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https://github.com/Nezumi-2711/9router.git
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Brokenc0deandCursor
ab5ec52f28
OpenAI-compatible apikey provider (chat/embedding/image) with dynamic model discovery via modelsFetcher + passthroughModels. No executor needed. Co-authored-by: Cursor <cursoragent@cursor.com>
57 lines
2.3 KiB
JavaScript
57 lines
2.3 KiB
JavaScript
export default {
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id: "venice",
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priority: 115,
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alias: "venice",
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aliases: [
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"vn",
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],
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uiAlias: "venice",
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display: {
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name: "Venice AI",
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icon: "shield",
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color: "#DC2626",
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textIcon: "VE",
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website: "https://venice.ai",
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notice: {
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text: "OpenAI-compatible. Private inference + uncensored models (Venice Uncensored, GLM, Qwen, DeepSeek, Llama).",
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apiKeyUrl: "https://venice.ai/settings/api",
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},
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},
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category: "apikey",
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transport: {
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baseUrl: "https://api.venice.ai/api/v1/chat/completions",
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validateUrl: "https://api.venice.ai/api/v1/models",
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thinkingFormat: "openai",
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},
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// Curated seed; the full live catalogue (90+ text models) is fetched via
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// modelsFetcher and any other id is accepted via passthroughModels.
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models: [
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{ id: "venice-uncensored-1-2", name: "Venice Uncensored 1.2" },
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{ id: "zai-org-glm-5", name: "GLM-5" },
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{ id: "qwen3-235b-a22b-instruct-2507", name: "Qwen3 235B A22B Instruct" },
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{ id: "qwen3-coder-480b-a35b-instruct-turbo", name: "Qwen3 Coder 480B A35B Turbo" },
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{ id: "qwen3-vl-235b-a22b", name: "Qwen3 VL 235B A22B" },
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{ id: "deepseek-v4-pro", name: "DeepSeek V4 Pro" },
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{ id: "llama-3.3-70b", name: "Llama 3.3 70B" },
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{ id: "hermes-3-llama-3.1-405b", name: "Hermes 3 Llama 3.1 405B" },
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{ id: "mistral-small-3-2-24b-instruct", name: "Mistral Small 3.2 24B" },
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{ id: "text-embedding-3-large", name: "Text Embedding 3 Large", kind: "embedding" },
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{ id: "text-embedding-bge-m3", name: "BGE-M3 Embedding", kind: "embedding" },
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{ id: "text-embedding-qwen3-8b", name: "Qwen3 8B Embedding", kind: "embedding" },
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{ id: "venice-sd35", name: "Venice SD3.5", params: ["n", "size"], kind: "image" },
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{ id: "flux-2-pro", name: "FLUX.2 Pro", params: ["n", "size"], kind: "image" },
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{ id: "gpt-image-2", name: "GPT Image 2 (via Venice)", params: ["n", "size", "quality"], kind: "image" },
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],
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serviceKinds: ["llm", "embedding", "image"],
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embeddingConfig: {
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baseUrl: "https://api.venice.ai/api/v1/embeddings",
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authType: "apikey",
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authHeader: "bearer",
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},
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imageConfig: {
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baseUrl: "https://api.venice.ai/api/v1/images/generations",
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},
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modelsFetcher: { url: "https://api.venice.ai/api/v1/models", type: "openai" },
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passthroughModels: true,
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};
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