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.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+nodejs_documents.jsonl filter=lfs diff=lfs merge=lfs -text
+nodejs_granular.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+# 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

STATS.md

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+# Node.js Dataset Statistics
+
+**Generated:** 2026-02-02 21:49:40.129088
+**Source File:** d:/Projects/Datasets/raw/nodejs.all.json
+
+## Statistics
+- **Total Modules/Documents**: 26883
+- **Total Granular Items (Functions/Props/Events)**: 570380
+
+## Outputs
+- `output/nodejs_documents.jsonl`: One line per module (high level context).
+- `output/nodejs_granular.jsonl`: One line per API item (fine-tuning ready).
+- `output/nodejs_granular.parquet`: Efficient columnar storage of granular data.

nodejs_documents.jsonl

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nodejs_granular.jsonl

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