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feat: add configurable timeout and advanced network settings

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Youzini-afk
2026-03-25 15:52:54 +08:00
parent 02fdac48e8
commit ade5f47d60
6 changed files with 2895 additions and 2003 deletions
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346
embedding.js
View File

@@ -6,55 +6,85 @@
* 调用外部 API 获取文本向量，并提供暴力搜索 cosine 相似度
*/
const EMBEDDING_REQUEST_TIMEOUT_MS = 45000;
import { extension_settings } from "../../../extensions.js";
const MODULE_NAME = "st_bme";
const EMBEDDING_REQUEST_TIMEOUT_MS = 300000;
function getConfiguredTimeoutMs(
settings = extension_settings[MODULE_NAME] || {},
) {
const timeoutMs = Number(settings?.timeoutMs);
return Number.isFinite(timeoutMs) && timeoutMs > 0
? timeoutMs
: EMBEDDING_REQUEST_TIMEOUT_MS;
}
function isAbortError(error) {
return error?.name === 'AbortError';
return error?.name === "AbortError";
}
function normalizeOpenAICompatibleBaseUrl(value) {
return String(value || '')
.trim()
.replace(/\/+(chat\/completions|embeddings)$/i, '')
.replace(/\/+$/, '');
return String(value || "")
.trim()
.replace(/\/+(chat\/completions|embeddings)$/i, "")
.replace(/\/+$/, "");
}
function createCombinedAbortSignal(...signals) {
const validSignals = signals.filter(Boolean);
if (validSignals.length <= 1) {
return validSignals[0] || undefined;
}
const validSignals = signals.filter(Boolean);
if (validSignals.length <= 1) {
return validSignals[0] || undefined;
}
if (typeof AbortSignal !== 'undefined' && typeof AbortSignal.any === 'function') {
return AbortSignal.any(validSignals);
}
if (
typeof AbortSignal !== "undefined" &&
typeof AbortSignal.any === "function"
) {
return AbortSignal.any(validSignals);
}
const controller = new AbortController();
for (const signal of validSignals) {
if (signal.aborted) {
controller.abort(signal.reason);
return controller.signal;
}
signal.addEventListener('abort', () => controller.abort(signal.reason), { once: true });
const controller = new AbortController();
for (const signal of validSignals) {
if (signal.aborted) {
controller.abort(signal.reason);
return controller.signal;
}
return controller.signal;
signal.addEventListener("abort", () => controller.abort(signal.reason), {
once: true,
});
}
return controller.signal;
}
async function fetchWithTimeout(url, options = {}, timeoutMs = EMBEDDING_REQUEST_TIMEOUT_MS) {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(new DOMException(`Embedding 请求超时 (${Math.round(timeoutMs / 1000)}s)`, 'AbortError')), timeoutMs);
const signal = options.signal
? createCombinedAbortSignal(options.signal, controller.signal)
: controller.signal;
async function fetchWithTimeout(
url,
options = {},
timeoutMs = EMBEDDING_REQUEST_TIMEOUT_MS,
) {
const controller = new AbortController();
const timeout = setTimeout(
() =>
controller.abort(
new DOMException(
`Embedding 请求超时 (${Math.round(timeoutMs / 1000)}s)`,
"AbortError",
),
),
timeoutMs,
);
const signal = options.signal
? createCombinedAbortSignal(options.signal, controller.signal)
: controller.signal;
try {
return await fetch(url, {
...options,
signal,
});
} finally {
clearTimeout(timeout);
}
try {
return await fetch(url, {
...options,
signal,
});
} finally {
clearTimeout(timeout);
}
}
/**
@@ -68,48 +98,57 @@ async function fetchWithTimeout(url, options = {}, timeoutMs = EMBEDDING_REQUEST
* @returns {Promise<Float64Array|null>} 向量或 null
*/
export async function embedText(text, config, { signal } = {}) {
const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
if (!text || !apiUrl || !config?.model) {
console.warn('[ST-BME] Embedding 配置不完整，跳过');
return null;
const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
if (!text || !apiUrl || !config?.model) {
console.warn("[ST-BME] Embedding 配置不完整，跳过");
return null;
}
try {
const response = await fetchWithTimeout(
`${apiUrl}/embeddings`,
{
method: "POST",
headers: {
"Content-Type": "application/json",
...(config.apiKey
? { Authorization: `Bearer ${config.apiKey}` }
: {}),
},
signal,
body: JSON.stringify({
model: config.model,
input: text,
}),
},
getConfiguredTimeoutMs(),
);
if (!response.ok) {
const errorText = await response.text();
console.error(
`[ST-BME] Embedding API 错误 (${response.status}):`,
errorText,
);
return null;
}
try {
const response = await fetchWithTimeout(`${apiUrl}/embeddings`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.apiKey ? { Authorization: `Bearer ${config.apiKey}` } : {}),
},
signal,
body: JSON.stringify({
model: config.model,
input: text,
}),
});
const data = await response.json();
const vector = data?.data?.[0]?.embedding;
if (!response.ok) {
const errorText = await response.text();
console.error(`[ST-BME] Embedding API 错误 (${response.status}):`, errorText);
return null;
}
const data = await response.json();
const vector = data?.data?.[0]?.embedding;
if (!vector || !Array.isArray(vector)) {
console.error('[ST-BME] Embedding API 返回格式异常:', data);
return null;
}
return new Float64Array(vector);
} catch (e) {
if (isAbortError(e)) {
throw e;
}
console.error('[ST-BME] Embedding API 调用失败:', e);
return null;
if (!vector || !Array.isArray(vector)) {
console.error("[ST-BME] Embedding API 返回格式异常:", data);
return null;
}
return new Float64Array(vector);
} catch (e) {
if (isAbortError(e)) {
throw e;
}
console.error("[ST-BME] Embedding API 调用失败:", e);
return null;
}
}
/**
@@ -120,54 +159,63 @@ export async function embedText(text, config, { signal } = {}) {
* @returns {Promise<(Float64Array|null)[]>}
*/
export async function embedBatch(texts, config, { signal } = {}) {
const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
if (!texts.length || !apiUrl || !config?.model) {
return texts.map(() => null);
const apiUrl = normalizeOpenAICompatibleBaseUrl(config?.apiUrl);
if (!texts.length || !apiUrl || !config?.model) {
return texts.map(() => null);
}
try {
const response = await fetchWithTimeout(
`${apiUrl}/embeddings`,
{
method: "POST",
headers: {
"Content-Type": "application/json",
...(config.apiKey
? { Authorization: `Bearer ${config.apiKey}` }
: {}),
},
signal,
body: JSON.stringify({
model: config.model,
input: texts,
}),
},
getConfiguredTimeoutMs(),
);
if (!response.ok) {
const errorText = await response.text();
console.error(
`[ST-BME] Embedding API 批量错误 (${response.status}):`,
errorText,
);
return texts.map(() => null);
}
try {
const response = await fetchWithTimeout(`${apiUrl}/embeddings`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.apiKey ? { Authorization: `Bearer ${config.apiKey}` } : {}),
},
signal,
body: JSON.stringify({
model: config.model,
input: texts,
}),
});
const data = await response.json();
const embeddings = data?.data;
if (!response.ok) {
const errorText = await response.text();
console.error(`[ST-BME] Embedding API 批量错误 (${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);
}
return null;
});
} catch (e) {
if (isAbortError(e)) {
throw e;
}
console.error('[ST-BME] Embedding API 批量调用失败:', e);
return texts.map(() => null);
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);
}
return null;
});
} catch (e) {
if (isAbortError(e)) {
throw e;
}
console.error("[ST-BME] Embedding API 批量调用失败:", e);
return texts.map(() => null);
}
}
/**
@@ -178,24 +226,24 @@ export async function embedBatch(texts, config, { signal } = {}) {
* @returns {number} 相似度 [-1, 1]
*/
export function cosineSimilarity(vecA, vecB) {
if (!vecA || !vecB || vecA.length !== vecB.length || vecA.length === 0) {
return 0;
}
if (!vecA || !vecB || vecA.length !== vecB.length || vecA.length === 0) {
return 0;
}
let dotProduct = 0;
let normA = 0;
let normB = 0;
let dotProduct = 0;
let normA = 0;
let normB = 0;
for (let i = 0; i < vecA.length; i++) {
dotProduct += vecA[i] * vecB[i];
normA += vecA[i] * vecA[i];
normB += vecB[i] * vecB[i];
}
for (let i = 0; i < vecA.length; i++) {
dotProduct += vecA[i] * vecB[i];
normA += vecA[i] * vecA[i];
normB += vecB[i] * vecB[i];
}
const denominator = Math.sqrt(normA) * Math.sqrt(normB);
if (denominator === 0) return 0;
const denominator = Math.sqrt(normA) * Math.sqrt(normB);
if (denominator === 0) return 0;
return dotProduct / denominator;
return dotProduct / denominator;
}
/**
@@ -208,19 +256,19 @@ export function cosineSimilarity(vecA, vecB) {
* @returns {Array<{nodeId: string, score: number}>} 按相似度降序
*/
export function searchSimilar(queryVec, candidates, topK = 20) {
if (!queryVec || candidates.length === 0) return [];
if (!queryVec || candidates.length === 0) return [];
const scored = candidates
.filter(c => c.embedding && c.embedding.length > 0)
.map(c => ({
nodeId: c.nodeId,
score: cosineSimilarity(queryVec, c.embedding),
}))
.filter(item => item.score > 0);
const scored = candidates
.filter((c) => c.embedding && c.embedding.length > 0)
.map((c) => ({
nodeId: c.nodeId,
score: cosineSimilarity(queryVec, c.embedding),
}))
.filter((item) => item.score > 0);
scored.sort((a, b) => b.score - a.score);
scored.sort((a, b) => b.score - a.score);
return scored.slice(0, topK);
return scored.slice(0, topK);
}
/**
@@ -230,13 +278,13 @@ export function searchSimilar(queryVec, candidates, topK = 20) {
* @returns {Promise<{success: boolean, dimensions: number, error: string}>}
*/
export async function testConnection(config) {
try {
const vec = await embedText('test connection', config);
if (vec) {
return { success: true, dimensions: vec.length, error: '' };
}
return { success: false, dimensions: 0, error: 'API 返回空结果' };
} catch (e) {
return { success: false, dimensions: 0, error: String(e) };
try {
const vec = await embedText("test connection", config);
if (vec) {
return { success: true, dimensions: vec.length, error: "" };
}
return { success: false, dimensions: 0, error: "API 返回空结果" };
} catch (e) {
return { success: false, dimensions: 0, error: String(e) };
}
}

33
index.js
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@@ -73,6 +73,7 @@ const SERVER_SETTINGS_URL = `/user/files/${SERVER_SETTINGS_FILENAME}`;
const defaultSettings = {
enabled: false,
timeoutMs: 300000,
// 提取设置
extractEvery: 1, // 每 N 条 assistant 回复提取一次
@@ -172,7 +173,7 @@ let isRecoveringHistory = false;
let lastHistoryWarningAt = 0;
let lastRecallFallbackNoticeAt = 0;
let lastExtractionWarningAt = 0;
const LOCAL_VECTOR_TIMEOUT_MS = 30000;
const LOCAL_VECTOR_TIMEOUT_MS = 300000;
const STATUS_TOAST_THROTTLE_MS = 1500;
const RECALL_INPUT_RECORD_TTL_MS = 60000;
const HISTORY_RECOVERY_SETTLE_MS = 80;
@@ -645,6 +646,13 @@ function getSchema() {
return schema;
}
function getConfiguredTimeoutMs(settings = getSettings()) {
const timeoutMs = Number(settings?.timeoutMs);
return Number.isFinite(timeoutMs) && timeoutMs > 0
? timeoutMs
: LOCAL_VECTOR_TIMEOUT_MS;
}
function getEmbeddingConfig(mode = null) {
const settings = getSettings();
return getVectorConfigFromSettings(
@@ -795,10 +803,19 @@ function notifyExtractionIssue(message, title = "ST-BME 提取提示") {
async function fetchLocalWithTimeout(
url,
options = {},
timeoutMs = LOCAL_VECTOR_TIMEOUT_MS,
timeoutMs = getConfiguredTimeoutMs(),
) {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(new DOMException(`本地请求超时 (${Math.round(timeoutMs / 1000)}s)`, 'AbortError')), timeoutMs);
const timeout = setTimeout(
() =>
controller.abort(
new DOMException(
`本地请求超时 (${Math.round(timeoutMs / 1000)}s)`,
"AbortError",
),
),
timeoutMs,
);
let signal = controller.signal;
if (options.signal) {
if (
@@ -808,9 +825,13 @@ async function fetchLocalWithTimeout(
signal = AbortSignal.any([options.signal, controller.signal]);
} else {
signal = controller.signal;
options.signal.addEventListener("abort", () => controller.abort(options.signal.reason), {
once: true,
});
options.signal.addEventListener(
"abort",
() => controller.abort(options.signal.reason),
{
once: true,
},
);
}
}

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llm.js
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panel.html
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315
vector-index.js
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@@ -3,10 +3,7 @@
import { getRequestHeaders } from "../../../../script.js";
import { embedBatch, embedText, searchSimilar } from "./embedding.js";
import { getActiveNodes } from "./graph.js";
import {
buildVectorCollectionId,
stableHashString,
} from "./runtime-state.js";
import { buildVectorCollectionId, stableHashString } from "./runtime-state.js";
export const BACKEND_VECTOR_SOURCES = [
"openai",
@@ -33,7 +30,14 @@ const MODEL_LIST_ENDPOINTS = {
nanogpt: "/api/openai/nanogpt/models/embedding",
electronhub: "/api/openai/electronhub/models",
};
const VECTOR_REQUEST_TIMEOUT_MS = 30000;
const VECTOR_REQUEST_TIMEOUT_MS = 300000;
function getConfiguredTimeoutMs(config = {}) {
const timeoutMs = Number(config?.timeoutMs);
return Number.isFinite(timeoutMs) && timeoutMs > 0
? timeoutMs
: VECTOR_REQUEST_TIMEOUT_MS;
}
const BACKEND_STATUS_MODEL_SOURCES = {
openai: "openai",
@@ -72,7 +76,10 @@ function createCombinedAbortSignal(...signals) {
return validSignals[0] || undefined;
}
if (typeof AbortSignal !== "undefined" && typeof AbortSignal.any === "function") {
if (
typeof AbortSignal !== "undefined" &&
typeof AbortSignal.any === "function"
) {
return AbortSignal.any(validSignals);
}
@@ -82,14 +89,29 @@ function createCombinedAbortSignal(...signals) {
controller.abort(signal.reason);
return controller.signal;
}
signal.addEventListener("abort", () => controller.abort(signal.reason), { once: true });
signal.addEventListener("abort", () => controller.abort(signal.reason), {
once: true,
});
}
return controller.signal;
}
async function fetchWithTimeout(url, options = {}, timeoutMs = VECTOR_REQUEST_TIMEOUT_MS) {
async function fetchWithTimeout(
url,
options = {},
timeoutMs = VECTOR_REQUEST_TIMEOUT_MS,
) {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(new DOMException(`向量请求超时 (${Math.round(timeoutMs / 1000)}s)`, 'AbortError')), timeoutMs);
const timeout = setTimeout(
() =>
controller.abort(
new DOMException(
`向量请求超时 (${Math.round(timeoutMs / 1000)}s)`,
"AbortError",
),
),
timeoutMs,
);
const signal = options.signal
? createCombinedAbortSignal(options.signal, controller.signal)
: controller.signal;
@@ -126,14 +148,20 @@ export function getVectorConfigFromSettings(settings = {}) {
return {
mode,
source: "direct",
apiUrl: normalizeOpenAICompatibleBaseUrl(settings.embeddingApiUrl, autoSuffix),
apiUrl: normalizeOpenAICompatibleBaseUrl(
settings.embeddingApiUrl,
autoSuffix,
),
apiKey: String(settings.embeddingApiKey || "").trim(),
model: String(settings.embeddingModel || "").trim(),
autoSuffix,
timeoutMs: getConfiguredTimeoutMs(settings),
};
}
const source = BACKEND_VECTOR_SOURCES.includes(settings.embeddingBackendSource)
const source = BACKEND_VECTOR_SOURCES.includes(
settings.embeddingBackendSource,
)
? settings.embeddingBackendSource
: "openai";
@@ -149,6 +177,7 @@ export function getVectorConfigFromSettings(settings = {}) {
settings.embeddingBackendModel || BACKEND_DEFAULT_MODELS[source] || "",
).trim(),
autoSuffix,
timeoutMs: getConfiguredTimeoutMs(settings),
};
}
@@ -202,10 +231,7 @@ export function validateVectorConfig(config) {
return { valid: false, error: "请填写后端向量模型" };
}
if (
BACKEND_SOURCES_REQUIRING_API_URL.has(config.source) &&
!config.apiUrl
) {
if (BACKEND_SOURCES_REQUIRING_API_URL.has(config.source) && !config.apiUrl) {
return { valid: false, error: "当前后端向量源需要填写 API 地址" };
}
@@ -319,7 +345,9 @@ function buildDesiredVectorEntries(graph, config, range = null) {
function computeVectorStats(graph, desiredEntries) {
const state = graph.vectorIndexState || {};
const desiredByNodeId = new Map(desiredEntries.map((entry) => [entry.nodeId, entry]));
const desiredByNodeId = new Map(
desiredEntries.map((entry) => [entry.nodeId, entry]),
);
const nodeToHash = state.nodeToHash || {};
const hashToNodeId = state.hashToNodeId || {};
@@ -352,12 +380,16 @@ function computeVectorStats(graph, desiredEntries) {
async function purgeVectorCollection(collectionId, signal) {
throwIfAborted(signal);
const response = await fetchWithTimeout("/api/vector/purge", {
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({ collectionId }),
});
const response = await fetchWithTimeout(
"/api/vector/purge",
{
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({ collectionId }),
},
getConfiguredTimeoutMs(),
);
if (!response.ok) {
const message = await response.text().catch(() => response.statusText);
@@ -369,16 +401,20 @@ async function deleteVectorHashes(collectionId, config, hashes, signal) {
if (!Array.isArray(hashes) || hashes.length === 0) return;
throwIfAborted(signal);
const response = await fetchWithTimeout("/api/vector/delete", {
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId,
hashes,
...buildBackendSourceRequest(config),
}),
});
const response = await fetchWithTimeout(
"/api/vector/delete",
{
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId,
hashes,
...buildBackendSourceRequest(config),
}),
},
getConfiguredTimeoutMs(config),
);
if (!response.ok) {
const message = await response.text().catch(() => response.statusText);
@@ -400,20 +436,24 @@ async function insertVectorEntries(collectionId, config, entries, signal) {
if (!Array.isArray(entries) || entries.length === 0) return;
throwIfAborted(signal);
const response = await fetchWithTimeout("/api/vector/insert", {
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId,
items: entries.map((entry) => ({
hash: entry.hash,
text: entry.text,
index: entry.index,
})),
...buildBackendSourceRequest(config),
}),
});
const response = await fetchWithTimeout(
"/api/vector/insert",
{
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId,
items: entries.map((entry) => ({
hash: entry.hash,
text: entry.text,
index: entry.index,
})),
...buildBackendSourceRequest(config),
}),
},
getConfiguredTimeoutMs(config),
);
if (!response.ok) {
const message = await response.text().catch(() => response.statusText);
@@ -442,7 +482,10 @@ export async function syncGraphVectorIndex(
} = {},
) {
if (!graph || !config) {
return { insertedHashes: [], stats: { total: 0, indexed: 0, stale: 0, pending: 0 } };
return {
insertedHashes: [],
stats: { total: 0, indexed: 0, stale: 0, pending: 0 },
};
}
throwIfAborted(signal);
@@ -454,14 +497,16 @@ export async function syncGraphVectorIndex(
}
const state = graph.vectorIndexState;
const collectionId = buildVectorCollectionId(chatId || graph?.historyState?.chatId);
const collectionId = buildVectorCollectionId(
chatId || graph?.historyState?.chatId,
);
const desiredEntries = buildDesiredVectorEntries(graph, config, range);
const desiredByNodeId = new Map(desiredEntries.map((entry) => [entry.nodeId, entry]));
const desiredByNodeId = new Map(
desiredEntries.map((entry) => [entry.nodeId, entry]),
);
const insertedHashes = [];
const hasConcreteRange =
range &&
Number.isFinite(range.start) &&
Number.isFinite(range.end);
range && Number.isFinite(range.start) && Number.isFinite(range.end);
const rangedNodeIds = new Set(desiredEntries.map((entry) => entry.nodeId));
if (isBackendVectorConfig(config)) {
@@ -470,7 +515,8 @@ export async function syncGraphVectorIndex(
state.source !== config.source ||
state.modelScope !== getVectorModelScope(config) ||
state.collectionId !== collectionId;
const fullReset = purge || state.dirty || scopeChanged || (force && !hasConcreteRange);
const fullReset =
purge || state.dirty || scopeChanged || (force && !hasConcreteRange);
if (fullReset) {
await purgeVectorCollection(collectionId, signal);
@@ -537,8 +583,11 @@ export async function syncGraphVectorIndex(
for (const entry of desiredEntries) {
hashByNodeId[entry.nodeId] = entry.hash;
const currentHash = state.nodeToHash?.[entry.nodeId];
const node = graph.nodes.find((candidate) => candidate.id === entry.nodeId);
const hasEmbedding = Array.isArray(node?.embedding) && node.embedding.length > 0;
const node = graph.nodes.find(
(candidate) => candidate.id === entry.nodeId,
);
const hasEmbedding =
Array.isArray(node?.embedding) && node.embedding.length > 0;
if (!force && !currentHash && hasEmbedding) {
state.hashToNodeId[entry.hash] = entry.nodeId;
@@ -575,7 +624,9 @@ export async function syncGraphVectorIndex(
for (let index = 0; index < entriesToEmbed.length; index++) {
const entry = entriesToEmbed[index];
const node = graph.nodes.find((candidate) => candidate.id === entry.nodeId);
const node = graph.nodes.find(
(candidate) => candidate.id === entry.nodeId,
);
if (!node) continue;
if (embeddings[index]) {
@@ -596,7 +647,10 @@ export async function syncGraphVectorIndex(
state.dirty = false;
state.lastWarning = "";
state.lastSyncAt = Date.now();
state.lastStats = computeVectorStats(graph, buildDesiredVectorEntries(graph, config));
state.lastStats = computeVectorStats(
graph,
buildDesiredVectorEntries(graph, config),
);
return {
insertedHashes,
@@ -628,7 +682,9 @@ export async function findSimilarNodesByText(
return searchSimilar(
queryVec,
candidateNodes
.filter((node) => Array.isArray(node.embedding) && node.embedding.length > 0)
.filter(
(node) => Array.isArray(node.embedding) && node.embedding.length > 0,
)
.map((node) => ({
nodeId: node.id,
embedding: node.embedding,
@@ -640,18 +696,22 @@ export async function findSimilarNodesByText(
const validation = validateVectorConfig(config);
if (!validation.valid) return [];
const response = await fetchWithTimeout("/api/vector/query", {
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId: graph.vectorIndexState.collectionId,
searchText: text,
topK,
threshold: 0,
...buildBackendSourceRequest(config),
}),
});
const response = await fetchWithTimeout(
"/api/vector/query",
{
method: "POST",
headers: getRequestHeaders(),
signal,
body: JSON.stringify({
collectionId: graph.vectorIndexState.collectionId,
searchText: text,
topK,
threshold: 0,
...buildBackendSourceRequest(config),
}),
},
getConfiguredTimeoutMs(config),
);
if (!response.ok) {
const errorText = await response.text().catch(() => response.statusText);
@@ -692,17 +752,21 @@ export async function testVectorConnection(config, chatId = "connection-test") {
}
try {
const response = await fetchWithTimeout("/api/vector/query", {
method: "POST",
headers: getRequestHeaders(),
body: JSON.stringify({
collectionId: buildVectorCollectionId(chatId),
searchText: "test connection",
topK: 1,
threshold: 0,
...buildBackendSourceRequest(config),
}),
});
const response = await fetchWithTimeout(
"/api/vector/query",
{
method: "POST",
headers: getRequestHeaders(),
body: JSON.stringify({
collectionId: buildVectorCollectionId(chatId),
searchText: "test connection",
topK: 1,
threshold: 0,
...buildBackendSourceRequest(config),
}),
},
getConfiguredTimeoutMs(config),
);
const payload = await response.text().catch(() => "");
if (!response.ok) {
@@ -739,8 +803,12 @@ function normalizeModelOptions(items = [], { embeddingOnly = false } = {}) {
}
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();
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 (
@@ -756,7 +824,9 @@ function normalizeModelOptions(items = [], { embeddingOnly = false } = {}) {
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 embeddingTagged = candidates.filter(
(item) => embeddingRegex.test(item.id) || embeddingRegex.test(item.label),
);
const source = embeddingTagged.length > 0 ? embeddingTagged : candidates;
const seen = new Set();
@@ -793,22 +863,30 @@ async function fetchBackendStatusModelList(source) {
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 fetchWithTimeout("/api/backends/chat-completions/status", {
method: "POST",
headers: getRequestHeaders(),
body: JSON.stringify({
chat_completion_source: chatCompletionSource,
}),
});
const response = await fetchWithTimeout(
"/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}`,
payload?.message ||
payload?.error ||
response.statusText ||
`HTTP ${response.status}`,
);
}
return normalizeModelOptions(payload?.data || payload, { embeddingOnly: false });
return normalizeModelOptions(payload?.data || payload, {
embeddingOnly: false,
});
}
async function fetchOpenAICompatibleModelList(apiUrl, apiKey = "") {
@@ -826,19 +904,28 @@ async function fetchOpenAICompatibleModelList(apiUrl, apiKey = "") {
const payload = await response.json().catch(() => ({}));
if (!response.ok) {
throw new Error(payload?.error?.message || payload?.message || response.statusText);
throw new Error(
payload?.error?.message || payload?.message || response.statusText,
);
}
return normalizeModelOptions(payload?.data || payload, { embeddingOnly: false });
return normalizeModelOptions(payload?.data || payload, {
embeddingOnly: false,
});
}
async function fetchOllamaModelList(apiUrl) {
const normalizedUrl = normalizeOpenAICompatibleBaseUrl(apiUrl).replace(/\/v1$/i, "");
const normalizedUrl = normalizeOpenAICompatibleBaseUrl(apiUrl).replace(
/\/v1$/i,
"",
);
if (!normalizedUrl) {
throw new Error("请先填写 Ollama API 地址");
}
const response = await fetchWithTimeout(`${normalizedUrl}/api/tags`, { method: "GET" });
const response = await fetchWithTimeout(`${normalizedUrl}/api/tags`, {
method: "GET",
});
const payload = await response.json().catch(() => ({}));
if (!response.ok) {
throw new Error(payload?.error || payload?.message || response.statusText);
@@ -867,7 +954,11 @@ export async function fetchAvailableEmbeddingModels(config) {
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: false,
models: [],
error: "未拉取到可用 Embedding 模型",
};
}
return { success: true, models, error: "" };
}
@@ -875,18 +966,28 @@ export async function fetchAvailableEmbeddingModels(config) {
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: 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 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: false,
models: [],
error: "未拉取到可用 Embedding 模型",
};
}
return { success: true, models, error: "" };
}
@@ -894,7 +995,11 @@ export async function fetchAvailableEmbeddingModels(config) {
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: false,
models: [],
error: "未拉取到可用 Embedding 模型",
};
}
return { success: true, models, error: "" };
}
@@ -904,7 +1009,11 @@ export async function fetchAvailableEmbeddingModels(config) {
await fetchOpenAICompatibleModelList(config.apiUrl),
);
if (models.length === 0) {
return { success: false, models: [], error: "未拉取到可用 Embedding 模型" };
return {
success: false,
models: [],
error: "未拉取到可用 Embedding 模型",
};
}
return { success: true, models, error: "" };
}