feat: enhance local knowledge retrieval

2026-08-12 21:45:47 +08:00
parent e0e5a8c1b3
commit 111f487e20
65 changed files with 13517 additions and 1155 deletions
@@ -0,0 +1,71 @@
+import { mkdtemp, rm, writeFile } from 'node:fs/promises'
+import { tmpdir } from 'node:os'
+import { join } from 'node:path'
+import { describe, expect, it } from 'vitest'
+import { KnowledgeService } from './knowledge-service'
+import { OpenAIEmbeddingClient } from './openai-embedding-client'
+
+const endpoint =
+  process.env.GOODBUDDY_LIVE_EMBEDDING_ENDPOINT?.trim()
+const model = process.env.GOODBUDDY_LIVE_EMBEDDING_MODEL?.trim()
+const liveIt = endpoint && model ? it : it.skip
+
+describe('live knowledge embeddings', () => {
+  liveIt(
+    'uses the configured provider for indexing and semantic retrieval',
+    async () => {
+      const directory = await mkdtemp(
+        join(tmpdir(), 'goodbuddy-live-embedding-')
+      )
+      const client = new OpenAIEmbeddingClient({
+        endpoint: endpoint!,
+        model: model!,
+        apiKey:
+          process.env.GOODBUDDY_LIVE_EMBEDDING_API_KEY,
+        batchSize: 8,
+        timeoutMs: 60_000
+      })
+      const service = new KnowledgeService({
+        databasePath: join(directory, 'knowledge.sqlite'),
+        managedRoot: join(directory, 'managed'),
+        embeddingProvider: client
+      })
+      try {
+        await service.initialize()
+        const sourcePath = join(directory, 'offline-guide.txt')
+        await writeFile(
+          sourcePath,
+          '在没有网络的环境中，先准备经过校验的安装包，再导入本地部署。',
+          'utf8'
+        )
+        const library = service.createLibrary({
+          name: 'Live embedding test',
+          storageMode: 'reference',
+          graphEnabled: false
+        })
+        await service.importPaths(library.id, [sourcePath])
+
+        const response = await service.retrieve({
+          knowledgeBaseId: library.id,
+          query: '断网时怎样安装软件？',
+          settings: {
+            ...library.retrievalSettings,
+            ftsWeight: 0,
+            vectorWeight: 1,
+            graphWeight: 0,
+            minimumVectorSimilarity: 0
+          }
+        })
+        expect(response.diagnostics.vectorScannedCount).toBeGreaterThan(0)
+        expect(response.results[0]?.channels).toContain('vector')
+        expect(response.results[0]?.documentTitle).toBe(
+          'offline-guide'
+        )
+      } finally {
+        await service.dispose()
+        await rm(directory, { recursive: true, force: true })
+      }
+    },
+    180_000
+  )
+})