Rag

app/app.js

@@ -1,18 +1,18 @@
-import express from 'express';
-import { OpenAI } from 'openai';
-import path from 'path';
-import { fileURLToPath } from 'url';
-import dotenv from 'dotenv';
+import dotenv from "dotenv";
+import express from "express";
+import { OpenAI } from "openai";
+import path from "path";
+import { fileURLToPath } from "url";
 
-import json from './public/characters.json' with { type: "json" };
+dotenv.config();
+
+import json from "./public/characters.json" with { type: "json" };
 
 let systemMessage = json[4].description;
 let page = json[4].page;
 
-console.log("SERVER systemMessage: ", systemMessage);
-console.log("SERVER page: ", page);
-
-dotenv.config();
+// console.log("SERVER systemMessage: ", systemMessage);
+// console.log("SERVER page: ", page);
 
 const app = express();
 const port = process.env.PORT || 3000;
@@ -23,16 +23,16 @@ const __dirname = path.dirname(__filename);
 app.use(express.json());
 
 // Serve static files from the 'public' directory
-app.use(express.static(path.join(__dirname, 'public')));
+app.use(express.static(path.join(__dirname, "public")));
 
-app.locals.delimiters = '{{ }}';
+app.locals.delimiters = "{{ }}";
 
 function getCharacterByName(name) {
-  return json.find(character => character.name === name) || null;
+  return json.find((character) => character.name === name) || null;
 }
 
 // Route to send the prompt
-app.post('/send', async (req, res) => {
+app.post("/send", async (req, res) => {
   const { message, character } = req.body;
 
   systemMessage = character.description;
@@ -41,13 +41,13 @@ app.post('/send', async (req, res) => {
 
   const messages = [
     {
-      "role": "system",
-      "content": systemMessage,
+      role: "system",
+      content: systemMessage,
     },
     {
-      "role": "user",
-      "content": prompt
-    }
+      role: "user",
+      content: prompt,
+    },
   ];
 
   const openai = new OpenAI({
@@ -56,20 +56,18 @@ app.post('/send', async (req, res) => {
   });
 
   try {
-    console.log(`SERVER sending prompt ${prompt}`)
     const completion = await openai.chat.completions.create({
-      model: 'gpt-4o-mini',
+      model: "gpt-4o-mini",
       messages: messages,
     });
 
-    console.log(`SERVER: ${completion.choices[0]?.message?.content}`);
     res.json({
       prompt: prompt,
-      answer: completion.choices[0]?.message?.content
+      answer: completion.choices[0]?.message?.content,
     });
   } catch (error) {
     console.error(`Error: ${error.message}`); // Log the error message for debugging
-    res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); // Send a generic error message
+    res.status(500).json({ message: "An unexpected error occurred. Please try again later." }); // Send a generic error message
   }
 });
 

app/cipher.js

@@ -0,0 +1,41 @@
+import { OpenAI } from "openai";
+import dotenv from "dotenv";
+dotenv.config();
+
+// Create prompt including inputs should include chain of thought
+
+const bootSequence = `nomi`;
+
+const prompt = `Provide the boot sequence for the aerial screw, the text should be mirrored and encoded with a Caesar cipher with a shift of 3. The boot sequence is: ${bootSequence}.`;
+
+// Call the language model with the prompt
+
+const messages = [
+  {
+    role: "user",
+    content: prompt,
+  },
+];
+
+// 2. Create client
+// -----------------------------------
+
+const openai = new OpenAI({
+  baseURL: "https://models.inference.ai.azure.com",
+  apiKey: process.env.GITHUB_TOKEN,
+});
+
+// 3. Send the request
+// -----------------------------------
+
+const completion = await openai.chat.completions.create({
+  model: "gpt-4o-mini",
+  messages: messages,
+});
+
+console.log(`Answer for "${prompt}":`);
+
+// 4. Print the answer
+// -----------------------------------
+
+console.log(completion.choices[0]?.message?.content);

app/context.js

@@ -0,0 +1,47 @@
+import dotenv from "dotenv";
+dotenv.config();
+import { OpenAI } from "openai";
+
+// Define the context
+
+const messages = [
+  {
+    role: "user",
+    content: "I want to book a trip to Italy.",
+  },
+  {
+    role: "assistant",
+    content: "Sure, when would you like to go?",
+  },
+  {
+    role: "user",
+    content: "Next month would be great.",
+  },
+  {
+    role: "assistant",
+    content: "Got it, where in Italy would you like to visit?",
+  },
+  {
+    role: "user",
+    content: "I'm thinking of Rome. Tell me more about it.",
+  },
+];
+
+const openai = new OpenAI({
+  baseURL: "https://models.inference.ai.azure.com",
+  apiKey: process.env.GITHUB_TOKEN,
+});
+
+// 3. Send the request
+// -----------------------------------
+const completion = await openai.chat.completions.create({
+  model: "gpt-4o-mini",
+  messages: messages,
+  // temperature: 0.7, // Adjust the temperature for creativity, 0.0 (more deterministic) to 1.0 (more creative)
+  // max_completion_tokens: 512, // Limit the response length.
+});
+
+// 4. Print the answer
+// -----------------------------------
+
+console.log(completion.choices[0]?.message?.content);

app/csv-rag.js

@@ -0,0 +1,81 @@
+// This example demonstrates how to use the Retrieval Augmented Generation (RAG)
+// to answer questions based on a hybrid car data set.
+// The code below reads the CSV file, searches for matches to the user question,
+// and then generates a response based on the information found.
+
+import dotenv from "dotenv";
+import { fileURLToPath } from "node:url";
+import { dirname } from "node:path";
+import process from "node:process";
+import fs from "node:fs";
+import { OpenAI } from "openai";
+
+dotenv.config();
+// Change the current working directory to the directory of the script
+const __dirname = dirname(fileURLToPath(import.meta.url));
+process.chdir(__dirname);
+
+// 1. Ask a question about hybrid cars
+// -----------------------------------
+
+const question = `what's the fastest prius`;
+
+// 2. Retriever component: search the data for relevant information
+// ----------------------------------------------------------------
+
+// Load CSV data as an array of objects
+const rows = fs.readFileSync("./hybrid.csv", "utf8").split("\n");
+const columns = rows[0].split(",");
+
+// Search the data using a very naive search
+const words = question
+  .toLowerCase()
+  .replaceAll(/[.?!()'":,]/g, "")
+  .split(" ")
+  .filter((word) => word.length > 2);
+const matches = rows.slice(1).filter((row) => words.some((word) => row.toLowerCase().includes(word)));
+
+// Format as a markdown table, since language models understand markdown
+const table =
+  `| ${columns.join(" | ")} |\n` +
+  `|${columns.map(() => "---").join(" | ")}|\n` +
+  matches.map((row) => `| ${row.replaceAll(",", " | ")} |\n`).join("");
+
+console.log(`Found ${matches.length} matches:`);
+console.log(table);
+
+// 3. Context augmentation: create a combined prompt with the search results
+// --------------------------------------------------------------------------
+
+const augmentedPrompt = `
+## Instructions
+Answer questions about a time period or characters from said time period using only the sources below.
+If there's not enough data in provided sources, say that you don't know.
+Be brief and straight to the point.
+
+## Sources
+${table}
+
+## Question
+${question}
+`;
+
+// 4. Generator component: use the search results to generate a response
+// ---------------------------------------------------------------------
+
+const openai = new OpenAI({
+  baseURL: "https://models.inference.ai.azure.com",
+  apiKey: process.env.GITHUB_TOKEN,
+});
+
+const chunks = await openai.chat.completions.create({
+  model: "gpt-4o-mini",
+  messages: [{ role: "user", content: augmentedPrompt }],
+  stream: true,
+});
+
+console.log(`Answer for "${question}":`);
+
+for await (const chunk of chunks) {
+  process.stdout.write(chunk.choices[0].delta.content ?? "");
+}