The rest of the files

app.py

@@ -0,0 +1,75 @@
+from flask import Flask, request, jsonify
+import asyncio
+import axios
+
+app = Flask(__name__)
+
+# Load Markdown files
+def loadMarkdownFiles(directory):
+  const files = fs.readdirSync(directory);
+  const markdownFiles = [];
+
+  for (const file of files) {
+    if (path.extname(file).toLowerCase() === '.md') {
+      const filePath = path.join(directory, file);
+      const content = fs.readFileSync(filePath, 'utf-8');
+      markdownFiles.push({ name: file, content });
+    }
+  }
+
+  return markdownFiles
+
+# Initialize RAG model and tokenizer
+async def initRagModel():
+  const tokenizer = await AutoTokenizer.from_pretrained('facebook/rag-token-nq');
+  const model = await RagTokenForGeneration.from_pretrained('facebook/rag-token-nq');
+
+  return { tokenizer, model }
+
+# Retrieve relevant information from Markdown files using Ollama API
+async def retrieveInformation(query):
+  try:
+    response = await axios.post('http://localhost:8080/chat', { query });
+    return response.data.response;
+  except (axios.AxiosError, Exception) as error:
+    print(f'Error: {error.message}')
+    raise
+
+# Chatbot logic
+async def chatbot():
+  const directory = './notes'; // Directory containing Markdown files
+  const markdownFiles = loadMarkdownFiles(directory);
+  const ragModel = await initRagModel();
+
+  print('Chatbot is ready! Ask your questions.')
+
+  process.stdin.on('data', async (data) => {
+    const query = data.toString().trim();
+    if (query.toLowerCase() === 'exit') {
+      process.exit(0);
+    }
+
+    try:
+      const response = await retrieveInformation(query);
+      print(f'Chatbot: {response}')
+    except Exception as error:
+      print(f'Error: {error.message}')
+
+# Flask route to handle chat requests
+@app.route('/chat', methods=['POST'])
+async def chat():
+  data = request.json
+  query = data.get('query')
+
+  if not query:
+    return jsonify({'error': 'No query provided'}), 400
+
+  try:
+    response = await retrieveInformation(query)
+    return jsonify({'response': response})
+  except Exception as error:
+    return jsonify({'error': str(error)}), 500
+
+if __name__ == '__main__':
+  asyncio.run(chatbot())
+  app.run(debug=True)

index.js

@@ -0,0 +1,70 @@
+const { AutoTokenizer, RagTokenForGeneration } = require('@huggingface/transformers');
+const fs = require('fs');
+const path = require('path');
+const axios = require('axios');
+const configparser = require('configparser');
+
+// Load Markdown files
+function loadMarkdownFiles(directory) {
+  const files = fs.readdirSync(directory);
+  const markdownFiles = [];
+
+  for (const file of files) {
+    if (path.extname(file).toLowerCase() === '.md') {
+      const filePath = path.join(directory, file);
+      const content = fs.readFileSync(filePath, 'utf-8');
+      markdownFiles.push({ name: file, content });
+    }
+  }
+
+  return markdownFiles;
+}
+
+// Initialize RAG model and tokenizer
+async function initRagModel() {
+  const tokenizer = await AutoTokenizer.from_pretrained('facebook/rag-token-nq');
+  const model = await RagTokenForGeneration.from_pretrained('facebook/rag-token-nq');
+
+  return { tokenizer, model };
+}
+
+// Retrieve relevant information from Markdown files using Ollama API
+async function retrieveInformation(query) {
+  try {
+    const config = new configparser.ConfigParser();
+    config.read('ollama.ini');
+    const host = config.get('Ollama', 'host');
+    const port = config.get('Ollama', 'port');
+
+    const response = await axios.post(`http://${host}:${port}/chat`, { query });
+    return response.data.response;
+  } catch (error) {
+    console.error('Error:', error.message);
+    throw error;
+  }
+}
+
+// Chatbot logic
+async function chatbot() {
+  const directory = './notes'; // Directory containing Markdown files
+  const markdownFiles = loadMarkdownFiles(directory);
+  const ragModel = await initRagModel();
+
+  console.log('Chatbot is ready! Ask your questions.');
+
+  process.stdin.on('data', async (data) => {
+    const query = data.toString().trim();
+    if (query.toLowerCase() === 'exit') {
+      process.exit(0);
+    }
+
+    try {
+      const response = await retrieveInformation(query);
+      console.log(`Chatbot: ${response}`);
+    } catch (error) {
+      console.error('Error:', error.message);
+    }
+  });
+}
+
+chatbot();

ollama.ini.example

@@ -0,0 +1,3 @@
+[Ollama]
+host = localhost
+port = 8080

package.json

@@ -0,0 +1,17 @@
+{
+  "name": "chatbot",
+  "version": "1.0.0",
+  "description": "An AI chatbot using RAG for retrieval from Markdown files.",
+  "main": "index.js",
+  "scripts": {
+    "start": "node index.js"
+  },
+  "devDependencies": {
+    "@huggingface/transformers": "^3.4.1",
+    "axios": "^1.8.4",
+    "configparser": "^0.3.10",
+    "flask": "^0.2.10",
+    "fs": "^0.0.1-security",
+    "path": "^0.12.7"
+  }
+}