Files
9router/open-sse/handlers/embeddingsCore.js
decolua 0b8bed5793 Enhance image and embedding provider support
- Added new image models for GPT 5.2, 5.3, and 5.4, including capabilities for text-to-image and editing.
- Updated embedding handling to include optional dimensions in requests.
- Introduced support for custom embedding providers, allowing dynamic fetching and validation of custom nodes.
- Improved image generation handling with Codex integration, including progress tracking and error handling.
- Enhanced UI components to support adding custom embeddings and displaying their status.
2026-04-25 16:22:30 +07:00

310 lines
10 KiB
JavaScript

import { getModelTargetFormat, PROVIDER_ID_TO_ALIAS } from "../config/providerModels.js";
import { createErrorResult, parseUpstreamError, formatProviderError } from "../utils/error.js";
import { HTTP_STATUS } from "../config/runtimeConfig.js";
import { getExecutor } from "../executors/index.js";
import { refreshWithRetry } from "../services/tokenRefresh.js";
// Google AI (Gemini) provider aliases / identifiers
const GEMINI_PROVIDERS = new Set(["gemini", "google_ai_studio"]);
/**
* Check whether a provider targets the Google AI (Gemini) embeddings API.
* @param {string} provider
*/
function isGeminiProvider(provider) {
return GEMINI_PROVIDERS.has(provider);
}
/**
* Build the embeddings request body for the target provider.
*
* - OpenAI / openai-compatible / openrouter: standard { model, input } format.
* - Google AI (Gemini): different format per API spec.
* - Single input → embedContent body: { model, content: { parts: [{ text }] } }
* - Batch input → batchEmbedContents body: { requests: [{ model, content: { parts: [{ text }] } }] }
*/
function buildEmbeddingsBody(provider, model, input, encodingFormat, dimensions) {
if (isGeminiProvider(provider)) {
// Normalize model name: Gemini API expects "models/<model>" prefix
const geminiModel = model.startsWith("models/") ? model : `models/${model}`;
if (Array.isArray(input)) {
// Batch request
return {
requests: input.map((text) => ({
model: geminiModel,
content: { parts: [{ text: String(text) }] }
}))
};
} else {
// Single request
return {
model: geminiModel,
content: { parts: [{ text: String(input) }] }
};
}
}
// Default: OpenAI format
const body = { model, input };
if (encodingFormat) {
body.encoding_format = encodingFormat;
}
if (dimensions != null && dimensions !== "") {
const dim = Number(dimensions);
if (Number.isFinite(dim) && dim > 0) body.dimensions = dim;
}
return body;
}
/**
* Build the URL for the embeddings endpoint based on the provider.
* @param {string} provider
* @param {string} model
* @param {object} credentials
* @param {string|string[]} input - used to select single vs batch endpoint for Gemini
*/
function buildEmbeddingsUrl(provider, model, credentials, input) {
if (isGeminiProvider(provider)) {
const apiKey = credentials.apiKey || credentials.accessToken;
// Normalize model name for URL path
const modelPath = model.startsWith("models/") ? model : `models/${model}`;
if (Array.isArray(input)) {
// batchEmbedContents for array input (keeps response format consistent even for length=1)
return `https://generativelanguage.googleapis.com/v1beta/${modelPath}:batchEmbedContents?key=${encodeURIComponent(apiKey)}`;
}
return `https://generativelanguage.googleapis.com/v1beta/${modelPath}:embedContent?key=${encodeURIComponent(apiKey)}`;
}
switch (provider) {
case "openai":
return "https://api.openai.com/v1/embeddings";
case "openrouter":
return "https://openrouter.ai/api/v1/embeddings";
default:
// openai-compatible & custom-embedding providers: use their baseUrl + /embeddings
if (provider?.startsWith?.("openai-compatible-") || provider?.startsWith?.("custom-embedding-")) {
const rawBaseUrl = credentials?.providerSpecificData?.baseUrl || "https://api.openai.com/v1";
// Defensive: strip trailing slash and accidental /embeddings to avoid double-append
const baseUrl = rawBaseUrl.replace(/\/$/, "").replace(/\/embeddings$/, "");
return `${baseUrl}/embeddings`;
}
// For other providers, attempt to use their base URL pattern with /embeddings path
return null;
}
}
/**
* Build headers for the embeddings request.
*/
function buildEmbeddingsHeaders(provider, credentials) {
const headers = { "Content-Type": "application/json" };
if (isGeminiProvider(provider)) {
// Gemini API uses API key as query param — no Authorization header needed
return headers;
}
switch (provider) {
case "openai":
case "openrouter":
headers["Authorization"] = `Bearer ${credentials.apiKey || credentials.accessToken}`;
if (provider === "openrouter") {
headers["HTTP-Referer"] = "https://endpoint-proxy.local";
headers["X-Title"] = "Endpoint Proxy";
}
break;
default:
headers["Authorization"] = `Bearer ${credentials.apiKey || credentials.accessToken}`;
}
return headers;
}
/**
* Normalize the embeddings response to OpenAI format.
*
* Gemini single response:
* { embedding: { values: [0.1, 0.2, ...] } }
*
* Gemini batch response:
* { embeddings: [{ values: [...] }, ...] }
*
* Target OpenAI format:
* { object: "list", data: [{ object: "embedding", index: 0, embedding: [...] }], model, usage: {...} }
*/
function normalizeEmbeddingsResponse(responseBody, model, provider) {
// Already in OpenAI format
if (responseBody.object === "list" && Array.isArray(responseBody.data)) {
return responseBody;
}
if (isGeminiProvider(provider)) {
let embeddingItems = [];
if (Array.isArray(responseBody.embeddings)) {
// Batch response
embeddingItems = responseBody.embeddings.map((emb, idx) => ({
object: "embedding",
index: idx,
embedding: emb.values || []
}));
} else if (responseBody.embedding?.values) {
// Single response
embeddingItems = [{
object: "embedding",
index: 0,
embedding: responseBody.embedding.values
}];
}
return {
object: "list",
data: embeddingItems,
model,
usage: {
prompt_tokens: 0,
total_tokens: 0
}
};
}
// Try to handle alternate formats gracefully
return responseBody;
}
/**
* Core embeddings handler — shared between Worker and SSE server.
*
* @param {object} options
* @param {object} options.body - Parsed request body { model, input, encoding_format }
* @param {object} options.modelInfo - { provider, model }
* @param {object} options.credentials - Provider credentials
* @param {object} [options.log] - Logger
* @param {function} [options.onCredentialsRefreshed] - Called when creds are refreshed
* @param {function} [options.onRequestSuccess] - Called on success (clear error state)
* @returns {Promise<{ success: boolean, response: Response, status?: number, error?: string }>}
*/
export async function handleEmbeddingsCore({
body,
modelInfo,
credentials,
log,
onCredentialsRefreshed,
onRequestSuccess
}) {
const { provider, model } = modelInfo;
// Validate input
const input = body.input;
if (!input) {
return createErrorResult(HTTP_STATUS.BAD_REQUEST, "Missing required field: input");
}
if (typeof input !== "string" && !Array.isArray(input)) {
return createErrorResult(HTTP_STATUS.BAD_REQUEST, "input must be a string or array of strings");
}
const encodingFormat = body.encoding_format || "float";
// Determine embeddings URL
const url = buildEmbeddingsUrl(provider, model, credentials, input);
if (!url) {
return createErrorResult(
HTTP_STATUS.BAD_REQUEST,
`Provider '${provider}' does not support embeddings. Use openai, openrouter, gemini, or an openai-compatible provider.`
);
}
const headers = buildEmbeddingsHeaders(provider, credentials);
const requestBody = buildEmbeddingsBody(provider, model, input, encodingFormat, body.dimensions);
log?.debug?.("EMBEDDINGS", `${provider.toUpperCase()} | ${model} | input_type=${Array.isArray(input) ? `array[${input.length}]` : "string"}`);
let providerResponse;
try {
providerResponse = await fetch(url, {
method: "POST",
headers,
body: JSON.stringify(requestBody)
});
} catch (error) {
const errMsg = formatProviderError(error, provider, model, HTTP_STATUS.BAD_GATEWAY);
log?.debug?.("EMBEDDINGS", `Fetch error: ${errMsg}`);
return createErrorResult(HTTP_STATUS.BAD_GATEWAY, errMsg);
}
// Handle 401/403 — try token refresh (skip for noAuth providers)
const executor = getExecutor(provider);
if (
!executor.noAuth &&
(providerResponse.status === HTTP_STATUS.UNAUTHORIZED ||
providerResponse.status === HTTP_STATUS.FORBIDDEN)
) {
const newCredentials = await refreshWithRetry(
() => executor.refreshCredentials(credentials, log),
3,
log
);
if (newCredentials?.accessToken || newCredentials?.apiKey) {
log?.info?.("TOKEN", `${provider.toUpperCase()} | refreshed for embeddings`);
Object.assign(credentials, newCredentials);
if (onCredentialsRefreshed && newCredentials) {
await onCredentialsRefreshed(newCredentials);
}
// Retry with refreshed credentials
try {
const retryHeaders = buildEmbeddingsHeaders(provider, credentials);
// Rebuild URL for Gemini since API key is embedded in query param
const retryUrl = isGeminiProvider(provider)
? buildEmbeddingsUrl(provider, model, credentials, input)
: url;
providerResponse = await fetch(retryUrl, {
method: "POST",
headers: retryHeaders,
body: JSON.stringify(requestBody)
});
} catch (retryError) {
log?.warn?.("TOKEN", `${provider.toUpperCase()} | retry after refresh failed`);
}
} else {
log?.warn?.("TOKEN", `${provider.toUpperCase()} | refresh failed`);
}
}
if (!providerResponse.ok) {
const { statusCode, message } = await parseUpstreamError(providerResponse);
const errMsg = formatProviderError(new Error(message), provider, model, statusCode);
log?.debug?.("EMBEDDINGS", `Provider error: ${errMsg}`);
return createErrorResult(statusCode, errMsg);
}
let responseBody;
try {
responseBody = await providerResponse.json();
} catch (parseError) {
return createErrorResult(HTTP_STATUS.BAD_GATEWAY, `Invalid JSON response from ${provider}`);
}
if (onRequestSuccess) {
await onRequestSuccess();
}
const normalized = normalizeEmbeddingsResponse(responseBody, model, provider);
log?.debug?.("EMBEDDINGS", `Success | usage=${JSON.stringify(normalized.usage || {})}`);
return {
success: true,
response: new Response(JSON.stringify(normalized), {
headers: {
"Content-Type": "application/json",
"Access-Control-Allow-Origin": "*"
}
})
};
}