Feat : Skills
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decolua
@@ -1,196 +1,13 @@
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import { getModelTargetFormat, PROVIDER_ID_TO_ALIAS } from "../config/providerModels.js";
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import { createErrorResult, parseUpstreamError, formatProviderError } from "../utils/error.js";
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import { HTTP_STATUS } from "../config/runtimeConfig.js";
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import { getExecutor } from "../executors/index.js";
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import { refreshWithRetry } from "../services/tokenRefresh.js";
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// Google AI (Gemini) provider aliases / identifiers
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const GEMINI_PROVIDERS = new Set(["gemini", "google_ai_studio"]);
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// Static map: provider id → embeddings endpoint (OpenAI-compatible body format)
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const EMBEDDING_URLS = {
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openai: "https://api.openai.com/v1/embeddings",
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openrouter: "https://openrouter.ai/api/v1/embeddings",
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mistral: "https://api.mistral.ai/v1/embeddings",
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"voyage-ai": "https://api.voyageai.com/v1/embeddings",
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fireworks: "https://api.fireworks.ai/inference/v1/embeddings",
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together: "https://api.together.xyz/v1/embeddings",
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nebius: "https://api.tokenfactory.nebius.com/v1/embeddings",
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github: "https://models.github.ai/inference/embeddings",
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nvidia: "https://integrate.api.nvidia.com/v1/embeddings",
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};
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import { getEmbeddingAdapter } from "./embeddingProviders/index.js";
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/**
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* Check whether a provider targets the Google AI (Gemini) embeddings API.
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* @param {string} provider
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*/
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function isGeminiProvider(provider) {
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return GEMINI_PROVIDERS.has(provider);
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}
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/**
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* Build the embeddings request body for the target provider.
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* Core embeddings handler — orchestrator only. Provider-specific URL/headers/body/normalize
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* live in `./embeddingProviders/{id}.js`.
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*
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* - OpenAI / openai-compatible / openrouter: standard { model, input } format.
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* - Google AI (Gemini): different format per API spec.
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* - Single input → embedContent body: { model, content: { parts: [{ text }] } }
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* - Batch input → batchEmbedContents body: { requests: [{ model, content: { parts: [{ text }] } }] }
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*/
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function buildEmbeddingsBody(provider, model, input, encodingFormat, dimensions) {
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if (isGeminiProvider(provider)) {
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// Normalize model name: Gemini API expects "models/<model>" prefix
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const geminiModel = model.startsWith("models/") ? model : `models/${model}`;
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if (Array.isArray(input)) {
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// Batch request
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return {
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requests: input.map((text) => ({
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model: geminiModel,
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content: { parts: [{ text: String(text) }] }
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}))
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};
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} else {
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// Single request
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return {
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model: geminiModel,
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content: { parts: [{ text: String(input) }] }
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};
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}
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}
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// Default: OpenAI format
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const body = { model, input };
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if (encodingFormat) {
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body.encoding_format = encodingFormat;
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}
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if (dimensions != null && dimensions !== "") {
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const dim = Number(dimensions);
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if (Number.isFinite(dim) && dim > 0) body.dimensions = dim;
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}
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return body;
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}
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/**
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* Build the URL for the embeddings endpoint based on the provider.
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* @param {string} provider
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* @param {string} model
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* @param {object} credentials
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* @param {string|string[]} input - used to select single vs batch endpoint for Gemini
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*/
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function buildEmbeddingsUrl(provider, model, credentials, input) {
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if (isGeminiProvider(provider)) {
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const apiKey = credentials.apiKey || credentials.accessToken;
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// Normalize model name for URL path
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const modelPath = model.startsWith("models/") ? model : `models/${model}`;
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if (Array.isArray(input)) {
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// batchEmbedContents for array input (keeps response format consistent even for length=1)
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return `https://generativelanguage.googleapis.com/v1beta/${modelPath}:batchEmbedContents?key=${encodeURIComponent(apiKey)}`;
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}
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return `https://generativelanguage.googleapis.com/v1beta/${modelPath}:embedContent?key=${encodeURIComponent(apiKey)}`;
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}
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if (EMBEDDING_URLS[provider]) return EMBEDDING_URLS[provider];
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// openai-compatible & custom-embedding providers: use their baseUrl + /embeddings
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if (provider?.startsWith?.("openai-compatible-") || provider?.startsWith?.("custom-embedding-")) {
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const rawBaseUrl = credentials?.providerSpecificData?.baseUrl || "https://api.openai.com/v1";
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// Defensive: strip trailing slash and accidental /embeddings to avoid double-append
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const baseUrl = rawBaseUrl.replace(/\/$/, "").replace(/\/embeddings$/, "");
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return `${baseUrl}/embeddings`;
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}
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return null;
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}
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/**
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* Build headers for the embeddings request.
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*/
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function buildEmbeddingsHeaders(provider, credentials) {
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const headers = { "Content-Type": "application/json" };
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if (isGeminiProvider(provider)) {
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// Gemini API uses API key as query param — no Authorization header needed
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return headers;
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}
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switch (provider) {
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case "openai":
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case "openrouter":
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headers["Authorization"] = `Bearer ${credentials.apiKey || credentials.accessToken}`;
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if (provider === "openrouter") {
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headers["HTTP-Referer"] = "https://endpoint-proxy.local";
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headers["X-Title"] = "Endpoint Proxy";
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}
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break;
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default:
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headers["Authorization"] = `Bearer ${credentials.apiKey || credentials.accessToken}`;
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}
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return headers;
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}
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/**
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* Normalize the embeddings response to OpenAI format.
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*
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* Gemini single response:
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* { embedding: { values: [0.1, 0.2, ...] } }
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*
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* Gemini batch response:
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* { embeddings: [{ values: [...] }, ...] }
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*
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* Target OpenAI format:
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* { object: "list", data: [{ object: "embedding", index: 0, embedding: [...] }], model, usage: {...} }
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*/
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function normalizeEmbeddingsResponse(responseBody, model, provider) {
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// Already in OpenAI format
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if (responseBody.object === "list" && Array.isArray(responseBody.data)) {
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return responseBody;
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}
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if (isGeminiProvider(provider)) {
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let embeddingItems = [];
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if (Array.isArray(responseBody.embeddings)) {
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// Batch response
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embeddingItems = responseBody.embeddings.map((emb, idx) => ({
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object: "embedding",
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index: idx,
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embedding: emb.values || []
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}));
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} else if (responseBody.embedding?.values) {
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// Single response
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embeddingItems = [{
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object: "embedding",
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index: 0,
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embedding: responseBody.embedding.values
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}];
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}
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return {
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object: "list",
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data: embeddingItems,
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model,
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usage: {
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prompt_tokens: 0,
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total_tokens: 0
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}
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};
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}
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// Try to handle alternate formats gracefully
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return responseBody;
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}
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/**
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* Core embeddings handler — shared between Worker and SSE server.
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*
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* @param {object} options
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* @param {object} options.body - Parsed request body { model, input, encoding_format }
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* @param {object} options.modelInfo - { provider, model }
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* @param {object} options.credentials - Provider credentials
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* @param {object} [options.log] - Logger
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* @param {function} [options.onCredentialsRefreshed] - Called when creds are refreshed
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* @param {function} [options.onRequestSuccess] - Called on success (clear error state)
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* @returns {Promise<{ success: boolean, response: Response, status?: number, error?: string }>}
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*/
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export async function handleEmbeddingsCore({
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@@ -199,7 +16,7 @@ export async function handleEmbeddingsCore({
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credentials,
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log,
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onCredentialsRefreshed,
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onRequestSuccess
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onRequestSuccess,
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}) {
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const { provider, model } = modelInfo;
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@@ -212,19 +29,22 @@ export async function handleEmbeddingsCore({
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return createErrorResult(HTTP_STATUS.BAD_REQUEST, "input must be a string or array of strings");
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}
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const encodingFormat = body.encoding_format || "float";
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// Determine embeddings URL
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const url = buildEmbeddingsUrl(provider, model, credentials, input);
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if (!url) {
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const adapter = getEmbeddingAdapter(provider);
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if (!adapter) {
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return createErrorResult(
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HTTP_STATUS.BAD_REQUEST,
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`Provider '${provider}' does not support embeddings. Use openai, openrouter, gemini, or an openai-compatible provider.`
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`Provider '${provider}' does not support embeddings.`
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);
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}
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const headers = buildEmbeddingsHeaders(provider, credentials);
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const requestBody = buildEmbeddingsBody(provider, model, input, encodingFormat, body.dimensions);
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const ctx = { input };
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const url = adapter.buildUrl(model, credentials, ctx);
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const headers = adapter.buildHeaders(credentials, ctx);
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const requestBody = adapter.buildBody(model, {
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input,
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encoding_format: body.encoding_format || "float",
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dimensions: body.dimensions,
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});
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log?.debug?.("EMBEDDINGS", `${provider.toUpperCase()} | ${model} | input_type=${Array.isArray(input) ? `array[${input.length}]` : "string"}`);
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@@ -233,7 +53,7 @@ export async function handleEmbeddingsCore({
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providerResponse = await fetch(url, {
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method: "POST",
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headers,
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body: JSON.stringify(requestBody)
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body: JSON.stringify(requestBody),
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});
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} catch (error) {
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const errMsg = formatProviderError(error, provider, model, HTTP_STATUS.BAD_GATEWAY);
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@@ -244,9 +64,9 @@ export async function handleEmbeddingsCore({
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// Handle 401/403 — try token refresh (skip for noAuth providers)
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const executor = getExecutor(provider);
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if (
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!executor.noAuth &&
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!executor?.noAuth &&
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(providerResponse.status === HTTP_STATUS.UNAUTHORIZED ||
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providerResponse.status === HTTP_STATUS.FORBIDDEN)
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providerResponse.status === HTTP_STATUS.FORBIDDEN)
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) {
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const newCredentials = await refreshWithRetry(
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() => executor.refreshCredentials(credentials, log),
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@@ -257,24 +77,17 @@ export async function handleEmbeddingsCore({
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if (newCredentials?.accessToken || newCredentials?.apiKey) {
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log?.info?.("TOKEN", `${provider.toUpperCase()} | refreshed for embeddings`);
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Object.assign(credentials, newCredentials);
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if (onCredentialsRefreshed && newCredentials) {
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await onCredentialsRefreshed(newCredentials);
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}
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if (onCredentialsRefreshed) await onCredentialsRefreshed(newCredentials);
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// Retry with refreshed credentials
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try {
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const retryHeaders = buildEmbeddingsHeaders(provider, credentials);
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// Rebuild URL for Gemini since API key is embedded in query param
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const retryUrl = isGeminiProvider(provider)
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? buildEmbeddingsUrl(provider, model, credentials, input)
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: url;
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const retryHeaders = adapter.buildHeaders(credentials, ctx);
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const retryUrl = adapter.buildUrl(model, credentials, ctx);
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providerResponse = await fetch(retryUrl, {
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method: "POST",
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headers: retryHeaders,
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body: JSON.stringify(requestBody)
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body: JSON.stringify(requestBody),
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});
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} catch (retryError) {
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} catch {
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log?.warn?.("TOKEN", `${provider.toUpperCase()} | retry after refresh failed`);
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}
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} else {
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@@ -292,16 +105,13 @@ export async function handleEmbeddingsCore({
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let responseBody;
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try {
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responseBody = await providerResponse.json();
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} catch (parseError) {
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} catch {
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return createErrorResult(HTTP_STATUS.BAD_GATEWAY, `Invalid JSON response from ${provider}`);
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}
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if (onRequestSuccess) {
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await onRequestSuccess();
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}
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const normalized = normalizeEmbeddingsResponse(responseBody, model, provider);
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if (onRequestSuccess) await onRequestSuccess();
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const normalized = adapter.normalize(responseBody, model);
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log?.debug?.("EMBEDDINGS", `Success | usage=${JSON.stringify(normalized.usage || {})}`);
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return {
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@@ -309,8 +119,8 @@ export async function handleEmbeddingsCore({
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response: new Response(JSON.stringify(normalized), {
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headers: {
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"Content-Type": "application/json",
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"Access-Control-Allow-Origin": "*"
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}
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})
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"Access-Control-Allow-Origin": "*",
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},
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}),
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};
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}
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