Publish artifacts for production-ai-observability-20260820

README.md

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+---
+license: mit
+library_name: custom
+pipeline_tag: text-classification
+datasets:
+- RKB109/production-ai-observability-20260820-dataset
+tags:
+- synthetic-data
+- transparent-baseline
+- ai-observability
+- text-classification
+- token-classification
+- summarization
+- zero-shot-classification
+metrics:
+- accuracy
+---
+
+# Production AI Observability Monitor Baseline Model
+
+## Model Description
+
+This repository contains a small, transparent prototype model for
+**Production AI teams need trace-level signals for latency, token growth, tool failures, and low-quality outputs.**
+
+The model combines per-label token weights with IDF-weighted evidence
+retrieval. It was generated for reproducible architecture demonstrations and
+does not call a hosted LLM.
+
+## Evaluation
+
+- Held-out synthetic examples: 4
+- Accuracy: 1
+- Intended metrics: failure_class_accuracy, alert_precision, trace_coverage
+
+## Intended Use
+
+- Architecture prototyping
+- CI and evaluation examples
+- Local baseline comparisons
+- Educational experimentation
+
+## Hugging Face Task Coverage
+
+- `text-classification`
+- `token-classification`
+- `summarization`
+- `zero-shot-classification`
+
+## Limitations and Risks
+
+Thresholds are demonstration defaults and need calibration against each production workload.
+
+The dataset is synthetic and small. Do not use this model for consequential
+decisions without representative data, expert review, and production-grade
+evaluation.
+
+## Reproducibility
+
+The linked GitHub repository includes `train.py`, the exact dataset split,
+evaluation code, and the model JSON format.

evaluation.json

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+{
+  "test_examples": 4,
+  "accuracy": 1,
+  "synthetic_evaluation": true
+}

inference.py

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+"""Transparent baseline pipeline for the generated AI project."""
+
+from __future__ import annotations
+
+import json
+import math
+import re
+from pathlib import Path
+
+
+def tokenize(value: str) -> list[str]:
+    return re.findall(r"[a-z0-9]+", value.lower())
+
+
+class Pipeline:
+    def __init__(self, model: dict):
+        self.model = model
+
+    @classmethod
+    def from_file(cls, path: str | Path) -> "Pipeline":
+        return cls(json.loads(Path(path).read_text(encoding="utf-8")))
+
+    def classify(self, text: str) -> tuple[str, float]:
+        tokens = tokenize(text)
+        scores = {
+            label: sum(weights.get(token, 0) for token in tokens)
+            for label, weights in self.model["prototypes"].items()
+        }
+        ranked = sorted(scores.items(), key=lambda item: (-item[1], item[0]))
+        label, best = ranked[0]
+        total = sum(max(score, 0) for _, score in ranked) or 1
+        return label, best / total
+
+    def search(self, query: str, limit: int = 3) -> list[dict]:
+        query_tokens = set(tokenize(query))
+        ranked = []
+        for document in self.model["documents"]:
+            document_tokens = set(tokenize(document["text"]))
+            lexical = sum(
+                self.model["idf"].get(token, 1.0)
+                for token in query_tokens & document_tokens
+            )
+            ranked.append({**document, "score": round(lexical, 6)})
+        return sorted(ranked, key=lambda item: (-item["score"], item["id"]))[:limit]
+
+    def graph_evidence(self, text: str) -> list[dict]:
+        tokens = set(tokenize(text))
+        matches = []
+        for subject, relation, target in self.model.get("graph_edges", []):
+            edge_tokens = set(tokenize(f"{subject} {relation} {target}"))
+            overlap = len(tokens & edge_tokens)
+            if overlap:
+                matches.append(
+                    {
+                        "subject": subject,
+                        "relation": relation,
+                        "target": target,
+                        "overlap": overlap,
+                    }
+                )
+        return sorted(matches, key=lambda item: -item["overlap"])
+
+    def run(self, text: str) -> dict:
+        label, confidence = self.classify(text)
+        evidence = self.search(text)
+        result = {
+            "prediction": label,
+            "confidence": round(confidence, 4),
+            "requires_review": confidence < self.model["confidence_threshold"],
+            "evidence": evidence,
+        }
+        if self.model["mode"] == "graph":
+            result["graph_evidence"] = self.graph_evidence(text)
+        if self.model["mode"] == "agent":
+            result["proposed_tool"] = label
+            result["approval_required"] = label in {
+                "request-approval",
+                "request-human-help",
+            }
+        return result

model.json

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+{
+  "format": "daily-project-prototype-v1",
+  "project": "ai-observability",
+  "title": "Production AI Observability Monitor",
+  "domain": "ai-observability",
+  "mode": "classifier",
+  "labels": [
+    "latency-regression",
+    "token-spike",
+    "tool-failure",
+    "quality-regression"
+  ],
+  "prototypes": {
+    "latency-regression": {
+      "request": 3,
+      "latency": 6,
+      "rose": 3,
+      "above": 3,
+      "the": 9,
+      "service": 3,
+      "objective": 3,
+      "end": 6,
+      "to": 3,
+      "exceeded": 3,
+      "approved": 3,
+      "percentile": 3,
+      "threshold": 3,
+      "in": 2,
+      "an": 4,
+      "operations": 2,
+      "review": 2,
+      "for": 2,
+      "evaluation": 2,
+      "case": 2,
+      "agent": 3,
+      "stayed": 3,
+      "within": 3,
+      "quality": 6,
+      "limits": 3,
+      "but": 3,
+      "became": 3,
+      "slower": 3,
+      "performance": 3,
+      "changed": 3,
+      "without": 3,
+      "a": 3,
+      "matching": 3,
+      "improvement": 3
+    },
+    "token-spike": {
+      "in": 1,
+      "an": 2,
+      "operations": 1,
+      "review": 1,
+      "prompt": 2,
+      "tokens": 2,
+      "doubled": 2,
+      "after": 2,
+      "a": 2,
+      "template": 2,
+      "change": 2,
+      "token": 2,
+      "consumption": 2,
+      "increased": 2,
+      "beyond": 2,
+      "the": 2,
+      "cost": 2,
+      "and": 2,
+      "context": 2,
+      "baseline": 2,
+      "for": 1,
+      "evaluation": 1,
+      "case": 1
+    },
+    "tool-failure": {
+      "the": 4,
+      "retrieval": 2,
+      "tool": 4,
+      "returned": 2,
+      "a": 4,
+      "timeout": 2,
+      "exception": 2,
+      "required": 2,
+      "external": 2,
+      "failed": 4,
+      "during": 2,
+      "execution": 2,
+      "for": 2,
+      "an": 3,
+      "evaluation": 2,
+      "case": 2,
+      "in": 1,
+      "operations": 1,
+      "review": 1,
+      "search": 2,
+      "calls": 2,
+      "with": 2,
+      "repeated": 2,
+      "connection": 2,
+      "errors": 4,
+      "dependency": 2,
+      "prevented": 2,
+      "workflow": 2,
+      "from": 2,
+      "completing": 2
+    },
+    "quality-regression": {
+      "grounded": 2,
+      "answer": 2,
+      "score": 2,
+      "dropped": 2,
+      "after": 2,
+      "deployment": 2,
+      "evaluation": 2,
+      "quality": 2,
+      "regressed": 2,
+      "relative": 2,
+      "to": 2,
+      "the": 2,
+      "release": 2,
+      "baseline": 2,
+      "in": 1,
+      "an": 1,
+      "operations": 1,
+      "review": 1
+    }
+  },
+  "idf": {
+    "end": 2.252763,
+    "to": 1.847298,
+    "latency": 2.252763,
+    "exceeded": 2.252763,
+    "the": 1.336472,
+    "approved": 2.252763,
+    "percentile": 2.252763,
+    "threshold": 2.252763,
+    "token": 2.252763,
+    "consumption": 2.252763,
+    "increased": 2.252763,
+    "beyond": 2.252763,
+    "cost": 2.252763,
+    "and": 2.252763,
+    "context": 2.252763,
+    "baseline": 1.847298,
+    "a": 1.847298,
+    "required": 2.252763,
+    "external": 2.252763,
+    "tool": 2.252763,
+    "failed": 2.252763,
+    "during": 2.252763,
+    "execution": 2.252763,
+    "evaluation": 2.252763,
+    "quality": 1.847298,
+    "regressed": 2.252763,
+    "relative": 2.252763,
+    "release": 2.252763,
+    "performance": 2.252763,
+    "changed": 2.252763,
+    "without": 2.252763,
+    "matching": 2.252763,
+    "improvement": 2.252763,
+    "dependency": 2.252763,
+    "errors": 2.252763,
+    "prevented": 2.252763,
+    "workflow": 2.252763,
+    "from": 2.252763,
+    "completing": 2.252763
+  },
+  "documents": [
+    {
+      "id": "trace-01",
+      "label": "latency-regression",
+      "text": "End-to-end latency exceeded the approved percentile threshold.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    },
+    {
+      "id": "trace-02",
+      "label": "token-spike",
+      "text": "Token consumption increased beyond the cost and context baseline.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    },
+    {
+      "id": "trace-03",
+      "label": "tool-failure",
+      "text": "A required external tool failed during execution.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    },
+    {
+      "id": "trace-04",
+      "label": "quality-regression",
+      "text": "Evaluation quality regressed relative to the release baseline.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    },
+    {
+      "id": "trace-05",
+      "label": "latency-regression",
+      "text": "Performance changed without a matching quality improvement.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    },
+    {
+      "id": "trace-06",
+      "label": "tool-failure",
+      "text": "Dependency errors prevented the workflow from completing.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "ai-observability"
+      }
+    }
+  ],
+  "graph_edges": [],
+  "confidence_threshold": 0.18,
+  "trained_on_synthetic_data": true
+}

project.json

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+{
+  "name": "Production AI Observability Monitor",
+  "problem": "Production AI teams need trace-level signals for latency, token growth, tool failures, and low-quality outputs.",
+  "domain": "ai-observability",
+  "architecture": "classifier",
+  "hugging_face_tasks": [
+    "text-classification",
+    "token-classification",
+    "summarization",
+    "zero-shot-classification"
+  ],
+  "recommended_stack": [
+    "OpenTelemetry GenAI semantic conventions",
+    "OpenTelemetry Collector for vendor-neutral ingestion",
+    "ClickHouse or PostgreSQL for trace analytics",
+    "Prometheus and Grafana for service-level metrics",
+    "MLflow for model and prompt version linkage",
+    "FastAPI for trace search and regression APIs"
+  ],
+  "real_world_data_sources": [
+    {
+      "name": "Prometheus HTTP API",
+      "url": "https://prometheus.demo.do.prometheus.io/api/v1/query?query=up",
+      "purpose": "Real time-series telemetry for anomaly pipelines"
+    },
+    {
+      "name": "GitHub Events API",
+      "url": "https://api.github.com/events?per_page=10",
+      "purpose": "Deployment-correlated public event metadata"
+    }
+  ],
+  "job_description_skills": [
+    "LLMOps observability and trace instrumentation",
+    "Prompt, model, dataset, and deployment lineage",
+    "SLOs, anomaly detection, and incident diagnostics",
+    "High-volume telemetry storage and aggregation",
+    "Evaluation-driven production monitoring"
+  ],
+  "impact_targets": [
+    "Ingest 1,000 synthetic traces/second without loss",
+    "Detect seeded latency and quality regressions with >= 0.90 precision",
+    "Link 100% of traces to model, prompt, and dataset versions",
+    "Generate a release health report in under 60 seconds"
+  ],
+  "baseline_evaluation": {
+    "test_examples": 4,
+    "accuracy": 1,
+    "synthetic_evaluation": true
+  },
+  "estimated_delivery": "8-12 weeks for one engineer",
+  "generated_baseline_is_production_ready": false
+}