| --- |
| license: mit |
| tags: |
| - nodejs |
| pretty_name: nodejs-all.json |
| --- |
| # Node.js API Dataset |
| > **Source**: [Node.js](https://nodejs.org/) official documentation (JSON variant) |
| > **Processing Type**: Extractive, Hierarchical Flattening. |
|
|
| ## Overview |
| This dataset contains a structured representation of the Node.js API, derived from the official `nodejs.json` distribution. Unlike raw documentation dumps, this dataset has been processed into two distinct formats to serve different machine learning and analytical purposes: **Macro-level (Documents)** and **Micro-level (Granular Items)**. |
|
|
| This "Dataset-as-a-Repo" approach ensures that the data is not just a transient output of a pipeline but a versioned, maintained artifact suitable for training high-quality code models. |
|
|
| ## Methodology & Design Choices |
|
|
| ### 1. The "Abstract-to-Concrete" Philosophy |
| The core design philosophy here is that "code intelligence" requires understanding both the *forest* (modules, high-level concepts) and the *trees* (individual function signatures, property types). |
| - **Raw Input**: The `nodejs.all.json` is a massive, nested structure that can be overwhelming for simple sequential models. |
| - **Transformation**: We "pulled apart" the JSON to create focused training examples. |
|
|
| ### 2. Dual-Format Output |
| We intentionally avoided a "one-size-fits-all" schema. |
| - **Documents (`nodejs_documents.jsonl`)**: Preserves the cohesiveness of a module. Good for teaching a model "concept association" (e.g., that `fs.readFile` belongs with `fs.writeFile`). |
| - **Granular (`nodejs_granular.jsonl`)**: Isolates every single function and property. Good for "instruction tuning" (e.g., "Write a function signature for `http.createServer`"). |
|
|
| ### 3. File Formats |
| - **JSONL**: Chosen for its streaming capabilities and human readability. Perfect for NLP pipelines. |
| - **Parquet**: Chosen for the "Granular" dataset to allow fast columnar access, filtering, and analysis (e.g., "Find all methods with > 3 arguments"). |
|
|
| ## Dataset Structure |
|
|
| ### Output Location |
| All processed files are located in `output/`: |
| ```text |
| output/ |
| ├── nodejs_documents.jsonl # High-level module data |
| ├── nodejs_granular.jsonl # Individual API items |
| └── nodejs_granular.parquet # Parquet version of granular data |
| ``` |
|
|
| ### Schema: Documents (`nodejs_documents.jsonl`) |
| Representing a whole Module (e.g., `Buffer`, `http`). |
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `module_name` | string | Name of the module (e.g., `fs`). | |
| | `type` | string | Usually `module` or `global`. | |
| | `description` | string | Raw HTML/Markdown description of the module. | |
| | `content` | json-string | Full nested JSON blob of the module's contents (methods, props). | |
|
|
| ### Schema: Granular (`nodejs_granular.jsonl`) |
| Representing a single API item (Function, Property, Event). |
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | string | Unique namespaced ID (e.g., `fs.readFile`). | |
| | `parent` | string | Parent module (e.g., `fs`). | |
| | `type` | string | `method`, `property`, `event`, etc. | |
| | `name` | string | Short name (e.g., `readFile`). | |
| | `description` | string | Description of *just* this item. | |
| | `metadata` | json-string | Detailed signatures, params, stability indices. | |
| |
| ## Use Cases |
| |
| ### 1. Pre-Training Code Models |
| Feed `nodejs_documents.jsonl` into a language model to teach it the general structure and API surface of Node.js. The large context windows of modern LLMs can easily ingest entire modules. |
|
|
| ### 2. Instruction Tuning / RAG |
| Use `nodejs_granular.jsonl` to build a Retrieval Augmented Generation (RAG) system. |
| - **Query**: "How do I read a file in Node?" |
| - **Retrieval**: Search against the `description` field in the granular dataset. |
| - **Context**: Retrieve the exact `metadata` (signature) for `fs.readFile`. |
|
|
| ### 3. API Analysis |
| Use `nodejs_granular.parquet` with Pandas/DuckDB to answer meta-questions: |
| - *Which Node.js APIs are marked as Experimental?* |
| - *What is the average number of arguments for `fs` methods vs `http` methods?* |
|
|
| ## Provenance |
| - **Original File**: `datasets/raw/nodejs.all.json` |
| - **Script**: `src/process_nodejs.py` |
| - **Maintainer**: Antigravity (Agent) / User |
