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refactor(extensions): privatize small plugin internals (#107774)
* refactor(llama-cpp): privatize embedding internals * refactor(parallel): privatize MCP response helpers * refactor(logbook): privatize analysis parsers * refactor(crabbox): narrow worker provider exports * refactor(acpx): privatize process reaper internals * chore(deadcode): refresh unused-export baseline
This commit is contained in:
Peter Steinberger
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GitHub
GitHub
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534300beed
commit
2eb9c7ebd7
@@ -22,12 +22,23 @@ vi.mock("openclaw/plugin-sdk/memory-core-host-engine-embeddings", () => ({
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}));
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import llamaCppPlugin from "./index.js";
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import {
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DEFAULT_LLAMA_CPP_EMBEDDING_MODEL,
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createLlamaCppMemoryEmbeddingProvider,
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formatLlamaCppSetupError,
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llamaCppEmbeddingProviderAdapter,
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} from "./src/embedding-provider.js";
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import { llamaCppEmbeddingProviderAdapter } from "./src/embedding-provider.js";
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const DEFAULT_LLAMA_CPP_EMBEDDING_MODEL =
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"hf:ggml-org/embeddinggemma-300m-qat-q8_0-GGUF/embeddinggemma-300m-qat-Q8_0.gguf";
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type AdapterCreateOptions = Parameters<typeof llamaCppEmbeddingProviderAdapter.create>[0];
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type MemoryCreateTestOptions = AdapterCreateOptions & {
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fallback?: "none";
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outputDimensionality?: number;
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};
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async function createLlamaCppMemoryEmbeddingProvider(options: MemoryCreateTestOptions) {
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const { fallback: _fallback, outputDimensionality, ...adapterOptions } = options;
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return await llamaCppEmbeddingProviderAdapter.create({
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...adapterOptions,
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dimensions: outputDimensionality,
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});
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}
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afterEach(() => {
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clearEmbeddingProviders();
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@@ -148,16 +159,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL,
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outputDimensionality: 512,
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL,
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outputDimensionality: 512,
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});
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const resolvedIdentity = llamaCppEmbeddingProviderAdapter.resolveIndexIdentity?.({
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config: {},
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provider: "local",
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@@ -197,16 +205,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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});
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expect(result.provider?.model).toBe(DEFAULT_LLAMA_CPP_EMBEDDING_MODEL);
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expect(result.runtime?.cacheKeyData).toEqual({
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@@ -265,7 +270,7 @@ describe("llama.cpp provider plugin", () => {
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local: { modelPath },
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}),
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{
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nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js",
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nodeLlamaCppImportUrl: expect.stringContaining("node-llama-cpp"),
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},
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);
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});
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@@ -283,16 +288,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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});
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expect(result.provider?.model).toBe(modelPath);
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expect(result.runtime?.cacheKeyData).toEqual({
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@@ -316,16 +318,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath },
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});
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expect(result.provider?.model).toBe(modelPath);
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expect(result.runtime).not.toHaveProperty("indexIdentityAliases");
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@@ -341,16 +340,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL,
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local: { modelPath: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL, modelCacheDir },
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL,
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local: { modelPath: DEFAULT_LLAMA_CPP_EMBEDDING_MODEL, modelCacheDir },
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});
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expect(result.provider?.model).toBe(DEFAULT_LLAMA_CPP_EMBEDDING_MODEL);
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expect(result.runtime?.cacheKeyData).toEqual({
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@@ -432,16 +428,13 @@ describe("llama.cpp provider plugin", () => {
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embedBatch: vi.fn(),
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});
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const result = await createLlamaCppMemoryEmbeddingProvider(
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{
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath, modelCacheDir },
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},
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{ nodeLlamaCppImportUrl: "file:///plugin/node-llama-cpp.js" },
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);
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const result = await createLlamaCppMemoryEmbeddingProvider({
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config: {},
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provider: "local",
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fallback: "none",
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model: modelPath,
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local: { modelPath, modelCacheDir },
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});
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expect(result.provider?.model).toBe(DEFAULT_LLAMA_CPP_EMBEDDING_MODEL);
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expect(result.runtime?.indexIdentityAliases).toEqual([
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@@ -467,7 +460,7 @@ describe("llama.cpp provider plugin", () => {
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code: "ERR_MODULE_NOT_FOUND",
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});
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expect(formatLlamaCppSetupError(err)).toContain(
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expect(llamaCppEmbeddingProviderAdapter.formatSetupError?.(err)).toContain(
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"openclaw plugins install @openclaw/llama-cpp-provider",
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);
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});
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@@ -29,7 +29,7 @@ type LlamaCppEmbeddingProviderRuntimeOptions = {
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const LLAMA_CPP_EMBEDDING_PROVIDER_ID = "local";
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const LOCAL_EMBEDDING_RUNTIME_FACTS = Symbol.for("openclaw.localEmbeddingRuntimeFacts");
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export const DEFAULT_LLAMA_CPP_EMBEDDING_MODEL =
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const DEFAULT_LLAMA_CPP_EMBEDDING_MODEL =
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"hf:ggml-org/embeddinggemma-300m-qat-q8_0-GGUF/embeddinggemma-300m-qat-Q8_0.gguf";
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const DEFAULT_LLAMA_CPP_EMBEDDING_MODEL_CACHE_FILE_NAME =
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"hf_ggml-org_embeddinggemma-300m-qat-Q8_0.gguf";
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@@ -133,7 +133,7 @@ function formatErrorMessage(err: unknown): string {
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return String(err);
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}
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export function formatLlamaCppSetupError(err: unknown): string {
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function formatLlamaCppSetupError(err: unknown): string {
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const detail = formatErrorMessage(err);
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const missing = isNodeLlamaCppMissing(err);
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return [
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@@ -195,7 +195,7 @@ function adaptMemoryEmbeddingProvider(provider: MemoryEmbeddingProvider): Embedd
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return adapted;
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}
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export async function createLlamaCppMemoryEmbeddingProvider(
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async function createLlamaCppMemoryEmbeddingProvider(
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options: MemoryEmbeddingProviderCreateOptions,
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runtimeOptions: LlamaCppEmbeddingProviderRuntimeOptions = {},
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): Promise<MemoryEmbeddingProviderCreateResult> {
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