githubProvider.ts

import {
  LLM_PROVIDER,
  LLMResponse,
  MODEL_META,
  ChatMessage,
  IConfigPresenter
} from '@shared/presenter'
import { DEFAULT_MODEL_CONTEXT_LENGTH, DEFAULT_MODEL_MAX_TOKENS } from '@shared/modelConfigDefaults'
import { OpenAICompatibleProvider } from './openAICompatibleProvider'
import { ModelsPage } from 'openai/resources'
import type { ProviderMcpRuntimePort } from '../runtimePorts'

export class GithubProvider extends OpenAICompatibleProvider {
  constructor(
    provider: LLM_PROVIDER,
    configPresenter: IConfigPresenter,
    mcpRuntime?: ProviderMcpRuntimePort
  ) {
    super(provider, configPresenter, mcpRuntime)
  }
  protected async fetchOpenAIModels(options?: { timeout: number }): Promise<MODEL_META[]> {
    const response = (await this.openai.models.list(options)) as ModelsPage & {
      body: {
        id: string
        name: string
        description: string
      }[]
    }
    return response.body.map((model) => ({
      id: model.name,
      name: model.name,
      group: 'default',
      providerId: this.provider.id,
      isCustom: false,
      contextLength: DEFAULT_MODEL_CONTEXT_LENGTH,
      maxTokens: DEFAULT_MODEL_MAX_TOKENS,
      description: model.description
    }))
  }
  async completions(
    messages: ChatMessage[],
    modelId: string,
    temperature?: number,
    maxTokens?: number
  ): Promise<LLMResponse> {
    return this.openAICompletion(messages, modelId, temperature, maxTokens)
  }

  async generateText(
    prompt: string,
    modelId: string,
    temperature?: number,
    maxTokens?: number
  ): Promise<LLMResponse> {
    return this.openAICompletion(
      [
        {
          role: 'user',
          content: prompt
        }
      ],
      modelId,
      temperature,
      maxTokens
    )
  }
}