From 8ed06e495f551cc0eab77fe555213682a5b1634d Mon Sep 17 00:00:00 2001 From: bendtherules Date: Thu, 26 Mar 2026 19:04:48 +0530 Subject: [PATCH] build: basic setup (gemini-3-flash) --- .../plans/1774523669864-glowing-wizard.md | 83 ++ Readme.md | 46 + agent.mjs | 133 +++ build_graph.mjs | 99 ++ ingest.mjs | 119 +++ package-lock.json | 965 ++++++++++++++++++ package.json | 25 + 7 files changed, 1470 insertions(+) create mode 100644 .opencode/plans/1774523669864-glowing-wizard.md create mode 100644 agent.mjs create mode 100644 build_graph.mjs create mode 100644 ingest.mjs create mode 100644 package-lock.json create mode 100644 package.json diff --git a/.opencode/plans/1774523669864-glowing-wizard.md b/.opencode/plans/1774523669864-glowing-wizard.md new file mode 100644 index 0000000..5dd561e --- /dev/null +++ b/.opencode/plans/1774523669864-glowing-wizard.md @@ -0,0 +1,83 @@ +# Plan for building a RAG-like pipeline for language specification exploration + +**Technology Stack:** +- **Framework:** LlamaIndex.js +- **Graph Storage:** LlamaIndex `SimpleGraphStore` (file-based) +- **Embeddings:** Local Ollama + +## High-Level Architecture + +The system will have four main components: + +1. **Data Ingestion:** A pipeline to process the HTML specification and the Javascript code. +2. **Data Storage:** A combination of a vector store and a graph store, both managed by LlamaIndex. +3. **RAG Pipeline:** A retrieval-augmented generation pipeline built with LlamaIndex. +4. **Agentic Chat Interface:** An interactive chat interface for the user to interact with the system. + +## Phase 1: Data Ingestion and Basic RAG + +This phase focuses on getting the basic RAG pipeline working with LlamaIndex. + +* **Task 1: Setup Project and Dependencies:** + * Initialize a Node.js project. + * Install `llamaindex`. + * Set up Ollama for local embeddings. + * Clone the `engine262` repository from `https://github.com/engine262/engine262` into the `./engine262` folder. + +* **Task 2: HTML Specification Parsing:** + * Write a script to recursively read all HTML files from the `./spec-built/multipage` folder. + * Use a library like `cheerio` to parse the HTML and extract the main text content. + * Use LlamaIndex's `SimpleNodeParser` to break down the content into meaningful chunks (nodes). + +* **Task 3: Javascript Code Parsing:** + * Write a script to read the Javascript files from the `./engine262/src` folder. + * Initially, treat the code as plain text and parse it into nodes. + +* **Task 4: Vector Database Integration with LlamaIndex:** + * Configure LlamaIndex to use your local Ollama instance for generating embeddings by using the `OllamaEmbedding` class. + * LlamaIndex will manage the documents and embeddings in a vector store. You can start with an in-memory store. + +* **Task 5: Basic RAG Pipeline:** + * Use LlamaIndex's `VectorStoreIndex` to build an index over your parsed documents. + * Create a `QueryEngine` from the index to ask questions. + +* **Task 6: Simple CLI Interface:** + * Create a simple command-line interface to ask questions and see the results from the LlamaIndex query engine. + +## Phase 2: Graph Integration with Graphology + +This phase will enhance the retrieval process by modeling the structure of the specification and the code using Graphology. LlamaIndex.js's native graph store is currently limited, so we'll use a custom mapping. + +* **Task 1: Graph Storage Setup:** + * Use `graphology` to build an in-memory graph. + * Persist the graph to a `graph.json` file for persistence. + +* **Task 2: Enhance Parsers to Extract Relationships:** + * **HTML Parser:** When parsing the spec, extract section titles (e.g., "If Statement") and their IDs. + * **JS Parser:** Use `acorn` or `@babel/parser` to create an AST of the Javascript code and extract function names. Create a mapping from function names to specification section titles (e.g., `Evaluate_IfStatement` -> `If Statement`). + +* **Task 3: Populate Graph Store:** + * Write a script to build the graph: + * Create nodes for specification sections (`SpecSection`) and Javascript functions (`JSFunction`). + * Create relationships for links between spec sections (`LINKS_TO`) and for functions implementing a spec section (`IMPLEMENTS`). + * Save this graph to `graph.json`. + +* **Task 4: Enhance Retriever with Custom Graph Lookup:** + * Create a custom retriever that first looks up the relevant code/spec in the graph and then uses the vector store for detailed retrieval. + +## Phase 3: Agentic Chat and Tool Use + +This phase focuses on building the interactive and "smart" agent using LlamaIndex's capabilities. + +* **Task 1: Agent Framework:** + * Use LlamaIndex's agent classes, like `ChatEngine` or `QueryEngine` with tools, to create the agent. + * Configure the agent to use your OpenAI-compatible service for generation. + +* **Task 2: Define Tools:** + * LlamaIndex allows you to define tools. The primary tool will be a `QueryEngineTool` that uses the combined vector and graph index from the previous phases. + * You could also create more specific tools, like one to directly query the graph for structural information. + +* **Task 3: Build the Chat Interface:** + * Create a web-based chat interface (e.g., using React or Vue). + * This interface will interact with the LlamaIndex agent endpoint. + diff --git a/Readme.md b/Readme.md index e69de29..e55aae1 100644 --- a/Readme.md +++ b/Readme.md @@ -0,0 +1,46 @@ +# 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**: + ```bash + npm install + ``` + +2. **Prepare environment**: + ```bash + 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**: + ```bash + 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**: + ```bash + node build_graph.mjs + ``` + +## Usage + +Ask the agent questions about how code relates to the specification: + +```bash +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. diff --git a/agent.mjs b/agent.mjs new file mode 100644 index 0000000..05f47b0 --- /dev/null +++ b/agent.mjs @@ -0,0 +1,133 @@ +import fs from 'fs'; +import { + VectorStoreIndex, + storageContextFromDefaults, + Settings, + QueryEngineTool, + ReActAgent +} from 'llamaindex'; +import { OllamaEmbedding } from '@llamaindex/ollama'; +import { OpenAI } from '@llamaindex/openai'; +import { Graph } from 'graphology'; + +const STORAGE_DIR = './storage'; +const GRAPH_FILE = './graph.json'; + +// Configure Settings +Settings.embedModel = new OllamaEmbedding({ + model: "nomic-embed-text-v2-moe", +}); + +const config = JSON.parse(fs.readFileSync('./config.json', 'utf-8')); +const apiKey = config.NVIDIA_API_KEY; +const baseURL = config.NVIDIA_API_BASE; + +if (!apiKey) { + console.warn("Please set NVIDIA_API_KEY in config.json."); +} + +const llm = new OpenAI({ + model: "openai/gpt-oss-120b", + apiKey: apiKey, + baseURL: baseURL, + temperature: 0 +}); +Settings.llm = llm; + +async function main() { + console.log("Loading indices and graph..."); + const storageContext = await storageContextFromDefaults({ + persistDir: STORAGE_DIR, + }); + + const index = await VectorStoreIndex.init({ + storageContext, + }); + + const graphData = JSON.parse(fs.readFileSync(GRAPH_FILE, 'utf-8')); + const graph = new Graph({ multi: true }); + graph.import(graphData); + + const queryEngine = index.asQueryEngine({ similarityTopK: 3 }); + + const queryEngineTool = new QueryEngineTool({ + queryEngine, + metadata: { + name: "spec_retriever", + description: "Queries the language specification for text content about specific sections or topics. Use this to get the detailed text of a section.", + }, + }); + + const graphTool = { + metadata: { + name: "graph_explorer", + description: "Explores structural relationships between specification sections and implementation code (functions). Use this to find which spec section a function implements. Input: section ID or function name.", + parameters: { + type: "object", + properties: { + query: { + type: "string", + description: "The section ID or function name to explore.", + }, + }, + required: ["query"], + }, + }, + call: async ({ query }) => { + console.log(`[Tool: graph_explorer] Querying for: ${query}`); + let nodeId = query; + if (!graph.hasNode(nodeId)) { + if (graph.hasNode(`func-${query}`)) { + nodeId = `func-${query}`; + } + } + + if (graph.hasNode(nodeId)) { + const neighbors = graph.neighbors(nodeId); + const nodeAttr = graph.getNodeAttributes(nodeId); + let result = `Information for ${nodeId} (${nodeAttr.type}):\n`; + if (nodeAttr.title) result += `- Title: ${nodeAttr.title}\n`; + if (nodeAttr.file) result += `- File: ${nodeAttr.file}\n`; + result += `\nConnected parts:\n`; + neighbors.forEach(neighbor => { + const attr = graph.getNodeAttributes(neighbor); + const edges = graph.edges(nodeId, neighbor); + const edgeAttr = graph.getEdgeAttributes(edges[0]); + result += `- ${neighbor} (${attr.type}) via ${edgeAttr.type}${attr.title ? ': ' + attr.title : ''}\n`; + }); + return result; + } + return `No information found in graph for ${query}. Use spec_retriever to search text.`; + } + }; + + const agent = new ReActAgent({ + tools: [queryEngineTool, graphTool], + llm: llm, + verbose: true, + systemPrompt: `You are an expert in the ECMAScript specification and its implementation in engine262. +Your goal is to explain how specific parts of the language work by combining information from the provided tools. + +CRITICAL INSTRUCTIONS: +1. ALWAYS prefer using the provided tools ('spec_retriever' and 'graph_explorer') to answer questions. +2. Do NOT rely on your internal knowledge of JavaScript or the ECMAScript specification. +3. If the user asks about a function, you MUST first use 'graph_explorer' to find the associated specification section. +4. You MUST then use 'spec_retriever' to read the actual text of that specification section before answering. +5. Base your explanations ONLY on the information retrieved from the tools. +6. If the tools do not provide enough information, state that clearly rather than guessing from your internal knowledge.` + }); + + console.log("Agent is ready!"); + + const message = process.argv[2] || "Which spec section does Evaluate_IfStatement implement? and what does that section say?"; + console.log(`User: ${message}`); + + const response = await agent.chat({ + message: message + }); + + console.log("\n--- Agent Response ---\n"); + console.log(response.toString()); +} + +main().catch(console.error); diff --git a/build_graph.mjs b/build_graph.mjs new file mode 100644 index 0000000..42a4c93 --- /dev/null +++ b/build_graph.mjs @@ -0,0 +1,99 @@ +import fs from 'fs'; +import path from 'path'; +import { glob } from 'glob'; +import * as cheerio from 'cheerio'; +import { Graph } from 'graphology'; + +const SPEC_DIR = './spec-built/multipage'; +const CODE_DIR = './engine262/src'; +const GRAPH_FILE = './graph.json'; + +async function buildGraph() { + const graph = new Graph({ multi: true }); + const htmlFiles = await glob(path.join(SPEC_DIR, '*.html')); + + console.log("Processing specification for graph..."); + for (const file of htmlFiles) { + const fileName = path.basename(file); + const content = fs.readFileSync(file, 'utf-8'); + const $ = cheerio.load(content); + + $('emu-clause').each((i, elem) => { + const id = $(elem).attr('id'); + const title = $(elem).find('h1').first().text().trim(); + + if (id) { + if (!graph.hasNode(id)) { + graph.addNode(id, { title, type: 'SpecSection', file: fileName }); + } + + // Extract internal links + $(elem).find('a[href]').each((j, link) => { + const href = $(link).attr('href'); + if (href && href.includes('#')) { + const [targetFile, targetId] = href.split('#'); + // We only care about links to other sections for now + if (targetId && targetId.startsWith('sec-')) { + // Add relationship later if nodes exist + } + } + }); + } + }); + } + + // Add edges for internal links + for (const file of htmlFiles) { + const content = fs.readFileSync(file, 'utf-8'); + const $ = cheerio.load(content); + $('emu-clause').each((i, elem) => { + const sourceId = $(elem).attr('id'); + if (sourceId && graph.hasNode(sourceId)) { + $(elem).find('a[href]').each((j, link) => { + const href = $(link).attr('href'); + if (href && href.includes('#')) { + const [, targetId] = href.split('#'); + if (targetId && graph.hasNode(targetId) && sourceId !== targetId) { + if (!graph.hasEdge(sourceId, targetId)) { + graph.addEdge(sourceId, targetId, { type: 'LINKS_TO' }); + } + } + } + }); + } + }); + } + + console.log("Processing code for graph..."); + const jsFiles = await glob(path.join(CODE_DIR, '**/*.mts')); + + for (const file of jsFiles) { + const fileName = path.basename(file); + const content = fs.readFileSync(file, 'utf-8'); + + // Simple heuristic: look for function names starting with Evaluate_ + const functionMatches = content.matchAll(/export function\*? (Evaluate_([a-zA-Z0-9_]+))/g); + for (const match of functionMatches) { + const fullFuncName = match[1]; + const shortName = match[2]; + const funcNodeId = `func-${fullFuncName}`; + + if (!graph.hasNode(funcNodeId)) { + graph.addNode(funcNodeId, { name: fullFuncName, type: 'JSFunction', file: fileName }); + } + + // Try to link to a spec section + // Heuristic: Evaluate_IfStatement -> sec-if-statement + const specId = `sec-${shortName.replace(/([a-z0-9])([A-Z])/g, '$1-$2').toLowerCase()}`; + if (graph.hasNode(specId)) { + graph.addEdge(funcNodeId, specId, { type: 'IMPLEMENTS' }); + } + } + } + + console.log(`Graph built with ${graph.order} nodes and ${graph.size} edges.`); + fs.writeFileSync(GRAPH_FILE, JSON.stringify(graph.export(), null, 2)); + console.log(`Graph saved to ${GRAPH_FILE}`); +} + +buildGraph().catch(console.error); diff --git a/ingest.mjs b/ingest.mjs new file mode 100644 index 0000000..7d72b7c --- /dev/null +++ b/ingest.mjs @@ -0,0 +1,119 @@ +import fs from 'fs'; +import path from 'path'; +import { glob } from 'glob'; +import * as cheerio from 'cheerio'; +import { + Document, + VectorStoreIndex, + Settings, + storageContextFromDefaults, + SentenceSplitter +} from 'llamaindex'; +import { OllamaEmbedding } from '@llamaindex/ollama'; + +// Configure Settings +Settings.embedModel = new OllamaEmbedding({ + model: "nomic-embed-text-v2-moe", +}); + +const SPEC_DIR = './spec-built/multipage'; +const CODE_DIR = './engine262/src'; +const STORAGE_DIR = './storage'; + +// Initialize a SentenceSplitter with even smaller chunk size +const sentenceSplitter = new SentenceSplitter({ chunkSize: 256, chunkOverlap: 20 }); + +async function ingestSpec() { + const htmlFiles = await glob(path.join(SPEC_DIR, '*.html')); + const documents = []; + + for (const file of htmlFiles) { + const content = fs.readFileSync(file, 'utf-8'); + const $ = cheerio.load(content); + + $('emu-clause').each((i, elem) => { + const id = $(elem).attr('id'); + const title = $(elem).find('h1').first().text().trim(); + // Only extract immediate text to avoid excessive chunking of child sections + const text = $(elem).clone().children('emu-clause').remove().end().text().trim(); + + if (id && title && text) { + documents.push(new Document({ + text, + metadata: { + source: file, + sectionId: id, + sectionTitle: title, + type: 'specification' + } + })); + } + }); + } + return documents; +} + +// ingestCode function removed as requested + +async function main() { + console.log("Ingesting specification..."); + const specDocs = await ingestSpec(); + console.log(`Ingested ${specDocs.length} specification sections.`); + + console.log("Splitting documents into nodes..."); + const rawNodes = sentenceSplitter.getNodesFromDocuments(specDocs); + console.log(`Total raw nodes generated: ${rawNodes.length}`); + + // Safety filter to ensure no node exceeds context limit + const nodes = rawNodes.filter(node => { + const contentLen = node.getContent().length; + if (contentLen > 2000) { + console.warn(`Skipping node with length ${contentLen} from ${node.metadata.source || 'unknown'}`); + return false; + } + return true; + }); + console.log(`Total valid nodes for indexing: ${nodes.length}`); + + console.log("Creating storage context..."); + const storageContext = await storageContextFromDefaults({ + persistDir: STORAGE_DIR, + }); + + console.log("Building index (this might take a while with local Ollama)..."); + + const BATCH_SIZE = 50; + let index; + + // Try to load existing index if any + try { + index = await VectorStoreIndex.init({ + storageContext, + }); + console.log("Existing index found, continuing ingestion..."); + } catch (e) { + console.log("No existing index found, starting fresh."); + } + + for (let i = 0; i < nodes.length; i += BATCH_SIZE) { + const batch = nodes.slice(i, i + BATCH_SIZE); + console.log(`Processing batch ${i / BATCH_SIZE + 1} / ${Math.ceil(nodes.length / BATCH_SIZE)}...`); + + if (!index) { + index = await VectorStoreIndex.init({ + storageContext, + nodes: batch + }); + } else { + // Here we'd ideally skip nodes already in the index, + // but for simplicity we'll just continue or assume + // we're starting fresh for this run if index was null. + // To truly resume, we need more logic. + await index.insertNodes(batch); 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