From 5242a30081d844d19b87ed85b241e2334c4945d7 Mon Sep 17 00:00:00 2001
From: bendtherules <abhasbhattacharya2@gmail.com>
Date: Tue, 31 Mar 2026 12:20:32 +0530
Subject: [PATCH] agent.ts - add comments

---
 agent.ts | 46 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 45 insertions(+), 1 deletion(-)

diff --git a/agent.ts b/agent.ts
index 340afa2..990497a 100644
--- a/agent.ts
+++ b/agent.ts
@@ -12,11 +12,14 @@ import {
 
 import { GRAPH_FILE, STORAGE_DIR } from "./constants";
 
-// Configure Settings
+// Configure LlamaIndex to use local Ollama embeddings for semantic search
+// This enables the query engine to perform similarity searches without external APIs
 Settings.embedModel = new OllamaEmbedding({
   model: "nomic-embed-text-v2-moe",
 });
 
+// Load API configuration from config.json
+// Expects NVIDIA_API_KEY and NVIDIA_API_BASE for accessing NVIDIA's API endpoint
 const config = JSON.parse(fs.readFileSync("./config.json", "utf-8"));
 const apiKey = config.NVIDIA_API_KEY;
 const baseURL = config.NVIDIA_API_BASE;
@@ -25,6 +28,8 @@ if (!apiKey) {
   console.warn("Please set NVIDIA_API_KEY in config.json.");
 }
 
+// Initialize the LLM using NVIDIA's OpenAI-compatible API endpoint
+// Model: openai/gpt-oss-120b with temperature 0 for deterministic responses
 const llm = new OpenAI({
   model: "openai/gpt-oss-120b",
   apiKey: apiKey,
@@ -33,8 +38,17 @@ const llm = new OpenAI({
 });
 Settings.llm = llm;
 
+/**
+ * Main function that initializes and runs the ECMAScript specification agent.
+ *
+ * The agent combines two information sources:
+ * 1. Vector search index (spec_retriever) - for semantic text search across spec sections
+ * 2. Graph knowledge base (graph_explorer) - for structural relationships between sections and code
+ */
 async function main() {
   console.log("Loading indices and graph...");
+
+  // Load the vector index from disk containing embedded spec sections
   const storageContext = await storageContextFromDefaults({
     persistDir: STORAGE_DIR,
   });
@@ -43,12 +57,18 @@ async function main() {
     storageContext,
   });
 
+  // Load the knowledge graph mapping spec sections to implementation functions
   const graphData = JSON.parse(fs.readFileSync(GRAPH_FILE, "utf-8"));
   const graph = new Graph({ multi: true });
   graph.import(graphData);
 
+  // Create a query engine with top-3 similarity results for text retrieval
   const queryEngine = index.asQueryEngine({ similarityTopK: 3 });
 
+  /**
+   * Tool for retrieving specification text via vector similarity search.
+   * Used to get detailed content of specific sections based on semantic queries.
+   */
   const queryEngineTool = new QueryEngineTool({
     queryEngine,
     metadata: {
@@ -58,6 +78,11 @@ async function main() {
     },
   });
 
+  /**
+   * Tool for exploring the knowledge graph connecting spec sections to implementation.
+   * Enables structural navigation: finding which spec section a function implements
+   * or which functions implement a spec section.
+   */
   const graphTool = {
     metadata: {
       name: "graph_explorer",
@@ -76,6 +101,8 @@ async function main() {
     },
     call: async ({ query }: { query: string }) => {
       console.log(`[Tool: graph_explorer] Querying for: ${query}`);
+
+      // Try exact node match, or prepend 'func-' prefix for function names
       let nodeId = query;
       if (!graph.hasNode(nodeId)) {
         if (graph.hasNode(`func-${query}`)) {
@@ -84,24 +111,39 @@ async function main() {
       }
 
       if (graph.hasNode(nodeId)) {
+        // Collect node info and all connected nodes
         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`;
+
+        // List all connected nodes with their relationship types
         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.`;
     },
   };
 
+  /**
+   * ReAct agent that reasons about ECMAScript specification.
+   *
+   * The agent follows this workflow:
+   * 1. For function queries: graph_explorer → spec_retriever → explanation
+   * 2. For section queries: spec_retriever → explanation
+   *
+   * Critical constraints ensure tool-based answers rather than internal knowledge.
+   */
   const agent = new ReActAgent({
     tools: [queryEngineTool, graphTool],
     llm: llm,
@@ -120,11 +162,13 @@ CRITICAL INSTRUCTIONS:
 
   console.log("Agent is ready!");
 
+  // Accept user query from command line argument, or use default question
   const message =
     process.argv[2] ||
     "Which spec section does Evaluate_IfStatement implement? and what does that section say?";
   console.log(`User: ${message}`);
 
+  // Execute the agent with the user's query
   const response = await agent.chat({
     message: message,
   });