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De Hilster","email":"contact@visualtext.org"},"license":"MIT","homepage":"https://visualtext.org/","keywords":["nlp","nlp++","text","text-analysis","parser","rule-based","visualtext","native","node-addon"],"repository":{"url":"git+https://github.com/VisualText/npm-package-nlpengine.git","type":"git"},"description":"Node.js bindings for the NLP++ text-analysis engine (peer of the Python package NLPPlus)","maintainers":[{"name":"visualtext","email":"admin@visualtext.org"}],"readme":"# nlpplus\n\n**NLP++ lets you build fully customized text analyzers using the [NLP++ VSCode language extension](https://vscode.visualtext.org), giving you 100% visibility into — and complete control over — every rule and decision your analyzer makes. Unlike other NLP packages that are statistical black boxes you cannot inspect or change, every NLP++ analyzer is glass-box code you own and can tailor to your exact needs.**\n\n[![NLP++ Textbook](https://github.com/VisualText/npm-package-nlpengine/raw/main/assets/TextbookLaunch01_LinkedIn%20Banner.png)](https://book.visualtext.org)\n\n## First Textbook on the NLP++ Programming Language\n\nThe first textbook on NLP++ is now available world-wide by [BPB Online](https://book.visualtext.org). NLP++ can replace LLMs when used in agentic flows. The code must be written by a human like any other programming language and this book will facilitate this process. NLP++ is no a statistical system that needs training. It relies on the ingenuity of the programmer to create a program that can parse text and extract information in a deterministic way.\n\n## The nlpplus Node.js Package\n\n[![Test](https://github.com/VisualText/npm-package-nlpengine/actions/workflows/test.yml/badge.svg)](https://github.com/VisualText/npm-package-nlpengine/actions/workflows/test.yml)\n[![Build prebuilds](https://github.com/VisualText/npm-package-nlpengine/actions/workflows/publish.yml/badge.svg)](https://github.com/VisualText/npm-package-nlpengine/actions/workflows/publish.yml)\n\nNode.js bindings for the [NLP++](https://visualtext.org) text-analysis\nengine — the Node peer of the Python package\n[NLPPlus](https://github.com/VisualText/py-package-nlpengine).\n\n`nlpplus` lets Node scripts call text and NLP analyzers written in\n[NLP++](https://visualtext.org). It links the C++ libraries of the\n[NLP Engine](https://github.com/VisualText/nlp-engine) directly into a\nnative Node-API addon, so calls run in-process — far more efficient than\nshelling out to the command-line `nlp.exe` (which is what\n[ts-nlp-engine](https://github.com/VisualText/ts-nlp-engine) does).\n\nThe major advantage of NLP++ over other NLP packages is that it is 100%\nrule-based and modifiable: any non-linguistic programmer can build text\nanalyzers 100% tailored to their needs, and every analyzer is human-readable\nglass-box code — no training, no statistical black box.\n\nAnalyzers can be run in two modes: **interpreted** (the default, runs\nstraight from the `.nlp` source) or **compiled** (analyzer code is compiled\nto a native shared library once and loaded at runtime). See\n[Compiled mode](#compiled-mode) below for the `cloudCompile()` one-call\nbuild path.\n\n## Requirements\n\n- Node.js 18 or newer\n- For building from source (no prebuilt binary for your platform): a working\n  C++ compiler and the ICU libraries — see [Building from source](#building-from-source).\n\n## Installation\n\n```sh\nnpm install nlpplus\n```\n\nWhen a prebuilt binary exists for your platform/Node-API version it is used\ndirectly. Otherwise the package builds from source on install (this requires\na C++ toolchain and ICU).\n\n## Usage\n\nVery basic usage — run the default US-English parser and get the parsing\nresults as XML:\n\n```js\nconst nlpplus = require('nlpplus');\n\nconst xml = nlpplus.analyze('Hello world.');\nconsole.log(xml);\n```\n\nDomain-specific analyzers are bundled too. These generally don't return text\n— they write a parse tree and `output.json` into the analyzer's output dir,\nwhich you read via the `Results` object from the `Engine` API:\n\n```js\nconst { Engine } = require('nlpplus');\n\nconst engine = new Engine();\ntry {\n  const results = engine.analyze('Reach me at hello@example.com', 'emailaddress');\n  console.log(results.output);     // parsed output.json as an object\n  console.log(results.finalTree);  // the NLP++ parse tree\n} finally {\n  engine.close();\n}\n```\n\nBundled analyzers:\n\n- `parse-en-us` — full English parser (default)\n- `address-parser` — extract addresses from text\n- `emailaddress` — extract email addresses\n- `links` — extract hyperlinks\n- `telephone` — extract telephone numbers\n\nBecause NLP++ is glass-box, every bundled analyzer can be edited with the\n[NLP++ VSCode extension](https://vscode.visualtext.org). Copy them somewhere\nwritable first with `copyLibraryAnalyzers()` so a package upgrade won't\noverwrite your edits.\n\n## API\n\n### Module-level functions\n\n| Function | Description |\n|---|---|\n| `analyze(text, parser = 'parse-en-us', develop = false, compiled = false)` | Run an analyzer on `text`, return its output text. |\n| `compile(analyzer = 'parse-en-us', develop = false, kbOnly = false, analyzerOnly = false)` | Generate the analyzer's C++ source trees (`-COMPILE`, or `-COMPILEKB` for `kbOnly`, or `-COMPILEANA` for `analyzerOnly`). |\n| `cloudCompile(analyzer = 'parse-en-us', opts?)` | End-to-end compile via the cloud build service (async). |\n| `setWorkingFolder(folder?, initialize = false)` | Re-point the default engine at another working folder. |\n| `setAnalyzersFolder(path)` | Set the analyzers directory used by the default engine. |\n| `copyLibraryAnalyzers(toDir, overwrite = true)` | Copy bundled analyzers somewhere writable. |\n| `inputText(analyzer, fileName)` | Read a file from the analyzer's `input/` dir. |\n| `putJsonFile(analyzer, jsonPath, name?)` | Copy a JSON file into the analyzer's `kb/user/` dir for its `json2kbb` pass. |\n| `putJsonObject(analyzer, obj, name)` | Serialize a JSON value into the analyzer's `kb/user/<name>.json`. |\n| `engineVersion()` | Bundled nlp-engine version string (e.g. `\"3.1.55\"`). |\n\nThese are thin wrappers over a module-level default `Engine`, exported as\n`nlpplus.engine`.\n\n### `Engine`\n\n```js\nconst engine = new Engine({ workingFolder, verbose, initialize });\n```\n\n- `workingFolder` *(string, optional)* — working folder. If omitted, a temp\n  directory is created and initialized with the bundled analyzers/data, and\n  is removed on `close()`. Otherwise it must contain `analyzers` and `data`\n  folders (unless `initialize: true`).\n- `verbose` *(bool, default false)* — more engine logging.\n- `initialize` *(bool, default false)* — populate `workingFolder` with the\n  bundled analyzers and data.\n\nMethods: `analyze`, `compile`, `cloudCompile`, `inputText`,\n`setAnalyzersFolder`, `copyLibraryAnalyzers`, `putJsonFile`, `putJsonObject`,\n`close`.\n\n> **Always call `engine.close()`** (e.g. in a `finally`) when you create an\n> `Engine` explicitly. On Windows the engine holds a handle on\n> `<workfolder>/logs/cgerr.log`, and `close()` is what lets the auto-created\n> temp working folder be removed. `close()` is idempotent.\n\n### `Results`\n\nReturned by `engine.analyze()`:\n\n- `outputText` — the raw string the engine returned (often empty).\n- `finalTree` — the NLP++ parse tree (`final.tree`), or `null`.\n- `outputJson` — the raw `output.json` text, or `null`.\n- `output` — `output.json` parsed into an object, or `null`.\n\n## Compiled mode\n\nAnalyzers normally run **interpreted** from their `.nlp` source — fine for\ndevelopment, slower on large inputs. **Compiled mode** builds native shared\nlibraries from the analyzer's `.nlp` files once, then loads them at analyze\ntime.\n\nThe simplest path is one call to `cloudCompile()`, which uses the public\n[nlp-compile-service](https://github.com/VisualText/nlp-compile-service) to\nbuild the right shared library for your platform:\n\n```js\nconst nlpplus = require('nlpplus');\n\n// Generate run/*.cpp + kb/*.cpp, ship to the cloud builder, download the\n// .so/.dylib/.dll, stage it into <analyzer>/bin/.\nawait nlpplus.cloudCompile('parse-en-us');\n\n// Now run with the compiled artifacts instead of the interpreter.\nconst xml = nlpplus.analyze('Hello world.', 'parse-en-us', false, true);\n```\n\n`cloudCompile()` options: `dispatcherUrl`, `kbOnly`, `analyzerOnly`,\n`develop`, `pollInterval`, `timeout` (default 30 min), `skipLocalCompile`.\nSet `analyzerOnly: true` to recompile just the analyzer rules when the KB\nis already compiled (`kbOnly` and `analyzerOnly` are mutually exclusive).\nThe cloud build takes ~1 min (small analyzer, cache hit) up to ~10 min\n(`parse-en-us`, cold Windows runner queue).\n\nIf you'd rather build the C++ trees yourself, use `compile()` for the\ncodegen step and run `cmake` against the engine's\n[published compile-libs](https://github.com/VisualText/nlp-engine/releases),\nthen stage the result as `<analyzer>/bin/run.<ext>` and\n`<analyzer>/bin/kb.<ext>`.\n\n## Building from source\n\n```sh\ngit clone --recurse-submodules https://github.com/VisualText/npm-package-nlpengine.git\ncd npm-package-nlpengine\nnpm install\n```\n\nThe native addon is built with [cmake-js](https://github.com/cmake-js/cmake-js)\nand [node-addon-api](https://github.com/nodejs/node-addon-api), driving the\nsame CMake build the engine uses.\n\n### Linux\n\n```sh\n# Ubuntu / Debian\nsudo apt install libicu-dev\n# CentOS / RHEL\nsudo yum install libicu-devel\n\nnpm run build\n```\n\n### macOS / other Unix\n\nICU usually isn't available system-wide, so use vcpkg:\n\n```sh\ngit clone --depth 1 https://github.com/Microsoft/vcpkg.git\n./vcpkg/bootstrap-vcpkg.sh\nbrew install autoconf-archive autoconf automake pkg-config\n\nCMAKE_TOOLCHAIN_FILE=$PWD/vcpkg/scripts/buildsystems/vcpkg.cmake npm run build\n```\n\n### Windows\n\nICU ships as DLLs that must be bundled with the addon. Use vcpkg\n(`x64-windows-release`) for ICU and build with the toolchain file as above.\nThe `vcpkg.json` manifest in this repo declares the ICU dependency.\n\n### Testing\n\n```sh\nnpm test\n```\n\nThe suite skips itself if the native addon hasn't been built yet.\n\n## Making a release\n\nReleases are managed by GitHub Actions. Bump the `version` in\n`package.json`, then push an annotated tag of the form `vX.Y.Z`:\n\n```sh\ngit tag -m 'Release 0.1.0' -a v0.1.0\ngit push --follow-tags\n```\n\nThe `publish` workflow builds prebuilds for Linux/macOS/Windows and publishes\nto npm (needs the `NPM_TOKEN` secret).\n\n## Learn more about NLP++\n\n- [VisualText website](https://visualtext.org)\n- [NLP++ VSCode extension](https://vscode.visualtext.org)\n- [Lectures on NLP++](http://talks.visualtext.org)\n- [YouTube tutorials](http://tutorials.visualtext.org)\n- [NLU Global Initiative](https://nluglob.org)\n\n## License\n\nMIT — see [LICENSE](LICENSE).\n","readmeFilename":"README.md"}