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fix(memory): hard-cap embedding inputs before batch
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@@ -122,6 +122,7 @@ Docs: https://docs.openclaw.ai
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- Memory/Embeddings: apply configured remote-base host pinning (`allowedHostnames`) across OpenAI/Voyage/Gemini embedding requests to keep private/self-hosted endpoints working without cross-host drift. (#18198) Thanks @ianpcook.
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- Memory/Batch: route OpenAI/Voyage/Gemini batch upload/create/status/download requests through the same guarded HTTP path for consistent SSRF policy enforcement.
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- Memory/Index: detect memory source-set changes (for example enabling `sessions` after an existing memory-only index) and trigger a full reindex so existing session transcripts are indexed without requiring `--force`. (#17576) Thanks @TarsAI-Agent.
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- Memory/Embeddings: enforce a per-input 8k safety cap before embedding batching and apply a conservative 2k fallback limit for local providers without declared input limits, preventing oversized session/memory chunks from triggering provider context-size failures during sync/indexing. (#6016) Thanks @batumilove.
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- Memory/QMD: on Windows, resolve bare `qmd`/`mcporter` command names to npm shim executables (`.cmd`) before spawning, so qmd boot updates and mcporter-backed searches no longer fail with `spawn ... ENOENT` on default npm installs. (#23899) Thanks @arcbuilder-ai.
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- Memory/QMD: parse plain-text `qmd collection list --json` output when older qmd builds ignore JSON mode, and retry memory searches once after re-ensuring managed collections when qmd returns `Collection not found ...`. (#23613) Thanks @leozhucn.
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- Signal/RPC: guard malformed Signal RPC JSON responses with a clear status-scoped error and add regression coverage for invalid JSON responses. (#22995) Thanks @adhitShet.
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@@ -13,6 +13,18 @@ function createProvider(maxInputTokens: number): EmbeddingProvider {
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};
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}
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function createProviderWithoutMaxInputTokens(params: {
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id: string;
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model: string;
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}): EmbeddingProvider {
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return {
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id: params.id,
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model: params.model,
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embedQuery: async () => [0],
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embedBatch: async () => [[0]],
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};
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}
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describe("embedding chunk limits", () => {
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it("splits oversized chunks so each embedding input stays <= maxInputTokens bytes", () => {
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const provider = createProvider(8192);
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@@ -49,4 +61,42 @@ describe("embedding chunk limits", () => {
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// If we split inside surrogate pairs we'd likely end up with replacement chars.
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expect(out.map((chunk) => chunk.text).join("")).not.toContain("\uFFFD");
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});
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it("uses conservative fallback limits for local providers without declared maxInputTokens", () => {
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const provider = createProviderWithoutMaxInputTokens({
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id: "local",
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model: "unknown-local-embedding",
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});
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const out = enforceEmbeddingMaxInputTokens(provider, [
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{
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startLine: 1,
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endLine: 1,
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text: "x".repeat(3000),
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hash: "ignored",
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},
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]);
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expect(out.length).toBeGreaterThan(1);
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expect(out.every((chunk) => estimateUtf8Bytes(chunk.text) <= 2048)).toBe(true);
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});
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it("honors hard safety caps lower than provider maxInputTokens", () => {
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const provider = createProvider(8192);
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const out = enforceEmbeddingMaxInputTokens(
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provider,
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[
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{
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startLine: 1,
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endLine: 1,
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text: "x".repeat(8100),
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hash: "ignored",
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},
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],
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8000,
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);
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expect(out.length).toBeGreaterThan(1);
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expect(out.every((chunk) => estimateUtf8Bytes(chunk.text) <= 8000)).toBe(true);
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});
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});
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@@ -6,8 +6,13 @@ import { hashText, type MemoryChunk } from "./internal.js";
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export function enforceEmbeddingMaxInputTokens(
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provider: EmbeddingProvider,
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chunks: MemoryChunk[],
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hardMaxInputTokens?: number,
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): MemoryChunk[] {
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const maxInputTokens = resolveEmbeddingMaxInputTokens(provider);
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const providerMaxInputTokens = resolveEmbeddingMaxInputTokens(provider);
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const maxInputTokens =
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typeof hardMaxInputTokens === "number" && hardMaxInputTokens > 0
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? Math.min(providerMaxInputTokens, hardMaxInputTokens)
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: providerMaxInputTokens;
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const out: MemoryChunk[] = [];
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for (const chunk of chunks) {
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@@ -1,6 +1,7 @@
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import type { EmbeddingProvider } from "./embeddings.js";
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const DEFAULT_EMBEDDING_MAX_INPUT_TOKENS = 8192;
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const DEFAULT_LOCAL_EMBEDDING_MAX_INPUT_TOKENS = 2048;
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const KNOWN_EMBEDDING_MAX_INPUT_TOKENS: Record<string, number> = {
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"openai:text-embedding-3-small": 8192,
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@@ -30,6 +31,9 @@ export function resolveEmbeddingMaxInputTokens(provider: EmbeddingProvider): num
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if (provider.id.toLowerCase() === "gemini") {
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return 2048;
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}
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if (provider.id.toLowerCase() === "local") {
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return DEFAULT_LOCAL_EMBEDDING_MAX_INPUT_TOKENS;
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}
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return DEFAULT_EMBEDDING_MAX_INPUT_TOKENS;
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}
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@@ -709,6 +709,7 @@ export abstract class MemoryManagerEmbeddingOps extends MemoryManagerSyncOps {
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chunkMarkdown(content, this.settings.chunking).filter(
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(chunk) => chunk.text.trim().length > 0,
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),
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EMBEDDING_BATCH_MAX_TOKENS,
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);
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if (options.source === "sessions" && "lineMap" in entry) {
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remapChunkLines(chunks, entry.lineMap);
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@@ -6,7 +6,7 @@ import { installEmbeddingManagerFixture } from "./embedding-manager.test-harness
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const fx = installEmbeddingManagerFixture({
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fixturePrefix: "openclaw-mem-",
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largeTokens: 1250,
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largeTokens: 4000,
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smallTokens: 200,
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createCfg: ({ workspaceDir, indexPath, tokens }) => ({
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agents: {
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@@ -50,6 +50,10 @@ describe("memory embedding batches", () => {
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);
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expect(totalTexts).toBe(status.chunks);
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expect(embedBatch.mock.calls.length).toBeGreaterThan(1);
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const inputs: string[] = embedBatch.mock.calls.flatMap(
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(call: unknown[]) => (call[0] as string[] | undefined) ?? [],
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);
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expect(inputs.every((text) => Buffer.byteLength(text, "utf8") <= 8000)).toBe(true);
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expect(updates.length).toBeGreaterThan(0);
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expect(updates.some((update) => update.label?.includes("/"))).toBe(true);
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const last = updates[updates.length - 1];
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