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AI Agent Context for liblognorm

Project Overview

liblognorm is a fast, samples-based log normalization library. It parses log messages (strings) into structured JSON objects based on a set of rules.

Critical Context

Important

READ THIS FIRST: The core engine logic is documented in doc/pdag_implementation_model.rst. This file contains critical "implementation deltas" that explain how the actual C code differs from the theoretical design (Master's thesis). Ignoring this file will lead to incorrect assumptions about the parser's behavior.

Key Directories

  • src/: Source code.
  • doc/: Documentation (Sphinx reStructuredText).
  • tests/: Test suite.

Architecture Map

For a detailed breakdown of which files implement which concepts, see: doc/ai_architecture_map.md

Coding Standards & Constraints

  1. C99: The codebase is written in C99.
  2. Memory Management: Be careful with memory. The PDAG engine relies on specific ownership rules (see pdag_implementation_model.rst).
  3. Thread Safety: The library is designed to be thread-safe.
  4. Error Handling: Uses goto done pattern for cleanup and error propagation.

Common Tasks

  • Adding a new parser: See src/parser.c. Use the PARSER_Parse, PARSER_Construct, etc. macros.
  • Debugging: The library has a debug callback system (ln_setDebugCB).

Commit Rules

  1. Contextual Messages: Commit messages must explain why a change was made, not just what changed. Relate changes to the project strategy (e.g., "AI first strategy") where applicable.
  2. Attribution: All AI-assisted commits must include the following footer: With the help of AI Agent: <agent-name>
  3. Atomic Changes: Separate documentation updates from code changes when possible, or group them logically if they are tightly coupled.
  4. Relative Paths: Always use relative paths in documentation (e.g., doc/file.md, not file:///...) to ensure portability across environments.