feat(server): improve context management (#15448)

#### PR Dependency Tree * **PR #15448** 👈 This tree was auto-generated by [Charcoal](https://github.com/danerwilliams/charcoal)  ## Summary by CodeRabbit * **New Features** * Added workspace artifact upload, browsing, removal, deduplication, and library ownership support. * Copilot now supports scoped document and artifact search, canvas reading, live editor context, and frontend tools. * Added scope and focus selectors with source-resolution receipts in chat. * Added embedding health, progress, synchronization, and retrieval capabilities. * Added BYOK policy visibility, provider restrictions, endpoint dialect selection, and validation. * Added delegated editor interactions and userdata document authorization. * **Bug Fixes** * Improved attachment handling, cancellation, access control, retrieval fallbacks, workspace synchronization, and configuration validation.
2026-08-19 02:51:47 +08:00 · 2026-08-10 09:27:58 +08:00
parent 42322d13fe
commit ee899a267b
311 changed files with 20468 additions and 14806 deletions
@@ -0,0 +1,201 @@
+import { Injectable, OnApplicationBootstrap } from '@nestjs/common';
+import { nanoid } from 'nanoid';
+
+import { metrics } from '../../../base';
+import { BackendRuntimeProvider } from '../../../core/backend-runtime';
+import type { DocChunkSimilarity } from '../../../models';
+import type {
+  RuntimeEmbeddingCandidate,
+  RuntimeRetrievalScope,
+} from '../../../native';
+import { CopilotRerankService } from './rerank';
+import type { EmbeddingRouteContext } from './route-context';
+
+@Injectable()
+export class NativeEmbeddingService implements OnApplicationBootstrap {
+  private supportEmbedding = false;
+
+  constructor(
+    private readonly runtime: BackendRuntimeProvider,
+    private readonly rerank: CopilotRerankService
+  ) {}
+
+  async onApplicationBootstrap() {
+    this.supportEmbedding = (await this.health()).enabled;
+  }
+
+  get canEmbedding() {
+    return this.supportEmbedding;
+  }
+
+  async health() {
+    const health = await this.runtime.embeddingHealth();
+    metrics.ai.counter('embedding_capability_check').add(1, {
+      state: health.state,
+      enabled: health.enabled,
+      reason: health.reason ?? 'none',
+      schema: String(health.schemaVersion ?? 0),
+      worker: health.workerRunning ? 'running' : 'stopped',
+    });
+    return health;
+  }
+
+  async progress(workspaceId: string) {
+    return await this.runtime.embeddingWorkspaceProgress(workspaceId);
+  }
+
+  async readSourceContent(
+    workspaceId: string,
+    sourceKind: 'document' | 'artifact',
+    sourceKey: string,
+    retrieval: RuntimeRetrievalScope,
+    maxChars?: number,
+    cursor?: string
+  ) {
+    return await this.runtime.readEmbeddingSourceContent({
+      workspaceId,
+      sourceKind,
+      sourceKey,
+      retrieval,
+      maxChars,
+      cursor,
+    });
+  }
+
+  async match(
+    workspaceId: string,
+    query: string,
+    sourceKind: 'document' | 'artifact',
+    retrieval: RuntimeRetrievalScope,
+    limit: number,
+    signal?: AbortSignal
+  ): Promise<RuntimeEmbeddingCandidate[]> {
+    const startedAt = performance.now();
+    signal?.throwIfAborted();
+    const requestId = nanoid();
+    const abort = () => {
+      void this.runtime
+        .cancelEmbeddingCandidateRequest(requestId)
+        .catch(() => {});
+    };
+    signal?.addEventListener('abort', abort, { once: true });
+    try {
+      const candidates = await this.runtime.matchEmbeddingCandidates({
+        requestId,
+        workspaceId,
+        query,
+        sourceKind,
+        retrieval,
+        limit,
+      });
+      signal?.throwIfAborted();
+      metrics.ai
+        .histogram('embedding_candidate_latency_ms')
+        .record(performance.now() - startedAt, {
+          corpus: sourceKind,
+          mode: retrieval.mode,
+          outcome: 'success',
+        });
+      return candidates;
+    } catch (error) {
+      metrics.ai.counter('embedding_operation_failure').add(1, {
+        operation: 'match',
+        kind: sourceKind,
+        code: embeddingErrorCode(error),
+      });
+      throw error;
+    } finally {
+      signal?.removeEventListener('abort', abort);
+    }
+  }
+
+  async matchWorkspaceDocCandidates(
+    workspaceId: string,
+    content: string,
+    topK = 5,
+    docIds?: string[]
+  ): Promise<DocChunkSimilarity[]> {
+    const retrieval: RuntimeRetrievalScope = {
+      mode: docIds ? 'required' : 'workspace',
+      requiredDocIds: docIds ?? [],
+      requiredArtifactIds: [],
+      preferredSourceIds: [],
+    };
+    return (
+      await this.match(workspaceId, content, 'document', retrieval, topK * 2)
+    )
+      .filter(candidate => candidate.docId)
+      .map(candidate => ({
+        docId: candidate.docId as string,
+        chunk: candidate.chunk,
+        content: candidate.content,
+        distance: candidate.distance,
+        unitId: candidate.unitId ?? '',
+        visibility: (candidate.visibility ?? 'page') as
+          | 'page'
+          | 'edgeless'
+          | 'both',
+        blockId: candidate.blockId ?? undefined,
+        elementId: candidate.elementId ?? undefined,
+        frameId: candidate.frameId ?? undefined,
+      }));
+  }
+
+  async rerankWorkspaceDocs(
+    workspaceId: string,
+    content: string,
+    candidates: DocChunkSimilarity[],
+    topK = 5,
+    routeContext?: EmbeddingRouteContext
+  ) {
+    if (!candidates.length) return [];
+    return await this.rerank.rerank(
+      content,
+      candidates,
+      topK,
+      workspaceId,
+      routeContext
+    );
+  }
+
+  async recordQueueCounts() {
+    const counts = await this.runtime.embeddingQueueCounts();
+    for (const status of [
+      'pending',
+      'running',
+      'retryWait',
+      'ready',
+      'failed',
+    ] as const) {
+      metrics.ai
+        .gauge('embedding_queue_status')
+        .record(Number(counts[status]), { status });
+    }
+    metrics.ai
+      .gauge('embedding_vector_rows')
+      .record(Number(counts.activeVectorRows), { state: 'active' });
+    metrics.ai
+      .gauge('embedding_vector_rows')
+      .record(Number(counts.inactiveVectorRows), { state: 'inactive' });
+    metrics.ai
+      .gauge('embedding_index_size_bytes')
+      .record(Number(counts.indexBytes));
+    metrics.ai
+      .gauge('embedding_index_retry')
+      .record(Number(counts.retryingIndexes), { measure: 'indexes' });
+    metrics.ai
+      .gauge('embedding_index_retry')
+      .record(Number(counts.maxIndexRetrySeconds), {
+        measure: 'max_delay_seconds',
+      });
+  }
+}
+
+function embeddingErrorCode(error: unknown) {
+  if (!(error instanceof Error)) return 'unknown';
+  if (error.message.includes('resource_exceeded')) return 'resource_exceeded';
+  if (error.message.includes('embedding_unavailable')) return 'unavailable';
+  if (error.message.includes('not_found')) return 'not_found';
+  if (error.message.includes('disabled')) return 'disabled';
+  return 'failed';
+}