build: basic setup (gemini-3-flash)

2026-08-18 13:21:55 +00:00 · 2026-03-26 19:04:48 +05:30
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+# 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.
+
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+# 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.
@@ -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);
@@ -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);
@@ -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);
+    }
+  }
+
+  console.log("Index built and persisted to ./storage");
+}
+
+main().catch(console.error);
@@ -0,0 +1,965 @@
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+  "name": "gemini-3-1-pro-preview",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "gemini-3-1-pro-preview",
+      "version": "1.0.0",
+      "license": "ISC",
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+        "@llamaindex/ollama": "^0.1.23",
+        "@llamaindex/openai": "^0.4.22",
+        "acorn": "^8.16.0",
+        "cheerio": "^1.2.0",
+        "glob": "^13.0.6",
+        "graphology": "^0.26.0",
+        "llamaindex": "^0.12.1"
+      }
+    },
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