{"version":3,"file":"couchbase.mjs","names":["#opts","#phys","#cluster","#cb","#bucket","#sleep","#scope","#scopeName","#coll","#dims","#metrics","#vecIndexName","#parseMeta","#project"],"sources":["../../../src/batteries/vector/couchbase/index.ts"],"sourcesContent":["/**\n * @module @nhtio/adk/batteries/vector/couchbase\n *\n * Couchbase (Enterprise) vector-store adapter. Vector search is Enterprise Edition only; the\n * Community Edition will throw \"vector typed fields not supported\". A logical collection maps to a\n * Couchbase scope.collection inside a bucket. KV operations (upsert/get/remove) use strong\n * consistency, while the scoped FTS vector index is async (settle-polled after writes).\n *\n * Scoring rule: do NOT trust FTS native scores (they vary per metric and are unbounded for\n * euclidean/l2_norm). Instead, use FTS only to retrieve the candidate id set, then KV-get each\n * candidate doc and RE-COMPUTE the similarity score from the stored vec vs query vec using\n * normalizeScore — guaranteeing the [0,1] contract regardless of backend metric quirks.\n *\n * Filter-scan, enumerate, and delete-by-filter use N1QL with RequestPlus for strong consistency.\n */\n\nimport { evaluateFilter } from '../filters'\nimport { normalizeScore } from '../helpers'\nimport { BaseVectorStore } from '../contract'\nimport { validateRecords } from '../validation'\nimport { isInstanceOf } from '@nhtio/adk/guards'\nimport {\n E_VECTOR_STORE_DRIVER_UNAVAILABLE,\n E_VECTOR_STORE_CONNECTION_FAILED,\n E_VECTOR_STORE_COLLECTION_FAILED,\n E_VECTOR_STORE_UPSERT_FAILED,\n E_VECTOR_STORE_SEARCH_FAILED,\n E_VECTOR_STORE_DELETE_FAILED,\n E_VECTOR_STORE_DIMENSION_MISMATCH,\n E_VECTOR_STORE_UNSUPPORTED_OPERATION,\n} from '../exceptions'\nimport type { SearchPlan, UpsertPlan, DeletePlan, CollectionSpec } from '../plan'\nimport type {\n VectorMatch,\n VectorStoreCapabilities,\n BaseVectorStoreOptions,\n VectorMetadata,\n DistanceMetric,\n} from '../types'\n\nconst similarity = (metric: DistanceMetric): string =>\n metric === 'cosine' ? 'cosine' : metric === 'dot' ? 'dot_product' : 'l2_norm'\n\nconst cosineSim = (a: number[], b: number[]): number => {\n let dot = 0\n let normA = 0\n let normB = 0\n a.forEach((av, i) => {\n const bv = b[i]\n dot += av * bv\n normA += av * av\n normB += bv * bv\n })\n const denom = Math.sqrt(normA) * Math.sqrt(normB)\n return denom === 0 ? 0 : dot / denom\n}\n\nconst dotProd = (a: number[], b: number[]): number => {\n let s = 0\n a.forEach((av, i) => {\n s += av * b[i]\n })\n return s\n}\n\nconst euclideanDist = (a: number[], b: number[]): number => {\n let s = 0\n a.forEach((av, i) => {\n const d = av - b[i]\n s += d * d\n })\n return Math.sqrt(s)\n}\n\nconst computeScore = (vec: number[], query: number[], metric: DistanceMetric): number => {\n if (metric === 'cosine') {\n const raw = cosineSim(vec, query)\n return normalizeScore(raw, 'cosine', 'similarity')\n } else if (metric === 'dot') {\n const raw = dotProd(vec, query)\n return normalizeScore(raw, 'dot', 'similarity')\n } else if (metric === 'euclidean') {\n const raw = euclideanDist(vec, query)\n return normalizeScore(raw, 'euclidean', 'distance')\n } else {\n // Fallback\n const raw = cosineSim(vec, query)\n return normalizeScore(raw, 'cosine', 'similarity')\n }\n}\n\nconst getCB = async (): Promise => {\n try {\n return await import('couchbase')\n } catch {\n throw new E_VECTOR_STORE_DRIVER_UNAVAILABLE(['couchbase'])\n }\n}\n\nexport interface CouchbaseVectorStoreOptions extends BaseVectorStoreOptions {\n /** Connection and authentication parameters for the backend. */\n connection: {\n url: string\n username: string\n password: string\n bucket: string\n scope?: string\n /**\n * When set, the physical Couchbase collection becomes `${collectionPrefix}${collection}`. The\n * logical collection the builder/base see is unchanged. Lets callers (and the test suite)\n * isolate otherwise identically-named collections — useful because the scoped FTS vector index\n * builds asynchronously, so a fresh collection per use avoids drop+rebuild-index churn.\n */\n collectionPrefix?: string\n }\n}\n\nexport class CouchbaseVectorStore extends BaseVectorStore {\n readonly capabilities: VectorStoreCapabilities = {\n transactions: false,\n namedVectors: false,\n rename: false,\n rawSql: false,\n builtInEncoding: false,\n consistency: { configurable: false, default: 'strong', modes: ['strong'] },\n }\n\n #cluster: any | null = null\n #bucket: any | null = null\n #cb: any | null = null\n #dims: Map = new Map()\n #metrics: Map = new Map()\n\n get #opts(): CouchbaseVectorStoreOptions {\n return this.options as CouchbaseVectorStoreOptions\n }\n\n #scopeName(): string {\n return this.#opts.connection.scope ?? '_default'\n }\n\n // Map a logical collection name to its physical Couchbase collection (optional prefix).\n #phys(collection: string): string {\n const prefix = this.#opts.connection.collectionPrefix\n return prefix ? `${prefix}${collection}` : collection\n }\n\n #vecIndexName(collection: string): string {\n return `${this.#phys(collection)}_vec`\n }\n\n /** Static availability probe: whether this adapter's runtime driver can load in the current environment. */\n static isAvailable(): boolean {\n return typeof process !== 'undefined'\n }\n\n isAvailable(): boolean {\n return typeof process !== 'undefined'\n }\n\n async connect(): Promise {\n if (this.#cluster) return\n const couchbase = await getCB()\n this.#cb = couchbase\n const c = this.#opts.connection\n try {\n this.#cluster = await couchbase.connect(c.url, { username: c.username, password: c.password })\n this.#bucket = this.#cluster.bucket(c.bucket)\n } catch (err) {\n throw new E_VECTOR_STORE_CONNECTION_FAILED([String(err)])\n }\n }\n\n async close(): Promise {\n if (this.#cluster) {\n await this.#cluster.close()\n this.#cluster = null\n this.#bucket = null\n }\n }\n\n async #sleep(ms: number): Promise {\n await new Promise((r) => setTimeout(r, ms))\n }\n\n async #scope(): Promise {\n if (!this.#bucket) await this.connect()\n return this.#bucket.scope(this.#scopeName())\n }\n\n async #coll(scope: any, collection: string): Promise {\n return scope.collection(this.#phys(collection))\n }\n\n async createCollection(spec: CollectionSpec, ifNotExists: boolean): Promise {\n if (!this.#bucket) await this.connect()\n const cm = this.#bucket.collections()\n const scopeName = this.#scopeName()\n this.#dims.set(spec.collection, spec.vector.dimensions)\n this.#metrics.set(spec.collection, spec.vector.metric)\n\n // Physical collection name (logical name + optional prefix); the FTS index, N1QL identifiers\n // and the collection-manager ops all key off the physical name.\n const collectionName = this.#phys(spec.collection)\n const indexName = this.#vecIndexName(spec.collection)\n\n try {\n // Create scope.collection\n try {\n await cm.createCollection({ name: collectionName, scopeName })\n } catch (err: any) {\n if (err.message && err.message.includes('CollectionExists')) {\n if (ifNotExists) {\n // Clear existing collection\n try {\n await this.#cluster.query(\n `DELETE FROM \\`${this.#opts.connection.bucket}\\`.\\`${scopeName}\\`.\\`${collectionName}\\``\n )\n } catch {\n // Ignore cleanup failures\n }\n } else {\n // Drop and recreate\n await cm.dropCollection(collectionName, scopeName)\n await cm.createCollection({ name: collectionName, scopeName })\n }\n } else {\n throw err\n }\n }\n\n // Wait for collection to be ready\n await this.#sleep(1000)\n\n // Create primary index\n try {\n await this.#cluster.query(\n `CREATE PRIMARY INDEX ON \\`${this.#opts.connection.bucket}\\`.\\`${scopeName}\\`.\\`${collectionName}\\``\n )\n } catch {\n // Ignore if already exists\n }\n\n // Create FTS vector index\n const mgr = this.#bucket.scope(scopeName).searchIndexes()\n try {\n await mgr.upsertIndex({\n name: indexName,\n sourceType: 'gocbcore',\n sourceName: this.#opts.connection.bucket,\n type: 'fulltext-index',\n planParams: { indexPartitions: 1 },\n params: {\n doc_config: {\n mode: 'scope.collection.type_field',\n type_field: 'type',\n },\n mapping: {\n default_mapping: { enabled: false },\n default_analyzer: 'standard',\n default_type: '_default',\n types: {\n [`${scopeName}.${collectionName}`]: {\n enabled: true,\n dynamic: false,\n properties: {\n vec: {\n enabled: true,\n fields: [\n {\n name: 'vec',\n type: 'vector',\n dims: spec.vector.dimensions,\n similarity: similarity(spec.vector.metric),\n index: true,\n },\n ],\n },\n },\n },\n },\n },\n },\n })\n } catch {\n // Ignore if index already exists\n }\n\n // Poll until index is queryable\n const scope = this.#bucket.scope(scopeName)\n const probe = new Array(spec.vector.dimensions).fill(0)\n probe[0] = 1\n for (let i = 0; i < 60; i++) {\n try {\n const req = this.#cb.SearchRequest.create(\n this.#cb.VectorSearch.fromVectorQuery(\n this.#cb.VectorQuery.create('vec', probe).numCandidates(10)\n )\n )\n await scope.search(indexName, req, { timeout: 10000 })\n break\n } catch {\n await this.#sleep(1000)\n }\n }\n } catch (err) {\n throw new E_VECTOR_STORE_COLLECTION_FAILED(['createCollection', String(err)])\n }\n }\n\n async dropCollection(collection: string, ifExists: boolean): Promise {\n if (!this.#bucket) await this.connect()\n const cm = this.#bucket.collections()\n const scopeName = this.#scopeName()\n const physical = this.#phys(collection)\n const indexName = this.#vecIndexName(collection)\n const mgr = this.#bucket.scope(scopeName).searchIndexes()\n\n try {\n // Drop FTS index\n try {\n await mgr.dropIndex(indexName)\n } catch {\n // Ignore if index doesn't exist\n }\n\n // Drop collection\n try {\n const scopes = await cm.getAllScopes()\n const scope = scopes.find((s: any) => s.name === scopeName)\n if (!scope) {\n if (ifExists) return\n throw new Error(`scope not found: ${scopeName}`)\n }\n const coll = scope.collections?.find((c: any) => c.name === physical)\n if (!coll) {\n if (ifExists) return\n throw new Error(`collection not found: ${collection}`)\n }\n } catch (err: any) {\n if (err.message && err.message.includes('not found')) {\n if (ifExists) return\n throw err\n }\n throw err\n }\n\n await cm.dropCollection(physical, scopeName)\n this.#dims.delete(collection)\n this.#metrics.delete(collection)\n } catch (err) {\n throw new E_VECTOR_STORE_COLLECTION_FAILED(['dropCollection', String(err)])\n }\n }\n\n async hasCollection(collection: string): Promise {\n if (!this.#bucket) await this.connect()\n const cm = this.#bucket.collections()\n const scopeName = this.#scopeName()\n try {\n const scopes = await cm.getAllScopes()\n const scope = scopes.find((s: any) => s.name === scopeName)\n if (!scope) return false\n return !!scope.collections?.find((c: any) => c.name === this.#phys(collection))\n } catch {\n return false\n }\n }\n\n async renameCollection(_from: string, _to: string): Promise {\n throw new E_VECTOR_STORE_UNSUPPORTED_OPERATION(['renameCollection', 'couchbase'])\n }\n\n async executeUpsert(plan: UpsertPlan): Promise {\n if (plan.records.length === 0) return\n validateRecords(plan.records)\n const scope = await this.#scope()\n const coll = await this.#coll(scope, plan.collection)\n const expected = this.#opts.dimensions ?? this.#dims.get(plan.collection)\n const dims = expected ?? 0\n const indexName = this.#vecIndexName(plan.collection)\n\n try {\n const ids: string[] = []\n for (const r of plan.records) {\n let vector = r.vector\n if (!vector && r.document) {\n const [v] = await this.encode([r.document], 'document')\n vector = v\n }\n if (!vector) {\n throw new E_VECTOR_STORE_UPSERT_FAILED(['Record missing vector and document'])\n }\n if (expected !== undefined && vector.length !== expected) {\n throw new E_VECTOR_STORE_DIMENSION_MISMATCH([expected, vector.length])\n }\n await coll.upsert(r.id, {\n id: r.id,\n vec: vector,\n document: r.document ?? '',\n metadata: r.metadata ? JSON.stringify(r.metadata) : '{}',\n type: plan.collection,\n })\n ids.push(r.id)\n }\n\n // Poll until FTS index shows all ids\n const probe = new Array(dims).fill(0)\n probe[0] = 1\n const deadline = Date.now() + 10_000\n while (Date.now() < deadline) {\n let seen = new Set()\n try {\n const req = this.#cb.SearchRequest.create(\n this.#cb.VectorSearch.fromVectorQuery(\n this.#cb.VectorQuery.create('vec', probe).numCandidates(100)\n )\n )\n const res: any = await scope.search(indexName, req, { timeout: 10000 })\n seen = new Set(res.rows?.map((row: any) => row.id) ?? [])\n } catch {\n // Continue polling\n }\n if (ids.every((id) => seen.has(id))) break\n await this.#sleep(300)\n }\n } catch (err) {\n if (\n isInstanceOf(err, 'E_VECTOR_STORE_DIMENSION_MISMATCH', E_VECTOR_STORE_DIMENSION_MISMATCH) ||\n isInstanceOf(err, 'E_VECTOR_STORE_UPSERT_FAILED', E_VECTOR_STORE_UPSERT_FAILED)\n ) {\n throw err\n }\n throw new E_VECTOR_STORE_UPSERT_FAILED([String(err)])\n }\n }\n\n async executeSearch(plan: SearchPlan): Promise {\n const scope = await this.#scope()\n const coll = await this.#coll(scope, plan.collection)\n const indexName = this.#vecIndexName(plan.collection)\n const metric: DistanceMetric =\n this.#metrics.get(plan.collection) ?? this.#opts.metric ?? 'cosine'\n const oversample = 10\n\n let queryVector: number[] | undefined\n if (plan.near) {\n if ('vector' in plan.near) {\n queryVector = plan.near.vector\n } else if ('serverText' in plan.near) {\n const [v] = await this.encode([plan.near.serverText], 'query')\n queryVector = v\n } else if ('id' in plan.near) {\n try {\n const got: any = await coll.get(plan.near.id)\n queryVector = got.content?.vec\n if (!queryVector) {\n throw new E_VECTOR_STORE_SEARCH_FAILED(['Referenced id not found: ' + plan.near.id])\n }\n } catch (err: any) {\n if (err.message && err.message.includes('DocumentNotFound')) {\n throw new E_VECTOR_STORE_SEARCH_FAILED(['Referenced id not found: ' + plan.near.id])\n }\n throw new E_VECTOR_STORE_SEARCH_FAILED([String(err)])\n }\n }\n }\n\n const offset = plan.offset ?? 0\n\n try {\n if (queryVector) {\n // Similarity search: FTS for candidates, then KV-get and re-score\n const k = (plan.filter ? 1000 : (plan.topK + offset) * oversample) || 100\n const req = this.#cb.SearchRequest.create(\n this.#cb.VectorSearch.fromVectorQuery(\n this.#cb.VectorQuery.create('vec', queryVector).numCandidates(Math.max(k, 100))\n )\n )\n const res: any = await scope.search(indexName, req, { timeout: 10000 })\n const candidateIds = res.rows?.map((row: any) => row.id) ?? []\n\n // KV-get each candidate to get full doc\n const candidates: any[] = []\n for (const id of candidateIds) {\n try {\n const got: any = await coll.get(id)\n candidates.push(got.content)\n } catch {\n // Skip docs that can't be read\n }\n }\n\n // Apply filter and compute scores\n const filtered = candidates.filter((d) =>\n evaluateFilter(plan.filter ?? {}, this.#parseMeta(d.metadata))\n )\n\n // Re-compute scores and sort\n const scored = filtered.map((d) => ({\n ...d,\n score: computeScore(d.vec, queryVector, metric),\n }))\n scored.sort((a, b) => b.score - a.score)\n\n return scored\n .slice(offset, offset + plan.topK)\n .map((d: any) => this.#project(d, plan, true))\n } else {\n // Filter-scan: N1QL\n const bucket = this.#opts.connection.bucket\n const scopeName = this.#scopeName()\n const q = await this.#cluster.query(\n `SELECT d.id, d.vec, d.document, d.metadata FROM \\`${bucket}\\`.\\`${scopeName}\\`.\\`${this.#phys(plan.collection)}\\` d`,\n { scanConsistency: this.#cb.QueryScanConsistency.RequestPlus }\n )\n const docs: any[] = q.rows ?? []\n\n const filtered = docs.filter((d) =>\n evaluateFilter(plan.filter ?? {}, this.#parseMeta(d.metadata))\n )\n\n return filtered\n .slice(offset, offset + plan.topK)\n .map((d: any) => this.#project(d, plan, false))\n }\n } catch (err: any) {\n if (err?.message?.includes('Referenced id not found')) {\n throw err\n }\n throw new E_VECTOR_STORE_SEARCH_FAILED([String(err)])\n }\n }\n\n #parseMeta(val: unknown): VectorMetadata {\n if (typeof val === 'string') {\n try {\n return JSON.parse(val) as VectorMetadata\n } catch {\n return {}\n }\n }\n if (val && typeof val === 'object') return val as VectorMetadata\n return {}\n }\n\n #project(doc: any, plan: SearchPlan, hasScore: boolean): VectorMatch {\n const proj = plan.projection\n const out: VectorMatch = {}\n if (proj.id) out.id = doc.id as string\n if (proj.vector && Array.isArray(doc.vec)) out.vector = (doc.vec as number[]).map(Number)\n if (proj.document) out.document = (doc.document ?? undefined) as string | undefined\n if (proj.metadata) out.metadata = this.#parseMeta(doc.metadata)\n if (hasScore && typeof doc.score === 'number') {\n out.score = doc.score\n }\n return out\n }\n\n async executeDelete(plan: DeletePlan): Promise {\n const scope = await this.#scope()\n const coll = await this.#coll(scope, plan.collection)\n const bucket = this.#opts.connection.bucket\n const scopeName = this.#scopeName()\n\n try {\n if (plan.ids && plan.ids.length > 0) {\n // Delete by ids\n for (const id of plan.ids) {\n try {\n await coll.remove(id)\n } catch {\n // Ignore DocumentNotFoundError\n }\n }\n } else if (plan.filter) {\n // Enumerate via N1QL, filter, then delete\n const q = await this.#cluster.query(\n `SELECT d.id, d.metadata FROM \\`${bucket}\\`.\\`${scopeName}\\`.\\`${this.#phys(plan.collection)}\\` d`,\n { scanConsistency: this.#cb.QueryScanConsistency.RequestPlus }\n )\n const docs: any[] = q.rows ?? []\n\n const targets = docs\n .filter((d) => evaluateFilter(plan.filter!, this.#parseMeta(d.metadata)))\n .map((d: any) => d.id as string)\n\n for (const id of targets) {\n try {\n await coll.remove(id)\n } catch {\n // Ignore DocumentNotFoundError\n }\n }\n } else {\n // Delete all\n await this.#cluster.query(\n `DELETE FROM \\`${bucket}\\`.\\`${scopeName}\\`.\\`${this.#phys(plan.collection)}\\` d`,\n { scanConsistency: this.#cb.QueryScanConsistency.RequestPlus }\n )\n }\n } catch (err) {\n throw new E_VECTOR_STORE_DELETE_FAILED([String(err)])\n }\n 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