README.md

metadata

license: mit
tags:
  - nodejs
pretty_name: nodejs-all.json

Node.js API Dataset

Source: Node.js 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/:

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