socraticode/dist/services/provider-lmstudio.d.ts

SocratiCode — MCP server for local codebase indexing, semantic search, and code dependency graphs. All private, all local via Docker.

/**
 * LM Studio embedding provider.
 *
 * LM Studio's Local Server exposes an OpenAI-compatible /v1/embeddings endpoint,
 * so we reuse the OpenAI SDK with a custom baseURL. This provider is intentionally
 * separate from `provider-openai.ts` because:
 *   - LM Studio runs locally with no auth by default; OpenAI is cloud-only and requires a key.
 *   - LM Studio models have no Matryoshka support, so we never send a `dimensions` parameter.
 *   - Health check messaging, defaults, and error guidance differ meaningfully.
 *
 * Required env when using this provider:
 *   EMBEDDING_PROVIDER=lmstudio
 *   EMBEDDING_MODEL=<model-id-from-LM-Studio-Local-Server-tab>
 *   EMBEDDING_DIMENSIONS=<dim-of-loaded-model>
 *
 * Optional env:
 *   LMSTUDIO_URL=http://localhost:1234/v1   (default)
 *   LMSTUDIO_API_KEY=<key>                  (only if you've enabled API key auth in LM Studio)
 *   EMBEDDING_CONTEXT_LENGTH=<tokens>       (defaults to 2048 if model unknown)
 *   LMSTUDIO_ALLOW_MISSING_MODEL_LISTING=true  (accept servers without /v1/models, e.g. TEI;
 *                                           resolved in embedding-config.ts)
 */
import type { EmbeddingHealthStatus, EmbeddingProvider, EmbeddingReadinessResult } from "./embedding-types.js";
/** Reset client (for testing or LMSTUDIO_URL hot-swap). */
export declare function resetLMStudioClient(): void;
export declare class LMStudioEmbeddingProvider implements EmbeddingProvider {
    readonly name = "lmstudio";
    ensureReady(): Promise<EmbeddingReadinessResult>;
    embed(texts: string[]): Promise<number[][]>;
    embedSingle(text: string): Promise<number[]>;
    healthCheck(): Promise<EmbeddingHealthStatus>;
    private _embedBatch;
}
//# sourceMappingURL=provider-lmstudio.d.ts.map