3. **Memory Issues**: Adjust HNSW parameters. Use persistent storage instead of in-memory. Implement proper cleanup procedures.
4. **Embedding Errors**: Verify LiteLLM configuration. Check API keys and quotas. Handle rate limiting properly.
This comprehensive guide provides everything needed to integrate ChromaDB with Swarms agents for powerful RAG applications using the unified LiteLLM embeddings approach.
@ -256,24 +256,12 @@ index = faiss.IndexIVFPQ(quantizer, dimension, nlist, m, nbits)
### Common Issues
1. **Memory Errors**
- Reduce batch sizes or use product quantization
- Consider using memory mapping for large indices
- Monitor system memory usage
2. **Slow Search Performance**
- Check if IVF index is properly trained
- Adjust nprobe parameter (higher = slower but more accurate)
- Consider using GPU acceleration
3. **Low Search Accuracy**
- Increase nlist for IVF indices
- Adjust efSearch for HNSW indices
- Verify embedding normalization
4. **Index Loading Issues**
- Check file permissions and disk space
- Verify FAISS version compatibility
- Ensure consistent data types (float32)
1. **Memory Errors**: Reduce batch sizes or use product quantization. Consider using memory mapping for large indices. Monitor system memory usage.
2. **Slow Search Performance**: Check if IVF index is properly trained. Adjust nprobe parameter (higher = slower but more accurate). Consider using GPU acceleration.
3. **Low Search Accuracy**: Increase nlist for IVF indices. Adjust efSearch for HNSW indices. Verify embedding normalization.
4. **Index Loading Issues**: Check file permissions and disk space. Verify FAISS version compatibility. Ensure consistent data types (float32).
This comprehensive guide provides everything needed to integrate FAISS with Swarms agents for high-performance RAG applications using the unified LiteLLM embeddings approach.
- Check network connectivity and firewall settings
- Confirm cloud region accessibility
2. **Performance Issues**
- Monitor collection size and index type appropriateness
- Check query complexity and filter expressions
- Review auto-scaling configuration
3. **Search Accuracy Issues**
- Verify embedding model consistency
- Check vector normalization if using cosine similarity
- Review index parameters and search parameters
4. **Quota and Billing Issues**
- Monitor usage against plan limits
- Review auto-scaling settings
- Check billing alerts and notifications
1. **Connection Errors**: Verify MILVUS_CLOUD_URI and MILVUS_CLOUD_TOKEN. Check network connectivity and firewall settings. Confirm cloud region accessibility.
2. **Performance Issues**: Monitor collection size and index type appropriateness. Check query complexity and filter expressions. Review auto-scaling configuration.
3. **Search Accuracy Issues**: Verify embedding model consistency. Check vector normalization if using cosine similarity. Review index parameters and search parameters.
4. **Quota and Billing Issues**: Monitor usage against plan limits. Review auto-scaling settings. Check billing alerts and notifications.
This comprehensive guide provides everything needed to integrate Milvus Cloud with Swarms agents for enterprise-scale RAG applications using the unified LiteLLM embeddings approach.
- Ensure directory exists before creating database
- Handle concurrent access properly
2. **Performance Issues**
- Monitor database size relative to available memory
- Consider index type optimization for dataset size
- Batch operations for better throughput
3. **Memory Usage**
- Use appropriate index parameters for available RAM
- Monitor system memory usage
- Consider data compression techniques
4. **Data Corruption**
- Implement proper backup and recovery procedures
- Handle application crashes gracefully
- Use database validation tools
1. **Database File Errors**: Check file permissions and disk space. Ensure directory exists before creating database. Handle concurrent access properly.
2. **Performance Issues**: Monitor database size relative to available memory. Consider index type optimization for dataset size. Batch operations for better throughput.
3. **Memory Usage**: Use appropriate index parameters for available RAM. Monitor system memory usage. Consider data compression techniques.
4. **Data Corruption**: Implement proper backup and recovery procedures. Handle application crashes gracefully. Use database validation tools.
This comprehensive guide provides everything needed to integrate Milvus Lite with Swarms agents for local, lightweight RAG applications using the unified LiteLLM embeddings approach.
@ -346,25 +346,13 @@ class MonitoredPineconeMemory(PineconeMemory):
### Common Issues
1. **API Quota Exceeded**
- Monitor API usage and implement rate limiting
- Consider upgrading plan or optimizing query patterns
- Use batch operations to reduce API calls
2. **High Latency**
- Check pod type and consider upgrading
- Verify regional configuration
- Optimize query complexity and top_k values
3. **Index Capacity Issues**
- Monitor index fullness metrics
- Consider scaling up pod type or adding shards
- Implement data archival strategies
4. **Connection Errors**
- Verify API key and environment configuration
- Check network connectivity and firewall settings
- Implement retry logic with exponential backoff
1. **API Quota Exceeded**: Monitor API usage and implement rate limiting. Consider upgrading plan or optimizing query patterns. Use batch operations to reduce API calls.
2. **High Latency**: Check pod type and consider upgrading. Verify regional configuration. Optimize query complexity and top_k values.
3. **Index Capacity Issues**: Monitor index fullness metrics. Consider scaling up pod type or adding shards. Implement data archival strategies.
4. **Connection Errors**: Verify API key and environment configuration. Check network connectivity and firewall settings. Implement retry logic with exponential backoff.
harshalmore31
1 month ago