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It currently supports distributed engines including forward-chaining, E-graph, and more.\n\n## Design Philosophy\n\nDDSS adopts a modular architecture that decomposes the deductive system into independent but collaborative sub-systems:\n\n1. **Separation of Concerns**: Each module focuses on a specific reasoning task.\n2. **Concurrent Execution**: All modules collaborate asynchronously through a shared database, fully utilizing system resources.\n3. **Persistent Storage**: Uses a database to store facts and ideas, ensuring data consistency.\n\nThe system uses a database as the central hub, with two tables (`facts` and `ideas`) for interaction between sub-systems:\n\n- **Eager engines** (e.g., forward-chaining): Read facts and eagerly produce new facts. They also add ideas to broadcast \"I want this XXX\" - indicating what new facts they need to produce more results.\n\n- **Lazy engines** (e.g., E-graph): Could produce too many facts if eager, so they quietly accept facts without producing many. They only produce facts when they see ideas from other engines that they can (partially) fulfill.\n\n## Modules\n\nThe system consists of the following modules, implemented symmetrically in `ddss/*.py` and `ddss/*.ts`:\n\n- **Input** (`ddss/input.py`, `ddss/input.ts`): Interactive input interface with BNF syntax parsing\n- **Output** (`ddss/output.py`, `ddss/output.ts`): Real-time display of facts and ideas from the database\n- **Load** (`ddss/load.py`, `ddss/load.ts`): Batch import of facts from standard input\n- **Dump** (`ddss/dump.py`, `ddss/dump.ts`): Export all facts and ideas to output\n- **Search** (`ddss/search.py`, `ddss/search.ts`): Forward-chaining deductive search engine\n- **Chain** (`ddss/chain.py`, `ddss/chain.ts`): Alternative forward-chaining engine using Chain API\n- **Egg** (`ddss/egg.py`, `ddss/egg.ts`): E-graph based equality reasoning engine\n\n## Installation\n\nYou can choose either the Python or TypeScript version. Both provide the same `ddss` command-line interface.\n\n### Python Version\n\n#### Using uvx (Recommended)\n```bash\nuvx ddss\n```\n\n#### Using pip\n```bash\npip install ddss\nddss\n```\n\n### TypeScript Version\n\n#### Using npx (Recommended)\n```bash\nnpx @hzhangxyz/ddss\n```\n\n#### Using npm\n```bash\nnpm install -g @hzhangxyz/ddss\nddss\n```\n\n## Usage\n\nThe usage, command-line options, and interactive syntax are identical regardless of the implementation language used.\n\n### Basic Usage\n\nRun DDSS with a temporary SQLite database:\n\n```bash\nddss\n```\n\n### Specifying a Database\n\nDDSS supports multiple database backends using the `-a` or `--addr` option:\n\n```bash\n# SQLite (persistent)\nddss --addr sqlite:///path/to/database.db\n\n# MySQL\nddss --addr mysql://user:password@host:port/database\n\n# MariaDB\nddss --addr mariadb://user:password@host:port/database\n\n# PostgreSQL\nddss --addr postgresql://user:password@host:port/database\n```\n\n### Selecting Components\n\nBy default, DDSS runs with all interactive components (`input`, `output`, `search`, `egg`). You can select specific components using the `-c` or `--component` option:\n\n```bash\n# Run only input and output (no inference engines)\nddss --component input output\n\n# Run with only the forward-chaining engine\nddss --component input output search\n\n# Run with only the E-graph engine\nddss --component input output egg\n```\n\nAvailable components:\n- `input`: Interactive input interface\n- `output`: Real-time display of facts and ideas\n- `search`: Forward-chaining deductive search engine\n- `chain`: Alternative forward-chaining engine using Chain API\n- `egg`: E-graph based equality reasoning engine\n- `load`: Batch import facts from standard input\n- `dump`: Export all facts and ideas to output\n\n### Interactive Usage\n\nAfter starting, input facts and rules at the `input:` prompt. The syntax follows the format `premise => conclusion`:\n\n**Example 1: Simple Inference**\n\nInput a fact stating `a` is true:\n```\ninput: => a\n```\n\nInput a rule stating if `a` then `b`:\n```\ninput: a => b\n```\n\nThe system automatically derives and displays `=> b`:\n```\nfact: => b\n```\n\n**Example 2: Equality Reasoning**\n\nInput an equality relation `a == b`:\n```\ninput: => a == b\n```\n\nInput an idea for `b == a` by creating a rule that requires it:\n```\ninput: b == a => target\n```\n\nThe system will derive both the idea and facts:\n```\nidea: => b == a\nfact: => b == a\nfact: => target\n```\n\n## License\n\nThis project is licensed under the GNU Affero General Public License v3.0 or later. See [LICENSE.md](LICENSE.md) for details.\n\n## Links\n\n- Documentation: https://ustc-knowledgecomputinglab.github.io/ddss/\n- GitHub: https://github.com/USTC-KnowledgeComputingLab/ddss\n","readmeFilename":"README.md"}