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91 lines
2.5 KiB
Markdown
91 lines
2.5 KiB
Markdown
# Vectorize Patterns
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## Workers AI Integration
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```typescript
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// Generate embedding + query
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const result = await env.AI.run("@cf/baai/bge-base-en-v1.5", { text: [query] });
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const matches = await env.VECTORIZE.query(result.data[0], { topK: 5 }); // Pass data[0]!
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```
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| Model | Dimensions |
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|-------|------------|
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| `@cf/baai/bge-small-en-v1.5` | 384 |
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| `@cf/baai/bge-base-en-v1.5` | 768 (recommended) |
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| `@cf/baai/bge-large-en-v1.5` | 1024 |
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## OpenAI Integration
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```typescript
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const response = await openai.embeddings.create({ model: "text-embedding-ada-002", input: query });
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const matches = await env.VECTORIZE.query(response.data[0].embedding, { topK: 5 });
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```
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## RAG Pattern
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```typescript
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// 1. Embed query
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const emb = await env.AI.run("@cf/baai/bge-base-en-v1.5", { text: [query] });
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// 2. Search vectors
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const matches = await env.VECTORIZE.query(emb.data[0], { topK: 5, returnMetadata: "indexed" });
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// 3. Fetch full docs from R2/D1/KV
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const docs = await Promise.all(matches.matches.map(m => env.R2.get(m.metadata.key).then(o => o?.text())));
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// 4. Generate with context
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const answer = await env.AI.run("@cf/meta/llama-3-8b-instruct", {
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prompt: `Context:\n${docs.filter(Boolean).join("\n\n")}\n\nQuestion: ${query}\n\nAnswer:`
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});
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```
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## Multi-Tenant
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### Namespaces (< 50K tenants, fastest)
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```typescript
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await env.VECTORIZE.upsert([{ id: "1", values: emb, namespace: `tenant-${id}` }]);
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await env.VECTORIZE.query(vec, { namespace: `tenant-${id}`, topK: 10 });
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```
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### Metadata Filter (> 50K tenants)
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```bash
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wrangler vectorize create-metadata-index my-index --property-name=tenantId --type=string
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```
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```typescript
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await env.VECTORIZE.upsert([{ id: "1", values: emb, metadata: { tenantId: id } }]);
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await env.VECTORIZE.query(vec, { filter: { tenantId: id }, topK: 10 });
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```
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## Hybrid Search
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```typescript
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const matches = await env.VECTORIZE.query(vec, {
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topK: 20,
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filter: {
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category: { $in: ["tech", "science"] },
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published: { $gte: lastMonthTimestamp }
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}
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});
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```
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## Batch Ingestion
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```typescript
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const BATCH = 500;
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for (let i = 0; i < vectors.length; i += BATCH) {
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await env.VECTORIZE.upsert(vectors.slice(i, i + BATCH));
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}
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```
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## Best Practices
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1. **Pass `data[0]`** not `data` or full response
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2. **Batch 500** vectors per upsert
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3. **Create metadata indexes** before inserting
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4. **Use namespaces** for tenant isolation (faster than filters)
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5. **`returnMetadata: "indexed"`** for best speed/data balance
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6. **Handle 5-10s mutation delay** in async operations
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