Files
OmniRoute/open-sse/config/embeddingRegistry.ts
diegosouzapw 71d14209a4 feat: OmniRoute v1.0.0 — Intelligent AI Gateway & Universal LLM Proxy
OmniRoute is an intelligent API gateway that unifies 20+ AI providers behind a single
OpenAI-compatible endpoint. Features include intelligent routing with 6 strategies,
multi-format translation (OpenAI/Claude/Gemini/Responses API), circuit breakers,
semantic caching, combo fallback chains, real-time health monitoring, and a full
dashboard with provider management, analytics, and CLI tool integration.

Key highlights:
- 20+ providers (Claude Code, Codex, Gemini CLI, GitHub Copilot, iFlow, Qwen, Kiro, etc.)
- 6 routing strategies (Fill First, Round Robin, P2C, Random, Least Used, Cost Optimized)
- Export/Import database backup with full archive support
- Translator Playground with 4 modes (Playground, Chat Tester, Test Bench, Live Monitor)
- 100% TypeScript across src/ and open-sse/
- Docker support with multi-stage builds
- Comprehensive documentation and 9 dashboard screenshots
2026-02-18 00:02:15 -03:00

127 lines
3.7 KiB
TypeScript

/**
* Embedding Provider Registry
*
* Defines providers that support the /v1/embeddings endpoint.
* All providers use the OpenAI-compatible format.
*
* API keys are stored in the same provider credentials system,
* keyed by provider ID (e.g. "nebius", "openai").
*/
export const EMBEDDING_PROVIDERS = {
nebius: {
id: "nebius",
baseUrl: "https://api.tokenfactory.nebius.com/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [{ id: "Qwen/Qwen3-Embedding-8B", name: "Qwen3 Embedding 8B", dimensions: 4096 }],
},
openai: {
id: "openai",
baseUrl: "https://api.openai.com/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [
{ id: "text-embedding-3-small", name: "Text Embedding 3 Small", dimensions: 1536 },
{ id: "text-embedding-3-large", name: "Text Embedding 3 Large", dimensions: 3072 },
{ id: "text-embedding-ada-002", name: "Text Embedding Ada 002", dimensions: 1536 },
],
},
mistral: {
id: "mistral",
baseUrl: "https://api.mistral.ai/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [{ id: "mistral-embed", name: "Mistral Embed", dimensions: 1024 }],
},
together: {
id: "together",
baseUrl: "https://api.together.xyz/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [
{ id: "BAAI/bge-large-en-v1.5", name: "BGE Large EN v1.5", dimensions: 1024 },
{ id: "togethercomputer/m2-bert-80M-8k-retrieval", name: "M2 BERT 80M 8K", dimensions: 768 },
],
},
fireworks: {
id: "fireworks",
baseUrl: "https://api.fireworks.ai/inference/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [
{ id: "nomic-ai/nomic-embed-text-v1.5", name: "Nomic Embed Text v1.5", dimensions: 768 },
],
},
nvidia: {
id: "nvidia",
baseUrl: "https://integrate.api.nvidia.com/v1/embeddings",
authType: "apikey",
authHeader: "bearer",
models: [{ id: "nvidia/nv-embedqa-e5-v5", name: "NV EmbedQA E5 v5", dimensions: 1024 }],
},
};
/**
* Get embedding provider config by ID
*/
export function getEmbeddingProvider(providerId) {
return EMBEDDING_PROVIDERS[providerId] || null;
}
/**
* Parse embedding model string (format: "provider/model" or just "model")
* Returns { provider, model }
*/
export function parseEmbeddingModel(modelStr) {
if (!modelStr) return { provider: null, model: null };
// Check for "provider/model" format
const slashIdx = modelStr.indexOf("/");
if (slashIdx > 0) {
// Handle nested model IDs like "nebius/Qwen/Qwen3-Embedding-8B"
// Try each provider prefix
for (const [providerId, config] of Object.entries(EMBEDDING_PROVIDERS)) {
if (modelStr.startsWith(providerId + "/")) {
return { provider: providerId, model: modelStr.slice(providerId.length + 1) };
}
}
// Fallback: first segment is provider
const provider = modelStr.slice(0, slashIdx);
const model = modelStr.slice(slashIdx + 1);
return { provider, model };
}
// No provider prefix — search all providers for the model
for (const [providerId, config] of Object.entries(EMBEDDING_PROVIDERS)) {
if (config.models.some((m) => m.id === modelStr)) {
return { provider: providerId, model: modelStr };
}
}
return { provider: null, model: modelStr };
}
/**
* Get all embedding models as a flat list
*/
export function getAllEmbeddingModels() {
const models = [];
for (const [providerId, config] of Object.entries(EMBEDDING_PROVIDERS)) {
for (const model of config.models) {
models.push({
id: `${providerId}/${model.id}`,
name: model.name,
provider: providerId,
dimensions: model.dimensions,
});
}
}
return models;
}