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148 lines
4.9 KiB
TypeScript
148 lines
4.9 KiB
TypeScript
import { createSubsystemLogger } from "openclaw/plugin-sdk/logging-core";
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import {
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buildRemoteBaseUrlPolicy,
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createRemoteEmbeddingProvider,
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normalizeEmbeddingModelWithPrefixes,
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type MemoryEmbeddingProvider,
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type MemoryEmbeddingProviderCreateOptions,
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} from "openclaw/plugin-sdk/memory-core-host-engine-embeddings";
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import { resolveMemorySecretInputString } from "openclaw/plugin-sdk/memory-core-host-secret";
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import { formatErrorMessage, type SsrFPolicy } from "openclaw/plugin-sdk/ssrf-runtime";
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import { LMSTUDIO_DEFAULT_EMBEDDING_MODEL, LMSTUDIO_PROVIDER_ID } from "./defaults.js";
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import { ensureLmstudioModelLoaded } from "./models.fetch.js";
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import { resolveLmstudioInferenceBase } from "./models.js";
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import {
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buildLmstudioAuthHeaders,
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resolveLmstudioProviderHeaders,
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resolveLmstudioRuntimeApiKey,
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} from "./runtime.js";
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const log = createSubsystemLogger("memory/embeddings");
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type LmstudioEmbeddingClient = {
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baseUrl: string;
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headers: Record<string, string>;
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ssrfPolicy?: SsrFPolicy;
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model: string;
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};
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export const DEFAULT_LMSTUDIO_EMBEDDING_MODEL = LMSTUDIO_DEFAULT_EMBEDDING_MODEL;
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/** Normalizes LM Studio embedding model refs and accepts `lmstudio/` prefix. */
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function normalizeLmstudioModel(model: string): string {
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return normalizeEmbeddingModelWithPrefixes({
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model,
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defaultModel: DEFAULT_LMSTUDIO_EMBEDDING_MODEL,
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prefixes: ["lmstudio/"],
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});
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}
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function hasAuthorizationHeader(headers: Record<string, string> | undefined): boolean {
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if (!headers) {
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return false;
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}
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return Object.entries(headers).some(
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([headerName, value]) =>
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headerName.trim().toLowerCase() === "authorization" && value.trim().length > 0,
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);
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}
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/** Resolves API key (real or synthetic placeholder) from runtime/provider auth config. */
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async function resolveLmstudioApiKey(
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options: MemoryEmbeddingProviderCreateOptions,
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): Promise<string | undefined> {
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try {
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return await resolveLmstudioRuntimeApiKey({
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config: options.config,
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agentDir: options.agentDir,
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});
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} catch (error) {
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// Embeddings can target local LM Studio instances that do not require auth.
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if (/LM Studio API key is required/i.test(formatErrorMessage(error))) {
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return undefined;
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}
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throw error;
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}
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}
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/** Creates the LM Studio embedding provider client and preloads the target model before return. */
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export async function createLmstudioEmbeddingProvider(
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options: MemoryEmbeddingProviderCreateOptions,
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): Promise<{ provider: MemoryEmbeddingProvider; client: LmstudioEmbeddingClient }> {
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const providerConfig = options.config.models?.providers?.lmstudio;
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const providerBaseUrl = providerConfig?.baseUrl?.trim();
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const isFallbackActivation = options.fallback === "lmstudio" && options.provider !== "lmstudio";
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const remoteBaseUrl = options.remote?.baseUrl?.trim();
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const remoteApiKey = !isFallbackActivation
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? resolveMemorySecretInputString({
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value: options.remote?.apiKey,
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path: "agents.*.memorySearch.remote.apiKey",
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})
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: undefined;
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// memorySearch.remote is shared across primary + fallback providers.
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// Ignore it during fallback activation to avoid inheriting another provider's
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// endpoint/headers/credentials when LM Studio activates as a fallback.
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const baseUrlSource = !isFallbackActivation ? remoteBaseUrl : undefined;
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const configuredBaseUrl =
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baseUrlSource && baseUrlSource.length > 0
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? baseUrlSource
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: providerBaseUrl && providerBaseUrl.length > 0
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? providerBaseUrl
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: undefined;
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const baseUrl = resolveLmstudioInferenceBase(configuredBaseUrl);
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const model = normalizeLmstudioModel(options.model);
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const providerHeaders = await resolveLmstudioProviderHeaders({
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config: options.config,
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env: process.env,
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headers: Object.assign(
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{},
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providerConfig?.headers,
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!isFallbackActivation ? options.remote?.headers : {},
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),
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});
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const apiKey = hasAuthorizationHeader(providerHeaders)
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? undefined
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: !isFallbackActivation
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? remoteApiKey?.trim() || (await resolveLmstudioApiKey(options))
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: await resolveLmstudioApiKey(options);
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const headerOverrides = Object.assign({}, providerHeaders);
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const headers =
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buildLmstudioAuthHeaders({
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apiKey,
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json: true,
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headers: headerOverrides,
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}) ?? {};
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const ssrfPolicy = buildRemoteBaseUrlPolicy(baseUrl);
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const client: LmstudioEmbeddingClient = {
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baseUrl,
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model,
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headers,
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ssrfPolicy,
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};
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try {
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await ensureLmstudioModelLoaded({
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baseUrl,
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apiKey,
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headers: headerOverrides,
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ssrfPolicy,
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modelKey: model,
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timeoutMs: 120_000,
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});
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} catch (error) {
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log.warn("lmstudio embeddings warmup failed; continuing without preload", {
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baseUrl,
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model,
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error: formatErrorMessage(error),
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});
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}
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return {
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provider: createRemoteEmbeddingProvider({
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id: LMSTUDIO_PROVIDER_ID,
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client,
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errorPrefix: "lmstudio embeddings failed",
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}),
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client,
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};
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}
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