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Weaviate

Weaviate

Observe your semantic cache efficiency to reduce cost and latency for LLM apps.

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Overview

Vector databases, such as Weaviate play an important role as semantic caches within state-of-the-art Large Language Model (LLM) service stacks.

Semantic caches reduce the latency for well-known and frequent user prompts while at the same time help to reduce the overall cost of pre-trained model cloud service costs. Monitoring cache efficiency and memory utilization ensures optimal resource allocation, while adaptability to dynamic contexts measures the cache's ability to respond accurately to evolving conversation dynamics. Additionally, cache warm-up times contribute to faster availability of cached information. For vector databases, query performance and indexing speed are critical indicators, directly impacting the system's ability to handle similarity searches efficiently. Scalability, accuracy of vector representations, and storage efficiency are essential for managing growing datasets effectively. Factors such as update, and deletion performance and query throughput further contribute to the overall effectiveness of these systems in providing real-time and accurate responses in natural language processing and similarity search applications. Striking the right balance across these KPIs ensures that both semantic LLM caches and vector databases deliver optimal performance across diverse use cases. In summary, vector databases such as Weaviate aim to address performance-related challenges, enhance efficiency, and contribute to a more seamless and responsive experience in various natural language processing applications.

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Setting up full stack observability for your GenAI applications is possible with Traceloop's OpenLLMetry, with OpenTelemetry under the hood, which can seamlessly provide comprehensive end-to-end insights into your production environments.

To set up OpenLLMetry with Dynatrace, see Dynatrace Documentation.

Details

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Weaviate can expose Prometheus-compatible metrics for monitoring. A standard Prometheus setup lets you visualize them on dashboards in your Dynatrace environment, covering request latencies, import speed, vector vs. object storage time, memory usage, and more.

Key metrics for observing index health and performance:

batch_durations_ms: duration of a batch operation (ms); dimensions 'operation' and 'class_name' identify the operation type and affected class. batch_delete_durations_ms: duration of batch deletions; 'operation' refines what was measured. objects_durations_ms: duration of individual operations. object_count: number of objects per object class. async_operations_running: number of running async operations, by 'operation'. lsm_active_segments: number of current segments per shard in the LSM tree. lsm_bloom_filter_duration_ms: duration of a bloom filter operation per shard (ms). lsm_segment_objects: entries per LSM segment, by level. lsm_segment_size: LSM segment size. lsm_segment_count: number of LSM segments. vector_index_tombstones: active tombstones in the vector index; rises on delete, falls after repair. vector_index_tombstone_cleanup_threads: active threads repairing/cleaning the vector index post-delete. vector_index_tombstone_cleaned: total vectors deleted and removed after repair. vector_index_operations: total mutating operations on the vector index, by 'operation'. vector_index_size: total capacity of the vector index. vector_index_maintenance_durations_ms: duration of sync/async index maintenance, by 'operation'. vector_index_durations_ms: duration of regular index operations (insert, delete), by 'operation' and 'step'. startup_durations_ms: duration of individual startup operations (ms). startup_diskio_throughput: disk I/O throughput (bytes/s) during startup, e.g. HNSW index read or LSM recovery. requests_total: tracks all user requests and whether they succeeded or failed.

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