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Your window into the Elastic Stack
2387e3b88c
Instrument the inference chatComplete API with OpenTelemetry, and export helper functions to create spans w/ the right semconv attributes. Additionally, optionally export to Langfuse or Phoenix. ## Centralizes OpenTelemetry setup As this is the first instance of OpenTelemetry based _tracing_ (we already have metrics in the MonitoringCollection plugin), some bootstrapping code is necessary to centrally configure OpenTelemetry. To this end, I've added the following config settings: - `telemetry.tracing.enabled`: whether OpenTelemetry tracing is enabled (defaults to undefined, if undefined, falls back to `telemetry.enabled`) - `telemetry.tracing.sample_rate` (defaults to 1) The naming of these configuration settings is mostly in-line with [the Elasticsearch tracing settings](https://github.com/elastic/elasticsearch/blob/main/TRACING.md). The following packages (containing bootstrapping logic, utility functions, types and config schemas) were added: - `@kbn/telemetry` - `@kbn/telemetry-config` - `@kbn/tracing` The OpenTelemetry bootstrapping depends on @kbn/apm-config-loader, as it has the same constraints - it needs to run before any other code, and it needs to read the raw config. Additionally, a root `telemetry` logger was added that captures OpenTelemetry logs. Note that there is no default exporter for spans, which means that although spans are being recorded, they do not get exported. ## Instrument chatComplete calls Calls to `chatComplete` now create OpenTelemetry spans, roughly following semantic conventions (which for GenAI are very much in flux). Some helper functions were added to create other inference spans. These helper functions use baggage to determine whether the created inference span is the "root" of an inference trace. This allows us to export these spans as if it were root spans - something that is needed to be able to easily visualize these in other tools. Leveraging these inference spans, two exporters are added. One for [Phoenix](https://github.com/Arize-ai/phoenix) and one for [Langfuse](https://github.com/langfuse/langfuse/tree/main): two open-source LLM Observability suites. This allows engineers that use the Inference plugin to be able to inspect and improve their LLM-based workflows with much less effort. For both Phoenix and Langfuse, two service scripts were added. Run `node scripts/phoenix` or `node scripts/langfuse` to get started. Both scripts work with zero-config - they will log generated Kibana config to stdout. --------- Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com> Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com> |
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Kibana
Kibana is your window into the Elastic Stack. Specifically, it's a browser-based analytics and search dashboard for Elasticsearch.
- Getting Started
- Documentation
- Version Compatibility with Elasticsearch
- Questions? Problems? Suggestions?
Getting Started
If you just want to try Kibana out, check out the Elastic Stack Getting Started Page to give it a whirl.
If you're interested in diving a bit deeper and getting a taste of Kibana's capabilities, head over to the Kibana Getting Started Page.
Using a Kibana Release
If you want to use a Kibana release in production, give it a test run, or just play around:
- Download the latest version on the Kibana Download Page.
- Learn more about Kibana's features and capabilities on the Kibana Product Page.
- We also offer a hosted version of Kibana on our Cloud Service.
Building and Running Kibana, and/or Contributing Code
You might want to build Kibana locally to contribute some code, test out the latest features, or try out an open PR:
- CONTRIBUTING.md will help you get Kibana up and running.
- If you would like to contribute code, please follow our STYLEGUIDE.mdx.
- For all other questions, check out the FAQ.md and wiki.
Documentation
Visit Elastic.co for the full Kibana documentation.
For information about building the documentation, see the README in elastic/docs.
Version Compatibility with Elasticsearch
Ideally, you should be running Elasticsearch and Kibana with matching version numbers. If your Elasticsearch has an older version number or a newer major number than Kibana, then Kibana will fail to run. If Elasticsearch has a newer minor or patch number than Kibana, then the Kibana Server will log a warning.
Note: The version numbers below are only examples, meant to illustrate the relationships between different types of version numbers.
| Situation | Example Kibana version | Example ES version | Outcome |
|---|---|---|---|
| Versions are the same. | 7.15.1 | 7.15.1 | 💚 OK |
| ES patch number is newer. | 7.15.0 | 7.15.1 | ⚠️ Logged warning |
| ES minor number is newer. | 7.14.2 | 7.15.0 | ⚠️ Logged warning |
| ES major number is newer. | 7.15.1 | 8.0.0 | 🚫 Fatal error |
| ES patch number is older. | 7.15.1 | 7.15.0 | ⚠️ Logged warning |
| ES minor number is older. | 7.15.1 | 7.14.2 | 🚫 Fatal error |
| ES major number is older. | 8.0.0 | 7.15.1 | 🚫 Fatal error |
Questions? Problems? Suggestions?
- If you've found a bug or want to request a feature, please create a GitHub Issue. Please check to make sure someone else hasn't already created an issue for the same topic.
- Need help using Kibana? Ask away on our Kibana Discuss Forum and a fellow community member or Elastic engineer will be glad to help you out.