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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>
11 lines
550 B
JavaScript
11 lines
550 B
JavaScript
/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the "Elastic License
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* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
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* Public License v 1"; you may not use this file except in compliance with, at
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* your election, the "Elastic License 2.0", the "GNU Affero General Public
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* License v3.0 only", or the "Server Side Public License, v 1".
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*/
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require('../src/setup_node_env');
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require('@kbn/inference-cli/scripts/phoenix');
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