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LogSight-AI

CI CodeQL Python License: MIT

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.

Production Readiness Guide

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.

CI License

Architecture flowchart

flowchart LR
    Input --> Validate[Schema + data checks] --> Model[Versioned model] --> Serve[API / dashboard] --> Observe[Metrics + drift]
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Quickstart and local validation

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 -q

If 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.

Research-style metrics and benchmarks

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.

Extended Q&A

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.

Architecture

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"]
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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.

Quick start

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 stdin

Useful controls:

logsight analyze application.log --threshold 3.0 --window 200 --spike-threshold 0.20

Verified metrics

Measured 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.

Engineering controls

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.

Documentation

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.

Development

pip install -e ".[dev]"
ruff format .
ruff check .
mypy
pytest

Contributions should include tests and documentation for behavioral changes. Report vulnerabilities privately as described in SECURITY.md.