Function buildPiModel

packages/node-runtime/src/ai/llm-builder.ts:112–175

(config: PiModelConfig, options?: BuildPiModelOptions)

}

export function buildPiModel(config: PiModelConfig, options?: BuildPiModelOptions): PiModel<PiApi> {
  const providerDef = BUILTIN_PROVIDERS.find((p) => p.id === config.provider)
  const baseUrl = config.baseUrl || providerDef?.defaultBaseUrl || ''
  const modelId = config.model || ''

  const findModel = options?.findModelFn ?? defaultFindModel
  const modelDef = findModel(config.provider, modelId)
  const contextWindow = modelDef?.contextWindow ?? DEFAULT_CONTEXT_WINDOW

  const apiFormat: PiApi = (config.apiFormat as PiApi) || BUILTIN_PROVIDER_API[config.provider] || 'openai-completions'

  if (apiFormat === 'google-generative-ai') {
    return {
      id: modelId,
      name: modelId,
      api: 'google-generative-ai',
      provider: 'google',
      baseUrl,
      reasoning: isReasoningModel(config.provider, modelId),
      input: ['text'],
      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
      contextWindow,
      maxTokens: config.maxTokens ?? 8192,
    }
  }

  if (apiFormat === 'anthropic-messages') {
    return {
      id: modelId,
      name: modelId,
      api: 'anthropic-messages',
      provider: 'anthropic',
      baseUrl: normalizeAnthropicBaseUrl(baseUrl),
      reasoning: false,
      input: ['text'],
      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
      contextWindow,
      maxTokens: config.maxTokens ?? 8192,
    }
  }

  const resolvedBaseUrl =
    config.provider === 'openai-compatible' && (apiFormat === 'openai-completions' || apiFormat === 'openai-responses')
      ? normalizeOpenAICompatibleBaseUrl(baseUrl)
      : baseUrl

  const { reasoning, compat, thinkingLevelMap } = inferReasoning(config.provider, modelId, modelDef)

  return {
    id: modelId,
    name: modelId,
    api: apiFormat,
    provider: config.provider,
    baseUrl: resolvedBaseUrl,
    headers: options?.headers,
    reasoning,
    thinkingLevelMap,
    input: ['text'],