mirror of
https://github.com/Wx-2025/ST-Amily2-Chat-Optimisation.git
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0e11f85031
### 新功能 - Function Call 填表开关下方新增公益站风险提示横幅：部分公益站会屏蔽 tools 参数，请确认支持情况避免被意外封禁 ### 修复 - **Function Call 填表**： - 修复 ST 代理以 HTTP 200 + error body 形式返回错误、导致降级重试机制从未触发的问题 - 修复思考模式模型（如 DeepSeek v4-flash）因 tool_choice 不兼容返回 Bad Request 后正确降级并重试 - 重试时自动追加强制调用指令，防止思考模型绕过工具直接输出文本造成无效二次开销 - **超级记忆 / 翰林院**： - 修复 `getRagSettings()` 读写顶层路径而非嵌套路径，导致打开超级记忆面板后向量化、归档等开关在重载时被全默认值覆盖的问题 - 修复自动归档失效问题 - 修复归档管理器在同一事件中被三次触发的回归问题 - 修复翰林院设置旧版迁移逻辑异常
430 lines
15 KiB
JavaScript
430 lines
15 KiB
JavaScript
'use strict';
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import {
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extension_settings
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} from '/scripts/extensions.js';
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import {
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buildGoogleEmbeddingRequest,
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parseGoogleEmbeddingResponse,
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buildGoogleEmbeddingApiUrl
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} from './utils/googleAdapter.js';
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import { getSlotProfile } from './api/api-resolver.js';
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import { extensionName } from '../utils/settings.js';
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const MODULE_NAME = 'hanlinyuan-rag-core';
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const GOOGLE_API_BASE_URL = 'https://generativelanguage.googleapis.com';
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function getSettings() {
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const root = extension_settings[extensionName];
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const nested = root && root[MODULE_NAME];
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if (nested) return nested;
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// 读侧兼容：若迁移尚未触发（极早期调用），回退至旧顶层位置，避免空配置。
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const legacy = extension_settings[MODULE_NAME];
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if (legacy) return legacy;
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console.error('[翰林院-API] 无法获取设置，API调用可能失败。');
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return { retrieval: {}, rerank: {} };
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}
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/**
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* 获取 Embedding 配置，优先从 ragEmbed 槽位 Profile 读取。
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* Profile 存在时映射为 custom endpoint，覆盖旧 settings。
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*/
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export async function getEmbedRetrievalSettings() {
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const profile = await getSlotProfile('ragEmbed');
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if (profile) {
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return {
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apiEndpoint: profile.provider === 'google' ? 'google_direct' : 'custom',
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customApiUrl: profile.apiUrl,
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apiKey: profile.apiKey ?? '',
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embeddingModel: profile.model,
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batchSize: getSettings().retrieval?.batchSize ?? 5,
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};
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}
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return getSettings().retrieval || {};
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}
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/**
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* 获取 Rerank 配置，优先从 ragRerank 槽位 Profile 读取。
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*/
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export async function getRerankSettings() {
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const profile = await getSlotProfile('ragRerank');
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if (profile) {
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const manualSettings = getSettings().rerank || {};
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return {
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url: profile.apiUrl,
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apiKey: profile.apiKey ?? '',
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model: profile.model,
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top_n: manualSettings.top_n ?? 10,
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apiMode: manualSettings.apiMode ?? 'custom',
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};
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}
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return getSettings().rerank || {};
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}
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function normalizeApiResponse(responseData) {
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let data = responseData;
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if (typeof data === 'string') {
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try {
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data = JSON.parse(data);
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} catch (e) {
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console.error(`[翰林院-API] API响应JSON解析失败:`, e);
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return { error: { message: 'Invalid JSON response' } };
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}
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}
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if (data && typeof data.data === 'object' && data.data !== null && !Array.isArray(data.data)) {
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if (Object.hasOwn(data.data, 'data')) {
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data = data.data;
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}
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}
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if (data && data.data) { // for /v1/models
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return { data: data.data };
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}
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if (data && data.error) {
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return { error: data.error };
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}
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return data;
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}
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export function getSanitizedBaseUrl(rawApiUrl) {
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let baseUrl = rawApiUrl.trim();
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if (baseUrl.endsWith('/')) {
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baseUrl = baseUrl.slice(0, -1);
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}
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if (baseUrl.endsWith('/v1')) {
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baseUrl = baseUrl.slice(0, -3);
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}
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// 兼容处理 /embeddings
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if (baseUrl.endsWith('/embeddings')) {
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baseUrl = baseUrl.slice(0, -11);
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}
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return baseUrl;
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}
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export async function fetchEmbeddingModels(overrideSettings = null) {
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const settings = overrideSettings || await getEmbedRetrievalSettings();
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const { apiEndpoint, apiKey, customApiUrl } = settings;
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let modelsUrl;
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let headers = {};
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let responseParser;
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switch (apiEndpoint) {
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case 'google_direct':
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if (!apiKey) throw new Error("Google直连模式需要API Key。");
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const fetchGoogleModels = async (version) => {
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const url = `${GOOGLE_API_BASE_URL}/${version}/models`;
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console.log(`[翰林院] 正在从 Google API (${version}) 获取模型列表: ${url}`);
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const response = await fetch(url, {
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headers: { 'x-goog-api-key': apiKey },
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});
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if (!response.ok) {
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console.warn(`获取 Google API (${version}) 模型列表失败: ${response.status}`);
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return [];
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}
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const json = await response.json();
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if (!json.models || !Array.isArray(json.models)) {
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return [];
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}
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return json.models
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.filter(model => model.supportedGenerationMethods?.includes('embedContent') || model.supportedGenerationMethods?.includes('batchEmbedContents'))
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.map(model => model.name.replace('models/', ''));
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};
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const [v1Models, v1betaModels] = await Promise.all([
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fetchGoogleModels('v1'),
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fetchGoogleModels('v1beta')
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]);
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const allModels = [...new Set([...v1Models, ...v1betaModels])].sort();
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return allModels;
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case 'custom':
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if (!customApiUrl) throw new Error("自定义模式需要API URL。");
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if (!apiKey) throw new Error("自定义模式需要API Key。");
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const customBaseUrl = getSanitizedBaseUrl(customApiUrl);
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modelsUrl = `${customBaseUrl}/v1/models`;
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headers = getApiHeaders(settings); // 这些模式需要认证头
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console.log(`[翰林院] 正在从 ${modelsUrl} 获取模型列表 (需要认证)...`);
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responseParser = (json) => {
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if (!json.data || !Array.isArray(json.data)) {
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throw new Error("模型API的响应格式无效: 未找到 'data' 数组。");
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}
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return json.data.map(m => m.id).sort();
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};
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// 对于custom模式，需要继续执行下面的fetch
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break;
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case 'local_proxy':
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default:
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if (!customApiUrl) throw new Error("本地代理模式需要API URL。");
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const proxyBaseUrl = getSanitizedBaseUrl(customApiUrl);
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modelsUrl = `${proxyBaseUrl}/v1/models`;
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// 本地代理通常不需要认证头
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headers = { 'Content-Type': 'application/json' };
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console.log(`[翰林院] 正在从 ${modelsUrl} 获取模型列表 (无需认证)...`);
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responseParser = (json) => {
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if (!json.data || !Array.isArray(json.data)) {
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throw new Error("模型API的响应格式无效: 未找到 'data' 数组。");
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}
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return json.data.map(m => m.id).sort();
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};
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break;
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}
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// 注意：st_backend case 和 google_direct case 已经提前返回，不会执行到这里
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if (!modelsUrl) {
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// 这个分支理论上不应该被执行，因为所有case都处理了
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throw new Error('无法确定获取模型的有效路径。');
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}
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const response = await fetch(modelsUrl, {
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method: 'GET',
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headers: headers,
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});
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if (!response.ok) {
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const errorBody = await response.text();
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throw new Error(`获取模型列表失败 (${response.status}): ${errorBody}`);
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}
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const data = await response.json();
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return responseParser(data);
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}
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export function getRerankBaseUrl(rawApiUrl) {
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let baseUrl = rawApiUrl.trim();
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if (baseUrl.endsWith('/')) {
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baseUrl = baseUrl.slice(0, -1);
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}
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if (baseUrl.endsWith('/v1')) {
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baseUrl = baseUrl.slice(0, -3);
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}
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// 兼容处理 /rerank
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if (baseUrl.endsWith('/rerank')) {
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baseUrl = baseUrl.slice(0, -7);
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}
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return baseUrl;
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}
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export async function fetchRerankModels() {
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const settings = await getRerankSettings();
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const { url, apiKey, apiMode = 'custom' } = settings;
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if (!url) {
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throw new Error("Rerank API URL 未提供。");
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}
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if (apiMode === 'custom' && !apiKey) {
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throw new Error("自定义模式下，Rerank API Key 未提供。");
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}
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const baseUrl = getRerankBaseUrl(url);
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const modelsUrl = `${baseUrl}/v1/models`;
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const headers = { 'Content-Type': 'application/json' };
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if (apiMode === 'custom') {
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headers['Authorization'] = `Bearer ${apiKey}`;
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}
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console.log(`[翰林院-Rerank] 正在从 ${modelsUrl} 获取模型列表 (模式: ${apiMode})...`);
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const response = await fetch(modelsUrl, {
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method: 'GET',
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headers: headers,
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});
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if (!response.ok) {
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const errorBody = await response.text();
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throw new Error(`获取Rerank模型列表失败 (${response.status}): ${errorBody}`);
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}
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const data = await response.json();
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if (!data.data || !Array.isArray(data.data)) {
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throw new Error("Rerank模型API的响应格式无效: 未找到 'data' 数组。");
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}
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return data.data
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.map(m => m.id)
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.sort();
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}
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export async function executeRerank(query, documents, rerankSettings = null) {
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const resolved = rerankSettings || await getRerankSettings();
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const { url, apiKey, model, top_n, apiMode = 'custom' } = resolved;
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if (!url) throw new Error("Rerank API URL 未提供。");
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if (apiMode === 'custom' && !apiKey) throw new Error("自定义模式下，Rerank API Key 未提供。");
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const baseUrl = getRerankBaseUrl(url);
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const rerankUrl = `${baseUrl}/v1/rerank`;
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const headers = { 'Content-Type': 'application/json' };
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if (apiMode === 'custom') {
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headers['Authorization'] = `Bearer ${apiKey}`;
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}
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const body = JSON.stringify({
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query: query,
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documents: documents,
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model: model,
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top_n: top_n,
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});
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console.log(`[翰林院-Rerank] 正在向 ${rerankUrl} 发送请求 (模式: ${apiMode})...`);
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const response = await fetch(rerankUrl, {
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method: 'POST',
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headers: headers,
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body: body,
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});
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if (!response.ok) {
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throw new Error(`Rerank API 请求失败 (${response.status}): ${await response.text()}`);
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}
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return await response.json();
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}
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export function getApiEndpointUrl(raw = false, overrideRetrieval = null) {
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const {
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apiEndpoint,
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customApiUrl
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} = overrideRetrieval || getSettings().retrieval;
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let url;
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switch (apiEndpoint) {
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case 'openai':
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url = 'https://api.openai.com';
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break;
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case 'azure':
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case 'custom':
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url = customApiUrl;
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break;
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default:
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url = 'https://api.openai.com';
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break;
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}
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if (raw) {
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return url;
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}
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// 默认情况下，返回拼接好 /v1/embeddings 的完整URL
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return getSanitizedBaseUrl(url) + '/v1/embeddings';
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}
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export function getApiHeaders(overrideRetrieval = null) {
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const headers = {
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'Content-Type': 'application/json'
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};
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const {
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apiKey,
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apiEndpoint
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} = overrideRetrieval || getSettings().retrieval;
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switch (apiEndpoint) {
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case 'openai':
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case 'custom':
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headers['Authorization'] = `Bearer ${apiKey}`;
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break;
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case 'azure':
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headers['api-key'] = apiKey;
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break;
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}
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return headers;
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}
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export async function getEmbeddings(texts, signal = null) {
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const settings = await getEmbedRetrievalSettings();
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const { apiEndpoint, customApiUrl, apiKey, embeddingModel, batchSize = 5 } = settings;
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const allEmbeddings = [];
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for (let i = 0; i < texts.length; i += batchSize) {
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if (signal?.aborted) throw new Error('AbortError');
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const batch = texts.slice(i, i + batchSize);
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let batchEmbeddings = [];
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switch (apiEndpoint) {
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case 'google_direct':
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console.log('[翰林院-API] 使用Google直连模式获取向量。');
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if (!apiKey) throw new Error('Google直连模式需要API Key。');
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// 使用适配器构建URL和请求体；Key 通过 x-goog-api-key 头传递避免 URL 泄露
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const googleUrl = buildGoogleEmbeddingApiUrl(GOOGLE_API_BASE_URL, embeddingModel);
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const googleBody = buildGoogleEmbeddingRequest(batch, embeddingModel);
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console.log(`[翰林院-API] 发送到 Google API 的请求 URL: ${googleUrl}`);
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console.log(`[翰林院-API] 发送到 Google API 的请求体:`, JSON.stringify(googleBody, null, 2));
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const googleResponse = await fetch(googleUrl, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'x-goog-api-key': apiKey,
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},
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body: JSON.stringify(googleBody),
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signal: signal,
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});
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if (!googleResponse.ok) {
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const errorText = await googleResponse.text();
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console.error(`[翰林院-API] Google API 错误响应: ${errorText}`);
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throw new Error(`Google API Error: ${googleResponse.status} ${errorText}`);
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}
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const googleData = await googleResponse.json();
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console.log(`[翰林院-API] 从 Google API 收到的响应:`, JSON.stringify(googleData, null, 2));
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// 使用适配器解析响应
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batchEmbeddings = parseGoogleEmbeddingResponse(googleData, batch);
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break;
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case 'custom':
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case 'local_proxy':
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default:
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console.log(`[翰林院-API] 使用前端直连模式 (${apiEndpoint}) 获取向量。`);
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if (!apiKey && apiEndpoint === 'custom') {
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// 本地代理可以没有key，但自定义通常需要
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// throw new Error('自定义模式需要API Key。');
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}
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const url = getApiEndpointUrl(false, settings); // 使用已解析的 settings
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const headers = getApiHeaders(settings); // 使用已解析的 settings
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const response = await fetch(url, {
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method: 'POST',
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headers,
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body: JSON.stringify({
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input: batch,
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model: embeddingModel
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}),
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signal: signal,
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});
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if (!response.ok) {
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const errorText = await response.text();
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throw new Error(`神力获取失败 ${response.status}: ${errorText}`);
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}
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const result = await response.json();
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if (result.data && Array.isArray(result.data)) {
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batchEmbeddings = result.data.map(item => item.embedding);
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} else {
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throw new Error('API返回的向量数据格式不正确。');
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}
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break;
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}
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if (batchEmbeddings.length !== batch.length) {
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throw new Error('获取到的向量数量与发送的文本数量不匹配。');
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}
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allEmbeddings.push(...batchEmbeddings);
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// 避免速率限制
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if (i + batchSize < texts.length) {
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await new Promise(resolve => setTimeout(resolve, 200));
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}
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}
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return allEmbeddings;
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}
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export async function testApiConnection() {
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await getEmbeddings(['测试连接']);
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}
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