text stringlengths 0 59.1k |
|---|
- Need quick prototyping |
- Want to avoid cloud costs |
- Have < 1M vectors |
- Need full control over data |
### For Production at Scale |
**Choose Pinecone or Milvus** if you: |
- Have > 10M vectors |
- Need 99.9% uptime SLA |
- Want managed infrastructure |
- Require horizontal scaling |
### For Hybrid Workloads |
**Choose Redis or Weaviate** if you: |
- Already use Redis/have existing infrastructure |
- Need both vector and traditional queries |
- Want multi-modal search capabilities |
- Require sub-10ms latency |
### Cost Considerations |
| Solution | Cost Model | Approximate Monthly Cost (1M vectors) | |
| ------------ | -------------------- | ------------------------------------- | |
| Pinecone | Per vector + queries | $70-150 | |
| Qdrant Cloud | Per cluster | $100-300 | |
| Self-hosted | Infrastructure only | $50-200 (AWS/GCP) | |
| Chroma | Free (local) | $0 | |
## Common Pitfalls and Best Practices |
### Pitfalls to Avoid |
1. **Dimension Mismatch** |
- Problem: Using different embedding models for indexing and querying |
- Solution: Always use the same model version and dimensions |
2. **Not Normalizing Vectors** |
- Problem: Inconsistent similarity scores |
- Solution: Normalize vectors before indexing when using cosine similarity |
3. **Ignoring Metadata Filtering** |
- Problem: Returning irrelevant results despite high similarity |
- Solution: Use metadata filters (date, category, access control) |
4. **Over-indexing** |
- Problem: Slow inserts and high memory usage |
- Solution: Choose appropriate index parameters for your use case |
### Best Practices |
1. **Chunk Your Documents Wisely** |
```python |
# Good: Semantic boundaries |
chunks = split_by_paragraphs(document, max_tokens=512) |
# Bad: Fixed character count |
chunks = [doc[i:i+1000] for i in range(0, len(doc), 1000)] |
``` |
2. **Implement Hybrid Search** |
- Combine vector search with keyword search for better results |
- Use BM25 for keyword relevance + vector similarity |
3. **Monitor Search Quality** |
- Track metrics: precision@k, recall@k, MRR |
- A/B test different embedding models |
- Log user feedback on search results |
4. **Handle Edge Cases** |
- Empty queries: Provide fallback behavior |
- Out-of-domain queries: Set similarity thresholds |
- Rate limiting: Implement query throttling |
## Scalability and Production Considerations |
### Scaling Strategies |
1. **Vertical Scaling** |
- Add more RAM for larger indexes |
- Use SSDs for faster disk operations |
- GPU acceleration for similarity computation |
2. **Horizontal Scaling** |
- Shard by metadata (e.g., user_id, tenant_id) |
- Replicate for read-heavy workloads |
- Use load balancers for query distribution |
3. **Index Optimization** |
- **IVF (Inverted File)**: Good for 1M-10M vectors |
- **HNSW**: Best for < 1M vectors with high recall |
- **LSH**: Suitable for streaming data |
### Production Checklist |
- [ ] **Backup Strategy**: Regular snapshots, point-in-time recovery |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.