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公明
@@ -2178,11 +2178,27 @@
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"subIndexFilter": "Sub-index filter (optional)",
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"subIndexFilterPlaceholder": "e.g. prod, must match an indexing sub_indexes tag",
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"subIndexFilterHint": "Empty = no filter. When set, only rows whose sub_indexes contain this tag (legacy rows with empty sub_indexes still match).",
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"ragPipelineHeader": "RAG pipeline (MultiQuery + Rerank)",
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"ragPipelineHint": "MultiQuery and rerank are always on: LLM query rewrite → vector prefetch & fusion → HTTP rerank → dedupe & budget truncate.",
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"multiQueryMaxQueries": "MultiQuery rewrite variant limit",
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"multiQueryMaxQueriesPlaceholder": "4",
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"multiQueryMaxQueriesHint": "Max LLM-generated retrieval variants (including paraphrases of the original query). Recommended 3–4, max 8.",
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"rerankProvider": "Rerank provider",
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"rerankProviderAuto": "Auto (infer from Base URL)",
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"rerankProviderCohere": "Cohere-compatible API",
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"rerankProviderHint": "DashScope uses gte-rerank; other compatible endpoints use /v1/rerank. Leave empty to infer from Base URL below.",
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"rerankModel": "Rerank model (optional)",
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"rerankModelPlaceholder": "Empty: DashScope→gte-rerank, Cohere→rerank-multilingual-v3.0",
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"rerankBaseUrl": "Rerank Base URL (optional)",
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"rerankBaseUrlPlaceholder": "Leave empty to reuse embedding / OpenAI base_url",
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"rerankApiKey": "Rerank API Key (optional)",
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"rerankApiKeyPlaceholder": "Leave empty to reuse embedding / OpenAI api_key",
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"rerankApiKeyHint": "On rerank failure, results fall back to fusion order; search still works.",
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"postRetrieveHeader": "Post-retrieval (dedupe / budget)",
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"postRetrieveDedupeAuto": "Results are always deduped by normalized text (whitespace-collapsed bodies). No setting required.",
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"prefetchTopK": "Prefetch candidates (vector stage)",
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"prefetchTopKPlaceholder": "0",
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"prefetchTopKHint": "0 = same as Top-K; larger values fetch more vector hits before dedupe/truncate (max 200).",
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"prefetchTopKPlaceholder": "20",
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"prefetchTopKHint": "Vector candidates per MultiQuery variant; 0 uses built-in max(top_k×4, 20) (max 200).",
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"maxContextChars": "Max returned characters (Unicode)",
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"maxContextCharsPlaceholder": "0",
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"maxContextCharsHint": "0 = unlimited; keeps whole chunks in rank order until the budget is exceeded.",
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@@ -2166,11 +2166,27 @@
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"subIndexFilter": "子索引过滤（可选）",
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"subIndexFilterPlaceholder": "如 prod，与索引 sub_indexes 一致",
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"subIndexFilterHint": "留空不过滤；填写后仅检索向量行 sub_indexes 中含该标签的结果（未打标旧行仍保留）。",
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"ragPipelineHeader": "RAG 管线（MultiQuery + Rerank）",
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"ragPipelineHint": "MultiQuery 与精排始终启用：LLM 改写多路检索 → 向量预取与融合 → HTTP 精排 → 去重与预算截断。",
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"multiQueryMaxQueries": "MultiQuery 改写变体上限",
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"multiQueryMaxQueriesPlaceholder": "4",
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"multiQueryMaxQueriesHint": "LLM 生成的检索变体数量上限（含原问语义覆盖）；建议 3~4，最大 8。",
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"rerankProvider": "精排提供商",
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"rerankProviderAuto": "自动（按 Base URL 推断）",
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"rerankProviderCohere": "Cohere 兼容 API",
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"rerankProviderHint": "DashScope 使用 gte-rerank；其他兼容端点走 /v1/rerank。留空时按下方 Base URL 自动推断。",
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"rerankModel": "精排模型（可选）",
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"rerankModelPlaceholder": "留空：DashScope→gte-rerank，Cohere→rerank-multilingual-v3.0",
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"rerankBaseUrl": "精排 Base URL（可选）",
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"rerankBaseUrlPlaceholder": "留空则复用嵌入 / OpenAI 的 base_url",
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"rerankApiKey": "精排 API Key（可选）",
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"rerankApiKeyPlaceholder": "留空则复用嵌入 / OpenAI 的 api_key",
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"rerankApiKeyHint": "精排失败时自动降级为融合排序，检索仍可用。",
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"postRetrieveHeader": "检索后处理（去重 / 预算）",
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"postRetrieveDedupeAuto": "检索结果会自动按正文规范化去重（合并仅空白不同的重复片段），无需配置。",
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"prefetchTopK": "预取候选数（向量阶段）",
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"prefetchTopKPlaceholder": "0",
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"prefetchTopKHint": "0 表示与 Top-K 相同；大于 Top-K 时先多取候选再经去重/截断回到 Top-K（上限 200）。",
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"prefetchTopKPlaceholder": "20",
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"prefetchTopKHint": "每条 MultiQuery 变体的向量候选数；0 表示内置 max(top_k×4, 20)（上限 200）。",
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"maxContextChars": "返回内容最大字符数（Unicode）",
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"maxContextCharsPlaceholder": "0",
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"maxContextCharsHint": "0 表示不限制；按检索顺序整段保留 chunk，超出则丢弃后续。",
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@@ -389,10 +389,35 @@ async function loadConfig(loadTools = true) {
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subIdxFilterInput.value = knowledge.retrieval?.sub_index_filter || '';
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}
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const mq = knowledge.retrieval?.multi_query || {};
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const mqMaxInput = document.getElementById('knowledge-multi-query-max-queries');
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if (mqMaxInput) {
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const mqVal = parseInt(mq.max_queries, 10);
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mqMaxInput.value = (!isNaN(mqVal) && mqVal > 0) ? mqVal : 4;
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}
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const rr = knowledge.retrieval?.rerank || {};
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const rerankProviderSelect = document.getElementById('knowledge-rerank-provider');
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if (rerankProviderSelect) {
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const p = (rr.provider || '').toLowerCase();
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rerankProviderSelect.value = (p === 'dashscope' || p === 'cohere') ? p : '';
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}
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const rerankModelInput = document.getElementById('knowledge-rerank-model');
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if (rerankModelInput) {
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rerankModelInput.value = rr.model || '';
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}
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const rerankBaseUrlInput = document.getElementById('knowledge-rerank-base-url');
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if (rerankBaseUrlInput) {
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rerankBaseUrlInput.value = rr.base_url || '';
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}
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const rerankApiKeyInput = document.getElementById('knowledge-rerank-api-key');
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if (rerankApiKeyInput) {
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rerankApiKeyInput.value = rr.api_key || '';
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}
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const post = knowledge.retrieval?.post_retrieve || {};
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const prefetchInput = document.getElementById('knowledge-post-retrieve-prefetch-top-k');
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if (prefetchInput) {
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prefetchInput.value = post.prefetch_top_k ?? 0;
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prefetchInput.value = post.prefetch_top_k ?? 20;
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}
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const maxCharsInput = document.getElementById('knowledge-post-retrieve-max-chars');
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if (maxCharsInput) {
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@@ -1273,8 +1298,25 @@ async function applySettings() {
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return isNaN(val) ? 0.7 : val;
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})(),
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sub_index_filter: document.getElementById('knowledge-retrieval-sub-index-filter')?.value?.trim() || '',
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multi_query: {
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max_queries: (() => {
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const v = parseInt(document.getElementById('knowledge-multi-query-max-queries')?.value, 10);
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if (isNaN(v) || v <= 0) return 4;
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return Math.min(8, v);
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})()
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},
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rerank: {
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provider: document.getElementById('knowledge-rerank-provider')?.value?.trim() || '',
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model: document.getElementById('knowledge-rerank-model')?.value?.trim() || '',
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base_url: document.getElementById('knowledge-rerank-base-url')?.value?.trim() || '',
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api_key: document.getElementById('knowledge-rerank-api-key')?.value?.trim() || ''
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},
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post_retrieve: {
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prefetch_top_k: parseInt(document.getElementById('knowledge-post-retrieve-prefetch-top-k')?.value, 10) || 0,
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prefetch_top_k: (() => {
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const raw = document.getElementById('knowledge-post-retrieve-prefetch-top-k')?.value;
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const v = parseInt(raw, 10);
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return isNaN(v) ? 20 : Math.max(0, v);
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})(),
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max_context_chars: parseInt(document.getElementById('knowledge-post-retrieve-max-chars')?.value, 10) || 0,
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max_context_tokens: parseInt(document.getElementById('knowledge-post-retrieve-max-tokens')?.value, 10) || 0
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}
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@@ -3009,14 +3009,46 @@
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<small class="form-hint" data-i18n="settingsBasic.subIndexFilterHint">留空表示不过滤；非空时仅检索 sub_indexes 含该标签的向量行（未打标旧数据仍会命中）。</small>
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</div>
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<div class="settings-subsection-header">
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<h5 data-i18n="settingsBasic.ragPipelineHeader">RAG 管线（MultiQuery + Rerank）</h5>
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</div>
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<p class="form-hint" style="margin: 0 0 12px 0;" data-i18n="settingsBasic.ragPipelineHint">MultiQuery 与精排始终启用：LLM 改写多路检索 → 向量预取与融合 → HTTP 精排 → 去重与预算截断。</p>
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<div class="form-group">
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<label for="knowledge-multi-query-max-queries" data-i18n="settingsBasic.multiQueryMaxQueries">MultiQuery 改写变体上限</label>
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<input type="number" id="knowledge-multi-query-max-queries" min="1" max="8" data-i18n="settingsBasic.multiQueryMaxQueriesPlaceholder" data-i18n-attr="placeholder" placeholder="4" />
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<small class="form-hint" data-i18n="settingsBasic.multiQueryMaxQueriesHint">LLM 生成的检索变体数量上限（含原问语义覆盖）；建议 3~4，最大 8。</small>
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</div>
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<div class="form-group">
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<label for="knowledge-rerank-provider" data-i18n="settingsBasic.rerankProvider">精排提供商</label>
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<select id="knowledge-rerank-provider">
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<option value="" data-i18n="settingsBasic.rerankProviderAuto">自动（按 Base URL 推断）</option>
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<option value="dashscope">DashScope</option>
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<option value="cohere" data-i18n="settingsBasic.rerankProviderCohere">Cohere 兼容 API</option>
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</select>
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<small class="form-hint" data-i18n="settingsBasic.rerankProviderHint">DashScope 使用 gte-rerank；其他兼容端点走 /v1/rerank。留空时按下方 Base URL 自动推断。</small>
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</div>
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<div class="form-group">
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<label for="knowledge-rerank-model" data-i18n="settingsBasic.rerankModel">精排模型（可选）</label>
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<input type="text" id="knowledge-rerank-model" data-i18n="settingsBasic.rerankModelPlaceholder" data-i18n-attr="placeholder" placeholder="留空：DashScope→gte-rerank，Cohere→rerank-multilingual-v3.0" />
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</div>
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<div class="form-group">
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<label for="knowledge-rerank-base-url" data-i18n="settingsBasic.rerankBaseUrl">精排 Base URL（可选）</label>
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<input type="text" id="knowledge-rerank-base-url" data-i18n="settingsBasic.rerankBaseUrlPlaceholder" data-i18n-attr="placeholder" placeholder="留空则复用嵌入 / OpenAI 的 base_url" />
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</div>
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<div class="form-group">
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<label for="knowledge-rerank-api-key" data-i18n="settingsBasic.rerankApiKey">精排 API Key（可选）</label>
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<input type="password" id="knowledge-rerank-api-key" data-i18n="settingsBasic.rerankApiKeyPlaceholder" data-i18n-attr="placeholder" placeholder="留空则复用嵌入 / OpenAI 的 api_key" />
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<small class="form-hint" data-i18n="settingsBasic.rerankApiKeyHint">精排失败时自动降级为融合排序，检索仍可用。</small>
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</div>
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<div class="settings-subsection-header">
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<h5 data-i18n="settingsBasic.postRetrieveHeader">检索后处理（去重 / 预算）</h5>
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</div>
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<p class="form-hint" style="margin: 0 0 12px 0;" data-i18n="settingsBasic.postRetrieveDedupeAuto">检索结果会自动按正文规范化去重（合并仅空白不同的重复片段），无需配置。</p>
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<div class="form-group">
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<label for="knowledge-post-retrieve-prefetch-top-k" data-i18n="settingsBasic.prefetchTopK">预取候选数（向量阶段）</label>
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<input type="number" id="knowledge-post-retrieve-prefetch-top-k" min="0" max="200" data-i18n="settingsBasic.prefetchTopKPlaceholder" data-i18n-attr="placeholder" placeholder="0" />
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<small class="form-hint" data-i18n="settingsBasic.prefetchTopKHint">0 表示与 Top-K 相同；大于 Top-K 时先多取候选再经去重/截断回到 Top-K（上限 200）。</small>
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<input type="number" id="knowledge-post-retrieve-prefetch-top-k" min="0" max="200" data-i18n="settingsBasic.prefetchTopKPlaceholder" data-i18n-attr="placeholder" placeholder="20" />
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<small class="form-hint" data-i18n="settingsBasic.prefetchTopKHint">每条 MultiQuery 变体的向量候选数；0 表示内置 max(top_k×4, 20)（上限 200）。</small>
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</div>
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<div class="form-group">
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<label for="knowledge-post-retrieve-max-chars" data-i18n="settingsBasic.maxContextChars">返回内容最大字符数（Unicode）</label>
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