but whose FTS index entry lingers until the next index rebuild.
// Validate that the entry actually exists in the store before including it.
if (!vectorResult && bm25Result) {
try {
const exists = await this.store.hasId(id);
if (!exists) continue; // Skip ghost entry
} catch {
// If hasId fails, keep the result (fail-open)
}
}
// Use the result with more complete data (prefer vector result if both exist)
const baseResult = vectorResult || bm25Result!;
// Use vector similarity as the base score.
// BM25 hit acts as a bonus (keyword match confirms relevance).
const vectorScore = vectorResult ? vectorResult.score : 0;
const bm25Score = bm25Result ? bm25Result.score : 0;
// Weighted fusion: vectorWeight/bm25Weight directly control score blending.
// BM25 high-score floor (>= 0.75) preserves exact keyword matches
// (e.g. API keys, ticket numbers) that may have low vector similarity.
const weightedFusion = (vectorScore * this.config.vectorWeight)
+ (bm25Score * this.config.bm25Weight);
const fusedScore = vectorResult
? clamp01(
Math.max(
weightedFusion,
bm25Score >= 0.75 ? bm25Score * 0.92 : 0,
),
0.1,
)
: clamp01(bm25Result!.score, 0.1);
fusedResults.push({
entry: baseResult.entry,
score: fusedScore,
sources: {
vector: vectorResult
? { score: vectorResult.score, rank: vectorResult.rank }
: undefined,
bm25: bm25Result
? { score: bm25Result.score, rank: bm25Result.rank }
: undefined,
fused: { score: fusedScore },
},
});
}
// Sort by fused score descending
return fusedResults.sort((a, b) => b.score - a.score);
}
/**
* Rerank results using cross-encoder API (Jina, Pinecone, or compatible).
* Falls back to cosine similarity if API is unavailable or fails.
*/
private async rerankResults(
query: string,
queryVector: number[],
results: RetrievalResult[],
): Promise<RetrievalResult[]> {
if (results.length === 0) {
return results;
}
// Try cross-encoder rerank via configured provider API
if (this.config.rerank === "cross-encoder" && this.config.rerankApiKey) {
try {
const provider = this.config.rerankProvider || "jina";
const model = this.config.rerankModel || "jina-reranker-v3";
const endpoint =
this.config.rerankEndpoint || "https://api.jina.ai/v1/rerank";
const documents = results.map((r) => r.entry.text);
// Build provider-specific request
const { headers, body } = buildRerankRequest(
provider,
this.config.rerankApiKey,
model,
query,
documents,
results.length,
);
// Timeout: configurable via rerankTimeoutMs (default: 5000ms)
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), this.config.rerankTimeoutMs ?? 5000);
let response: Response;
try {
response = await fetch(endpoint, {
method: "POST",
headers,
body: JSON.stringify(body),
signal: controller.signal,
});
} finally {
clearTimeout(timeout);
}
if (response.ok) {
const data: unknown = await response.json();
// Parse provider-specific response into unified format
const parsed = parseRerankResponse(provider, data);
if (!parsed) {
console.warn(
"Rerank API: invalid response shape, falling back to cosine",
);
} else {
// Build a Set of returned indices to identify unreturned candidates
const returnedIndices = new Set(parsed.map((r) => r.index));
const reranked = parsed
.filter((item) => item.index >= 0 && item.index < results.length)
.map((item) => {
const original = results[item.index];
const floor = this.getRerankPreservationFloor(original, false);
// Blend: 60% cross-encoder score + 40% original fused score
const blendedScore = clamp01WithFloor(
item.score * 0.6 + original.score * 0.4,
floor,
);
return {
...original,
score: blendedScore,
sources: {
...original.sources,
reranked: { score: item.score },
},
};
});
// Keep unreturned candidates with their original scores (slightly penalized)
const unreturned = results
.filter((_, idx) => !returnedIndices.has(idx))
.map(r => ({
...r,
score: clamp01WithFloor(
r.score * 0.8,
this.getRerankPreservationFloor(r, true),
),
}));
return [...reranked, ...unreturned].sort(
(a, b) => b.score - a.score,
);
}
} else {
const errText = await response.text().catch(() => "");
console.warn(
`Rerank API returned ${response.status}: ${errText.slice(0, 200)}, falling back to cosine`,
);
}
} catch (error) {
if (error instanceof Error && error.name === "AbortError") {
console.warn(`Rerank API timed out (${this.config.rerankTimeoutMs ?? 5000}ms), falling back to cosine`);
} else {
console.warn("Rerank API failed, falling back to cosine:", error);
}
}
}
// Fallback: lightweight cosine similarity rerank
try {
const reranked = results.map((result) => {
const cosineScore = cosineSimilarity(queryVector, result.entry.vector);
const combinedScore = result.score * 0.7 + cosineScore * 0.3;
return {
...result,
score: clamp01(combinedScore, result.score),
sources: {
...result.sources,
reranked: { score: cosineScore },
},
};
});
return reranked.sort((a, b) => b.score - a.score);
} catch (error) {
console.warn("Reranking failed, returning original results:", error);
return results;
}
}
private getRerankPreservationFloor(result: RetrievalResult, unreturned: boolean): number {
const bm25Score = result.sources.bm25?.score ?? 0;
// Exact lexical hits (IDs, env vars, ticket numbers) should not disappear
// just because a reranker under-scores symbolic or mixed-language queries.
if (bm25Score >= 0.75) {
return result.score * (unreturned ? 1.0 : 0.95);
}
if (bm25Score >= 0.6) {
return result.score * (unreturned ? 0.95 : 0.9);
}
return result.score * (unreturned ? 0.8 : 0.5);
}
/**
* Apply recency boost: newer memories get a small score bonus.
* This ensures corrections/updates naturally outrank older entries
* when semantic similarity is close.
* Formula: boost = exp(-ageDays / halfLife) * weight
*/
private applyRecencyBoost(results: RetrievalResult[]): RetrievalResult[] {
const { recencyHalfLifeDays, recencyWeight } = this.config;
if (!recencyHalfLifeDays || recencyHalfLifeDays <= 0 || !recencyWeight) {
return results;
}
const now = Date.now();
const boosted = results.map((r) => {
const ts =
r.entry.timestamp && r.entry.timestamp > 0 ? r.entry.timestamp : now;
const ageDays = (now - ts) / 86_400_000;
const boost = Math.exp(-ageDays / recencyHalfLifeDays) * recencyWeight;
return {
...r,