gadget/packages/ai/src/openai.ts

// src/openai.ts
// Copyright (C) 2026 Rob Colbert <rob.colbert@openplatform.us>
// Licensed under the Apache License, Version 2.0

import OpenAI from "openai";
import numeral from "numeral";
import {
  AiApi,
  IAiChatOptions,
  IAiChatResponse,
  IToolCallResult,
  IAiGenerateOptions,
  IAiGenerateResponse,
  IAiLogger,
  IAiModelConfig,
  IAiModelListResult,
  IAiModelProbeResult,
  IAiProvider,
  IAiResponseStreamFn,
} from "./api.js";
import {
  ChatCompletionAssistantMessageParam,
  ChatCompletionFunctionTool,
  ChatCompletionMessageParam,
  ChatCompletionTool,
  ChatCompletionToolMessageParam,
} from "openai/resources";
import { IAiEnvironment } from "./config/env.ts";

interface GabAiCapabilities {
  text?: boolean;
  images?: boolean;
  video?: boolean;
  audio?: boolean;
  streaming?: boolean;
  thinking?: boolean;
  web_search?: boolean;
  function_calling?: boolean;
  embeddings?: boolean;
  image_input?: boolean;
  file_input?: boolean;
  audio_input?: boolean;
  video_input?: boolean;
}

interface OpenAIModelInfo {
  id: string;
  created: number;
  object: "model";
  owned_by: string;
  supported_methods?: string[];
  groups?: string[];
  features?: string[];
  max_tokens?: number;
  capabilities?: GabAiCapabilities;
  context_window?: number;
}

export class OpenAiApi extends AiApi {
  protected client: OpenAI;

  constructor(env: IAiEnvironment, provider: IAiProvider, logger?: IAiLogger) {
    super(env, provider, logger);
    this.client = new OpenAI({
      baseURL: provider.baseUrl,
      apiKey: provider.apiKey,
    });
  }

  async listModels(): Promise<IAiModelListResult> {
    await this.log.debug("OpenAiApi.listModels called");

    const response = await this.client.models.list();
    await this.log.debug("OpenAI models list response", {
      data: response.data,
    });

    const models = response.data.map((model) => {
      const modelInfo = model as unknown as OpenAIModelInfo;
      const maxTokens = modelInfo.max_tokens || modelInfo.context_window;

      return {
        id: model.id,
        name: model.id,
        parameterLabel: undefined,
        parameterCount: undefined,
        contextWindow: maxTokens,
      };
    });

    return { models };
  }

  async probeModel(modelId: string): Promise<IAiModelProbeResult> {
    await this.log.debug("OpenAiApi.probeModel called", { modelId });

    try {
      const response = await this.client.models.retrieve(modelId);
      const modelInfo = response as unknown as OpenAIModelInfo;

      await this.log.debug("OpenAI model retrieve response", {
        modelId,
        features: modelInfo.features,
        supported_methods: modelInfo.supported_methods,
        capabilities: modelInfo.capabilities,
      });

      const capabilities = this.analyzeCapabilities(modelInfo);

      return {
        capabilities,
        settings: undefined,
      };
    } catch (error) {
      await this.log.debug("Failed to retrieve model details, using fallback", {
        modelId,
        error: (error as Error).message,
      });

      const listResponse = await this.client.models.list();
      const modelFromList = listResponse.data.find((m) => m.id === modelId);

      if (modelFromList) {
        const modelInfo = modelFromList as unknown as OpenAIModelInfo;
        if (modelInfo.capabilities) {
          await this.log.debug("Using capabilities from list endpoint", {
            modelId,
          });
          return {
            capabilities: this.analyzeCapabilities(modelInfo),
            settings: undefined,
          };
        }
      }

      return {
        capabilities: {
          canCallTools: modelId.toLowerCase().includes("gpt"),
          hasVision:
            modelId.toLowerCase().includes("vision") ||
            modelId.toLowerCase().includes("4o") ||
            modelId.toLowerCase().includes("image"),
          hasEmbedding:
            modelId.toLowerCase().includes("embedding") ||
            modelId.toLowerCase().includes("embed"),
          hasThinking:
            modelId.toLowerCase().includes("o1") ||
            modelId.toLowerCase().includes("o3") ||
            modelId.toLowerCase().includes("reasoning"),
          isInstructTuned: true,
        },
        settings: undefined,
      };
    }
  }

  private analyzeCapabilities(
    modelInfo: OpenAIModelInfo,
  ): IAiModelProbeResult["capabilities"] {
    const features = modelInfo.features || [];
    const supportedMethods = modelInfo.supported_methods || [];
    const caps = modelInfo.capabilities;

    if (caps) {
      return {
        canCallTools: !!caps.function_calling,
        hasVision: !!caps.images || !!caps.image_input,
        hasEmbedding: !!caps.embeddings,
        hasThinking: !!caps.thinking,
        isInstructTuned: !!caps.text,
      };
    }

    return {
      canCallTools:
        features.includes("function_calling") ||
        features.includes("parallel_tool_calls"),
      hasVision: features.includes("image_content"),
      hasEmbedding: supportedMethods.includes("embedding"),
      hasThinking: features.includes("reasoning_effort"),
      isInstructTuned: supportedMethods.includes("chat.completions"),
    };
  }

  async generate(
    model: IAiModelConfig,
    options: IAiGenerateOptions,
    streamCallback?: IAiResponseStreamFn,
  ): Promise<IAiGenerateResponse> {
    await this.log.debug("OpenAiApi.generate called", {
      provider: model.provider.name,
      modelId: model.modelId,
    });

    const startTime = Date.now();
    const response = await this.client.chat.completions.create({
      model: model.modelId,
      messages: [
        ...(options.systemPrompt
          ? [{ role: "system" as const, content: options.systemPrompt }]
          : []),
        { role: "user" as const, content: options.prompt },
      ],
      stream: false,
    });

    const choice = response.choices[0];
    const endTime = Date.now();
    const durationMs = endTime - startTime;

    return {
      done: true,
      response: choice.message.content || "",
      thinking: undefined,
      stats: {
        duration: {
          seconds: durationMs / 1000,
          text: numeral(durationMs / 1000).format("hh:mm:ss"),
        },
        tokenCounts: {
          input: response.usage?.prompt_tokens || 0,
          response: response.usage?.completion_tokens || 0,
          thinking: 0,
        },
      },
    };
  }

  async chat(
    model: IAiModelConfig,
    options: IAiChatOptions,
    streamCallback?: IAiResponseStreamFn,
  ): Promise<IAiChatResponse> {
    await this.log.debug("OpenAiApi.chat called", {
      provider: model.provider.name,
      modelId: model.modelId,
    });

    const startTime = Date.now();
    const maxIterations = options.maxToolIterations ?? 5;
    let iteration = 0;

    const messages: ChatCompletionMessageParam[] = [];
    if (options.systemPrompt) {
      messages.push({ role: "system", content: options.systemPrompt });
    }
    if (options.context) {
      for (const msg of options.context) {
        messages.push({
          role: msg.role as "user" | "assistant" | "system",
          content: msg.content,
        });
      }
    }
    if (options.userPrompt) {
      messages.push({ role: "user", content: options.userPrompt });
    }

    const allToolCallResults: IToolCallResult[] = [];

    while (iteration < maxIterations) {
      iteration++;

      const tools: ChatCompletionTool[] = options.tools
        ? options.tools.map((tool) => {
            const openaiTool: ChatCompletionFunctionTool = {
              type: tool.definition.type,
              function: {
                name: tool.definition.function.name,
                description: tool.definition.function.description,
                parameters: tool.definition.function.parameters,
              },
            };
            return openaiTool;
          })
        : [];

      const response = await this.client.chat.completions.create({
        model: model.modelId,
        messages,
        tools,
        stream: false,
      });

      const choice = response.choices[0];
      const endTime = Date.now();
      const durationMs = endTime - startTime;

      const toolCalls = choice.message.tool_calls
        ?.filter((tc) => tc.type === "function")
        .map((tc) => ({
          callId: tc.id,
          function: {
            name: tc.function.name,
            arguments: tc.function.arguments,
          },
        }));

      if (!toolCalls || toolCalls.length === 0) {
        return {
          done: true,
          response: choice.message.content || "",
          thinking: undefined,
          toolCalls: undefined,
          toolCallResults: allToolCallResults.length > 0 ? allToolCallResults : undefined,
          stats: {
            duration: {
              seconds: durationMs / 1000,
              text: numeral(durationMs / 1000).format("hh:mm:ss"),
            },
            tokenCounts: {
              input: response.usage?.prompt_tokens || 0,
              response: response.usage?.completion_tokens || 0,
              thinking: 0,
            },
          },
        };
      }

      const toolCallResults = await this.executeToolCalls(
        toolCalls,
        options.tools || [],
      );
      allToolCallResults.push(...toolCallResults);

      const assistantMsg: ChatCompletionAssistantMessageParam = {
        role: "assistant",
        content: choice.message.content,
      };
      if (choice.message.tool_calls) {
        assistantMsg.tool_calls = choice.message.tool_calls;
      }
      messages.push(assistantMsg);

      for (const result of toolCallResults) {
        const toolMsg: ChatCompletionToolMessageParam = {
          role: "tool",
          tool_call_id: result.callId,
          content: result.error || result.result,
        };
        messages.push(toolMsg);
      }
    }

    const endTime = Date.now();
    const durationMs = endTime - startTime;

    return {
      done: false,
      doneReason: "max_tool_iterations_reached",
      response: "",
      thinking: undefined,
      toolCalls: undefined,
      toolCallResults: allToolCallResults,
      stats: {
        duration: {
          seconds: durationMs / 1000,
          text: numeral(durationMs / 1000).format("hh:mm:ss"),
        },
        tokenCounts: {
          input: 0,
          response: 0,
          thinking: 0,
        },
      },
    };
  }
}