feat: add Venice AI provider integration
Venice AI is a privacy-focused AI inference provider with support for uncensored models and access to major proprietary models via their anonymized proxy. This integration adds: - Complete model catalog with 25 models: - 15 private models (Llama, Qwen, DeepSeek, Venice Uncensored, etc.) - 10 anonymized models (Claude, GPT-5.2, Gemini, Grok, Kimi, MiniMax) - Auto-discovery from Venice API with fallback to static catalog - VENICE_API_KEY environment variable support - Interactive onboarding via 'venice-api-key' auth choice - Model selection prompt showing all available Venice models - Provider auto-registration when API key is detected - Comprehensive documentation covering: - Privacy modes (private vs anonymized) - All 25 models with context windows and features - Streaming, function calling, and vision support - Model selection recommendations Privacy modes: - Private: Fully private, no logging (open-source models) - Anonymized: Proxied through Venice (proprietary models) Default model: venice/llama-3.3-70b (good balance of capability + privacy) Venice API: https://api.venice.ai/api/v1 (OpenAI-compatible)
This commit is contained in:
jonisjongithub
committed by
Peter Steinberger
Peter Steinberger
parent
fc0e303e05
commit
7540d1e8c1
@@ -282,6 +282,7 @@ export function resolveEnvApiKey(provider: string): EnvApiKeyResult | null {
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"kimi-code": "KIMICODE_API_KEY",
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minimax: "MINIMAX_API_KEY",
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synthetic: "SYNTHETIC_API_KEY",
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venice: "VENICE_API_KEY",
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mistral: "MISTRAL_API_KEY",
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opencode: "OPENCODE_API_KEY",
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};
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@@ -12,6 +12,12 @@ import {
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SYNTHETIC_BASE_URL,
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SYNTHETIC_MODEL_CATALOG,
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} from "./synthetic-models.js";
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import {
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buildVeniceModelDefinition,
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discoverVeniceModels,
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VENICE_BASE_URL,
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VENICE_MODEL_CATALOG,
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} from "./venice-models.js";
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type ModelsConfig = NonNullable<ClawdbotConfig["models"]>;
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export type ProviderConfig = NonNullable<ModelsConfig["providers"]>[string];
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@@ -340,6 +346,15 @@ function buildSyntheticProvider(): ProviderConfig {
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};
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}
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async function buildVeniceProvider(): Promise<ProviderConfig> {
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const models = await discoverVeniceModels();
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return {
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baseUrl: VENICE_BASE_URL,
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api: "openai-completions",
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models,
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};
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}
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async function buildOllamaProvider(): Promise<ProviderConfig> {
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const models = await discoverOllamaModels();
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return {
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@@ -385,6 +400,13 @@ export async function resolveImplicitProviders(params: {
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providers.synthetic = { ...buildSyntheticProvider(), apiKey: syntheticKey };
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}
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const veniceKey =
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resolveEnvApiKeyVarName("venice") ??
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resolveApiKeyFromProfiles({ provider: "venice", store: authStore });
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if (veniceKey) {
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providers.venice = { ...(await buildVeniceProvider()), apiKey: veniceKey };
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}
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const qwenProfiles = listProfilesForProvider(authStore, "qwen-portal");
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if (qwenProfiles.length > 0) {
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providers["qwen-portal"] = {
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389
src/agents/venice-models.ts
Normal file
389
src/agents/venice-models.ts
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@@ -0,0 +1,389 @@
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import type { ModelDefinitionConfig } from "../config/types.js";
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export const VENICE_BASE_URL = "https://api.venice.ai/api/v1";
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export const VENICE_DEFAULT_MODEL_ID = "llama-3.3-70b";
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export const VENICE_DEFAULT_MODEL_REF = `venice/${VENICE_DEFAULT_MODEL_ID}`;
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// Venice uses credit-based pricing, not per-token costs.
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// Set to 0 as costs vary by model and account type.
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export const VENICE_DEFAULT_COST = {
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input: 0,
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output: 0,
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cacheRead: 0,
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cacheWrite: 0,
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};
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/**
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* Complete catalog of Venice AI models.
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*
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* Venice provides two privacy modes:
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* - "private": Fully private inference, no logging, ephemeral
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* - "anonymized": Proxied through Venice with metadata stripped (for proprietary models)
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*
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* Note: The `privacy` field is included for documentation purposes but is not
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* propagated to ModelDefinitionConfig as it's not part of the core model schema.
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* Privacy mode is determined by the model itself, not configurable at runtime.
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*
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* This catalog serves as a fallback when the Venice API is unreachable.
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*/
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export const VENICE_MODEL_CATALOG = [
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// ============================================
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// PRIVATE MODELS (Fully private, no logging)
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// ============================================
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// Llama models
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{
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id: "llama-3.3-70b",
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name: "Llama 3.3 70B",
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reasoning: false,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "llama-3.2-3b",
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name: "Llama 3.2 3B",
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reasoning: false,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "hermes-3-llama-3.1-405b",
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name: "Hermes 3 Llama 3.1 405B",
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reasoning: false,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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// Qwen models
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{
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id: "qwen3-235b-a22b-thinking-2507",
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name: "Qwen3 235B Thinking",
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reasoning: true,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "qwen3-235b-a22b-instruct-2507",
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name: "Qwen3 235B Instruct",
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reasoning: false,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "qwen3-coder-480b-a35b-instruct",
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name: "Qwen3 Coder 480B",
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reasoning: false,
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input: ["text"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "qwen3-next-80b",
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name: "Qwen3 Next 80B",
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reasoning: false,
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input: ["text"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "qwen3-vl-235b-a22b",
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name: "Qwen3 VL 235B (Vision)",
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reasoning: false,
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input: ["text", "image"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "qwen3-4b",
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name: "Venice Small (Qwen3 4B)",
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reasoning: true,
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input: ["text"],
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contextWindow: 32768,
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maxTokens: 8192,
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privacy: "private",
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},
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// DeepSeek
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{
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id: "deepseek-v3.2",
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name: "DeepSeek V3.2",
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reasoning: true,
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input: ["text"],
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contextWindow: 163840,
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maxTokens: 8192,
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privacy: "private",
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},
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// Venice-specific models
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{
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id: "venice-uncensored",
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name: "Venice Uncensored (Dolphin-Mistral)",
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reasoning: false,
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input: ["text"],
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contextWindow: 32768,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "mistral-31-24b",
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name: "Venice Medium (Mistral)",
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reasoning: false,
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input: ["text", "image"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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// Other private models
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{
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id: "google-gemma-3-27b-it",
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name: "Google Gemma 3 27B Instruct",
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reasoning: false,
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input: ["text", "image"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "openai-gpt-oss-120b",
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name: "OpenAI GPT OSS 120B",
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reasoning: false,
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input: ["text"],
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contextWindow: 131072,
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maxTokens: 8192,
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privacy: "private",
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},
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{
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id: "zai-org-glm-4.7",
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name: "GLM 4.7",
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reasoning: true,
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input: ["text"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "private",
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},
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// ============================================
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// ANONYMIZED MODELS (Proxied through Venice)
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// These are proprietary models accessed via Venice's proxy
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// ============================================
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// Anthropic (via Venice)
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{
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id: "claude-opus-45",
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name: "Claude Opus 4.5 (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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{
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id: "claude-sonnet-45",
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name: "Claude Sonnet 4.5 (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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// OpenAI (via Venice)
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{
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id: "openai-gpt-52",
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name: "GPT-5.2 (via Venice)",
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reasoning: true,
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input: ["text"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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{
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id: "openai-gpt-52-codex",
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name: "GPT-5.2 Codex (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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// Google (via Venice)
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{
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id: "gemini-3-pro-preview",
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name: "Gemini 3 Pro (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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{
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id: "gemini-3-flash-preview",
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name: "Gemini 3 Flash (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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// xAI (via Venice)
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{
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id: "grok-41-fast",
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name: "Grok 4.1 Fast (via Venice)",
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reasoning: true,
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input: ["text", "image"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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{
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id: "grok-code-fast-1",
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name: "Grok Code Fast 1 (via Venice)",
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reasoning: true,
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input: ["text"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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// Other anonymized models
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{
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id: "kimi-k2-thinking",
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name: "Kimi K2 Thinking (via Venice)",
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reasoning: true,
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input: ["text"],
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contextWindow: 262144,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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{
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id: "minimax-m21",
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name: "MiniMax M2.1 (via Venice)",
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reasoning: true,
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input: ["text"],
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contextWindow: 202752,
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maxTokens: 8192,
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privacy: "anonymized",
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},
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] as const;
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export type VeniceCatalogEntry = (typeof VENICE_MODEL_CATALOG)[number];
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/**
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* Build a ModelDefinitionConfig from a Venice catalog entry.
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*
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* Note: The `privacy` field from the catalog is not included in the output
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* as ModelDefinitionConfig doesn't support custom metadata fields. Privacy
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* mode is inherent to each model and documented in the catalog/docs.
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*/
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export function buildVeniceModelDefinition(entry: VeniceCatalogEntry): ModelDefinitionConfig {
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return {
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id: entry.id,
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name: entry.name,
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reasoning: entry.reasoning,
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input: [...entry.input],
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cost: VENICE_DEFAULT_COST,
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contextWindow: entry.contextWindow,
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maxTokens: entry.maxTokens,
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};
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}
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// Venice API response types
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interface VeniceModelSpec {
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name: string;
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privacy: "private" | "anonymized";
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availableContextTokens: number;
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capabilities: {
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supportsReasoning: boolean;
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supportsVision: boolean;
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supportsFunctionCalling: boolean;
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};
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}
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interface VeniceModel {
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id: string;
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model_spec: VeniceModelSpec;
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}
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interface VeniceModelsResponse {
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data: VeniceModel[];
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}
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/**
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* Discover models from Venice API with fallback to static catalog.
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* The /models endpoint is public and doesn't require authentication.
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*/
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export async function discoverVeniceModels(): Promise<ModelDefinitionConfig[]> {
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// Skip API discovery in test environment
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if (process.env.NODE_ENV === "test" || process.env.VITEST) {
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return VENICE_MODEL_CATALOG.map(buildVeniceModelDefinition);
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}
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try {
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const response = await fetch(`${VENICE_BASE_URL}/models`, {
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signal: AbortSignal.timeout(5000),
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});
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if (!response.ok) {
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console.warn(`[venice-models] Failed to discover models: HTTP ${response.status}, using static catalog`);
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return VENICE_MODEL_CATALOG.map(buildVeniceModelDefinition);
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}
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const data = (await response.json()) as VeniceModelsResponse;
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if (!Array.isArray(data.data) || data.data.length === 0) {
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console.warn("[venice-models] No models found from API, using static catalog");
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return VENICE_MODEL_CATALOG.map(buildVeniceModelDefinition);
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}
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// Merge discovered models with catalog metadata
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const catalogById = new Map(VENICE_MODEL_CATALOG.map((m) => [m.id, m]));
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const models: ModelDefinitionConfig[] = [];
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for (const apiModel of data.data) {
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const catalogEntry = catalogById.get(apiModel.id);
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if (catalogEntry) {
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// Use catalog metadata for known models
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models.push(buildVeniceModelDefinition(catalogEntry));
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} else {
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// Create definition for newly discovered models not in catalog
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const isReasoning =
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apiModel.model_spec.capabilities.supportsReasoning ||
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apiModel.id.toLowerCase().includes("thinking") ||
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apiModel.id.toLowerCase().includes("reason") ||
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apiModel.id.toLowerCase().includes("r1");
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const hasVision = apiModel.model_spec.capabilities.supportsVision;
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models.push({
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id: apiModel.id,
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name: apiModel.model_spec.name || apiModel.id,
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reasoning: isReasoning,
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input: hasVision ? ["text", "image"] : ["text"],
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cost: VENICE_DEFAULT_COST,
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contextWindow: apiModel.model_spec.availableContextTokens || 128000,
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maxTokens: 8192,
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});
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}
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}
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return models.length > 0 ? models : VENICE_MODEL_CATALOG.map(buildVeniceModelDefinition);
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} catch (error) {
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console.warn(`[venice-models] Discovery failed: ${String(error)}, using static catalog`);
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return VENICE_MODEL_CATALOG.map(buildVeniceModelDefinition);
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}
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}
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Block a user