{"version":3,"file":"redis.mjs","names":["#opts","#client","#ensure","#fields","#materialize","#parseSearchReply","#project","#decode"],"sources":["../../../src/batteries/vector/redis/index.ts"],"sourcesContent":["/**\n * @module @nhtio/adk/batteries/vector/redis\n *\n * One adapter for the whole Redis/Valkey family. The vector engine is RediSearch\n * (`FT.CREATE` / `FT.SEARCH` with a `VECTOR` field over a HASH keyspace); records are\n * stored as Redis hashes keyed `${collection}:${id}`, with the vector packed as a\n * little-endian Float32 buffer. Metadata scalars are stored as TAG/NUMERIC fields so the\n * neutral filter tree compiles to RediSearch query syntax.\n *\n * Works against `redis/redis-stack-server` (bundles RediSearch) and any Redis/Valkey with\n * the RediSearch module loaded.\n */\n\nimport { BaseVectorStore } from '../contract'\nimport { validateRecords } from '../validation'\nimport { isInstanceOf } from '@nhtio/adk/guards'\nimport { normalizeScore, sanitizeMetadata } from '../helpers'\nimport { isFilterCondition, isRawFilter, isFilterGroup } from '../filters'\nimport {\n E_VECTOR_STORE_DRIVER_UNAVAILABLE,\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_FILTER_OPERATOR,\n E_VECTOR_STORE_UNSUPPORTED_OPERATION,\n E_VECTOR_STORE_CONNECTION_FAILED,\n} from '../exceptions'\nimport type { VectorFilter } from '../filters'\nimport type { SearchPlan, UpsertPlan, DeletePlan, CollectionSpec } from '../plan'\nimport type {\n VectorMatch,\n VectorStoreCapabilities,\n BaseVectorStoreOptions,\n VectorMetadata,\n DistanceMetric,\n} from '../types'\n\nexport interface RedisVectorStoreOptions extends BaseVectorStoreOptions {\n /** Connection and authentication parameters for the backend. */\n connection?: { url?: string; username?: string; password?: string }\n}\n\nconst getRedisClient = async () => {\n try {\n const mod = await import('redis')\n return mod.createClient\n } catch {\n throw new E_VECTOR_STORE_DRIVER_UNAVAILABLE(['redis'])\n }\n}\n\n// RediSearch index name + key prefix derived from the logical collection.\nconst indexName = (collection: string): string => `idx:${collection}`\nconst keyPrefix = (collection: string): string => `${collection}:`\nconst keyFor = (collection: string, id: string): string => `${collection}:${id}`\n\n// Pack a number[] into a little-endian Float32 Buffer (RediSearch FLOAT32 vector blob).\nconst packVector = (vector: number[]): Buffer => {\n const buf = Buffer.allocUnsafe(vector.length * 4)\n for (let i = 0; i < vector.length; i++) buf.writeFloatLE(vector[i], i * 4)\n return buf\n}\n\nconst unpackVector = (buf: Buffer): number[] => {\n const out: number[] = []\n for (let i = 0; i < buf.length; i += 4) out.push(buf.readFloatLE(i))\n return out\n}\n\n// RediSearch distance metric for the index. Cosine + L2 are native; 'dot' maps to IP.\nconst metricToRedis = (metric: DistanceMetric): string =>\n metric === 'euclidean' ? 'L2' : metric === 'dot' ? 'IP' : 'COSINE'\n\n// Escape a value for a RediSearch TAG filter (`@field:{value}`). Special chars must be escaped.\nconst escapeTag = (value: string): string =>\n value.replace(/[,.<>{}[\\]\"':;!@#$%^&*()\\-+=~|/\\\\ ]/g, '\\\\$&')\n\n/**\n * Compile the neutral filter tree to a RediSearch query fragment. TAG fields hold string /\n * boolean metadata; NUMERIC fields hold numbers. Returns a fragment to AND with the KNN\n * clause (or to use alone for a filter-scan). Throws on operators RediSearch can't express.\n */\nexport const translateRedisFilter = (filter?: VectorFilter): string => {\n if (!filter) return '*'\n if (isRawFilter(filter)) {\n if (filter.$dialect === 'redis' && typeof filter.$raw === 'string') {\n return filter.$raw\n }\n throw new E_VECTOR_STORE_UNSUPPORTED_FILTER_OPERATOR(['redis', String(filter.$dialect)])\n }\n if (isFilterCondition(filter)) {\n const { field, op, value } = filter\n const tag = (v: unknown): string => `@${field}:{${escapeTag(String(v))}}`\n const num = (lo: string, hi: string): string => `@${field}:[${lo} ${hi}]`\n // Range bounds must be finite numbers — a non-numeric value would be concatenated\n // raw into the query fragment, letting it break out of the `[lo hi]` bracket.\n const bound = (v: unknown): string => {\n const n = Number(v)\n if (!Number.isFinite(n)) {\n throw new E_VECTOR_STORE_UNSUPPORTED_FILTER_OPERATOR([\n 'redis',\n `${op} with non-numeric value`,\n ])\n }\n return String(n)\n }\n switch (op) {\n case 'eq':\n return typeof value === 'number' ? num(bound(value), bound(value)) : tag(value)\n case 'ne':\n return typeof value === 'number' ? `-${num(bound(value), bound(value))}` : `-${tag(value)}`\n case 'gt':\n return num(`(${bound(value)}`, '+inf')\n case 'gte':\n return num(bound(value), '+inf')\n case 'lt':\n return num('-inf', `(${bound(value)}`)\n case 'lte':\n return num('-inf', bound(value))\n case 'in': {\n if (!Array.isArray(value) || value.length === 0) return '@__never:{__never}'\n return `@${field}:{${value.map((v) => escapeTag(String(v))).join('|')}}`\n }\n case 'nin': {\n if (!Array.isArray(value) || value.length === 0) return '*'\n return `-@${field}:{${value.map((v) => escapeTag(String(v))).join('|')}}`\n }\n case 'exists':\n return `@${field}:*`\n default:\n throw new E_VECTOR_STORE_UNSUPPORTED_FILTER_OPERATOR(['redis', op])\n }\n }\n if (isFilterGroup(filter)) {\n const { and, or, not } = filter\n if (and) {\n const parts = and.map(translateRedisFilter).filter((p) => p !== '*')\n return parts.length === 0 ? '*' : `(${parts.join(' ')})`\n }\n if (or) {\n const parts = or.map(translateRedisFilter).filter((p) => p !== '*')\n return parts.length === 0 ? '*' : `(${parts.join(' | ')})`\n }\n if (not) {\n const inner = translateRedisFilter(not)\n return inner === '*' ? '*' : `-${inner}`\n }\n return '*'\n }\n return '*'\n}\n\nexport class RedisVectorStore extends BaseVectorStore {\n readonly capabilities: VectorStoreCapabilities = {\n transactions: false,\n namedVectors: false,\n rename: false,\n rawSql: false,\n builtInEncoding: false,\n // RediSearch indexes synchronously on HSET; a write is visible on resolve. No-op option.\n consistency: { configurable: false, default: 'strong', modes: ['strong'] },\n }\n #client: any | null = null\n // Per-collection declared metadata fields, so FT.CREATE can declare TAG/NUMERIC schema.\n #fields: Map = new Map()\n\n get #opts(): RedisVectorStoreOptions {\n return this.options as RedisVectorStoreOptions\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 isAvailable(): boolean {\n return typeof process !== 'undefined'\n }\n\n async connect(): Promise {\n if (this.#client) return\n const createClient = await getRedisClient()\n const c = this.#opts.connection || {}\n try {\n this.#client = createClient({\n url: c.url ?? 'redis://localhost:6379',\n username: c.username,\n password: c.password,\n })\n await this.#client.connect()\n } catch (err) {\n throw new E_VECTOR_STORE_CONNECTION_FAILED([String(err)])\n }\n }\n\n async close(): Promise {\n if (this.#client) {\n try {\n await this.#client.quit()\n } catch {\n // ignore quit errors on teardown\n }\n this.#client = null\n }\n }\n\n async #ensure(): Promise {\n if (!this.#client) await this.connect()\n return this.#client!\n }\n\n async createCollection(spec: CollectionSpec, ifNotExists: boolean): Promise {\n const client = await this.#ensure()\n this.#fields.set(spec.collection, spec.fields)\n if (ifNotExists && (await this.hasCollection(spec.collection))) return\n const dim = spec.vector.dimensions\n const distance = metricToRedis(spec.vector.metric)\n // Build the FT.CREATE schema: the vector field + a TAG/NUMERIC field per declared payload\n // column. Declared fields are advisory; undeclared metadata is still stored on the hash\n // (just not independently filterable).\n const schema: string[] = [\n '__vector',\n 'VECTOR',\n 'FLAT',\n '6',\n 'TYPE',\n 'FLOAT32',\n 'DIM',\n String(dim),\n 'DISTANCE_METRIC',\n distance,\n ]\n for (const f of spec.fields) {\n schema.push(f.name)\n schema.push(f.type === 'integer' || f.type === 'number' ? 'NUMERIC' : 'TAG')\n }\n try {\n await client.sendCommand([\n 'FT.CREATE',\n indexName(spec.collection),\n 'ON',\n 'HASH',\n 'PREFIX',\n '1',\n keyPrefix(spec.collection),\n 'SCHEMA',\n ...schema,\n ])\n } catch (err) {\n const msg = String(err)\n if (msg.includes('Index already exists')) return\n throw new E_VECTOR_STORE_COLLECTION_FAILED(['createCollection', msg])\n }\n }\n\n async dropCollection(collection: string, ifExists: boolean): Promise {\n const client = await this.#ensure()\n if (ifExists && !(await this.hasCollection(collection))) return\n try {\n // DD drops the indexed documents too.\n await client.sendCommand(['FT.DROPINDEX', indexName(collection), 'DD'])\n } catch (err) {\n const msg = String(err)\n if (ifExists && msg.includes('Unknown')) return\n throw new E_VECTOR_STORE_COLLECTION_FAILED(['dropCollection', msg])\n }\n }\n\n async hasCollection(collection: string): Promise {\n const client = await this.#ensure()\n try {\n await client.sendCommand(['FT.INFO', indexName(collection)])\n return true\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', 'redis'])\n }\n\n async executeUpsert(plan: UpsertPlan): Promise {\n if (plan.records.length === 0) return\n validateRecords(plan.records)\n const client = await this.#ensure()\n const expected = this.#opts.dimensions\n try {\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 // Hash fields: the vector blob, the document, and each metadata scalar (stringified\n // for TAG, numeric kept as-is). __id/__document are reserved internal fields.\n const blob = packVector(vector)\n const entries: Record = {\n // Raw FLOAT32 blob for RediSearch to index on...\n __vector: blob,\n // ...plus a base64 copy so the vector survives a plain (string) hGetAll read-back\n // (node-redis v6's buffer-mode read API is version-fragile; base64 is portable).\n __vecb64: blob.toString('base64'),\n __id: r.id,\n }\n if (r.document !== undefined) entries.__document = r.document\n if (r.metadata) {\n for (const [k, v] of Object.entries(sanitizeMetadata(r.metadata))) {\n if (v === null || v === undefined) continue\n entries[k] = typeof v === 'object' ? JSON.stringify(v) : String(v)\n }\n }\n await client.hSet(keyFor(plan.collection, r.id), entries)\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 throw new E_VECTOR_STORE_UPSERT_FAILED([String(err)])\n }\n }\n\n async executeSearch(plan: SearchPlan): Promise {\n const client = await this.#ensure()\n const metric = this.#opts.metric ?? 'cosine'\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 const raw = await client.hGetAll(keyFor(plan.collection, plan.near.id))\n const b64 = raw?.__vecb64\n if (!b64) {\n throw new E_VECTOR_STORE_SEARCH_FAILED(['Referenced id not found: ' + plan.near.id])\n }\n queryVector = unpackVector(Buffer.from(String(b64), 'base64'))\n }\n }\n\n try {\n if (queryVector) {\n const filterFrag = translateRedisFilter(plan.filter)\n const k = plan.topK + (plan.offset ?? 0)\n const query = `(${filterFrag})=>[KNN ${k} @__vector $BLOB AS __score]`\n const reply: any = await client.sendCommand([\n 'FT.SEARCH',\n indexName(plan.collection),\n query,\n 'PARAMS',\n '2',\n 'BLOB',\n packVector(queryVector),\n 'SORTBY',\n '__score',\n 'DIALECT',\n '2',\n 'LIMIT',\n String(plan.offset ?? 0),\n String(plan.topK),\n 'RETURN',\n '1',\n '__score',\n ])\n return await this.#materialize(client, reply, plan, metric, true)\n } else {\n // Filter-scan: FT.SEARCH with the filter fragment, no KNN.\n const filterFrag = translateRedisFilter(plan.filter)\n const reply: any = await client.sendCommand([\n 'FT.SEARCH',\n indexName(plan.collection),\n filterFrag,\n 'DIALECT',\n '2',\n 'LIMIT',\n String(plan.offset ?? 0),\n String(plan.topK),\n 'NOCONTENT',\n ])\n return await this.#materialize(client, reply, plan, metric, false)\n }\n } catch (err) {\n throw new E_VECTOR_STORE_SEARCH_FAILED([String(err)])\n }\n }\n\n // Parse a node-redis v6 FT.SEARCH reply (structured object OR legacy flat array) into\n // [{ key, score? }] pairs. Score is read from extra_attributes.__score on KNN searches.\n #parseSearchReply(reply: any): Array<{ key: string; score?: number }> {\n // node-redis v6 returns { total_results, results: [{ id, extra_attributes }] }\n if (reply && typeof reply === 'object' && Array.isArray(reply.results)) {\n return reply.results.map((r: any) => ({\n key: String(r.id),\n score:\n r.extra_attributes && r.extra_attributes.__score !== undefined\n ? Number(r.extra_attributes.__score)\n : undefined,\n }))\n }\n // Legacy flat array reply: [total, key, key, ...]\n if (Array.isArray(reply)) {\n return reply.filter((_: unknown, i: number) => i > 0).map((k: any) => ({ key: String(k) }))\n }\n return []\n }\n\n // Parse the FT.SEARCH reply for keys (+ KNN score), then re-fetch each hash as buffers to\n // project. Re-fetching keeps the vector blob intact (RediSearch's per-field reply mangles it).\n async #materialize(\n client: any,\n reply: any,\n plan: SearchPlan,\n metric: string,\n isKnn: boolean\n ): Promise {\n const hits = this.#parseSearchReply(reply)\n const out: VectorMatch[] = []\n for (const hit of hits) {\n const raw = await client.hGetAll(hit.key)\n if (!raw) continue\n out.push(this.#project(raw, plan, metric, isKnn, hit.score))\n }\n return out\n }\n\n #decode(buf: unknown): string | undefined {\n if (buf === undefined || buf === null) return undefined\n return Buffer.isBuffer(buf) ? buf.toString('utf8') : String(buf)\n }\n\n #project(\n raw: Record,\n plan: SearchPlan,\n metric: string,\n isKnn: boolean,\n rawScore?: number\n ): VectorMatch {\n const proj = plan.projection\n const out: VectorMatch = {}\n if (proj.id) out.id = this.#decode(raw.__id)\n if (proj.vector && raw.__vecb64) {\n out.vector = unpackVector(Buffer.from(String(this.#decode(raw.__vecb64)), 'base64'))\n }\n if (proj.document) out.document = this.#decode(raw.__document)\n if (proj.metadata) {\n const meta: VectorMetadata = {}\n const reserved = new Set(['__vector', '__vecb64', '__id', '__document', '__score'])\n for (const [k, v] of Object.entries(raw)) {\n if (reserved.has(k)) continue\n meta[k] = this.#decode(v) as string\n }\n out.metadata = meta\n }\n if (isKnn && rawScore !== undefined) {\n // RediSearch returns the raw distance for the metric; normalize to [0,1] higher-is-better.\n out.score = normalizeScore(rawScore, metric as DistanceMetric, 'distance')\n }\n return out\n }\n\n async executeDelete(plan: DeletePlan): Promise {\n const client = await this.#ensure()\n try {\n if (plan.ids && plan.ids.length > 0) {\n await client.del(plan.ids.map((id) => keyFor(plan.collection, id)))\n } else if (plan.filter) {\n // Resolve ids via a filter-scan, then delete the hashes.\n const filterFrag = translateRedisFilter(plan.filter)\n const reply: any = await client.sendCommand([\n 'FT.SEARCH',\n indexName(plan.collection),\n filterFrag,\n 'DIALECT',\n '2',\n 'LIMIT',\n '0',\n '10000',\n 'NOCONTENT',\n ])\n const keys = this.#parseSearchReply(reply).map((h) => h.key)\n if (keys.length > 0) await client.del(keys)\n } else {\n // Delete-all: drop every hash under the prefix via the index.\n const reply: any = await client.sendCommand([\n 'FT.SEARCH',\n indexName(plan.collection),\n '*',\n 'DIALECT',\n '2',\n 'LIMIT',\n '0',\n '10000',\n 'NOCONTENT',\n ])\n const keys = this.#parseSearchReply(reply).map((h) => h.key)\n if (keys.length > 0) await client.del(keys)\n }\n } catch (err) {\n throw new E_VECTOR_STORE_DELETE_FAILED([String(err)])\n }\n 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