fix(vector): harden runtime embedding sync

vector/embedding.js

@@ -12,6 +12,8 @@ import { resolveConfiguredTimeoutMs } from "../runtime/request-timeout.js";
 
 const MODULE_NAME = "st_bme";
 const EMBEDDING_REQUEST_TIMEOUT_MS = 300000;
+const DEFAULT_EMBEDDING_BATCH_SIZE = 10;
+const MAX_EMBEDDING_BATCH_SIZE = 100;
 const BACKEND_SOURCES_REQUIRING_API_URL = new Set([
   "ollama",
   "llamacpp",
@@ -110,6 +112,94 @@ async function requestBackendEmbeddings(config = {}, payload = {}, { signal } =
   return await response.json().catch(() => ({}));
 }
 
+function getEmbeddingBatchSize(config = {}) {
+  const parsed = Number(config?.embeddingBatchSize ?? config?.batchSize);
+  if (!Number.isFinite(parsed) || parsed <= 0) {
+    return DEFAULT_EMBEDDING_BATCH_SIZE;
+  }
+  return Math.min(MAX_EMBEDDING_BATCH_SIZE, Math.max(1, Math.trunc(parsed)));
+}
+
+function chunkTexts(texts = [], size = DEFAULT_EMBEDDING_BATCH_SIZE) {
+  const chunks = [];
+  for (let start = 0; start < texts.length; start += size) {
+    chunks.push({ start, texts: texts.slice(start, start + size) });
+  }
+  return chunks;
+}
+
+async function requestDirectEmbeddingBatch(texts, config = {}, { signal } = {}) {
+  const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
+  const response = await fetchWithTimeout(
+    apiUrl + "/embeddings",
+    {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        ...(config.apiKey ? { Authorization: "Bearer " + config.apiKey } : {}),
+      },
+      signal,
+      body: JSON.stringify({
+        model: config.model,
+        input: texts,
+      }),
+    },
+    getConfiguredTimeoutMs(config),
+  );
+
+  if (!response.ok) {
+    const errorText = await response.text().catch(() => response.statusText);
+    const error = new Error(errorText || response.statusText || "HTTP " + response.status);
+    error.status = response.status;
+    throw error;
+  }
+
+  const data = await response.json().catch(() => ({}));
+  const embeddings = Array.isArray(data?.data) ? data.data : null;
+  if (!embeddings) {
+    throw new Error("Embedding API 返回格式异常");
+  }
+
+  const results = new Array(texts.length).fill(null);
+  embeddings.forEach((item, order) => {
+    const rawIndex = Number(item?.index);
+    const index = Number.isInteger(rawIndex) ? rawIndex : order;
+    if (index >= 0 && index < results.length) {
+      results[index] = normalizeVector(item?.embedding);
+    }
+  });
+  return results;
+}
+
+async function requestBackendEmbeddingBatch(texts, config = {}, { signal, isQuery = false } = {}) {
+  const payload = await requestBackendEmbeddings(
+    config,
+    { texts, isQuery },
+    { signal },
+  );
+  const vectors = Array.isArray(payload?.vectors) ? payload.vectors : null;
+  if (!vectors) {
+    throw new Error("Backend Embedding API 返回格式异常");
+  }
+  return texts.map((_, index) => normalizeVector(vectors[index]));
+}
+
+async function fallbackEmbedChunkTexts(texts, config = {}, { signal, isQuery = false } = {}) {
+  const vectors = [];
+  for (const text of texts) {
+    try {
+      vectors.push(await embedText(text, config, { signal, isQuery }));
+    } catch (error) {
+      if (isAbortError(error)) {
+        throw error;
+      }
+      console.error("[ST-BME] Embedding 单条回退失败:", error);
+      vectors.push(null);
+    }
+  }
+  return vectors;
+}
+
 function createCombinedAbortSignal(...signals) {
   const validSignals = signals.filter(Boolean);
   if (validSignals.length <= 1) {
@@ -264,91 +354,78 @@ export async function embedText(text, config, { signal, isQuery = false } = {})
  * @returns {Promise<(Float64Array|null)[]>}
  */
 export async function embedBatch(texts, config, { signal, isQuery = false } = {}) {
+  const normalizedTexts = Array.isArray(texts)
+    ? texts.map((item) => String(item ?? ""))
+    : [];
   const override = getEmbeddingTestOverride("embedBatch");
   if (override) {
-    return await override(texts, config, { signal, isQuery });
+    return await override(normalizedTexts, config, { signal, isQuery });
   }
 
-  if (readEmbeddingMode(config) === "backend") {
-    if (!texts.length || !config?.model) {
-      return texts.map(() => null);
-    }
-    try {
-      const payload = await requestBackendEmbeddings(
-        config,
-        { texts, isQuery },
-        { signal },
-      );
-      const vectors = Array.isArray(payload?.vectors) ? payload.vectors : [];
-      return texts.map((_, index) => normalizeVector(vectors[index]));
-    } catch (e) {
-      if (isAbortError(e)) {
-        throw e;
-      }
-      console.error("[ST-BME] Backend Embedding 批量调用失败:", e);
-      return texts.map(() => null);
-    }
+  if (!normalizedTexts.length) {
+    return [];
   }
 
+  const isBackend = readEmbeddingMode(config) === "backend";
   const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
-  if (!texts.length || !apiUrl || !config?.model) {
-    return texts.map(() => null);
+  if (!config?.model || (!isBackend && !apiUrl)) {
+    return normalizedTexts.map(() => null);
   }
 
-  try {
-    const response = await fetchWithTimeout(
-      `${apiUrl}/embeddings`,
-      {
-        method: "POST",
-        headers: {
-          "Content-Type": "application/json",
-          ...(config.apiKey
-            ? { Authorization: `Bearer ${config.apiKey}` }
-            : {}),
-        },
-        signal,
-        body: JSON.stringify({
-          model: config.model,
-          input: texts,
-        }),
-      },
-      getConfiguredTimeoutMs(config),
-    );
-
-    if (!response.ok) {
-      const errorText = await response.text();
-      console.error(
-        `[ST-BME] Embedding API 批量错误 (${response.status}):`,
-        errorText,
-      );
-      return texts.map(() => null);
-    }
-
-    const data = await response.json();
-    const embeddings = data?.data;
-
-    if (!Array.isArray(embeddings)) {
-      return texts.map(() => null);
-    }
-
-    // 按 index 排序（API 可能不保证顺序）
-    embeddings.sort((a, b) => a.index - b.index);
-
-    return embeddings.map((item) => {
-      if (item?.embedding && Array.isArray(item.embedding)) {
-        return new Float64Array(item.embedding);
+  const results = new Array(normalizedTexts.length).fill(null);
+  const batchSize = getEmbeddingBatchSize(config);
+  for (const chunk of chunkTexts(normalizedTexts, batchSize)) {
+    let vectors = null;
+    try {
+      vectors = isBackend
+        ? await requestBackendEmbeddingBatch(chunk.texts, config, { signal, isQuery })
+        : await requestDirectEmbeddingBatch(chunk.texts, config, { signal });
+    } catch (error) {
+      if (isAbortError(error)) {
+        throw error;
       }
-      return null;
-    });
-  } catch (e) {
-    if (isAbortError(e)) {
-      throw e;
+      console.error(
+        isBackend
+          ? "[ST-BME] Backend Embedding 批量调用失败:"
+          : "[ST-BME] Embedding API 批量调用失败:",
+        error,
+      );
     }
-    console.error("[ST-BME] Embedding API 批量调用失败:", e);
-    return texts.map(() => null);
-  }
-}
 
+    if (!vectors || vectors.length < chunk.texts.length) {
+      vectors = await fallbackEmbedChunkTexts(chunk.texts, config, {
+        signal,
+        isQuery,
+      });
+    } else {
+      const missingIndexes = [];
+      for (let index = 0; index < chunk.texts.length; index++) {
+        if (!vectors[index]) {
+          missingIndexes.push(index);
+        }
+      }
+      if (missingIndexes.length > 0) {
+        const fallbackVectors = await fallbackEmbedChunkTexts(
+          missingIndexes.map((index) => chunk.texts[index]),
+          config,
+          {
+            signal,
+            isQuery,
+          },
+        );
+        missingIndexes.forEach((missingIndex, fallbackIndex) => {
+          vectors[missingIndex] = fallbackVectors[fallbackIndex] || null;
+        });
+      }
+    }
+
+    for (let index = 0; index < chunk.texts.length; index++) {
+      results[chunk.start + index] = vectors[index] || null;
+    }
+  }
+
+  return results;
+}
 /**
  * 计算两个向量的 cosine 相似度
  *