72 lines
2.3 KiB
TypeScript
72 lines
2.3 KiB
TypeScript
import { mkdtemp, rm, writeFile } from 'node:fs/promises'
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import { tmpdir } from 'node:os'
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import { join } from 'node:path'
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import { describe, expect, it } from 'vitest'
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import { KnowledgeService } from './knowledge-service'
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import { OpenAIEmbeddingClient } from './openai-embedding-client'
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const endpoint =
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process.env.GOODBUDDY_LIVE_EMBEDDING_ENDPOINT?.trim()
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const model = process.env.GOODBUDDY_LIVE_EMBEDDING_MODEL?.trim()
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const liveIt = endpoint && model ? it : it.skip
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describe('live knowledge embeddings', () => {
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liveIt(
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'uses the configured provider for indexing and semantic retrieval',
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async () => {
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const directory = await mkdtemp(
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join(tmpdir(), 'goodbuddy-live-embedding-')
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)
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const client = new OpenAIEmbeddingClient({
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endpoint: endpoint!,
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model: model!,
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apiKey:
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process.env.GOODBUDDY_LIVE_EMBEDDING_API_KEY,
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batchSize: 8,
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timeoutMs: 60_000
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})
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const service = new KnowledgeService({
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databasePath: join(directory, 'knowledge.sqlite'),
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managedRoot: join(directory, 'managed'),
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embeddingProvider: client
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})
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try {
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await service.initialize()
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const sourcePath = join(directory, 'offline-guide.txt')
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await writeFile(
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sourcePath,
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'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。',
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'utf8'
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)
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const library = service.createLibrary({
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name: 'Live embedding test',
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storageMode: 'reference',
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graphEnabled: false
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})
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await service.importPaths(library.id, [sourcePath])
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const response = await service.retrieve({
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knowledgeBaseId: library.id,
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query: '断网时怎样安装软件？',
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settings: {
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...library.retrievalSettings,
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ftsWeight: 0,
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vectorWeight: 1,
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graphWeight: 0,
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minimumVectorSimilarity: 0
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}
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})
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expect(response.diagnostics.vectorScannedCount).toBeGreaterThan(0)
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expect(response.results[0]?.channels).toContain('vector')
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expect(response.results[0]?.documentTitle).toBe(
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'offline-guide'
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)
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} finally {
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await service.dispose()
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await rm(directory, { recursive: true, force: true })
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}
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},
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180_000
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)
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})
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