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Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](https://www.contributor-covenant.org/version/2/1/code_of_conduct/)\n\n> Main NLPTools package - Complete suite of NLP algorithms and utilities\n\nThis is the main NLPTools package (`@nlptools/nlptools`) that exports all algorithms and utilities from the entire toolkit. It provides a single entry point to access all string distance, similarity algorithms, text splitting, and tokenization utilities.\n\n## Features\n\n- **All-in-One**: Complete access to all NLPTools algorithms\n- **Convenient**: Single import for all functionality\n- **Text Splitting**: Document chunking and text processing utilities\n- **Tokenization**: Fast text encoding and decoding for LLM models\n- **Distance & Similarity**: Comprehensive string comparison algorithms\n- **Locality-Sensitive Hashing**: Fast approximate nearest neighbor search\n- **TypeScript First**: Full type safety with comprehensive API\n- **Easy to Use**: Consistent API across all algorithms\n\n## Installation\n\n```bash\n# Install with npm\nnpm install @nlptools/nlptools\n\n# Install with yarn\nyarn add @nlptools/nlptools\n\n# Install with pnpm\npnpm add @nlptools/nlptools\n```\n\n## Usage\n\n### Basic Setup\n\n```typescript\nimport * as nlptools from \"@nlptools/nlptools\";\n\n// Edit distance\nconsole.log(nlptools.levenshtein(\"kitten\", \"sitting\")); // 3\nconsole.log(nlptools.levenshteinNormalized(\"cat\", \"bat\")); // 0.6666666666666666\n\n// Token-based similarity\nconsole.log(nlptools.jaccard(\"abc\", \"bcd\")); // 0.3333333333333333\nconsole.log(nlptools.cosine(\"hello\", \"hallo\")); // 0.8\nconsole.log(nlptools.sorensen(\"abc\", \"bcd\")); // 0.5\n```\n\n### Distance vs Similarity\n\nMost algorithms have both distance and normalized versions:\n\n```typescript\n// Distance algorithms (lower is more similar)\nconst distance = nlptools.levenshtein(\"cat\", \"bat\"); // 1\n\n// Similarity algorithms (higher is more similar, 0-1 range)\nconst similarity = nlptools.levenshteinNormalized(\"cat\", \"bat\"); // 0.6666666666666666\n```\n\n### Text Splitting\n\nThis package includes text splitters from `@nlptools/splitter`:\n\n```typescript\nimport { RecursiveCharacterTextSplitter } from \"@nlptools/nlptools\";\n\nconst splitter = new RecursiveCharacterTextSplitter({\n  chunkSize: 1000,\n  chunkOverlap: 200,\n});\n\nconst text = \"Your long document text here...\";\nconst chunks = await splitter.splitText(text);\nconsole.log(chunks);\n```\n\n### Tokenization\n\nThis package includes tokenization utilities from `@nlptools/tokenizer`:\n\n```typescript\nimport { Tokenizer } from \"@nlptools/nlptools\";\n\n// Load tokenizer from HuggingFace Hub\nconst modelId = \"HuggingFaceTB/SmolLM3-3B\";\nconst tokenizerJson = await fetch(\n  `https://huggingface.co/${modelId}/resolve/main/tokenizer.json`,\n).then((res) => res.json());\nconst tokenizerConfig = await fetch(\n  `https://huggingface.co/${modelId}/resolve/main/tokenizer_config.json`,\n).then((res) => res.json());\n\nconst tokenizer = new Tokenizer(tokenizerJson, tokenizerConfig);\n\n// Encode text\nconst encoded = tokenizer.encode(\"Hello World\");\nconsole.log(encoded.ids); // [9906, 4435]\nconsole.log(encoded.tokens); // ['Hello', 'ĠWorld']\n\n// Get token count\nconst tokenCount = tokenizer.encode(\"This is a sentence.\").ids.length;\nconsole.log(`Token count: ${tokenCount}`);\n```\n\n### Available Algorithm Categories\n\nThis package includes all algorithms from `@nlptools/distance`, `@nlptools/splitter`, and `@nlptools/tokenizer`:\n\n#### Edit Distance\n\n- `levenshtein` / `levenshteinNormalized` - Classic Levenshtein edit distance\n- `lcsDistance` / `lcsNormalized` - Longest Common Subsequence distance\n- `lcsLength` - LCS length\n- `lcsPairs` - LCS matching index pairs\n\n#### Token-based Similarity\n\n- `jaccard` / `jaccardNgram` - Jaccard similarity (character / n-gram)\n- `cosine` / `cosineNgram` - Cosine similarity (character / n-gram)\n- `sorensen` / `sorensenNgram` - Sorensen-Dice coefficient (character / n-gram)\n\n#### Hash-based Algorithms\n\n- `simhash` / `SimHasher` - Locality-sensitive document fingerprinting\n- `hammingDistance` / `hammingSimilarity` - Hamming distance for fingerprint comparison\n- `MinHash` - MinHash estimator for approximate Jaccard similarity\n- `LSH` - Locality-Sensitive Hashing index for fast approximate nearest neighbor search\n\n#### Diff\n\n- `diff` - Compute the difference between two sequences\n\n#### Text Splitters\n\n- `RecursiveCharacterTextSplitter` - Splits text recursively using different separators\n- `CharacterTextSplitter` - Splits text by character count\n- `MarkdownTextSplitter` - Specialized splitter for Markdown documents\n- `TokenTextSplitter` - Splits text by token count\n- `LatexTextSplitter` - Specialized splitter for LaTeX documents\n\n#### Tokenization Utilities\n\n- `Tokenizer` - Main tokenizer class for encoding and decoding text\n- `encode()` - Convert text to token IDs and tokens\n- `decode()` - Convert token IDs back to text\n- `tokenize()` - Split text into token strings\n- `AddedToken` - Custom token configuration class\n\n## License\n\n- [MIT](../../LICENSE) © [Demo Macro](https://www.demomacro.com/)\n","readmeFilename":"README.md"}