/**
 * Ollama provider plugin registration.
 *
 * ## Why this file exists separately from `models/*`
 *
 * Centralizes **plugin metadata**, **model-type → handler** wiring, and **init-time** logging
 * (base URL, model defaults) so model modules stay pure “call Ollama / AI SDK” logic.
 *
 * ## Text handlers and v5 parity
 *
 * `TEXT_*`, `RESPONSE_HANDLER`, and `ACTION_PLANNER` all route through `models/text.ts`, which uses:
 *
 * - **`generateText`** — default completion; structured output when `responseSchema` is set and
 *   streaming is not used for that shape; **`stream: true`** + schema-only (no tools) for nested
 *   extractors. **Why:** keeps JSON `format` on the supported completion path.
 * - **`streamText`** — plain SSE chat when `stream: true` with no tools/schema/`toolChoice`; and
 *   **`stream: true` + native tools** so Ollama can stream `/api/chat` with tools. For v5 planner
 *   model types under SSE, `models/text.ts` may yield a **single** `textStream` chunk of plan JSON
 *   so `useModel`’s concatenated string stays parseable. **Why:** core’s streaming path only
 *   accumulates `textStream` chunks, not the `text` promise; mixing arbitrary deltas with plan JSON
 *   breaks `parseMessageHandlerOutput`.
 *
 * Handlers return **`Promise<string | TextStreamResult>`** — **why:** `useModel` accepts either a
 * final string or a streaming object for text model keys; matching OpenRouter keeps orchestration
 * and SSE paths identical. ElizaOS v5 Stage 1 calls `RESPONSE_HANDLER` with **`messages`**,
 * **`tools`**, and **`toolChoice`**; the text adapter must accept the same shapes as
 * OpenRouter/OpenAI or local-only agents fail before the first reply. See `models/text.ts` and
 * `utils/ai-sdk-wire.ts` module comments for the full rationale.
 *
 * ## AI SDK log noise
 *
 * Suppresses noisy AI SDK warnings at load (`AI_SDK_LOG_WARNINGS`) because local inference
 * runs in tight loops during tests and desktop shells. **Why:** keeps CI and packaged logs readable.
 */
import type {
  GenerateTextParams,
  IAgentRuntime,
  Plugin,
  ProcessEnvLike,
  TextEmbeddingParams,
  TextStreamResult,
} from "@elizaos/core";
import { logger, ModelType } from "@elizaos/core";

const _globalThis = globalThis as typeof globalThis & {
  AI_SDK_LOG_WARNINGS?: boolean;
};
_globalThis.AI_SDK_LOG_WARNINGS ??= false;

import { handleTextEmbedding } from "./models/embedding";
import {
  handleActionPlanner,
  handleResponseHandler,
  handleTextLarge,
  handleTextMedium,
  handleTextMega,
  handleTextNano,
  handleTextSmall,
} from "./models/text";
import { getApiBase, getBaseURL } from "./utils/config";

function getProcessEnv(): ProcessEnvLike {
  if (typeof process === "undefined" || !process.env) {
    return {};
  }
  return process.env as ProcessEnvLike;
}

const env = getProcessEnv();
const TEXT_NANO_MODEL_TYPE = ModelType.TEXT_NANO as string;
const TEXT_MEDIUM_MODEL_TYPE = ModelType.TEXT_MEDIUM as string;
const TEXT_MEGA_MODEL_TYPE = ModelType.TEXT_MEGA as string;
const RESPONSE_HANDLER_MODEL_TYPE = ModelType.RESPONSE_HANDLER as string;
const ACTION_PLANNER_MODEL_TYPE = ModelType.ACTION_PLANNER as string;

export const ollamaPlugin: Plugin = {
  name: "ollama",
  description: "Ollama plugin for local LLM inference",
  autoEnable: {
    envKeys: ["OLLAMA_BASE_URL"],
  },

  config: {
    OLLAMA_API_ENDPOINT: env.OLLAMA_API_ENDPOINT ?? null,
    OLLAMA_NANO_MODEL: env.OLLAMA_NANO_MODEL ?? null,
    OLLAMA_SMALL_MODEL: env.OLLAMA_SMALL_MODEL ?? null,
    OLLAMA_MEDIUM_MODEL: env.OLLAMA_MEDIUM_MODEL ?? null,
    OLLAMA_LARGE_MODEL: env.OLLAMA_LARGE_MODEL ?? null,
    OLLAMA_MEGA_MODEL: env.OLLAMA_MEGA_MODEL ?? null,
    OLLAMA_RESPONSE_HANDLER_MODEL: env.OLLAMA_RESPONSE_HANDLER_MODEL ?? null,
    OLLAMA_SHOULD_RESPOND_MODEL: env.OLLAMA_SHOULD_RESPOND_MODEL ?? null,
    OLLAMA_ACTION_PLANNER_MODEL: env.OLLAMA_ACTION_PLANNER_MODEL ?? null,
    OLLAMA_PLANNER_MODEL: env.OLLAMA_PLANNER_MODEL ?? null,
    NANO_MODEL: env.NANO_MODEL ?? null,
    MEDIUM_MODEL: env.MEDIUM_MODEL ?? null,
    SMALL_MODEL: env.SMALL_MODEL ?? null,
    LARGE_MODEL: env.LARGE_MODEL ?? null,
    MEGA_MODEL: env.MEGA_MODEL ?? null,
    RESPONSE_HANDLER_MODEL: env.RESPONSE_HANDLER_MODEL ?? null,
    SHOULD_RESPOND_MODEL: env.SHOULD_RESPOND_MODEL ?? null,
    ACTION_PLANNER_MODEL: env.ACTION_PLANNER_MODEL ?? null,
    PLANNER_MODEL: env.PLANNER_MODEL ?? null,
    OLLAMA_EMBEDDING_MODEL: env.OLLAMA_EMBEDDING_MODEL ?? null,
    OLLAMA_DISABLE_STRUCTURED_OUTPUT: env.OLLAMA_DISABLE_STRUCTURED_OUTPUT ?? null,
  },

  async init(_config, runtime) {
    const baseURL = getBaseURL(runtime);
    const apiBase = getApiBase(runtime);

    if (!baseURL || baseURL === "http://localhost:11434/api") {
      const endpoint = runtime.getSetting("OLLAMA_API_ENDPOINT");
      if (!endpoint) {
        logger.warn("OLLAMA_API_ENDPOINT not set, using default localhost:11434");
      }
    }

    try {
      const fetchImpl = (runtime as { fetch?: typeof fetch }).fetch ?? fetch;
      const response = await fetchImpl(`${apiBase}/api/tags`, {
        method: "GET",
        headers: { "Content-Type": "application/json" },
      });

      if (!response.ok) {
        logger.warn(`Ollama API validation failed: ${response.statusText}`);
      }
    } catch (fetchError: unknown) {
      // error-policy:J4 explicit degrade — `init` runs a connectivity probe; a
      // daemon-down result must not crash plugin load (the agent can start with
      // Ollama offline and the operator brings it up later). The failure is
      // surfaced per-call by ensureModelAvailable (throws), not swallowed there.
      const message = fetchError instanceof Error ? fetchError.message : String(fetchError);
      logger.warn(`Ollama API validation error: ${message}`);
    }
  },

  models: {
    [ModelType.TEXT_EMBEDDING]: async (
      runtime: IAgentRuntime,
      params: TextEmbeddingParams | string | null
    ): Promise<number[]> => {
      return handleTextEmbedding(runtime, params);
    },

    [TEXT_NANO_MODEL_TYPE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleTextNano(runtime, params);
    },

    [ModelType.TEXT_SMALL]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleTextSmall(runtime, params);
    },

    [TEXT_MEDIUM_MODEL_TYPE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleTextMedium(runtime, params);
    },

    [ModelType.TEXT_LARGE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleTextLarge(runtime, params);
    },

    [TEXT_MEGA_MODEL_TYPE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleTextMega(runtime, params);
    },

    [RESPONSE_HANDLER_MODEL_TYPE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleResponseHandler(runtime, params);
    },

    [ACTION_PLANNER_MODEL_TYPE]: async (
      runtime: IAgentRuntime,
      params: GenerateTextParams
    ): Promise<string | TextStreamResult> => {
      return handleActionPlanner(runtime, params);
    },
  },

  tests: [
    {
      name: "ollama_plugin_tests",
      tests: [
        {
          name: "ollama_test_url_validation",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const apiBase = getApiBase(runtime);
              const response = await fetch(`${apiBase}/api/tags`);
              if (!response.ok) {
                logger.error(`Failed to validate Ollama API: ${response.statusText}`);
              }
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — a probe failure is logged
              // as the test result; it must not throw out of the test harness.
              logger.error({ error }, "Error in ollama_test_url_validation");
            }
          },
        },
        {
          name: "ollama_test_text_embedding",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const runModel = runtime.useModel.bind(runtime);
              const embedding = await runModel(ModelType.TEXT_EMBEDDING, {
                text: "Hello, world!",
              });
              logger.log({ embedding }, "Generated embedding");
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — logged as the test result.
              logger.error({ error }, "Error in test_text_embedding");
            }
          },
        },
        {
          name: "ollama_test_text_large",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const runModel = runtime.useModel.bind(runtime);
              const text = await runModel(ModelType.TEXT_LARGE, {
                prompt: "What is the nature of reality in 10 words?",
              });
              if (text.length === 0) {
                logger.error("Failed to generate text");
                return;
              }
              logger.log({ text }, "Generated with test_text_large");
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — logged as the test result.
              logger.error({ error }, "Error in test_text_large");
            }
          },
        },
        {
          name: "ollama_test_text_small",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const runModel = runtime.useModel.bind(runtime);
              const text = await runModel(ModelType.TEXT_SMALL, {
                prompt: "What is the nature of reality in 10 words?",
              });
              if (text.length === 0) {
                logger.error("Failed to generate text");
                return;
              }
              logger.log({ text }, "Generated with test_text_small");
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — logged as the test result.
              logger.error({ error }, "Error in test_text_small");
            }
          },
        },
        {
          name: "ollama_test_structured_output_via_text_small",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const runModel = runtime.useModel.bind(runtime);
              const result = await runModel(ModelType.TEXT_SMALL, {
                prompt:
                  "Generate a JSON object representing a user profile with name, age, and hobbies",
                temperature: 0.7,
                responseSchema: {
                  type: "object",
                  properties: {
                    name: { type: "string" },
                    age: { type: "number" },
                    hobbies: { type: "array", items: { type: "string" } },
                  },
                  required: ["name", "age", "hobbies"],
                },
              });
              logger.log({ result }, "Generated structured output via TEXT_SMALL");
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — logged as the test result.
              logger.error({ error }, "Error in test_structured_output_via_text_small");
            }
          },
        },
        {
          name: "ollama_test_structured_output_via_text_large",
          fn: async (runtime: IAgentRuntime) => {
            try {
              const runModel = runtime.useModel.bind(runtime);
              const result = await runModel(ModelType.TEXT_LARGE, {
                prompt:
                  "Generate a detailed JSON object representing a restaurant with name, cuisine type, menu items with prices, and customer reviews",
                temperature: 0.7,
                responseSchema: { type: "object" },
              });
              logger.log({ result }, "Generated structured output via TEXT_LARGE");
            } catch (error) {
              // error-policy:J7 plugin self-test diagnostic — logged as the test result.
              logger.error({ error }, "Error in test_structured_output_via_text_large");
            }
          },
        },
      ],
    },
  ],
};