feat: 支持拉取记忆与嵌入模型列表

index.js

@@ -30,7 +30,7 @@ import {
   getNode,
 } from "./graph.js";
 import { estimateTokens, formatInjection } from "./injector.js";
-import { testLLMConnection } from "./llm.js";
+import { fetchMemoryLLMModels, testLLMConnection } from "./llm.js";
 import { retrieve } from "./retriever.js";
 import {
   appendBatchJournal,
@@ -51,6 +51,7 @@ import {
   getVectorIndexStats,
   isBackendVectorConfig,
   isDirectVectorConfig,
+  fetchAvailableEmbeddingModels,
   syncGraphVectorIndex,
   testVectorConnection,
   validateVectorConfig,
@@ -251,8 +252,11 @@ function getSchema() {
   return schema;
 }
 
-function getEmbeddingConfig() {
-  return getVectorConfigFromSettings(getSettings());
+function getEmbeddingConfig(mode = null) {
+  const settings = getSettings();
+  return getVectorConfigFromSettings(
+    mode ? { ...settings, embeddingTransportMode: mode } : settings,
+  );
 }
 
 function getCurrentChatId(context = getContext()) {
@@ -1454,6 +1458,41 @@ async function onTestMemoryLLM() {
   }
 }
 
+async function onFetchMemoryLLMModels() {
+  toastr.info("正在拉取记忆 LLM 模型列表...");
+  const result = await fetchMemoryLLMModels();
+
+  if (result.success) {
+    toastr.success(`已拉取 ${result.models.length} 个记忆 LLM 模型`);
+  } else {
+    toastr.error(`拉取失败: ${result.error}`);
+  }
+
+  return result;
+}
+
+async function onFetchEmbeddingModels(mode = null) {
+  const config = getEmbeddingConfig(mode);
+  const targetMode = mode || config?.mode || "direct";
+  const validation = validateVectorConfig(config);
+  if (!validation.valid) {
+    toastr.warning(validation.error);
+    return { success: false, models: [], error: validation.error };
+  }
+
+  toastr.info("正在拉取 Embedding 模型列表...");
+  const result = await fetchAvailableEmbeddingModels(config);
+
+  if (result.success) {
+    const modeLabel = targetMode === "backend" ? "后端" : "直连";
+    toastr.success(`已拉取 ${result.models.length} 个${modeLabel} Embedding 模型`);
+  } else {
+    toastr.error(`拉取失败: ${result.error}`);
+  }
+
+  return result;
+}
+
 async function onManualExtract() {
   if (isExtracting) return;
   if (!(await recoverHistoryIfNeeded("manual-extract"))) return;
@@ -1650,6 +1689,8 @@ async function onReembedDirect() {
         evolve: onManualEvolve,
         testEmbedding: onTestEmbedding,
         testMemoryLLM: onTestMemoryLLM,
+        fetchMemoryLLMModels: onFetchMemoryLLMModels,
+        fetchEmbeddingModels: onFetchEmbeddingModels,
         rebuildVectorIndex: () => onRebuildVectorIndex(),
         rebuildVectorRange: (range) => onRebuildVectorIndex(range),
         reembedDirect: onReembedDirect,

llm.js

@@ -27,6 +27,32 @@ function hasDedicatedLLMConfig(config = getMemoryLLMConfig()) {
     return Boolean(config.apiUrl && config.model);
 }
 
+function normalizeModelList(items = []) {
+    if (!Array.isArray(items)) return [];
+
+    const seen = new Set();
+    const models = [];
+
+    for (const item of items) {
+        let id = "";
+        let label = "";
+
+        if (typeof item === "string") {
+            id = item.trim();
+            label = id;
+        } else if (item && typeof item === "object") {
+            id = String(item.id || item.name || item.value || item.slug || "").trim();
+            label = String(item.name || item.id || item.value || item.slug || "").trim();
+        }
+
+        if (!id || seen.has(id)) continue;
+        seen.add(id);
+        models.push({ id, label: label || id });
+    }
+
+    return models;
+}
+
 // 自动检测：如果 API 不支持 response_format，记住并跳过
 let _jsonModeSupported = true;
 
@@ -206,6 +232,48 @@ export async function testLLMConnection() {
     }
 }
 
+export async function fetchMemoryLLMModels() {
+    const config = getMemoryLLMConfig();
+    if (!config.apiUrl) {
+        return {
+            success: false,
+            models: [],
+            error: "请先填写记忆 LLM API 地址",
+        };
+    }
+
+    try {
+        const response = await fetch("/api/backends/chat-completions/status", {
+            method: "POST",
+            headers: getRequestHeaders(),
+            body: JSON.stringify({
+                chat_completion_source: chat_completion_sources.OPENAI,
+                reverse_proxy: config.apiUrl,
+                proxy_password: config.apiKey || "",
+            }),
+        });
+
+        const payload = await response.json().catch(() => ({}));
+        if (!response.ok) {
+            const message = payload?.error || payload?.message || response.statusText;
+            return { success: false, models: [], error: message || `HTTP ${response.status}` };
+        }
+
+        const models = normalizeModelList(payload?.data);
+        if (models.length === 0) {
+            return {
+                success: false,
+                models: [],
+                error: "未拉取到可用模型，请检查接口是否支持 /models",
+            };
+        }
+
+        return { success: true, models, error: "" };
+    } catch (error) {
+        return { success: false, models: [], error: String(error) };
+    }
+}
+
 /**
  * 从 LLM 响应文本中提取 JSON 对象
  * 处理各种常见格式：纯 JSON、markdown 代码块、混合文本等

panel.html

@@ -367,6 +367,15 @@
                       placeholder="gpt-4.1-mini / qwen-max / deepseek-chat"
                     />
                   </div>
+                  <div class="bme-model-fetch-block">
+                    <button class="bme-config-secondary-btn" id="bme-fetch-llm-models" type="button">
+                      <i class="fa-solid fa-rotate"></i>
+                      <span>拉取模型</span>
+                    </button>
+                    <select id="bme-select-llm-model" class="bme-config-input bme-model-select" style="display:none">
+                      <option value="">从拉取结果中选择模型</option>
+                    </select>
+                  </div>
                   <div class="bme-config-actions">
                     <button class="bme-config-test-btn" id="bme-test-llm" type="button">
                       <i class="fa-solid fa-plug"></i>
@@ -417,6 +426,15 @@
                         placeholder="text-embedding-3-small / nomic-embed-text / BAAI/bge-m3"
                       />
                     </div>
+                    <div class="bme-model-fetch-block">
+                      <button class="bme-config-secondary-btn" id="bme-fetch-embed-backend-models" type="button">
+                        <i class="fa-solid fa-rotate"></i>
+                        <span>拉取模型</span>
+                      </button>
+                      <select id="bme-select-embed-backend-model" class="bme-config-input bme-model-select" style="display:none">
+                        <option value="">从拉取结果中选择模型</option>
+                      </select>
+                    </div>
                     <div class="bme-config-row">
                       <label for="bme-setting-embed-backend-url">后端 API 地址</label>
                       <input
@@ -466,6 +484,15 @@
                         placeholder="text-embedding-3-small"
                       />
                     </div>
+                    <div class="bme-model-fetch-block">
+                      <button class="bme-config-secondary-btn" id="bme-fetch-embed-direct-models" type="button">
+                        <i class="fa-solid fa-rotate"></i>
+                        <span>拉取模型</span>
+                      </button>
+                      <select id="bme-select-embed-direct-model" class="bme-config-input bme-model-select" style="display:none">
+                        <option value="">从拉取结果中选择模型</option>
+                      </select>
+                    </div>
                   </div>
 
                   <div class="bme-config-actions">

panel.js

@@ -110,6 +110,9 @@ let graphRenderer = null;
 let mobileGraphRenderer = null;
 let currentTabId = "dashboard";
 let currentConfigSectionId = "api";
+let fetchedMemoryLLMModels = [];
+let fetchedBackendEmbeddingModels = [];
+let fetchedDirectEmbeddingModels = [];
 
 
 // 由 index.js 注入的引用
@@ -795,6 +798,7 @@ function _refreshConfigTab() {
     _setInputValue("bme-setting-synopsis-prompt", settings.synopsisPrompt || DEFAULT_PROMPTS.synopsis);
     _setInputValue("bme-setting-reflection-prompt", settings.reflectionPrompt || DEFAULT_PROMPTS.reflection);
 
+    _refreshFetchedModelSelects(settings);
     _refreshGuardedConfigStates(settings);
     _refreshStageCardStates(settings);
     _refreshPromptCardStates(settings);
@@ -1063,6 +1067,48 @@ function _bindConfigControls() {
     document.getElementById("bme-test-embedding")?.addEventListener("click", async () => {
         await _actionHandlers.testEmbedding?.();
     });
+    document.getElementById("bme-fetch-llm-models")?.addEventListener("click", async () => {
+        const result = await _actionHandlers.fetchMemoryLLMModels?.();
+        if (!result?.success) return;
+        fetchedMemoryLLMModels = result.models || [];
+        _renderFetchedModelOptions(
+            "bme-select-llm-model",
+            fetchedMemoryLLMModels,
+            (_getSettings?.() || {}).llmModel || "",
+        );
+    });
+    document.getElementById("bme-fetch-embed-backend-models")?.addEventListener("click", async () => {
+        const result = await _actionHandlers.fetchEmbeddingModels?.("backend");
+        if (!result?.success) return;
+        fetchedBackendEmbeddingModels = result.models || [];
+        _renderFetchedModelOptions(
+            "bme-select-embed-backend-model",
+            fetchedBackendEmbeddingModels,
+            (_getSettings?.() || {}).embeddingBackendModel || "",
+        );
+    });
+    document.getElementById("bme-fetch-embed-direct-models")?.addEventListener("click", async () => {
+        const result = await _actionHandlers.fetchEmbeddingModels?.("direct");
+        if (!result?.success) return;
+        fetchedDirectEmbeddingModels = result.models || [];
+        _renderFetchedModelOptions(
+            "bme-select-embed-direct-model",
+            fetchedDirectEmbeddingModels,
+            (_getSettings?.() || {}).embeddingModel || "",
+        );
+    });
+
+    bindSelectModel("bme-select-llm-model", "bme-setting-llm-model", "llmModel");
+    bindSelectModel(
+        "bme-select-embed-backend-model",
+        "bme-setting-embed-backend-model",
+        "embeddingBackendModel",
+    );
+    bindSelectModel(
+        "bme-select-embed-direct-model",
+        "bme-setting-embed-model",
+        "embeddingModel",
+    );
 
     panelEl.dataset.bmeConfigBound = "true";
 }
@@ -1122,6 +1168,17 @@ function bindPromptText(id, settingKey, promptKey) {
     element.dataset.bmeBound = "true";
 }
 
+function bindSelectModel(selectId, inputId, settingKey) {
+    const element = document.getElementById(selectId);
+    if (!element || element.dataset.bmeBound === "true") return;
+    element.addEventListener("change", () => {
+        if (!element.value) return;
+        _setInputValue(inputId, element.value);
+        _patchSettings({ [settingKey]: element.value });
+    });
+    element.dataset.bmeBound = "true";
+}
+
 // ==================== 工具函数 ====================
 
 function _setText(id, text) {
@@ -1186,6 +1243,57 @@ function _refreshStageCardStates(settings = _getSettings?.() || {}) {
     });
 }
 
+function _refreshFetchedModelSelects(settings = _getSettings?.() || {}) {
+    _renderFetchedModelOptions(
+        "bme-select-llm-model",
+        fetchedMemoryLLMModels,
+        settings.llmModel || "",
+    );
+    _renderFetchedModelOptions(
+        "bme-select-embed-backend-model",
+        fetchedBackendEmbeddingModels,
+        settings.embeddingBackendModel || "",
+    );
+    _renderFetchedModelOptions(
+        "bme-select-embed-direct-model",
+        fetchedDirectEmbeddingModels,
+        settings.embeddingModel || "",
+    );
+}
+
+function _renderFetchedModelOptions(selectId, models, currentValue = "") {
+    const select = document.getElementById(selectId);
+    if (!select) return;
+
+    const normalized = Array.isArray(models) ? models : [];
+    select.innerHTML = "";
+
+    const placeholder = document.createElement("option");
+    placeholder.value = "";
+    placeholder.textContent = normalized.length
+        ? "从拉取结果中选择模型"
+        : "暂无已拉取模型";
+    select.appendChild(placeholder);
+
+    normalized.forEach((model) => {
+        const option = document.createElement("option");
+        option.value = String(model?.id || "");
+        option.textContent = String(model?.label || model?.id || "");
+        select.appendChild(option);
+    });
+
+    if (
+        currentValue &&
+        normalized.some((model) => String(model?.id || "") === String(currentValue))
+    ) {
+        select.value = String(currentValue);
+    } else {
+        select.value = "";
+    }
+
+    select.style.display = normalized.length > 0 ? "" : "none";
+}
+
 function _refreshPromptCardStates(settings = _getSettings?.() || {}) {
     if (!panelEl) return;
     panelEl.querySelectorAll(".bme-prompt-card").forEach((card) => {

style.css

@@ -930,6 +930,46 @@
     margin-top: 8px;
 }
 
+.bme-model-fetch-block {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+    margin-top: -2px;
+    margin-bottom: 12px;
+}
+
+.bme-config-secondary-btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+    width: fit-content;
+    min-height: 38px;
+    padding: 0 14px;
+    border-radius: 10px;
+    border: 1px solid rgba(255, 255, 255, 0.08);
+    background: rgba(255, 255, 255, 0.03);
+    color: var(--bme-on-surface);
+    cursor: pointer;
+    font-size: 12px;
+    font-weight: 700;
+    transition: border-color 0.15s, background 0.15s, color 0.15s;
+}
+
+.bme-config-secondary-btn:hover {
+    border-color: var(--bme-primary);
+    background: rgba(255, 255, 255, 0.06);
+    color: var(--bme-primary);
+}
+
+.bme-config-secondary-btn i {
+    font-size: 12px;
+}
+
+.bme-model-select {
+    max-width: 100%;
+}
+
 .bme-config-test-btn {
     display: inline-flex !important;
     flex-direction: row !important;
@@ -1408,6 +1448,10 @@
         width: 100%;
     }
 
+    .bme-config-secondary-btn {
+        width: 100%;
+    }
+
     .bme-config-test-btn {
         width: 100%;
     }

vector-index.js

@@ -27,6 +27,19 @@ const BACKEND_SOURCES_REQUIRING_API_URL = new Set([
   "vllm",
 ]);
 
+const MODEL_LIST_ENDPOINTS = {
+  openrouter: "/api/openrouter/models/embedding",
+  chutes: "/api/openai/chutes/models/embedding",
+  nanogpt: "/api/openai/nanogpt/models/embedding",
+  electronhub: "/api/openai/electronhub/models",
+};
+
+const BACKEND_STATUS_MODEL_SOURCES = {
+  openai: "openai",
+  cohere: "cohere",
+  mistral: "mistralai",
+};
+
 export const BACKEND_DEFAULT_MODELS = {
   openai: "text-embedding-3-small",
   openrouter: "openai/text-embedding-3-small",
@@ -639,3 +652,195 @@ export function getVectorIndexStats(graph) {
   }
   return state.lastStats || { total: 0, indexed: 0, stale: 0, pending: 0 };
 }
+
+function normalizeModelOptions(items = [], { embeddingOnly = false } = {}) {
+  if (!Array.isArray(items)) return [];
+
+  const candidates = [];
+  for (const item of items) {
+    if (typeof item === "string") {
+      const id = item.trim();
+      if (id) candidates.push({ id, label: id, raw: item });
+      continue;
+    }
+
+    if (!item || typeof item !== "object") continue;
+    const id = String(item.id || item.name || item.label || item.slug || item.value || "").trim();
+    const label = String(item.label || item.name || item.id || item.slug || item.value || "").trim();
+    if (!id) continue;
+
+    if (
+      embeddingOnly &&
+      Array.isArray(item.endpoints) &&
+      !item.endpoints.includes("/v1/embeddings")
+    ) {
+      continue;
+    }
+
+    candidates.push({ id, label: label || id, raw: item });
+  }
+
+  const embeddingRegex =
+    /(embed|embedding|bge|e5|gte|nomic|voyage|mxbai|jina|minilm)/i;
+  const embeddingTagged = candidates.filter((item) => embeddingRegex.test(item.id) || embeddingRegex.test(item.label));
+  const source = embeddingTagged.length > 0 ? embeddingTagged : candidates;
+
+  const seen = new Set();
+  return source
+    .filter((item) => {
+      if (seen.has(item.id)) return false;
+      seen.add(item.id);
+      return true;
+    })
+    .map(({ id, label }) => ({ id, label }));
+}
+
+async function fetchJsonEndpoint(url, { method = "POST" } = {}) {
+  const response = await fetch(url, {
+    method,
+    headers: getRequestHeaders({ omitContentType: true }),
+  });
+
+  const payload = await response.json().catch(() => []);
+  if (!response.ok) {
+    throw new Error(
+      (typeof payload === "object" && payload?.error) ||
+        response.statusText ||
+        `HTTP ${response.status}`,
+    );
+  }
+
+  return payload;
+}
+
+async function fetchBackendStatusModelList(source) {
+  const chatCompletionSource = BACKEND_STATUS_MODEL_SOURCES[source];
+  if (!chatCompletionSource) {
+    throw new Error("当前后端向量源暂不支持自动拉取模型，请手动填写");
+  }
+
+  const response = await fetch("/api/backends/chat-completions/status", {
+    method: "POST",
+    headers: getRequestHeaders(),
+    body: JSON.stringify({
+      chat_completion_source: chatCompletionSource,
+    }),
+  });
+
+  const payload = await response.json().catch(() => ({}));
+  if (!response.ok || payload?.error) {
+    throw new Error(
+      payload?.message || payload?.error || response.statusText || `HTTP ${response.status}`,
+    );
+  }
+
+  return normalizeModelOptions(payload?.data || payload, { embeddingOnly: false });
+}
+
+async function fetchOpenAICompatibleModelList(apiUrl, apiKey = "") {
+  const normalizedUrl = normalizeOpenAICompatibleBaseUrl(apiUrl);
+  if (!normalizedUrl) {
+    throw new Error("请先填写 API 地址");
+  }
+
+  const response = await fetch(`${normalizedUrl}/models`, {
+    method: "GET",
+    headers: {
+      ...(apiKey ? { Authorization: `Bearer ${apiKey}` } : {}),
+    },
+  });
+
+  const payload = await response.json().catch(() => ({}));
+  if (!response.ok) {
+    throw new Error(payload?.error?.message || payload?.message || response.statusText);
+  }
+
+  return normalizeModelOptions(payload?.data || payload, { embeddingOnly: false });
+}
+
+async function fetchOllamaModelList(apiUrl) {
+  const normalizedUrl = normalizeOpenAICompatibleBaseUrl(apiUrl).replace(/\/v1$/i, "");
+  if (!normalizedUrl) {
+    throw new Error("请先填写 Ollama API 地址");
+  }
+
+  const response = await fetch(`${normalizedUrl}/api/tags`, { method: "GET" });
+  const payload = await response.json().catch(() => ({}));
+  if (!response.ok) {
+    throw new Error(payload?.error || payload?.message || response.statusText);
+  }
+
+  return normalizeModelOptions(
+    Array.isArray(payload?.models)
+      ? payload.models.map((item) => ({
+          id: item?.model || item?.name,
+          name: item?.model || item?.name,
+        }))
+      : [],
+    { embeddingOnly: false },
+  );
+}
+
+export async function fetchAvailableEmbeddingModels(config) {
+  const validation = validateVectorConfig(config);
+  if (!validation.valid) {
+    return { success: false, models: [], error: validation.error };
+  }
+
+  try {
+    if (isDirectVectorConfig(config)) {
+      const models = normalizeModelOptions(
+        await fetchOpenAICompatibleModelList(config.apiUrl, config.apiKey),
+      );
+      if (models.length === 0) {
+        return { success: false, models: [], error: "未拉取到可用 Embedding 模型" };
+      }
+      return { success: true, models, error: "" };
+    }
+
+    if (config.source === "ollama") {
+      const models = await fetchOllamaModelList(config.apiUrl);
+      if (models.length === 0) {
+        return { success: false, models: [], error: "未拉取到可用 Ollama 模型" };
+      }
+      return { success: true, models, error: "" };
+    }
+
+    if (MODEL_LIST_ENDPOINTS[config.source]) {
+      const payload = await fetchJsonEndpoint(MODEL_LIST_ENDPOINTS[config.source]);
+      const models = normalizeModelOptions(payload, {
+        embeddingOnly: config.source === "electronhub",
+      });
+      if (models.length === 0) {
+        return { success: false, models: [], error: "未拉取到可用 Embedding 模型" };
+      }
+      return { success: true, models, error: "" };
+    }
+
+    if (BACKEND_STATUS_MODEL_SOURCES[config.source]) {
+      const models = await fetchBackendStatusModelList(config.source);
+      if (models.length === 0) {
+        return { success: false, models: [], error: "未拉取到可用 Embedding 模型" };
+      }
+      return { success: true, models, error: "" };
+    }
+
+    if (config.apiUrl) {
+      const models = normalizeModelOptions(
+        await fetchOpenAICompatibleModelList(config.apiUrl),
+      );
+      if (models.length === 0) {
+        return { success: false, models: [], error: "未拉取到可用 Embedding 模型" };
+      }
+      return { success: true, models, error: "" };
+    }
+
+    return {
+      success: false,
+      models: [],
+      error: "当前后端向量源暂不支持自动拉取模型，请手动填写",
+    };
+  } catch (error) {
+    return { success: false, models: [], error: String(error) };
+  }
+}