Vector

npm install @upstash/vector
# 或
pip install upstash-vector

import { Index } from '@upstash/vector'

const index = new Index({
  url: 'UPSTASH_VECTOR_REST_URL',
  token: 'UPSTASH_VECTOR_REST_TOKEN',
})

// 插入向量（文本自动嵌入）
await index.upsert([{
  id: 'doc1',
  data: 'Paris is the capital of France.',
  metadata: { country: 'France' }
}])

// 或提供向量
await index.upsert([{
  id: 'doc2',
  vector: [0.1, 0.2, 0.3, ...],
  metadata: { country: 'Japan' }
}])

// 文本搜索（自动嵌入）
const results = await index.query('capital city', {
  topK: 5,
  includeMetadata: true,
})

// 向量搜索
const results = await index.query(
  [0.1, 0.2, 0.3, ...],
  { topK: 10 }
)

// 带过滤
const results = await index.query('capital', {
  topK: 5,
  filter: 'country = "France"',
})

import { Index } from '@upstash/vector'
import OpenAI from 'openai'

const index = new Index({ url, token })
const openai = new OpenAI()

// 1. 索引文档
async function indexDocument(text, id) {
  await index.upsert([{
    id,
    data: text,
    metadata: { text }
  }])
}

// 2. RAG 查询
async function ragQuery(question) {
  // 搜索相关文档
  const results = await index.query(question, { topK: 3 })
  const context = results.map(r => r.metadata?.text).join('\n\n')
  
  // 调用 LLM
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [
      { role: 'system', content: 'Use the context to answer.' },
      { role: 'user', content: `Context: ${context}\n\nQuestion: ${question}` }
    ]
  })
  
  return response.choices[0].message.content
}