RAG Pipeline for Language Specification Exploration

This project implements a RAG-based AI chat agent to explore the ECMAScript specification and its implementation in engine262.

Prerequisites

• Node.js: Version 18+
• Ollama: Installed locally with an embedding model (e.g., nomic-embed-text)
• OpenAI-compatible Endpoint: A hosted or local LLM service

Setup

1. Install dependencies:

   npm install
   

2. Prepare environment:

   export OPENAI_API_BASE="your_endpoint_base_url"
   export OPENAI_API_KEY="your_api_key"
   

3. Clone specification: (Ensure ./spec-built/multipage contains the HTML files)

4. Ingest data:

   node ingest.mjs
   

   Note: This will take significant time as it generates local embeddings via Ollama for both the spec and the implementation.

5. Build graph:

   node build_graph.mjs
   

Usage

Ask the agent questions about how code relates to the specification:

node agent.mjs "Explain how the 'if' statement works and show its implementation."

The agent will use tools to search the specification, explore the implementation code, and navigate the relationships between them using the graph.