export declare const llmConfigTemplate = "import type {\n  LLMConfig,\n  LLMProvider,\n  ProviderDefaults,\n} from \"@/services/llm/types\";\nconst PROVIDER_DEFAULTS: ProviderDefaults = {\n  openai: {\n    chat: \"gpt-4.1-nano\",\n    embedding: \"text-embedding-3-small\",\n  },\n  anthropic: {\n    chat: \"claude-sonnet-4-5-20250929\",\n    embedding: \"text-embedding-3-small\", // Fallback to OpenAI\n  },\n  google: {\n    chat: \"gemini-2.5-flash-lite\",\n    embedding: \"gemini-embedding-001\",\n  },\n};\nfunction validateAPIKeys(provider: LLMProvider): void {\n  const requiredKeys: Record<LLMProvider, string> = {\n    openai: \"OPENAI_API_KEY\",\n    anthropic: \"ANTHROPIC_API_KEY\",\n    google: \"GOOGLE_API_KEY\",\n  };\n  const keyName = requiredKeys[provider];\n  const keyValue = process.env[keyName];\n  if (!keyValue) {\n    throw new Error(\n      `Missing API key for ${provider}. Please set ${keyName} in your environment variables.`,\n    );\n  }\n  if (provider === \"anthropic\" && !process.env.OPENAI_API_KEY) {\n    throw new Error(\n      \"Anthropic provider requires OPENAI_API_KEY for embeddings. Please set OPENAI_API_KEY in your environment variables.\",\n    );\n  }\n}\nexport function isLLMAvailable(): boolean {\n  const provider = (process.env.LLM_PROVIDER || \"openai\") as LLMProvider;\n  const requiredKeys: Record<LLMProvider, string> = {\n    openai: \"OPENAI_API_KEY\",\n    anthropic: \"ANTHROPIC_API_KEY\",\n    google: \"GOOGLE_API_KEY\",\n  };\n  const keyName = requiredKeys[provider];\n  if (!keyName || !process.env[keyName]) return false;\n  if (provider === \"anthropic\" && !process.env.OPENAI_API_KEY) return false;\n  return true;\n}\nexport function getLLMConfig(): LLMConfig {\n  const provider = (process.env.LLM_PROVIDER || \"openai\") as LLMProvider;\n  if (![\"openai\", \"anthropic\", \"google\"].includes(provider)) {\n    throw new Error(\n      `Invalid LLM_PROVIDER: ${provider}. Must be one of: openai, anthropic, google`,\n    );\n  }\n  validateAPIKeys(provider);\n  const defaults = PROVIDER_DEFAULTS[provider];\n  // Vectors are Matryoshka-truncated to this many dimensions before being\n  // stored as int8, keeping the prebuilt search index small. Must match between\n  // build time and runtime; a mismatch forces a re-embed. Values >= the model's\n  // native dimension keep full precision (default: 512).\n  const rawDims = process.env.LLM_EMBEDDING_DIMS;\n  let embeddingDims = 512;\n  if (rawDims !== undefined && rawDims !== \"\") {\n    const parsed = parseInt(rawDims, 10);\n    if (Number.isFinite(parsed) && parsed >= 0) embeddingDims = parsed;\n  }\n  return {\n    provider,\n    chatModel: process.env.LLM_CHAT_MODEL || defaults.chat,\n    embeddingModel: process.env.LLM_EMBEDDING_MODEL || defaults.embedding,\n    embeddingDims,\n    temperature: parseFloat(process.env.LLM_TEMPERATURE || \"0\"),\n  };\n}\n";