refactor(test): dedupe openai batch test fetch mocks

src/memory/manager.batch.test.ts

@@ -59,6 +59,95 @@ describe("memory indexing with OpenAI batches", () => {
     return () => spy.mockRestore();
   }
 
+  async function readOpenAIBatchUploadRequests(body: FormData) {
+    let uploadedRequests: Array<{ custom_id?: string }> = [];
+    for (const [key, value] of body.entries()) {
+      if (key !== "file") {
+        continue;
+      }
+      const text = typeof value === "string" ? value : await value.text();
+      uploadedRequests = text
+        .split("\n")
+        .filter(Boolean)
+        .map((line) => JSON.parse(line) as { custom_id?: string });
+    }
+    return uploadedRequests;
+  }
+
+  function createOpenAIBatchFetchMock(options?: {
+    onCreateBatch?: (ctx: { batchCreates: number }) => Response | Promise<Response>;
+  }) {
+    let uploadedRequests: Array<{ custom_id?: string }> = [];
+    const state = { batchCreates: 0 };
+    const fetchMock = vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
+      const url =
+        typeof input === "string" ? input : input instanceof URL ? input.toString() : input.url;
+      if (url.endsWith("/files")) {
+        const body = init?.body;
+        if (!(body instanceof FormData)) {
+          throw new Error("expected FormData upload");
+        }
+        uploadedRequests = await readOpenAIBatchUploadRequests(body);
+        return new Response(JSON.stringify({ id: "file_1" }), {
+          status: 200,
+          headers: { "Content-Type": "application/json" },
+        });
+      }
+      if (url.endsWith("/batches")) {
+        state.batchCreates += 1;
+        if (options?.onCreateBatch) {
+          return await options.onCreateBatch({ batchCreates: state.batchCreates });
+        }
+        return new Response(JSON.stringify({ id: "batch_1", status: "in_progress" }), {
+          status: 200,
+          headers: { "Content-Type": "application/json" },
+        });
+      }
+      if (url.endsWith("/batches/batch_1")) {
+        return new Response(
+          JSON.stringify({ id: "batch_1", status: "completed", output_file_id: "file_out" }),
+          { status: 200, headers: { "Content-Type": "application/json" } },
+        );
+      }
+      if (url.endsWith("/files/file_out/content")) {
+        const lines = uploadedRequests.map((request, index) =>
+          JSON.stringify({
+            custom_id: request.custom_id,
+            response: {
+              status_code: 200,
+              body: { data: [{ embedding: [index + 1, 0, 0], index: 0 }] },
+            },
+          }),
+        );
+        return new Response(lines.join("\n"), {
+          status: 200,
+          headers: { "Content-Type": "application/jsonl" },
+        });
+      }
+      throw new Error(`unexpected fetch ${url}`);
+    });
+    return { fetchMock, state };
+  }
+
+  function createBatchCfg() {
+    return {
+      agents: {
+        defaults: {
+          workspace: workspaceDir,
+          memorySearch: {
+            provider: "openai",
+            model: "text-embedding-3-small",
+            store: { path: indexPath, vector: { enabled: false } },
+            sync: { watch: false, onSessionStart: false, onSearch: false },
+            query: { minScore: 0, hybrid: { enabled: false } },
+            remote: { batch: { enabled: true, wait: true, pollIntervalMs: 1 } },
+          },
+        },
+        list: [{ id: "main", default: true }],
+      },
+    };
+  }
+
   beforeAll(async () => {
     fixtureRoot = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-mem-batch-"));
   });
@@ -91,85 +180,11 @@ describe("memory indexing with OpenAI batches", () => {
     const content = ["hello", "from", "batch"].join("\n\n");
     await fs.writeFile(path.join(workspaceDir, "memory", "2026-01-07.md"), content);
 
-    let uploadedRequests: Array<{ custom_id?: string }> = [];
-    const fetchMock = vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
-      const url =
-        typeof input === "string" ? input : input instanceof URL ? input.toString() : input.url;
-      if (url.endsWith("/files")) {
-        const body = init?.body;
-        if (!(body instanceof FormData)) {
-          throw new Error("expected FormData upload");
-        }
-        for (const [key, value] of body.entries()) {
-          if (key !== "file") {
-            continue;
-          }
-          if (typeof value === "string") {
-            uploadedRequests = value
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          } else {
-            const text = await value.text();
-            uploadedRequests = text
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          }
-        }
-        return new Response(JSON.stringify({ id: "file_1" }), {
-          status: 200,
-          headers: { "Content-Type": "application/json" },
-        });
-      }
-      if (url.endsWith("/batches")) {
-        return new Response(JSON.stringify({ id: "batch_1", status: "in_progress" }), {
-          status: 200,
-          headers: { "Content-Type": "application/json" },
-        });
-      }
-      if (url.endsWith("/batches/batch_1")) {
-        return new Response(
-          JSON.stringify({ id: "batch_1", status: "completed", output_file_id: "file_out" }),
-          { status: 200, headers: { "Content-Type": "application/json" } },
-        );
-      }
-      if (url.endsWith("/files/file_out/content")) {
-        const lines = uploadedRequests.map((request, index) =>
-          JSON.stringify({
-            custom_id: request.custom_id,
-            response: {
-              status_code: 200,
-              body: { data: [{ embedding: [index + 1, 0, 0], index: 0 }] },
-            },
-          }),
-        );
-        return new Response(lines.join("\n"), {
-          status: 200,
-          headers: { "Content-Type": "application/jsonl" },
-        });
-      }
-      throw new Error(`unexpected fetch ${url}`);
-    });
+    const { fetchMock } = createOpenAIBatchFetchMock();
 
     vi.stubGlobal("fetch", fetchMock);
 
-    const cfg = {
-      agents: {
-        defaults: {
-          workspace: workspaceDir,
-          memorySearch: {
-            provider: "openai",
-            model: "text-embedding-3-small",
-            store: { path: indexPath, vector: { enabled: false } },
-            sync: { watch: false, onSessionStart: false, onSearch: false },
-            query: { minScore: 0, hybrid: { enabled: false } },
-            remote: { batch: { enabled: true, wait: true, pollIntervalMs: 1 } },
-          },
-        },
-        list: [{ id: "main", default: true }],
-      },
-    };
+    const cfg = createBatchCfg();
 
     try {
       const result = await getMemorySearchManager({ cfg, agentId: "main" });
@@ -202,40 +217,8 @@ describe("memory indexing with OpenAI batches", () => {
     const content = ["retry", "the", "batch"].join("\n\n");
     await fs.writeFile(path.join(workspaceDir, "memory", "2026-01-08.md"), content);
 
-    let uploadedRequests: Array<{ custom_id?: string }> = [];
-    let batchCreates = 0;
-    const fetchMock = vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
-      const url =
-        typeof input === "string" ? input : input instanceof URL ? input.toString() : input.url;
-      if (url.endsWith("/files")) {
-        const body = init?.body;
-        if (!(body instanceof FormData)) {
-          throw new Error("expected FormData upload");
-        }
-        for (const [key, value] of body.entries()) {
-          if (key !== "file") {
-            continue;
-          }
-          if (typeof value === "string") {
-            uploadedRequests = value
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          } else {
-            const text = await value.text();
-            uploadedRequests = text
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          }
-        }
-        return new Response(JSON.stringify({ id: "file_1" }), {
-          status: 200,
-          headers: { "Content-Type": "application/json" },
-        });
-      }
-      if (url.endsWith("/batches")) {
-        batchCreates += 1;
+    const { fetchMock, state } = createOpenAIBatchFetchMock({
+      onCreateBatch: ({ batchCreates }) => {
         if (batchCreates === 1) {
           return new Response("upstream connect error", { status: 503 });
         }
@@ -243,49 +226,12 @@ describe("memory indexing with OpenAI batches", () => {
           status: 200,
           headers: { "Content-Type": "application/json" },
         });
-      }
-      if (url.endsWith("/batches/batch_1")) {
-        return new Response(
-          JSON.stringify({ id: "batch_1", status: "completed", output_file_id: "file_out" }),
-          { status: 200, headers: { "Content-Type": "application/json" } },
-        );
-      }
-      if (url.endsWith("/files/file_out/content")) {
-        const lines = uploadedRequests.map((request, index) =>
-          JSON.stringify({
-            custom_id: request.custom_id,
-            response: {
-              status_code: 200,
-              body: { data: [{ embedding: [index + 1, 0, 0], index: 0 }] },
-            },
-          }),
-        );
-        return new Response(lines.join("\n"), {
-          status: 200,
-          headers: { "Content-Type": "application/jsonl" },
-        });
-      }
-      throw new Error(`unexpected fetch ${url}`);
+      },
     });
 
     vi.stubGlobal("fetch", fetchMock);
 
-    const cfg = {
-      agents: {
-        defaults: {
-          workspace: workspaceDir,
-          memorySearch: {
-            provider: "openai",
-            model: "text-embedding-3-small",
-            store: { path: indexPath, vector: { enabled: false } },
-            sync: { watch: false, onSessionStart: false, onSearch: false },
-            query: { minScore: 0, hybrid: { enabled: false } },
-            remote: { batch: { enabled: true, wait: true, pollIntervalMs: 1 } },
-          },
-        },
-        list: [{ id: "main", default: true }],
-      },
-    };
+    const cfg = createBatchCfg();
 
     try {
       const result = await getMemorySearchManager({ cfg, agentId: "main" });
@@ -298,7 +244,7 @@ describe("memory indexing with OpenAI batches", () => {
 
       const status = manager.status();
       expect(status.chunks).toBeGreaterThan(0);
-      expect(batchCreates).toBe(2);
+      expect(state.batchCreates).toBe(2);
     } finally {
       restoreTimeouts();
     }
@@ -318,39 +264,9 @@ describe("memory indexing with OpenAI batches", () => {
     await fs.writeFile(memoryFile, content);
     await touch();
 
-    let uploadedRequests: Array<{ custom_id?: string }> = [];
     let mode: "fail" | "ok" = "fail";
-    const fetchMock = vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
-      const url =
-        typeof input === "string" ? input : input instanceof URL ? input.toString() : input.url;
-      if (url.endsWith("/files")) {
-        const body = init?.body;
-        if (!(body instanceof FormData)) {
-          throw new Error("expected FormData upload");
-        }
-        for (const [key, value] of body.entries()) {
-          if (key !== "file") {
-            continue;
-          }
-          if (typeof value === "string") {
-            uploadedRequests = value
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          } else {
-            const text = await value.text();
-            uploadedRequests = text
-              .split("\n")
-              .filter(Boolean)
-              .map((line) => JSON.parse(line) as { custom_id?: string });
-          }
-        }
-        return new Response(JSON.stringify({ id: "file_1" }), {
-          status: 200,
-          headers: { "Content-Type": "application/json" },
-        });
-      }
-      if (url.endsWith("/batches")) {
+    const { fetchMock } = createOpenAIBatchFetchMock({
+      onCreateBatch: () => {
         if (mode === "fail") {
           return new Response("batch failed", { status: 400 });
         }
@@ -358,49 +274,12 @@ describe("memory indexing with OpenAI batches", () => {
           status: 200,
           headers: { "Content-Type": "application/json" },
         });
-      }
-      if (url.endsWith("/batches/batch_1")) {
-        return new Response(
-          JSON.stringify({ id: "batch_1", status: "completed", output_file_id: "file_out" }),
-          { status: 200, headers: { "Content-Type": "application/json" } },
-        );
-      }
-      if (url.endsWith("/files/file_out/content")) {
-        const lines = uploadedRequests.map((request, index) =>
-          JSON.stringify({
-            custom_id: request.custom_id,
-            response: {
-              status_code: 200,
-              body: { data: [{ embedding: [index + 1, 0, 0], index: 0 }] },
-            },
-          }),
-        );
-        return new Response(lines.join("\n"), {
-          status: 200,
-          headers: { "Content-Type": "application/jsonl" },
-        });
-      }
-      throw new Error(`unexpected fetch ${url}`);
+      },
     });
 
     vi.stubGlobal("fetch", fetchMock);
 
-    const cfg = {
-      agents: {
-        defaults: {
-          workspace: workspaceDir,
-          memorySearch: {
-            provider: "openai",
-            model: "text-embedding-3-small",
-            store: { path: indexPath, vector: { enabled: false } },
-            sync: { watch: false, onSessionStart: false, onSearch: false },
-            query: { minScore: 0, hybrid: { enabled: false } },
-            remote: { batch: { enabled: true, wait: true, pollIntervalMs: 1 } },
-          },
-        },
-        list: [{ id: "main", default: true }],
-      },
-    };
+    const cfg = createBatchCfg();
 
     try {
       const result = await getMemorySearchManager({ cfg, agentId: "main" });