openclaw/dist/embedding-provider-BTMtlCOH.js

Multi-channel AI gateway with extensible messaging integrations

import { n as resolveRemoteEmbeddingClient, r as fetchRemoteEmbeddingVectors } from "./memory-core-host-engine-embeddings-nTgmnTkX.js";
import { r as OPENAI_DEFAULT_EMBEDDING_MODEL } from "./default-models-CV6ZCMki.js";
//#region extensions/openai/embedding-provider.ts
const DEFAULT_OPENAI_BASE_URL = "https://api.openai.com/v1";
const DEFAULT_OPENAI_EMBEDDING_MODEL = OPENAI_DEFAULT_EMBEDDING_MODEL;
const OPENAI_MAX_INPUT_TOKENS = {
	"text-embedding-3-small": 8192,
	"text-embedding-3-large": 8192,
	"text-embedding-ada-002": 8191
};
function normalizeOpenAiModel(model) {
	const trimmed = model.trim();
	if (!trimmed) return DEFAULT_OPENAI_EMBEDDING_MODEL;
	return trimmed.startsWith("openai/") ? trimmed.slice(7) : trimmed;
}
async function createOpenAiEmbeddingProvider(options) {
	const client = await resolveOpenAiEmbeddingClient(options);
	const url = `${client.baseUrl.replace(/\/$/, "")}/embeddings`;
	const resolveInputType = (kind) => {
		const value = (kind === "query" ? client.queryInputType : client.documentInputType) ?? client.inputType;
		return typeof value === "string" && value.trim().length > 0 ? value.trim() : void 0;
	};
	const embed = async (input, kind, signal) => {
		if (input.length === 0) return [];
		const inputType = resolveInputType(kind);
		return await fetchRemoteEmbeddingVectors({
			url,
			headers: client.headers,
			ssrfPolicy: client.ssrfPolicy,
			fetchImpl: client.fetchImpl,
			signal,
			body: {
				model: client.model,
				input,
				...typeof client.outputDimensionality === "number" ? { dimensions: client.outputDimensionality } : {},
				...inputType ? { input_type: inputType } : {}
			},
			errorPrefix: "openai embeddings failed"
		});
	};
	return {
		provider: {
			id: "openai",
			model: client.model,
			...typeof OPENAI_MAX_INPUT_TOKENS[client.model] === "number" ? { maxInputTokens: OPENAI_MAX_INPUT_TOKENS[client.model] } : {},
			embedQuery: async (text, optionsValue) => {
				const [vec] = await embed([text], "query", optionsValue?.signal);
				return vec ?? [];
			},
			embedBatch: async (texts, optionsLocal) => await embed(texts, "document", optionsLocal?.signal)
		},
		client
	};
}
async function resolveOpenAiEmbeddingClient(options) {
	return {
		...await resolveRemoteEmbeddingClient({
			provider: options.provider ?? "openai",
			options,
			defaultBaseUrl: DEFAULT_OPENAI_BASE_URL,
			normalizeModel: normalizeOpenAiModel
		}),
		inputType: options.inputType,
		queryInputType: options.queryInputType,
		documentInputType: options.documentInputType,
		outputDimensionality: options.outputDimensionality
	};
}
//#endregion
export { createOpenAiEmbeddingProvider as n, DEFAULT_OPENAI_EMBEDDING_MODEL as t };