1242 lines
41 KiB
TypeScript
1242 lines
41 KiB
TypeScript
import fs from "node:fs/promises";
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import path from "node:path";
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import type { DatabaseSync } from "node:sqlite";
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import chokidar, { type FSWatcher } from "chokidar";
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import { resolveAgentDir, resolveAgentWorkspaceDir } from "../agents/agent-scope.js";
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import type { ResolvedMemorySearchConfig } from "../agents/memory-search.js";
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import { resolveMemorySearchConfig } from "../agents/memory-search.js";
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import type { ClawdbotConfig } from "../config/config.js";
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import { resolveSessionTranscriptsDirForAgent } from "../config/sessions/paths.js";
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import { createSubsystemLogger } from "../logging.js";
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import { onSessionTranscriptUpdate } from "../sessions/transcript-events.js";
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import { resolveUserPath } from "../utils.js";
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import {
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createEmbeddingProvider,
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type EmbeddingProvider,
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type EmbeddingProviderResult,
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type OpenAiEmbeddingClient,
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} from "./embeddings.js";
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import {
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OPENAI_BATCH_ENDPOINT,
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type OpenAiBatchRequest,
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runOpenAiEmbeddingBatches,
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} from "./openai-batch.js";
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import {
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chunkMarkdown,
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ensureDir,
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hashText,
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isMemoryPath,
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type MemoryChunk,
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type MemoryFileEntry,
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normalizeRelPath,
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parseEmbedding,
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} from "./internal.js";
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import { bm25RankToScore, buildFtsQuery, mergeHybridResults } from "./hybrid.js";
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import { searchKeyword, searchVector } from "./manager-search.js";
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import { ensureMemoryIndexSchema } from "./memory-schema.js";
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import { computeMemoryManagerCacheKey } from "./manager-cache-key.js";
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import { computeEmbeddingProviderKey } from "./provider-key.js";
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import { requireNodeSqlite } from "./sqlite.js";
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import { loadSqliteVecExtension } from "./sqlite-vec.js";
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import type { SessionFileEntry } from "./session-files.js";
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import { syncMemoryFiles } from "./sync-memory-files.js";
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import { syncSessionFiles } from "./sync-session-files.js";
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type MemorySource = "memory" | "sessions";
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export type MemorySearchResult = {
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path: string;
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startLine: number;
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endLine: number;
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score: number;
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snippet: string;
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source: MemorySource;
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};
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type MemoryIndexMeta = {
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model: string;
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provider: string;
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providerKey?: string;
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chunkTokens: number;
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chunkOverlap: number;
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vectorDims?: number;
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};
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type MemorySyncProgressUpdate = {
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completed: number;
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total: number;
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label?: string;
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};
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type MemorySyncProgressState = {
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completed: number;
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total: number;
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label?: string;
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report: (update: MemorySyncProgressUpdate) => void;
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};
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const META_KEY = "memory_index_meta_v1";
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const SNIPPET_MAX_CHARS = 700;
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const VECTOR_TABLE = "chunks_vec";
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const FTS_TABLE = "chunks_fts";
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const EMBEDDING_CACHE_TABLE = "embedding_cache";
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const SESSION_DIRTY_DEBOUNCE_MS = 5000;
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const EMBEDDING_BATCH_MAX_TOKENS = 8000;
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const EMBEDDING_APPROX_CHARS_PER_TOKEN = 1;
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const EMBEDDING_INDEX_CONCURRENCY = 4;
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const EMBEDDING_RETRY_MAX_ATTEMPTS = 3;
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const EMBEDDING_RETRY_BASE_DELAY_MS = 500;
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const EMBEDDING_RETRY_MAX_DELAY_MS = 8000;
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const log = createSubsystemLogger("memory");
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const INDEX_CACHE = new Map<string, MemoryIndexManager>();
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const vectorToBlob = (embedding: number[]): Buffer =>
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Buffer.from(new Float32Array(embedding).buffer);
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export class MemoryIndexManager {
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private readonly cacheKey: string;
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private readonly cfg: ClawdbotConfig;
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private readonly agentId: string;
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private readonly workspaceDir: string;
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private readonly settings: ResolvedMemorySearchConfig;
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private readonly provider: EmbeddingProvider;
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private readonly requestedProvider: "openai" | "local";
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private readonly fallbackReason?: string;
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private readonly openAi?: OpenAiEmbeddingClient;
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private readonly batch: {
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enabled: boolean;
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wait: boolean;
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concurrency: number;
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pollIntervalMs: number;
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timeoutMs: number;
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};
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private readonly db: DatabaseSync;
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private readonly sources: Set<MemorySource>;
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private readonly providerKey: string;
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private readonly cache: { enabled: boolean; maxEntries?: number };
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private readonly vector: {
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enabled: boolean;
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available: boolean | null;
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extensionPath?: string;
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loadError?: string;
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dims?: number;
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};
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private readonly fts: {
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enabled: boolean;
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available: boolean;
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loadError?: string;
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};
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private vectorReady: Promise<boolean> | null = null;
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private watcher: FSWatcher | null = null;
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private watchTimer: NodeJS.Timeout | null = null;
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private sessionWatchTimer: NodeJS.Timeout | null = null;
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private sessionUnsubscribe: (() => void) | null = null;
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private intervalTimer: NodeJS.Timeout | null = null;
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private closed = false;
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private dirty = false;
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private sessionsDirty = false;
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private sessionsDirtyFiles = new Set<string>();
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private sessionWarm = new Set<string>();
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private syncing: Promise<void> | null = null;
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static async get(params: {
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cfg: ClawdbotConfig;
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agentId: string;
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}): Promise<MemoryIndexManager | null> {
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const { cfg, agentId } = params;
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const settings = resolveMemorySearchConfig(cfg, agentId);
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if (!settings) return null;
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const workspaceDir = resolveAgentWorkspaceDir(cfg, agentId);
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const key = computeMemoryManagerCacheKey({ agentId, workspaceDir, settings });
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const existing = INDEX_CACHE.get(key);
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if (existing) return existing;
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const providerResult = await createEmbeddingProvider({
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config: cfg,
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agentDir: resolveAgentDir(cfg, agentId),
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provider: settings.provider,
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remote: settings.remote,
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model: settings.model,
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fallback: settings.fallback,
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local: settings.local,
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});
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const manager = new MemoryIndexManager({
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cacheKey: key,
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cfg,
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agentId,
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workspaceDir,
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settings,
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providerResult,
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});
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INDEX_CACHE.set(key, manager);
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return manager;
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}
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private constructor(params: {
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cacheKey: string;
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cfg: ClawdbotConfig;
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agentId: string;
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workspaceDir: string;
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settings: ResolvedMemorySearchConfig;
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providerResult: EmbeddingProviderResult;
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}) {
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this.cacheKey = params.cacheKey;
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this.cfg = params.cfg;
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this.agentId = params.agentId;
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this.workspaceDir = params.workspaceDir;
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this.settings = params.settings;
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this.provider = params.providerResult.provider;
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this.requestedProvider = params.providerResult.requestedProvider;
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this.fallbackReason = params.providerResult.fallbackReason;
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this.openAi = params.providerResult.openAi;
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this.sources = new Set(params.settings.sources);
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this.db = this.openDatabase();
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this.providerKey = computeEmbeddingProviderKey({
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providerId: this.provider.id,
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providerModel: this.provider.model,
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openAi: this.openAi
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? { baseUrl: this.openAi.baseUrl, model: this.openAi.model, headers: this.openAi.headers }
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: undefined,
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});
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this.cache = {
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enabled: params.settings.cache.enabled,
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maxEntries: params.settings.cache.maxEntries,
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};
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this.fts = { enabled: params.settings.query.hybrid.enabled, available: false };
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this.ensureSchema();
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this.vector = {
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enabled: params.settings.store.vector.enabled,
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available: null,
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extensionPath: params.settings.store.vector.extensionPath,
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};
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const meta = this.readMeta();
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if (meta?.vectorDims) {
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this.vector.dims = meta.vectorDims;
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}
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this.ensureWatcher();
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this.ensureSessionListener();
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this.ensureIntervalSync();
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this.dirty = this.sources.has("memory");
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if (this.sources.has("sessions")) {
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this.sessionsDirty = true;
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}
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const batch = params.settings.remote?.batch;
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this.batch = {
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enabled: Boolean(batch?.enabled && this.openAi && this.provider.id === "openai"),
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wait: batch?.wait ?? true,
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concurrency: Math.max(1, batch?.concurrency ?? 2),
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pollIntervalMs: batch?.pollIntervalMs ?? 2000,
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timeoutMs: (batch?.timeoutMinutes ?? 60) * 60 * 1000,
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};
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}
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async warmSession(sessionKey?: string): Promise<void> {
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if (!this.settings.sync.onSessionStart) return;
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const key = sessionKey?.trim() || "";
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if (key && this.sessionWarm.has(key)) return;
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await this.sync({ reason: "session-start" });
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if (key) this.sessionWarm.add(key);
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}
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async search(
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query: string,
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opts?: {
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maxResults?: number;
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minScore?: number;
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sessionKey?: string;
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},
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): Promise<MemorySearchResult[]> {
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await this.warmSession(opts?.sessionKey);
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if (this.settings.sync.onSearch && (this.dirty || this.sessionsDirty)) {
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await this.sync({ reason: "search" });
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}
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const cleaned = query.trim();
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if (!cleaned) return [];
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const minScore = opts?.minScore ?? this.settings.query.minScore;
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const maxResults = opts?.maxResults ?? this.settings.query.maxResults;
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const hybrid = this.settings.query.hybrid;
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const candidates = Math.min(
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200,
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Math.max(1, Math.floor(maxResults * hybrid.candidateMultiplier)),
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);
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const keywordResults = hybrid.enabled
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? await this.searchKeyword(cleaned, candidates).catch(() => [])
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: [];
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const queryVec = await this.provider.embedQuery(cleaned);
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const hasVector = queryVec.some((v) => v !== 0);
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const vectorResults = hasVector
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? await this.searchVector(queryVec, candidates).catch(() => [])
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: [];
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if (!hybrid.enabled) {
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return vectorResults.filter((entry) => entry.score >= minScore).slice(0, maxResults);
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}
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const merged = this.mergeHybridResults({
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vector: vectorResults,
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keyword: keywordResults,
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vectorWeight: hybrid.vectorWeight,
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textWeight: hybrid.textWeight,
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});
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return merged.filter((entry) => entry.score >= minScore).slice(0, maxResults);
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}
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private async searchVector(
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queryVec: number[],
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limit: number,
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): Promise<Array<MemorySearchResult & { id: string }>> {
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const results = await searchVector({
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db: this.db,
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vectorTable: VECTOR_TABLE,
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providerModel: this.provider.model,
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queryVec,
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limit,
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snippetMaxChars: SNIPPET_MAX_CHARS,
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ensureVectorReady: async (dimensions) => await this.ensureVectorReady(dimensions),
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sourceFilterVec: this.buildSourceFilter("c"),
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sourceFilterChunks: this.buildSourceFilter(),
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});
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return results.map((entry) => entry as MemorySearchResult & { id: string });
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}
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private buildFtsQuery(raw: string): string | null {
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return buildFtsQuery(raw);
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}
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private async searchKeyword(
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query: string,
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limit: number,
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): Promise<Array<MemorySearchResult & { id: string; textScore: number }>> {
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if (!this.fts.enabled || !this.fts.available) return [];
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const sourceFilter = this.buildSourceFilter();
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const results = await searchKeyword({
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db: this.db,
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ftsTable: FTS_TABLE,
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providerModel: this.provider.model,
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query,
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limit,
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snippetMaxChars: SNIPPET_MAX_CHARS,
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sourceFilter,
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buildFtsQuery: (raw) => this.buildFtsQuery(raw),
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bm25RankToScore,
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});
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return results.map((entry) => entry as MemorySearchResult & { id: string; textScore: number });
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}
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private mergeHybridResults(params: {
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vector: Array<MemorySearchResult & { id: string }>;
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keyword: Array<MemorySearchResult & { id: string; textScore: number }>;
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vectorWeight: number;
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textWeight: number;
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}): MemorySearchResult[] {
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const merged = mergeHybridResults({
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vector: params.vector.map((r) => ({
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id: r.id,
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path: r.path,
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startLine: r.startLine,
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endLine: r.endLine,
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source: r.source,
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snippet: r.snippet,
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vectorScore: r.score,
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})),
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keyword: params.keyword.map((r) => ({
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id: r.id,
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path: r.path,
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startLine: r.startLine,
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endLine: r.endLine,
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source: r.source,
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snippet: r.snippet,
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textScore: r.textScore,
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})),
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vectorWeight: params.vectorWeight,
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textWeight: params.textWeight,
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});
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return merged.map((entry) => entry as MemorySearchResult);
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}
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async sync(params?: {
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reason?: string;
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force?: boolean;
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progress?: (update: MemorySyncProgressUpdate) => void;
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}): Promise<void> {
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if (this.syncing) return this.syncing;
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this.syncing = this.runSync(params).finally(() => {
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this.syncing = null;
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});
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return this.syncing;
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}
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async readFile(params: {
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relPath: string;
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from?: number;
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lines?: number;
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}): Promise<{ text: string; path: string }> {
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const relPath = normalizeRelPath(params.relPath);
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if (!relPath || !isMemoryPath(relPath)) {
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throw new Error("path required");
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}
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const absPath = path.resolve(this.workspaceDir, relPath);
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if (!absPath.startsWith(this.workspaceDir)) {
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throw new Error("path escapes workspace");
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}
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const content = await fs.readFile(absPath, "utf-8");
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if (!params.from && !params.lines) {
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return { text: content, path: relPath };
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}
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const lines = content.split("\n");
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const start = Math.max(1, params.from ?? 1);
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const count = Math.max(1, params.lines ?? lines.length);
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const slice = lines.slice(start - 1, start - 1 + count);
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return { text: slice.join("\n"), path: relPath };
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}
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status(): {
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files: number;
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chunks: number;
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dirty: boolean;
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workspaceDir: string;
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dbPath: string;
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provider: string;
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model: string;
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requestedProvider: string;
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sources: MemorySource[];
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sourceCounts: Array<{ source: MemorySource; files: number; chunks: number }>;
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cache?: { enabled: boolean; entries?: number; maxEntries?: number };
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fts?: { enabled: boolean; available: boolean; error?: string };
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fallback?: { from: string; reason?: string };
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vector?: {
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enabled: boolean;
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available?: boolean;
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extensionPath?: string;
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loadError?: string;
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dims?: number;
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};
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} {
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const sourceFilter = this.buildSourceFilter();
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const files = this.db
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.prepare(`SELECT COUNT(*) as c FROM files WHERE 1=1${sourceFilter.sql}`)
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.get(...sourceFilter.params) as {
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c: number;
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};
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const chunks = this.db
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.prepare(`SELECT COUNT(*) as c FROM chunks WHERE 1=1${sourceFilter.sql}`)
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.get(...sourceFilter.params) as {
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c: number;
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};
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const sourceCounts = (() => {
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const sources = Array.from(this.sources);
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if (sources.length === 0) return [];
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const bySource = new Map<MemorySource, { files: number; chunks: number }>();
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for (const source of sources) {
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bySource.set(source, { files: 0, chunks: 0 });
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}
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const fileRows = this.db
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.prepare(
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`SELECT source, COUNT(*) as c FROM files WHERE 1=1${sourceFilter.sql} GROUP BY source`,
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)
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.all(...sourceFilter.params) as Array<{ source: MemorySource; c: number }>;
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for (const row of fileRows) {
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const entry = bySource.get(row.source) ?? { files: 0, chunks: 0 };
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entry.files = row.c ?? 0;
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bySource.set(row.source, entry);
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}
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const chunkRows = this.db
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.prepare(
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`SELECT source, COUNT(*) as c FROM chunks WHERE 1=1${sourceFilter.sql} GROUP BY source`,
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)
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.all(...sourceFilter.params) as Array<{ source: MemorySource; c: number }>;
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for (const row of chunkRows) {
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const entry = bySource.get(row.source) ?? { files: 0, chunks: 0 };
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entry.chunks = row.c ?? 0;
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bySource.set(row.source, entry);
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}
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return sources.map((source) => ({ source, ...bySource.get(source)! }));
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})();
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return {
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files: files?.c ?? 0,
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chunks: chunks?.c ?? 0,
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dirty: this.dirty,
|
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workspaceDir: this.workspaceDir,
|
|
dbPath: this.settings.store.path,
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provider: this.provider.id,
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model: this.provider.model,
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requestedProvider: this.requestedProvider,
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sources: Array.from(this.sources),
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sourceCounts,
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cache: this.cache.enabled
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? {
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enabled: true,
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entries:
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(
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this.db.prepare(`SELECT COUNT(*) as c FROM ${EMBEDDING_CACHE_TABLE}`).get() as
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| { c: number }
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| undefined
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)?.c ?? 0,
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maxEntries: this.cache.maxEntries,
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}
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: { enabled: false, maxEntries: this.cache.maxEntries },
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fts: {
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enabled: this.fts.enabled,
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available: this.fts.available,
|
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error: this.fts.loadError,
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},
|
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fallback: this.fallbackReason ? { from: "local", reason: this.fallbackReason } : undefined,
|
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vector: {
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enabled: this.vector.enabled,
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available: this.vector.available ?? undefined,
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extensionPath: this.vector.extensionPath,
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loadError: this.vector.loadError,
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dims: this.vector.dims,
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},
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};
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}
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|
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async probeVectorAvailability(): Promise<boolean> {
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if (!this.vector.enabled) return false;
|
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return this.ensureVectorReady();
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}
|
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|
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async probeEmbeddingAvailability(): Promise<{ ok: boolean; error?: string }> {
|
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try {
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await this.embedBatchWithRetry(["ping"]);
|
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return { ok: true };
|
|
} catch (err) {
|
|
const message = err instanceof Error ? err.message : String(err);
|
|
return { ok: false, error: message };
|
|
}
|
|
}
|
|
|
|
async close(): Promise<void> {
|
|
if (this.closed) return;
|
|
this.closed = true;
|
|
if (this.watchTimer) {
|
|
clearTimeout(this.watchTimer);
|
|
this.watchTimer = null;
|
|
}
|
|
if (this.sessionWatchTimer) {
|
|
clearTimeout(this.sessionWatchTimer);
|
|
this.sessionWatchTimer = null;
|
|
}
|
|
if (this.intervalTimer) {
|
|
clearInterval(this.intervalTimer);
|
|
this.intervalTimer = null;
|
|
}
|
|
if (this.watcher) {
|
|
await this.watcher.close();
|
|
this.watcher = null;
|
|
}
|
|
if (this.sessionUnsubscribe) {
|
|
this.sessionUnsubscribe();
|
|
this.sessionUnsubscribe = null;
|
|
}
|
|
this.db.close();
|
|
INDEX_CACHE.delete(this.cacheKey);
|
|
}
|
|
|
|
private async ensureVectorReady(dimensions?: number): Promise<boolean> {
|
|
if (!this.vector.enabled) return false;
|
|
if (!this.vectorReady) {
|
|
this.vectorReady = this.loadVectorExtension();
|
|
}
|
|
const ready = await this.vectorReady;
|
|
if (ready && typeof dimensions === "number" && dimensions > 0) {
|
|
this.ensureVectorTable(dimensions);
|
|
}
|
|
return ready;
|
|
}
|
|
|
|
private async loadVectorExtension(): Promise<boolean> {
|
|
if (this.vector.available !== null) return this.vector.available;
|
|
if (!this.vector.enabled) {
|
|
this.vector.available = false;
|
|
return false;
|
|
}
|
|
try {
|
|
const resolvedPath = this.vector.extensionPath?.trim()
|
|
? resolveUserPath(this.vector.extensionPath)
|
|
: undefined;
|
|
const loaded = await loadSqliteVecExtension({ db: this.db, extensionPath: resolvedPath });
|
|
if (!loaded.ok) throw new Error(loaded.error ?? "unknown sqlite-vec load error");
|
|
this.vector.extensionPath = loaded.extensionPath;
|
|
this.vector.available = true;
|
|
return true;
|
|
} catch (err) {
|
|
const message = err instanceof Error ? err.message : String(err);
|
|
this.vector.available = false;
|
|
this.vector.loadError = message;
|
|
log.warn(`sqlite-vec unavailable: ${message}`);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
private ensureVectorTable(dimensions: number): void {
|
|
if (this.vector.dims === dimensions) return;
|
|
if (this.vector.dims && this.vector.dims !== dimensions) {
|
|
this.dropVectorTable();
|
|
}
|
|
this.db.exec(
|
|
`CREATE VIRTUAL TABLE IF NOT EXISTS ${VECTOR_TABLE} USING vec0(\n` +
|
|
` id TEXT PRIMARY KEY,\n` +
|
|
` embedding FLOAT[${dimensions}]\n` +
|
|
`)`,
|
|
);
|
|
this.vector.dims = dimensions;
|
|
}
|
|
|
|
private dropVectorTable(): void {
|
|
try {
|
|
this.db.exec(`DROP TABLE IF EXISTS ${VECTOR_TABLE}`);
|
|
} catch (err) {
|
|
const message = err instanceof Error ? err.message : String(err);
|
|
log.debug(`Failed to drop ${VECTOR_TABLE}: ${message}`);
|
|
}
|
|
}
|
|
|
|
private buildSourceFilter(alias?: string): { sql: string; params: MemorySource[] } {
|
|
const sources = Array.from(this.sources);
|
|
if (sources.length === 0) return { sql: "", params: [] };
|
|
const column = alias ? `${alias}.source` : "source";
|
|
const placeholders = sources.map(() => "?").join(", ");
|
|
return { sql: ` AND ${column} IN (${placeholders})`, params: sources };
|
|
}
|
|
|
|
private openDatabase(): DatabaseSync {
|
|
const dbPath = resolveUserPath(this.settings.store.path);
|
|
const dir = path.dirname(dbPath);
|
|
ensureDir(dir);
|
|
const { DatabaseSync } = requireNodeSqlite();
|
|
return new DatabaseSync(dbPath, { allowExtension: this.settings.store.vector.enabled });
|
|
}
|
|
|
|
private ensureSchema() {
|
|
const result = ensureMemoryIndexSchema({
|
|
db: this.db,
|
|
embeddingCacheTable: EMBEDDING_CACHE_TABLE,
|
|
ftsTable: FTS_TABLE,
|
|
ftsEnabled: this.fts.enabled,
|
|
});
|
|
this.fts.available = result.ftsAvailable;
|
|
if (result.ftsError) {
|
|
this.fts.loadError = result.ftsError;
|
|
log.warn(`fts unavailable: ${result.ftsError}`);
|
|
}
|
|
}
|
|
|
|
private ensureWatcher() {
|
|
if (!this.sources.has("memory") || !this.settings.sync.watch || this.watcher) return;
|
|
const watchPaths = [
|
|
path.join(this.workspaceDir, "MEMORY.md"),
|
|
path.join(this.workspaceDir, "memory"),
|
|
];
|
|
this.watcher = chokidar.watch(watchPaths, {
|
|
ignoreInitial: true,
|
|
awaitWriteFinish: {
|
|
stabilityThreshold: this.settings.sync.watchDebounceMs,
|
|
pollInterval: 100,
|
|
},
|
|
});
|
|
const markDirty = () => {
|
|
this.dirty = true;
|
|
this.scheduleWatchSync();
|
|
};
|
|
this.watcher.on("add", markDirty);
|
|
this.watcher.on("change", markDirty);
|
|
this.watcher.on("unlink", markDirty);
|
|
}
|
|
|
|
private ensureSessionListener() {
|
|
if (!this.sources.has("sessions") || this.sessionUnsubscribe) return;
|
|
this.sessionUnsubscribe = onSessionTranscriptUpdate((update) => {
|
|
if (this.closed) return;
|
|
const sessionFile = update.sessionFile;
|
|
if (!this.isSessionFileForAgent(sessionFile)) return;
|
|
this.scheduleSessionDirty(sessionFile);
|
|
});
|
|
}
|
|
|
|
private scheduleSessionDirty(sessionFile: string) {
|
|
this.sessionsDirtyFiles.add(sessionFile);
|
|
if (this.sessionWatchTimer) return;
|
|
this.sessionWatchTimer = setTimeout(() => {
|
|
this.sessionWatchTimer = null;
|
|
this.sessionsDirty = true;
|
|
}, SESSION_DIRTY_DEBOUNCE_MS);
|
|
}
|
|
|
|
private isSessionFileForAgent(sessionFile: string): boolean {
|
|
if (!sessionFile) return false;
|
|
const sessionsDir = resolveSessionTranscriptsDirForAgent(this.agentId);
|
|
const resolvedFile = path.resolve(sessionFile);
|
|
const resolvedDir = path.resolve(sessionsDir);
|
|
return resolvedFile.startsWith(`${resolvedDir}${path.sep}`);
|
|
}
|
|
|
|
private ensureIntervalSync() {
|
|
const minutes = this.settings.sync.intervalMinutes;
|
|
if (!minutes || minutes <= 0 || this.intervalTimer) return;
|
|
const ms = minutes * 60 * 1000;
|
|
this.intervalTimer = setInterval(() => {
|
|
void this.sync({ reason: "interval" }).catch((err) => {
|
|
log.warn(`memory sync failed (interval): ${String(err)}`);
|
|
});
|
|
}, ms);
|
|
}
|
|
|
|
private scheduleWatchSync() {
|
|
if (!this.sources.has("memory") || !this.settings.sync.watch) return;
|
|
if (this.watchTimer) clearTimeout(this.watchTimer);
|
|
this.watchTimer = setTimeout(() => {
|
|
this.watchTimer = null;
|
|
void this.sync({ reason: "watch" }).catch((err) => {
|
|
log.warn(`memory sync failed (watch): ${String(err)}`);
|
|
});
|
|
}, this.settings.sync.watchDebounceMs);
|
|
}
|
|
|
|
private shouldSyncSessions(
|
|
params?: { reason?: string; force?: boolean },
|
|
needsFullReindex = false,
|
|
) {
|
|
if (!this.sources.has("sessions")) return false;
|
|
if (params?.force) return true;
|
|
const reason = params?.reason;
|
|
if (reason === "session-start" || reason === "watch") return false;
|
|
return this.sessionsDirty || needsFullReindex;
|
|
}
|
|
|
|
private async syncMemoryFiles(params: {
|
|
needsFullReindex: boolean;
|
|
progress?: MemorySyncProgressState;
|
|
}) {
|
|
await syncMemoryFiles({
|
|
workspaceDir: this.workspaceDir,
|
|
db: this.db,
|
|
needsFullReindex: params.needsFullReindex,
|
|
progress: params.progress,
|
|
batchEnabled: this.batch.enabled,
|
|
concurrency: this.getIndexConcurrency(),
|
|
runWithConcurrency: this.runWithConcurrency.bind(this),
|
|
indexFile: async (entry) => await this.indexFile(entry, { source: "memory" }),
|
|
vectorTable: VECTOR_TABLE,
|
|
ftsTable: FTS_TABLE,
|
|
ftsEnabled: this.fts.enabled,
|
|
ftsAvailable: this.fts.available,
|
|
model: this.provider.model,
|
|
});
|
|
}
|
|
|
|
private async syncSessionFiles(params: {
|
|
needsFullReindex: boolean;
|
|
progress?: MemorySyncProgressState;
|
|
}) {
|
|
await syncSessionFiles({
|
|
agentId: this.agentId,
|
|
db: this.db,
|
|
needsFullReindex: params.needsFullReindex,
|
|
progress: params.progress,
|
|
batchEnabled: this.batch.enabled,
|
|
concurrency: this.getIndexConcurrency(),
|
|
runWithConcurrency: this.runWithConcurrency.bind(this),
|
|
indexFile: async (entry) => await this.indexFile(entry, { source: "sessions", content: entry.content }),
|
|
vectorTable: VECTOR_TABLE,
|
|
ftsTable: FTS_TABLE,
|
|
ftsEnabled: this.fts.enabled,
|
|
ftsAvailable: this.fts.available,
|
|
model: this.provider.model,
|
|
dirtyFiles: this.sessionsDirtyFiles,
|
|
});
|
|
}
|
|
|
|
private createSyncProgress(
|
|
onProgress: (update: MemorySyncProgressUpdate) => void,
|
|
): MemorySyncProgressState {
|
|
const state: MemorySyncProgressState = {
|
|
completed: 0,
|
|
total: 0,
|
|
label: undefined,
|
|
report: (update) => {
|
|
if (update.label) state.label = update.label;
|
|
const label =
|
|
update.total > 0 && state.label
|
|
? `${state.label} ${update.completed}/${update.total}`
|
|
: state.label;
|
|
onProgress({
|
|
completed: update.completed,
|
|
total: update.total,
|
|
label,
|
|
});
|
|
},
|
|
};
|
|
return state;
|
|
}
|
|
|
|
private async runSync(params?: {
|
|
reason?: string;
|
|
force?: boolean;
|
|
progress?: (update: MemorySyncProgressUpdate) => void;
|
|
}) {
|
|
const progress = params?.progress ? this.createSyncProgress(params.progress) : undefined;
|
|
const vectorReady = await this.ensureVectorReady();
|
|
const meta = this.readMeta();
|
|
const needsFullReindex =
|
|
params?.force ||
|
|
!meta ||
|
|
meta.model !== this.provider.model ||
|
|
meta.provider !== this.provider.id ||
|
|
meta.providerKey !== this.providerKey ||
|
|
meta.chunkTokens !== this.settings.chunking.tokens ||
|
|
meta.chunkOverlap !== this.settings.chunking.overlap ||
|
|
(vectorReady && !meta?.vectorDims);
|
|
if (needsFullReindex) {
|
|
this.resetIndex();
|
|
}
|
|
|
|
const shouldSyncMemory =
|
|
this.sources.has("memory") && (params?.force || needsFullReindex || this.dirty);
|
|
const shouldSyncSessions = this.shouldSyncSessions(params, needsFullReindex);
|
|
|
|
if (shouldSyncMemory) {
|
|
await this.syncMemoryFiles({ needsFullReindex, progress: progress ?? undefined });
|
|
this.dirty = false;
|
|
}
|
|
|
|
if (shouldSyncSessions) {
|
|
await this.syncSessionFiles({ needsFullReindex, progress: progress ?? undefined });
|
|
this.sessionsDirty = false;
|
|
this.sessionsDirtyFiles.clear();
|
|
} else if (needsFullReindex && this.sources.has("sessions")) {
|
|
this.sessionsDirty = true;
|
|
}
|
|
|
|
const nextMeta: MemoryIndexMeta = {
|
|
model: this.provider.model,
|
|
provider: this.provider.id,
|
|
providerKey: this.providerKey,
|
|
chunkTokens: this.settings.chunking.tokens,
|
|
chunkOverlap: this.settings.chunking.overlap,
|
|
};
|
|
if (this.vector.available && this.vector.dims) {
|
|
nextMeta.vectorDims = this.vector.dims;
|
|
}
|
|
if (shouldSyncMemory || shouldSyncSessions || needsFullReindex) {
|
|
this.writeMeta(nextMeta);
|
|
}
|
|
if (shouldSyncMemory || shouldSyncSessions || needsFullReindex) {
|
|
this.pruneEmbeddingCacheIfNeeded();
|
|
}
|
|
}
|
|
|
|
private resetIndex() {
|
|
this.db.exec(`DELETE FROM files`);
|
|
this.db.exec(`DELETE FROM chunks`);
|
|
if (this.fts.enabled && this.fts.available) {
|
|
try {
|
|
this.db.exec(`DELETE FROM ${FTS_TABLE}`);
|
|
} catch {}
|
|
}
|
|
this.dropVectorTable();
|
|
this.vector.dims = undefined;
|
|
this.sessionsDirtyFiles.clear();
|
|
}
|
|
|
|
private readMeta(): MemoryIndexMeta | null {
|
|
const row = this.db.prepare(`SELECT value FROM meta WHERE key = ?`).get(META_KEY) as
|
|
| { value: string }
|
|
| undefined;
|
|
if (!row?.value) return null;
|
|
try {
|
|
return JSON.parse(row.value) as MemoryIndexMeta;
|
|
} catch {
|
|
return null;
|
|
}
|
|
}
|
|
|
|
private writeMeta(meta: MemoryIndexMeta) {
|
|
const value = JSON.stringify(meta);
|
|
this.db
|
|
.prepare(
|
|
`INSERT INTO meta (key, value) VALUES (?, ?) ON CONFLICT(key) DO UPDATE SET value=excluded.value`,
|
|
)
|
|
.run(META_KEY, value);
|
|
}
|
|
|
|
private estimateEmbeddingTokens(text: string): number {
|
|
if (!text) return 0;
|
|
return Math.ceil(text.length / EMBEDDING_APPROX_CHARS_PER_TOKEN);
|
|
}
|
|
|
|
private buildEmbeddingBatches(chunks: MemoryChunk[]): MemoryChunk[][] {
|
|
const batches: MemoryChunk[][] = [];
|
|
let current: MemoryChunk[] = [];
|
|
let currentTokens = 0;
|
|
|
|
for (const chunk of chunks) {
|
|
const estimate = this.estimateEmbeddingTokens(chunk.text);
|
|
const wouldExceed =
|
|
current.length > 0 && currentTokens + estimate > EMBEDDING_BATCH_MAX_TOKENS;
|
|
if (wouldExceed) {
|
|
batches.push(current);
|
|
current = [];
|
|
currentTokens = 0;
|
|
}
|
|
if (current.length === 0 && estimate > EMBEDDING_BATCH_MAX_TOKENS) {
|
|
batches.push([chunk]);
|
|
continue;
|
|
}
|
|
current.push(chunk);
|
|
currentTokens += estimate;
|
|
}
|
|
|
|
if (current.length > 0) {
|
|
batches.push(current);
|
|
}
|
|
return batches;
|
|
}
|
|
|
|
private loadEmbeddingCache(hashes: string[]): Map<string, number[]> {
|
|
if (!this.cache.enabled) return new Map();
|
|
if (hashes.length === 0) return new Map();
|
|
const unique: string[] = [];
|
|
const seen = new Set<string>();
|
|
for (const hash of hashes) {
|
|
if (!hash) continue;
|
|
if (seen.has(hash)) continue;
|
|
seen.add(hash);
|
|
unique.push(hash);
|
|
}
|
|
if (unique.length === 0) return new Map();
|
|
|
|
const out = new Map<string, number[]>();
|
|
const baseParams = [this.provider.id, this.provider.model, this.providerKey];
|
|
const batchSize = 400;
|
|
for (let start = 0; start < unique.length; start += batchSize) {
|
|
const batch = unique.slice(start, start + batchSize);
|
|
const placeholders = batch.map(() => "?").join(", ");
|
|
const rows = this.db
|
|
.prepare(
|
|
`SELECT hash, embedding FROM ${EMBEDDING_CACHE_TABLE}\n` +
|
|
` WHERE provider = ? AND model = ? AND provider_key = ? AND hash IN (${placeholders})`,
|
|
)
|
|
.all(...baseParams, ...batch) as Array<{ hash: string; embedding: string }>;
|
|
for (const row of rows) {
|
|
out.set(row.hash, parseEmbedding(row.embedding));
|
|
}
|
|
}
|
|
return out;
|
|
}
|
|
|
|
private upsertEmbeddingCache(entries: Array<{ hash: string; embedding: number[] }>): void {
|
|
if (!this.cache.enabled) return;
|
|
if (entries.length === 0) return;
|
|
const now = Date.now();
|
|
const stmt = this.db.prepare(
|
|
`INSERT INTO ${EMBEDDING_CACHE_TABLE} (provider, model, provider_key, hash, embedding, dims, updated_at)\n` +
|
|
` VALUES (?, ?, ?, ?, ?, ?, ?)\n` +
|
|
` ON CONFLICT(provider, model, provider_key, hash) DO UPDATE SET\n` +
|
|
` embedding=excluded.embedding,\n` +
|
|
` dims=excluded.dims,\n` +
|
|
` updated_at=excluded.updated_at`,
|
|
);
|
|
for (const entry of entries) {
|
|
const embedding = entry.embedding ?? [];
|
|
stmt.run(
|
|
this.provider.id,
|
|
this.provider.model,
|
|
this.providerKey,
|
|
entry.hash,
|
|
JSON.stringify(embedding),
|
|
embedding.length,
|
|
now,
|
|
);
|
|
}
|
|
}
|
|
|
|
private pruneEmbeddingCacheIfNeeded(): void {
|
|
if (!this.cache.enabled) return;
|
|
const max = this.cache.maxEntries;
|
|
if (!max || max <= 0) return;
|
|
const row = this.db.prepare(`SELECT COUNT(*) as c FROM ${EMBEDDING_CACHE_TABLE}`).get() as
|
|
| { c: number }
|
|
| undefined;
|
|
const count = row?.c ?? 0;
|
|
if (count <= max) return;
|
|
const excess = count - max;
|
|
this.db
|
|
.prepare(
|
|
`DELETE FROM ${EMBEDDING_CACHE_TABLE}\n` +
|
|
` WHERE rowid IN (\n` +
|
|
` SELECT rowid FROM ${EMBEDDING_CACHE_TABLE}\n` +
|
|
` ORDER BY updated_at ASC\n` +
|
|
` LIMIT ?\n` +
|
|
` )`,
|
|
)
|
|
.run(excess);
|
|
}
|
|
|
|
private async embedChunksInBatches(chunks: MemoryChunk[]): Promise<number[][]> {
|
|
if (chunks.length === 0) return [];
|
|
const cached = this.loadEmbeddingCache(chunks.map((chunk) => chunk.hash));
|
|
const embeddings: number[][] = Array.from({ length: chunks.length }, () => []);
|
|
const missing: Array<{ index: number; chunk: MemoryChunk }> = [];
|
|
|
|
for (let i = 0; i < chunks.length; i += 1) {
|
|
const chunk = chunks[i];
|
|
const hit = chunk?.hash ? cached.get(chunk.hash) : undefined;
|
|
if (hit && hit.length > 0) {
|
|
embeddings[i] = hit;
|
|
} else if (chunk) {
|
|
missing.push({ index: i, chunk });
|
|
}
|
|
}
|
|
|
|
if (missing.length === 0) return embeddings;
|
|
|
|
const missingChunks = missing.map((m) => m.chunk);
|
|
const batches = this.buildEmbeddingBatches(missingChunks);
|
|
const toCache: Array<{ hash: string; embedding: number[] }> = [];
|
|
let cursor = 0;
|
|
for (const batch of batches) {
|
|
const batchEmbeddings = await this.embedBatchWithRetry(batch.map((chunk) => chunk.text));
|
|
for (let i = 0; i < batch.length; i += 1) {
|
|
const item = missing[cursor + i];
|
|
const embedding = batchEmbeddings[i] ?? [];
|
|
if (item) {
|
|
embeddings[item.index] = embedding;
|
|
toCache.push({ hash: item.chunk.hash, embedding });
|
|
}
|
|
}
|
|
cursor += batch.length;
|
|
}
|
|
this.upsertEmbeddingCache(toCache);
|
|
return embeddings;
|
|
}
|
|
|
|
private async embedChunksWithBatch(
|
|
chunks: MemoryChunk[],
|
|
entry: MemoryFileEntry | SessionFileEntry,
|
|
source: MemorySource,
|
|
): Promise<number[][]> {
|
|
if (!this.openAi) {
|
|
return this.embedChunksInBatches(chunks);
|
|
}
|
|
if (chunks.length === 0) return [];
|
|
const cached = this.loadEmbeddingCache(chunks.map((chunk) => chunk.hash));
|
|
const embeddings: number[][] = Array.from({ length: chunks.length }, () => []);
|
|
const missing: Array<{ index: number; chunk: MemoryChunk }> = [];
|
|
|
|
for (let i = 0; i < chunks.length; i += 1) {
|
|
const chunk = chunks[i];
|
|
const hit = chunk?.hash ? cached.get(chunk.hash) : undefined;
|
|
if (hit && hit.length > 0) {
|
|
embeddings[i] = hit;
|
|
} else if (chunk) {
|
|
missing.push({ index: i, chunk });
|
|
}
|
|
}
|
|
|
|
if (missing.length === 0) return embeddings;
|
|
|
|
const requests: OpenAiBatchRequest[] = [];
|
|
const mapping = new Map<string, { index: number; hash: string }>();
|
|
for (const item of missing) {
|
|
const chunk = item.chunk;
|
|
const customId = hashText(
|
|
`${source}:${entry.path}:${chunk.startLine}:${chunk.endLine}:${chunk.hash}:${item.index}`,
|
|
);
|
|
mapping.set(customId, { index: item.index, hash: chunk.hash });
|
|
requests.push({
|
|
custom_id: customId,
|
|
method: "POST",
|
|
url: OPENAI_BATCH_ENDPOINT,
|
|
body: {
|
|
model: this.openAi?.model ?? this.provider.model,
|
|
input: chunk.text,
|
|
},
|
|
});
|
|
}
|
|
const byCustomId = await runOpenAiEmbeddingBatches({
|
|
openAi: this.openAi,
|
|
agentId: this.agentId,
|
|
requests,
|
|
wait: this.batch.wait,
|
|
concurrency: this.batch.concurrency,
|
|
pollIntervalMs: this.batch.pollIntervalMs,
|
|
timeoutMs: this.batch.timeoutMs,
|
|
debug: (message, data) => log.debug(message, { ...data, source, chunks: chunks.length }),
|
|
});
|
|
|
|
const toCache: Array<{ hash: string; embedding: number[] }> = [];
|
|
for (const [customId, embedding] of byCustomId.entries()) {
|
|
const mapped = mapping.get(customId);
|
|
if (!mapped) continue;
|
|
embeddings[mapped.index] = embedding;
|
|
toCache.push({ hash: mapped.hash, embedding });
|
|
}
|
|
this.upsertEmbeddingCache(toCache);
|
|
return embeddings;
|
|
}
|
|
|
|
private async embedBatchWithRetry(texts: string[]): Promise<number[][]> {
|
|
if (texts.length === 0) return [];
|
|
let attempt = 0;
|
|
let delayMs = EMBEDDING_RETRY_BASE_DELAY_MS;
|
|
while (true) {
|
|
try {
|
|
return await this.provider.embedBatch(texts);
|
|
} catch (err) {
|
|
const message = err instanceof Error ? err.message : String(err);
|
|
if (!this.isRetryableEmbeddingError(message) || attempt >= EMBEDDING_RETRY_MAX_ATTEMPTS) {
|
|
throw err;
|
|
}
|
|
const waitMs = Math.min(
|
|
EMBEDDING_RETRY_MAX_DELAY_MS,
|
|
Math.round(delayMs * (1 + Math.random() * 0.2)),
|
|
);
|
|
log.warn(`memory embeddings rate limited; retrying in ${waitMs}ms`);
|
|
await new Promise((resolve) => setTimeout(resolve, waitMs));
|
|
delayMs *= 2;
|
|
attempt += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
private isRetryableEmbeddingError(message: string): boolean {
|
|
return /(rate[_ ]limit|too many requests|429|resource has been exhausted|5\d\d|cloudflare)/i.test(
|
|
message,
|
|
);
|
|
}
|
|
|
|
private async runWithConcurrency<T>(tasks: Array<() => Promise<T>>, limit: number): Promise<T[]> {
|
|
if (tasks.length === 0) return [];
|
|
const resolvedLimit = Math.max(1, Math.min(limit, tasks.length));
|
|
const results: T[] = Array.from({ length: tasks.length });
|
|
let next = 0;
|
|
let firstError: unknown = null;
|
|
|
|
const workers = Array.from({ length: resolvedLimit }, async () => {
|
|
while (true) {
|
|
if (firstError) return;
|
|
const index = next;
|
|
next += 1;
|
|
if (index >= tasks.length) return;
|
|
try {
|
|
results[index] = await tasks[index]();
|
|
} catch (err) {
|
|
firstError = err;
|
|
return;
|
|
}
|
|
}
|
|
});
|
|
|
|
await Promise.allSettled(workers);
|
|
if (firstError) throw firstError;
|
|
return results;
|
|
}
|
|
|
|
private getIndexConcurrency(): number {
|
|
return this.batch.enabled ? this.batch.concurrency : EMBEDDING_INDEX_CONCURRENCY;
|
|
}
|
|
|
|
private async indexFile(
|
|
entry: MemoryFileEntry | SessionFileEntry,
|
|
options: { source: MemorySource; content?: string },
|
|
) {
|
|
const content = options.content ?? (await fs.readFile(entry.absPath, "utf-8"));
|
|
const chunks = chunkMarkdown(content, this.settings.chunking).filter(
|
|
(chunk) => chunk.text.trim().length > 0,
|
|
);
|
|
const embeddings = this.batch.enabled
|
|
? await this.embedChunksWithBatch(chunks, entry, options.source)
|
|
: await this.embedChunksInBatches(chunks);
|
|
const sample = embeddings.find((embedding) => embedding.length > 0);
|
|
const vectorReady = sample ? await this.ensureVectorReady(sample.length) : false;
|
|
const now = Date.now();
|
|
if (vectorReady) {
|
|
try {
|
|
this.db
|
|
.prepare(
|
|
`DELETE FROM ${VECTOR_TABLE} WHERE id IN (SELECT id FROM chunks WHERE path = ? AND source = ?)`,
|
|
)
|
|
.run(entry.path, options.source);
|
|
} catch {}
|
|
}
|
|
if (this.fts.enabled && this.fts.available) {
|
|
try {
|
|
this.db
|
|
.prepare(`DELETE FROM ${FTS_TABLE} WHERE path = ? AND source = ? AND model = ?`)
|
|
.run(entry.path, options.source, this.provider.model);
|
|
} catch {}
|
|
}
|
|
this.db
|
|
.prepare(`DELETE FROM chunks WHERE path = ? AND source = ?`)
|
|
.run(entry.path, options.source);
|
|
for (let i = 0; i < chunks.length; i++) {
|
|
const chunk = chunks[i];
|
|
const embedding = embeddings[i] ?? [];
|
|
const id = hashText(
|
|
`${options.source}:${entry.path}:${chunk.startLine}:${chunk.endLine}:${chunk.hash}:${this.provider.model}`,
|
|
);
|
|
this.db
|
|
.prepare(
|
|
`INSERT INTO chunks (id, path, source, start_line, end_line, hash, model, text, embedding, updated_at)
|
|
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|
ON CONFLICT(id) DO UPDATE SET
|
|
hash=excluded.hash,
|
|
model=excluded.model,
|
|
text=excluded.text,
|
|
embedding=excluded.embedding,
|
|
updated_at=excluded.updated_at`,
|
|
)
|
|
.run(
|
|
id,
|
|
entry.path,
|
|
options.source,
|
|
chunk.startLine,
|
|
chunk.endLine,
|
|
chunk.hash,
|
|
this.provider.model,
|
|
chunk.text,
|
|
JSON.stringify(embedding),
|
|
now,
|
|
);
|
|
if (vectorReady && embedding.length > 0) {
|
|
this.db
|
|
.prepare(`INSERT OR REPLACE INTO ${VECTOR_TABLE} (id, embedding) VALUES (?, ?)`)
|
|
.run(id, vectorToBlob(embedding));
|
|
}
|
|
if (this.fts.enabled && this.fts.available) {
|
|
this.db
|
|
.prepare(
|
|
`INSERT INTO ${FTS_TABLE} (text, id, path, source, model, start_line, end_line)\n` +
|
|
` VALUES (?, ?, ?, ?, ?, ?, ?)`,
|
|
)
|
|
.run(
|
|
chunk.text,
|
|
id,
|
|
entry.path,
|
|
options.source,
|
|
this.provider.model,
|
|
chunk.startLine,
|
|
chunk.endLine,
|
|
);
|
|
}
|
|
}
|
|
this.db
|
|
.prepare(
|
|
`INSERT INTO files (path, source, hash, mtime, size) VALUES (?, ?, ?, ?, ?)
|
|
ON CONFLICT(path) DO UPDATE SET
|
|
source=excluded.source,
|
|
hash=excluded.hash,
|
|
mtime=excluded.mtime,
|
|
size=excluded.size`,
|
|
)
|
|
.run(entry.path, options.source, entry.hash, entry.mtimeMs, entry.size);
|
|
}
|
|
}
|