tests/integration/memory-pipeline.test.ts

import test from "node:test";
import assert from "node:assert/strict";
import { mock } from "node:test";

import { createChatPipelineHarness } from "./_chatPipelineHarness.ts";

const harness = await createChatPipelineHarness("memory-pipeline");

// Dynamic imports — MUST happen after harness creation to avoid premature DB init.
// The harness sets DATA_DIR before importing DB modules, so these must resolve after that.
const { extractFactsFromText } = await import("../../src/lib/memory/extraction.ts");
const { retrieveMemories } = await import("../../src/lib/memory/retrieval.ts");
const { invalidateMemorySettingsCache } = await import("../../src/lib/memory/settings.ts");
const { injectMemory, formatMemoryContext } = await import("../../src/lib/memory/injection.ts");
const {
  BaseExecutor,
  buildOpenAIResponse,
  buildRequest,
  handleChat,
  memoryStore,
  memoryTools,
  resetStorage,
  seedApiKey,
  seedConnection,
  settingsDb,
  waitFor,
} = harness;

const { createMemory, listMemories } = memoryStore;

/** Drop FTS5 triggers/table that cause SQLITE_MISMATCH (TEXT id used as INTEGER rowid). */
function dropFts5Artifacts() {
  try {
    const db = harness.core.getDbInstance();
    db.exec(
      "DROP TRIGGER IF EXISTS memory_fts_ai;" +
        "DROP TRIGGER IF EXISTS memory_fts_ad;" +
        "DROP TRIGGER IF EXISTS memory_fts_au;" +
        "DROP TABLE IF EXISTS memory_fts;"
    );
  } catch (_: any) {
    /* ignore if already dropped or DB not yet initialized */
  }
}

test.beforeEach(async () => {
  BaseExecutor.RETRY_CONFIG.delayMs = 0;
  await resetStorage();
  invalidateMemorySettingsCache();
  dropFts5Artifacts();
});

test.afterEach(async () => {
  BaseExecutor.RETRY_CONFIG.delayMs = harness.originalRetryDelayMs;
  await resetStorage();
  invalidateMemorySettingsCache();
});

test.after(async () => {
  await harness.cleanup();
});

async function enableMemory(
  maxTokens = 400,
  strategy: "recent" | "semantic" | "hybrid" = "recent"
) {
  await settingsDb.updateSettings({
    memoryEnabled: true,
    memoryMaxTokens: maxTokens,
    memoryRetentionDays: 30,
    memoryStrategy: strategy,
  });
}

test("first request proceeds without injected context when the store is empty", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-memory-empty" });
  const apiKey = await seedApiKey();
  await enableMemory();

  const fetchCalls = [];
  globalThis.fetch = async (_url, init = {}) => {
    fetchCalls.push(init.body ? JSON.parse(String(init.body)) : null);
    return buildOpenAIResponse("No memory yet");
  };

  const response = await handleChat(
    buildRequest({
      authKey: apiKey.key,
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "First turn" }],
      },
    })
  );

  assert.equal(response.status, 200);
  assert.equal(fetchCalls.length, 1);
  assert.equal(fetchCalls[0].messages[0].role, "user");
  assert.equal(fetchCalls[0].messages[0].content, "First turn");
});

test("successful responses extract facts and persist them as memories", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-extract" });
  const apiKey = await seedApiKey();
  await enableMemory();

  globalThis.fetch = async () =>
    buildOpenAIResponse("I prefer concise answers. I usually answer in bullet points.");

  const response = await handleChat(
    buildRequest({
      authKey: apiKey.key,
      headers: { "x-omniroute-session-id": "session-extract" },
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "Remember my preferences" }],
      },
    })
  );

  const memories = await waitFor(async () => {
    dropFts5Artifacts();
    const result = await listMemories({ apiKeyId: apiKey.id });
    const list = Array.isArray(result) ? result : (result.data ?? []);
    return list.length >= 2 ? list : null;
  }, 5000);

  assert.equal(response.status, 200);
  assert.ok(memories, "expected extracted memories to be stored");
  assert.ok(memories.some((memory) => /concise answers/i.test(memory.content)));
  assert.ok(memories.some((memory) => /bullet points/i.test(memory.content)));
  assert.ok(memories.every((memory) => memory.sessionId === "session-extract"));
});

test("later requests inject retrieved memories into upstream messages", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-inject" });
  const apiKey = await seedApiKey();
  await enableMemory();

  await createMemory({
    apiKeyId: apiKey.id,
    sessionId: "session-inject",
    type: "factual",
    key: "preference:concise",
    content: "User prefers concise answers.",
    metadata: {},
    expiresAt: null,
  });

  const fetchCalls = [];
  globalThis.fetch = async (_url, init = {}) => {
    fetchCalls.push(init.body ? JSON.parse(String(init.body)) : null);
    return buildOpenAIResponse("Memory injected");
  };

  const response = await handleChat(
    buildRequest({
      authKey: apiKey.key,
      headers: { "x-omniroute-session-id": "session-inject" },
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "What do you remember?" }],
      },
    })
  );

  assert.equal(response.status, 200);
  assert.equal(fetchCalls.length, 1);
  assert.equal(fetchCalls[0].messages[0].role, "system");
  assert.match(fetchCalls[0].messages[0].content, /User prefers concise answers/);
});

test("memory search ranks query-relevant memories first", async () => {
  const apiKey = await seedApiKey();
  await enableMemory(400, "hybrid");

  await memoryTools.omniroute_memory_add.handler({
    apiKeyId: apiKey.id,
    sessionId: "search",
    type: "factual",
    key: "pref:language",
    content: "The user writes TypeScript services every day.",
    metadata: {},
  });
  await memoryTools.omniroute_memory_add.handler({
    apiKeyId: apiKey.id,
    sessionId: "search",
    type: "factual",
    key: "pref:hobby",
    content: "The user enjoys gardening on weekends.",
    metadata: {},
  });
  await memoryTools.omniroute_memory_add.handler({
    apiKeyId: apiKey.id,
    sessionId: "search",
    type: "factual",
    key: "pref:stack",
    content: "TypeScript and Node.js are the preferred backend stack.",
    metadata: {},
  });

  const result = await memoryTools.omniroute_memory_search.handler({
    apiKeyId: apiKey.id,
    query: "typescript backend",
    limit: 2,
  });

  assert.equal(result.success, true);
  assert.equal(result.data.count, 2);
  assert.match(result.data.memories[0].content, /TypeScript/i);
  assert.ok(result.data.memories.every((memory) => /TypeScript|backend/i.test(memory.content)));
});

test("memory injection respects the configured token budget", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-budget" });
  const apiKey = await seedApiKey();
  await enableMemory(20);

  await createMemory({
    apiKeyId: apiKey.id,
    sessionId: "budget",
    type: "factual",
    key: "older",
    content: "Older preference that should be trimmed when the context budget is tight.",
    metadata: {},
    expiresAt: null,
  });
  await new Promise((resolve) => setTimeout(resolve, 10));
  await createMemory({
    apiKeyId: apiKey.id,
    sessionId: "budget",
    type: "factual",
    key: "newer",
    content: "Newest preference should fit first.",
    metadata: {},
    expiresAt: null,
  });

  const fetchCalls = [];
  globalThis.fetch = async (_url, init = {}) => {
    fetchCalls.push(init.body ? JSON.parse(String(init.body)) : null);
    return buildOpenAIResponse("Budget respected");
  };

  const response = await handleChat(
    buildRequest({
      authKey: apiKey.key,
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "Use only the relevant memory." }],
      },
    })
  );

  assert.equal(response.status, 200);
  assert.equal(fetchCalls.length, 1);
  assert.match(fetchCalls[0].messages[0].content, /Newest preference should fit first/);
  assert.doesNotMatch(fetchCalls[0].messages[0].content, /Older preference that should be trimmed/);
});

test("disabled memory skips both extraction and injection", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-memory-off" });
  const apiKey = await seedApiKey();
  await settingsDb.updateSettings({
    memoryEnabled: false,
    memoryMaxTokens: 400,
    memoryRetentionDays: 30,
    memoryStrategy: "recent",
  });

  const fetchCalls = [];
  globalThis.fetch = async (_url, init = {}) => {
    fetchCalls.push(init.body ? JSON.parse(String(init.body)) : null);
    return buildOpenAIResponse("I prefer dark mode.");
  };

  const response = await handleChat(
    buildRequest({
      authKey: apiKey.key,
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "This should not be remembered." }],
      },
    })
  );

  const memories = await waitFor(async () => {
    const result = await listMemories({ apiKeyId: apiKey.id });
    const list = Array.isArray(result) ? result : (result.data ?? []);
    return list.length > 0 ? list : [];
  });

  assert.equal(response.status, 200);
  assert.equal(fetchCalls[0].messages[0].role, "user");
  assert.deepEqual(memories, []);
});

test("memory clear removes all stored memories for an API key", async () => {
  const apiKey = await seedApiKey();

  await memoryTools.omniroute_memory_add.handler({
    apiKeyId: apiKey.id,
    sessionId: "clear",
    type: "factual",
    key: "pref:one",
    content: "First memory",
    metadata: {},
  });
  await memoryTools.omniroute_memory_add.handler({
    apiKeyId: apiKey.id,
    sessionId: "clear",
    type: "episodic",
    key: "event:two",
    content: "Second memory",
    metadata: {},
  });

  const cleared = await memoryTools.omniroute_memory_clear.handler({
    apiKeyId: apiKey.id,
  });
  const remaining = await listMemories({ apiKeyId: apiKey.id });
  const remainingList = Array.isArray(remaining) ? remaining : (remaining.data ?? []);

  assert.equal(cleared.success, true);
  assert.equal(cleared.data.deletedCount, 2);
  assert.equal(remainingList.length, 0);
});

test("extracted memories remain isolated by session id", async () => {
  await seedConnection("openai", { apiKey: "sk-openai-session-memory" });
  const apiKey = await seedApiKey();
  await enableMemory();

  globalThis.fetch = async () => buildOpenAIResponse("I prefer tea.");
  await handleChat(
    buildRequest({
      authKey: apiKey.key,
      headers: { "x-omniroute-session-id": "session-a" },
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "Remember drink A" }],
      },
    })
  );

  globalThis.fetch = async () => buildOpenAIResponse("I prefer coffee.");
  await handleChat(
    buildRequest({
      authKey: apiKey.key,
      headers: { "x-omniroute-session-id": "session-b" },
      body: {
        model: "openai/gpt-4o-mini",
        stream: false,
        messages: [{ role: "user", content: "Remember drink B" }],
      },
    })
  );

  const sessionAMemories = await waitFor(async () => {
    dropFts5Artifacts();
    const result = await listMemories({ apiKeyId: apiKey.id, sessionId: "session-a" });
    const list = Array.isArray(result) ? result : (result.data ?? []);
    return list.length > 0 ? list : null;
  }, 5000);
  const sessionBMemories = await waitFor(async () => {
    dropFts5Artifacts();
    const result = await listMemories({ apiKeyId: apiKey.id, sessionId: "session-b" });
    const list = Array.isArray(result) ? result : (result.data ?? []);
    return list.length > 0 ? list : null;
  }, 5000);

  assert.ok(sessionAMemories, "expected session A memories");
  assert.ok(sessionBMemories, "expected session B memories");
  assert.ok(sessionAMemories.every((memory) => /tea/i.test(memory.content)));
  assert.ok(sessionBMemories.every((memory) => /coffee/i.test(memory.content)));
});

// ─── Module-to-Module Pipeline Tests ──────────────────────────────────────────

test("extraction→storage: extractFactsFromText output persists via createMemory", async () => {
  const apiKey = await seedApiKey();

  // 1. Extract facts synchronously (no LLM call)
  const text = "I prefer TypeScript. I usually write tests first. I'll use Vitest for unit tests.";
  const facts = extractFactsFromText(text);

  assert.ok(facts.length >= 3, `expected ≥3 facts, got ${facts.length}`);
  assert.ok(facts.some((f) => f.category === "preference"));
  assert.ok(facts.some((f) => f.category === "pattern"));
  assert.ok(facts.some((f) => f.category === "decision"));

  // 2. Store each extracted fact via createMemory
  const stored = [];
  for (const fact of facts) {
    const memory = await createMemory({
      apiKeyId: apiKey.id,
      sessionId: "extract-store-test",
      type: fact.type,
      key: fact.key,
      content: fact.content,
      metadata: { category: fact.category, source: "test" },
      expiresAt: null,
    });
    stored.push(memory);
  }

  // 3. Verify all are persisted in DB
  assert.equal(stored.length, facts.length);
  for (const mem of stored) {
    assert.ok(mem.id, "stored memory should have an id");
    assert.equal(mem.apiKeyId, apiKey.id);
    assert.equal(mem.sessionId, "extract-store-test");
  }

  // 4. Verify via listMemories
  const rows = await listMemories({ apiKeyId: apiKey.id, sessionId: "extract-store-test" });
  // listMemories may return { data, total } or flat array — handle both like existing tests
  const list = Array.isArray(rows) ? rows : (rows.data ?? []);
  assert.equal(list.length, facts.length, "all extracted facts should be persisted");
});

test("retrieval→injection: retrieveMemories feeds into injectMemory context", async () => {
  const apiKey = await seedApiKey();
  await enableMemory(2000);

  // 1. Seed two memories
  await createMemory({
    apiKeyId: apiKey.id,
    sessionId: "retrieval-inject-test",
    type: "factual",
    key: "pref:editor",
    content: "User prefers VS Code.",
    metadata: {},
    expiresAt: null,
  });
  await createMemory({
    apiKeyId: apiKey.id,
    sessionId: "retrieval-inject-test",
    type: "factual",
    key: "pref:lang",
    content: "User works with TypeScript.",
    metadata: {},
    expiresAt: null,
  });

  // 2. Retrieve memories via the retrieval module
  const memories = await retrieveMemories(apiKey.id, {
    maxTokens: 2000,
    retrievalStrategy: "exact",
    retentionDays: 30,
  });

  assert.ok(memories.length >= 2, `expected ≥2 memories, got ${memories.length}`);

  // 3. Inject into a request
  const request = {
    model: "openai/gpt-4o-mini",
    messages: [{ role: "user", content: "What editor do I use?" }],
  };
  const injected = injectMemory(request, memories, "openai");

  // 4. Verify injection
  assert.ok(injected.messages.length > request.messages.length, "should prepend memory message");
  assert.equal(injected.messages[0].role, "system", "memory should be injected as system message");
  assert.match(injected.messages[0].content, /Memory context:/);
  assert.match(injected.messages[0].content, /VS Code/);
  assert.match(injected.messages[0].content, /TypeScript/);
  // Original user message should still be present
  assert.equal(injected.messages[injected.messages.length - 1].content, "What editor do I use?");
});

test("full pipeline: extract → store → retrieve → inject end-to-end", async () => {
  const apiKey = await seedApiKey();
  await enableMemory(2000);

  // 1. Extract facts from simulated LLM response text
  const llmResponse =
    "I prefer dark mode editors. I usually commit small changes. I'll use pnpm for package management.";
  const facts = extractFactsFromText(llmResponse);
  assert.ok(facts.length >= 3, `expected ≥3 facts from LLM response, got ${facts.length}`);

  // 2. Store all extracted facts
  for (const fact of facts) {
    await createMemory({
      apiKeyId: apiKey.id,
      sessionId: "full-pipeline-test",
      type: fact.type,
      key: fact.key,
      content: fact.content,
      metadata: { category: fact.category, source: "llm_response" },
      expiresAt: null,
    });
  }

  // 3. Retrieve stored memories
  const memories = await retrieveMemories(apiKey.id, {
    maxTokens: 2000,
    retrievalStrategy: "exact",
    retentionDays: 30,
  });
  assert.ok(memories.length >= 3, `expected ≥3 retrieved memories, got ${memories.length}`);

  // 4. Inject into a new request
  const request = {
    model: "openai/gpt-4o-mini",
    messages: [{ role: "user", content: "What are my preferences?" }],
  };
  const injected = injectMemory(request, memories, "openai");

  // 5. Full pipeline assertions
  assert.equal(injected.messages[0].role, "system");
  assert.match(injected.messages[0].content, /Memory context:/);
  assert.match(injected.messages[0].content, /dark mode/);
  assert.match(injected.messages[0].content, /small changes/);
  assert.match(injected.messages[0].content, /pnpm/);
  assert.equal(injected.messages.length, 2, "system memory + original user message");

  // 6. Verify for non-system providers (o1-mini) — should inject as user message
  const injectedForO1 = injectMemory(request, memories, "o1-mini");
  assert.equal(injectedForO1.messages[0].role, "user", "o1-mini should get user-role memory");
  assert.match(injectedForO1.messages[0].content, /Memory context:/);
});

test("logging verification: observability logs fire during pipeline operations", async () => {
  const apiKey = await seedApiKey();
  await enableMemory(2000);

  // Spy on console methods used by the logger
  const logSpy = mock.method(console, "log", () => {});
  const debugSpy = mock.method(console, "debug", () => {});

  try {
    // 1. createMemory should trigger "memory.stored" log
    const mem = await createMemory({
      apiKeyId: apiKey.id,
      sessionId: "log-test",
      type: "factual",
      key: "pref:logging",
      content: "User likes verbose logging.",
      metadata: {},
      expiresAt: null,
    });
    assert.ok(mem.id, "memory should be created");

    // 2. retrieveMemories should trigger "memory.retrieval.start" + "memory.retrieval.complete"
    const memories = await retrieveMemories(apiKey.id, {
      maxTokens: 2000,
      retrievalStrategy: "exact",
      retentionDays: 30,
    });
    assert.ok(memories.length >= 1, "should retrieve at least one memory");

    // 3. injectMemory should trigger "memory.injection.injected"
    const request = {
      model: "openai/gpt-4o-mini",
      messages: [{ role: "user", content: "Test" }],
    };
    injectMemory(request, memories, "openai");

    // 4. injectMemory with empty memories should trigger "memory.injection.skipped"
    injectMemory(request, [], "openai");

    // 5. Verify that logs were emitted (console.log/debug were called)
    const allCalls = [...logSpy.mock.calls, ...debugSpy.mock.calls];
    assert.ok(
      allCalls.length > 0,
      "expected console.log or console.debug to be called by logger during pipeline operations"
    );

    // 6. Check for specific log event strings in the log output
    const allLogOutput = allCalls.map((c) => c.arguments.join(" ")).join("\n");
    assert.match(allLogOutput, /memory\.stored/i, "should log memory.stored event");
    assert.match(
      allLogOutput,
      /memory\.retrieval\.(start|complete)/i,
      "should log memory retrieval events"
    );
    assert.match(
      allLogOutput,
      /memory\.injection\.(injected|skipped)/i,
      "should log memory injection events"
    );
  } finally {
    // Restore console methods
    logSpy.mock.restore();
    debugSpy.mock.restore();
  }
});