LogSight-AI is a local-first Python CLI for parsing common log formats, summarizing error patterns, detecting message-length outliers, and locating error-rate spikes. The production package does not transmit logs or require credentials.
This section is the portfolio audit entry point for LogSight-AI. It describes an engineering promotion path; it is not a claim that the repository is already production-authorized.
flowchart LR
Input --> Validate[Schema + data checks] --> Model[Versioned model] --> Serve[API / dashboard] --> Observe[Metrics + drift]
The supported local path should be reproducible from a clean checkout. The inferred stack for this repository is Python/ML.
python -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt
pytest -qIf the project uses external services, model artifacts, cloud credentials, or private data, start them through documented local fixtures or mocks. Never place secrets or identifiable records in the repository.
| Evidence | Required record |
|---|---|
| Correctness | Test command, commit SHA, runtime, and pass/fail result |
| Performance | Warm-up, sample count, concurrency, median, p95, p99, throughput, and memory |
| Data/model quality | Dataset version, split strategy, leakage controls, calibration, subgroup results, and uncertainty |
| Runtime | Image digest, health-check latency, resource limits, and rollback target |
| Security | Dependency, secret, SAST, container, and SBOM results |
A benchmark number belongs in a versioned artifact tied to a commit and hardware/runtime description. Engineering benchmarks must not be presented as clinical, financial, safety, or model-quality validation without the appropriate domain evidence.
What is production-ready for this repository?
A reproducible build, tested public contract, controlled configuration, observable runtime, documented security boundary, versioned artifacts, and a tested rollback path.
What must remain explicit?
The intended use, excluded use, data/credential handling, model or algorithm limitations, and which metrics are measured versus aspirational.
What should be completed next?
Use the linked production-readiness issue for this repository as the checklist. Resolve missing tests, deployment instructions, observability, supply-chain controls, and release evidence before attaching a production claim.
flowchart LR
A["File or stdin"] --> B["Format parser"]
B --> C["Typed LogEntry records"]
C --> D["Statistics and anomaly analysis"]
D --> E["Rich CLI report"]
Supported formats include ISO-8601 application logs, syslog, nginx access logs, and generic level-prefixed lines. Detection is an explainable statistical heuristic; it is not a trained model and no accuracy claim is made without a labeled evaluation corpus.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e .
logsight health
logsight analyze application.log
cat application.log | logsight stdinUseful controls:
logsight analyze application.log --threshold 3.0 --window 200 --spike-threshold 0.20Measured locally on 2026-07-17; CI artifacts are the canonical per-commit record.
| Metric | Value |
|---|---|
| Automated tests | 50 passing |
| Core package coverage | 95.26% |
| Benchmark input | 1,000 lines |
| Median pipeline latency | 10.315 ms |
| Mean throughput | 96.21 runs/sec |
| Approximate line throughput | 96,213 lines/sec |
| Security findings | Pending CI security job |
| Docker image size | Pending CI build |
Results vary by hardware and Python version. See Benchmark Guide and Benchmark Report.
Every pull request runs formatting, linting, strict type checking, unit/integration/CLI tests, a 90% coverage gate, package and container validation, Bandit, dependency audit, SBOM generation, CodeQL, and a reproducible microbenchmark. Checks fail closed.
- Production audit
- Architecture
- Deployment and rollback checklist
- Benchmark methodology
- Runtime metrics
- Security policy
The Streamlit and external-LLM files are retained as demonstrations and are not part of the supported package or deployment contract; see the audit for the work required to promote them.
pip install -e ".[dev]"
ruff format .
ruff check .
mypy
pytestContributions should include tests and documentation for behavioral changes. Report vulnerabilities privately as described in SECURITY.md.