import assert from 'node:assert/strict';
import test from 'node:test';

import {
  __resetEmbeddingCacheForTests,
  embedQuery,
  embedText,
  probeEmbeddingAvailability,
} from './embed-memory-v2-openai-compat.mjs';

function mockFetch(responseFactory) {
  return async (url, init) => {
    const call = { url: String(url), init };
    return responseFactory(call);
  };
}

test('embedText uses OpenAI-compatible /embeddings and caches by text hash', async () => {
  __resetEmbeddingCacheForTests();
  const calls = [];
  const fetchImpl = mockFetch(({ url, init }) => {
    calls.push({ url, body: JSON.parse(String(init.body)) });
    return {
      ok: true,
      async json() {
        return { data: [{ embedding: [0.1, 0.2, 0.3] }] };
      },
    };
  });

  const first = await embedText('Sarah closed the curtains', {
    env: {
      MEMIND_EMBEDDING_PROVIDER: 'openai',
      MEMIND_EMBEDDING_API_KEY: 'test-key',
      MEMIND_EMBEDDING_BASE_URL: 'https://example.com/v1',
      MEMIND_EMBEDDING_MODEL: 'text-embedding-3-small',
      MEMIND_EMBEDDING_CACHE_PATH: '',
    },
    fetchImpl,
    persist: false,
  });
  const second = await embedText('Sarah closed the curtains', {
    env: {
      MEMIND_EMBEDDING_PROVIDER: 'openai',
      MEMIND_EMBEDDING_API_KEY: 'test-key',
      MEMIND_EMBEDDING_BASE_URL: 'https://example.com/v1',
      MEMIND_EMBEDDING_MODEL: 'text-embedding-3-small',
      MEMIND_EMBEDDING_CACHE_PATH: '',
    },
    fetchImpl,
    persist: false,
  });

  assert.deepEqual(first, [0.1, 0.2, 0.3]);
  assert.deepEqual(second, first);
  assert.equal(calls.length, 1);
  assert.equal(calls[0].url, 'https://example.com/v1/embeddings');
  assert.equal(calls[0].body.model, 'text-embedding-3-small');
});

test('embedQuery delegates to embedText', async () => {
  __resetEmbeddingCacheForTests();
  const vector = await embedQuery('probe question', {}, {
    env: {
      MEMIND_EMBEDDING_PROVIDER: 'openai',
      MEMIND_EMBEDDING_API_KEY: 'test-key',
      MEMIND_EMBEDDING_BASE_URL: 'https://example.com/v1',
      MEMIND_EMBEDDING_CACHE_PATH: '',
    },
    fetchImpl: mockFetch(() => ({
      ok: true,
      async json() {
        return { data: [{ embedding: [0.5, 0.6] }] };
      },
    })),
    persist: false,
  });
  assert.deepEqual(vector, [0.5, 0.6]);
});

test('ollama provider uses /api/embed without API key', async () => {
  __resetEmbeddingCacheForTests();
  let seenUrl = null;
  const vector = await embedText('local semantic probe', {
    env: {
      MEMIND_EMBEDDING_PROVIDER: 'ollama',
      MEMIND_EMBEDDING_BASE_URL: 'http://127.0.0.1:11434',
      MEMIND_EMBEDDING_MODEL: 'nomic-embed-text',
      MEMIND_EMBEDDING_CACHE_PATH: '',
    },
    fetchImpl: async (url) => {
      seenUrl = String(url);
      return {
        ok: true,
        async json() {
          return { embeddings: [[0.9, 0.1, 0.4]] };
        },
      };
    },
    persist: false,
  });
  assert.equal(seenUrl, 'http://127.0.0.1:11434/api/embed');
  assert.deepEqual(vector, [0.9, 0.1, 0.4]);
});

test('probeEmbeddingAvailability reports missing API key for openai provider', async () => {
  __resetEmbeddingCacheForTests();
  const probe = await probeEmbeddingAvailability({
    env: {
      MEMIND_EMBEDDING_PROVIDER: 'openai',
      MEMIND_EMBEDDING_CACHE_PATH: '',
    },
  });
  assert.equal(probe.available, false);
  assert.equal(probe.reason, 'embedding_api_key_missing');
});