0cfd0defa9
Root cause: Helm's `helm push` collapses the chart `description` field into a single-line OCI manifest annotation `org.opencontainers.image.description`. The GHCR manifest-PUT validator returns a deterministic 500 Internal Server Error when that annotation is long AND contains an ASCII apostrophe. bp-langfuse 1.0.0 was the only chart in the observability batch (PR #214) carrying both characteristics, so it was the only one that failed to publish. Fix: reword the affected sentence from "Langfuse's persistent state" to "the Langfuse persistent state" — drops the apostrophe, preserves the meaning, and crucially preserves every byte of the actual chart payload (values, templates, all 350 entries of the upstream langfuse-1.5.28 subchart with its 4-level-deep Bitnami vendoring). No runtime behavioural change; helm template renders the exact same 6 resources across 490 lines. The narrowing was done by progressively reducing the Chart.yaml from the failing version to a passing version while pushing to a scratch GHCR namespace, with the bp-langfuse repo deleted between attempts (verified via `DELETE /orgs/openova-io/packages/container/bp-langfuse` and re-querying). The trigger is reproducible: long description + apostrophe → 500; long description without apostrophe → push succeeds; short description with apostrophe → push succeeds. Added a multi-line WARNING comment immediately above `description:` documenting the trigger so future authors do not reintroduce a possessive form. Issue #215 captures the full reproduction. Closes #215 Co-authored-by: hatiyildiz <hatice.yildiz@openova.io> |
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LangFuse
LLM observability and analytics. Application Blueprint (see docs/PLATFORM-TECH-STACK.md §4.7). Traces every LLM call in bp-cortex — latency, tokens, cost, eval scores. Catalyst's general-purpose observability stack (Grafana/OTel) covers infrastructure; LangFuse covers the AI-specific dimensions (prompt/response, model drift, eval).
Category: AI Observability | Type: Application Blueprint
Overview
LangFuse provides tracing, evaluation, and analytics for LLM applications. It captures every LLM call with cost, latency, token usage, and evaluation scores. Complements Grafana (which handles infrastructure metrics) with AI-specific observability.
Key Features
- LLM call tracing (input, output, cost, latency, tokens)
- Prompt management and versioning
- Evaluation scoring and datasets
- User analytics and session tracking
- Cost attribution per model/user/feature
Integration
| Component | Integration |
|---|---|
| LLM Gateway | Automatic trace capture |
| Grafana | Infrastructure metrics complement |
| CNPG | PostgreSQL backend for traces |
| NeMo Guardrails | Traces guardrail activations |
Used By
- OpenOva Cortex - LLM observability for enterprise AI
Deployment
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
name: langfuse
namespace: flux-system
spec:
interval: 10m
path: ./platform/langfuse
prune: true
Part of OpenOva