Added gmail RAG example for week 5
This commit is contained in:
Armelle Moulin-Lantz
472
week5/community-contributions/day5_gmailRAG.ipynb
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472
week5/community-contributions/day5_gmailRAG.ipynb
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dfe37963-1af6-44fc-a841-8e462443f5e6",
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"metadata": {},
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"source": [
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"## gmail RAG assistant"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import glob\n",
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"from dotenv import load_dotenv\n",
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"import gradio as gr\n",
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"# NEW IMPORTS FOR GMAIL\n",
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"from google.auth.transport.requests import Request\n",
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"from google.oauth2.credentials import Credentials\n",
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"from google_auth_oauthlib.flow import InstalledAppFlow\n",
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"from googleapiclient.discovery import build\n",
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"from datetime import datetime\n",
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"import base64\n",
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"from email.mime.text import MIMEText\n",
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"import re"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "802137aa-8a74-45e0-a487-d1974927d7ca",
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"metadata": {},
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"outputs": [],
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"source": [
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||||||
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"# imports for langchain, plotly and Chroma\n",
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"\n",
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"from langchain.document_loaders import DirectoryLoader, TextLoader\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.schema import Document\n",
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"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
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"from langchain_chroma import Chroma\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.manifold import TSNE\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain.chains import ConversationalRetrievalChain\n",
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"from langchain.embeddings import HuggingFaceEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "58c85082-e417-4708-9efe-81a5d55d1424",
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"metadata": {},
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"outputs": [],
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"source": [
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"# price is a factor for our company, so we're going to use a low cost model\n",
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"\n",
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"MODEL = \"gpt-4o-mini\"\n",
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"db_name = \"vector_db\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ee78efcb-60fe-449e-a944-40bab26261af",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"\n",
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"load_dotenv(override=True)\n",
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
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"# NEW: Gmail API credentials\n",
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"SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']\n",
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"CREDENTIALS_FILE = 'credentials.json' # Download from Google Cloud Console\n",
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"TOKEN_FILE = 'token.json'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "730711a9-6ffe-4eee-8f48-d6cfb7314905",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Read in emails using LangChain's loaders\n",
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"# IMPORTANT: set the email received date range hard-coded below\n",
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"\n",
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"def authenticate_gmail():\n",
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" \"\"\"Authenticate and return Gmail service object\"\"\"\n",
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" creds = None\n",
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" if os.path.exists(TOKEN_FILE):\n",
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" creds = Credentials.from_authorized_user_file(TOKEN_FILE, SCOPES)\n",
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" \n",
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" if not creds or not creds.valid:\n",
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" if creds and creds.expired and creds.refresh_token:\n",
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" creds.refresh(Request())\n",
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" else:\n",
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" flow = InstalledAppFlow.from_client_secrets_file(CREDENTIALS_FILE, SCOPES)\n",
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" creds = flow.run_local_server(port=0)\n",
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" \n",
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" with open(TOKEN_FILE, 'w') as token:\n",
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" token.write(creds.to_json())\n",
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" \n",
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" return build('gmail', 'v1', credentials=creds)\n",
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"\n",
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"def get_email_content(service, message_id):\n",
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" \"\"\"Extract email content from message\"\"\"\n",
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" try:\n",
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" message = service.users().messages().get(userId='me', id=message_id, format='full').execute()\n",
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" \n",
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" # Extract basic info\n",
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" headers = message['payload'].get('headers', [])\n",
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" subject = next((h['value'] for h in headers if h['name'] == 'Subject'), 'No Subject')\n",
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" sender = next((h['value'] for h in headers if h['name'] == 'From'), 'Unknown Sender')\n",
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" date = next((h['value'] for h in headers if h['name'] == 'Date'), 'Unknown Date')\n",
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" \n",
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" # Extract body\n",
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" body = \"\"\n",
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" if 'parts' in message['payload']:\n",
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" for part in message['payload']['parts']:\n",
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" if part['mimeType'] == 'text/plain':\n",
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" data = part['body']['data']\n",
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" body = base64.urlsafe_b64decode(data).decode('utf-8')\n",
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" break\n",
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" else:\n",
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" if message['payload']['body'].get('data'):\n",
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" body = base64.urlsafe_b64decode(message['payload']['body']['data']).decode('utf-8')\n",
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" \n",
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" # Clean up body text\n",
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" body = re.sub(r'\\s+', ' ', body).strip()\n",
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" \n",
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" return {\n",
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" 'subject': subject,\n",
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" 'sender': sender,\n",
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" 'date': date,\n",
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" 'body': body,\n",
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" 'id': message_id\n",
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" }\n",
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" except Exception as e:\n",
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" print(f\"Error processing message {message_id}: {str(e)}\")\n",
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" return None\n",
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"\n",
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"def load_gmail_documents(start_date, end_date, max_emails=100):\n",
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" \"\"\"Load emails from Gmail between specified dates\"\"\"\n",
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" service = authenticate_gmail()\n",
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" \n",
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" # Format dates for Gmail API (YYYY/MM/DD)\n",
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" start_date_str = start_date.strftime('%Y/%m/%d')\n",
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" end_date_str = end_date.strftime('%Y/%m/%d')\n",
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" \n",
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" # Build query\n",
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" query = f'after:{start_date_str} before:{end_date_str}'\n",
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" \n",
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" # Get message list\n",
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" result = service.users().messages().list(userId='me', q=query, maxResults=max_emails).execute()\n",
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" messages = result.get('messages', [])\n",
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" \n",
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" print(f\"Found {len(messages)} emails between {start_date_str} and {end_date_str}\")\n",
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" \n",
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" # Convert to LangChain documents\n",
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" documents = []\n",
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" for i, message in enumerate(messages):\n",
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" print(f\"Processing email {i+1}/{len(messages)}\")\n",
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" email_data = get_email_content(service, message['id'])\n",
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" \n",
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" if email_data and email_data['body']:\n",
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" # Create document content\n",
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" content = f\"\"\"Subject: {email_data['subject']}\n",
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"From: {email_data['sender']}\n",
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"Date: {email_data['date']}\n",
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"\n",
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"{email_data['body']}\"\"\"\n",
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" \n",
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" # Create LangChain document\n",
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" doc = Document(\n",
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" page_content=content,\n",
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" metadata={\n",
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" \"doc_type\": \"email\",\n",
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" \"subject\": email_data['subject'],\n",
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" \"sender\": email_data['sender'],\n",
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" \"date\": email_data['date'],\n",
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" \"message_id\": email_data['id']\n",
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" }\n",
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" )\n",
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" documents.append(doc)\n",
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" \n",
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" return documents\n",
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"\n",
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"# SET YOUR DATE RANGE HERE\n",
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"start_date = datetime(2025, 6, 20) # YYYY, MM, DD\n",
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"end_date = datetime(2025, 6, 26) # YYYY, MM, DD\n",
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"\n",
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"# Load Gmail documents \n",
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"documents = load_gmail_documents(start_date, end_date, max_emails=200)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c59de72d-f965-44b3-8487-283e4c623b1d",
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"metadata": {},
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"outputs": [],
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"source": [
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
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"chunks = text_splitter.split_documents(documents)\n",
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"\n",
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"print(f\"Total number of chunks: {len(chunks)}\")\n",
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"print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "78998399-ac17-4e28-b15f-0b5f51e6ee23",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
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"# Chroma is a popular open source Vector Database based on SQLLite\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"\n",
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"# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n",
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"# Then replace embeddings = OpenAIEmbeddings()\n",
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"# with:\n",
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"# from langchain.embeddings import HuggingFaceEmbeddings\n",
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"# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
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"\n",
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"# Delete if already exists\n",
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"\n",
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"if os.path.exists(db_name):\n",
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" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
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"\n",
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"# Create vectorstore\n",
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"\n",
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"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
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"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff2e7687-60d4-4920-a1d7-a34b9f70a250",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's investigate the vectors\n",
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"\n",
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"collection = vectorstore._collection\n",
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"count = collection.count()\n",
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"\n",
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"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
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"dimensions = len(sample_embedding)\n",
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"print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b0d45462-a818-441c-b010-b85b32bcf618",
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"metadata": {},
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"source": [
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"## Visualizing the Vector Store\n",
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"\n",
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"Let's take a minute to look at the documents and their embedding vectors to see what's going on."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Prework (with thanks to Jon R for identifying and fixing a bug in this!)\n",
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"\n",
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"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
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"vectors = np.array(result['embeddings'])\n",
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"documents = result['documents']\n",
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"metadatas = result['metadatas']\n",
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"\n",
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"# Alternatively, color by sender:\n",
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"senders = [metadata.get('sender', 'unknown') for metadata in metadatas]\n",
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"unique_senders = list(set(senders))\n",
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"sender_colors = ['blue', 'green', 'red', 'orange', 'purple', 'brown', 'pink', 'gray']\n",
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"colors = [sender_colors[unique_senders.index(sender) % len(sender_colors)] for sender in senders]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21",
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"metadata": {},
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"outputs": [],
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"source": [
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"# We humans find it easier to visalize things in 2D!\n",
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"# Reduce the dimensionality of the vectors to 2D using t-SNE\n",
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"# (t-distributed stochastic neighbor embedding)\n",
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"\n",
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"tsne = TSNE(n_components=2, random_state=42)\n",
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"reduced_vectors = tsne.fit_transform(vectors)\n",
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"\n",
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"# Create the 2D scatter plot\n",
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"fig = go.Figure(data=[go.Scatter(\n",
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" x=reduced_vectors[:, 0],\n",
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" y=reduced_vectors[:, 1],\n",
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" mode='markers',\n",
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" marker=dict(size=5, color=colors, opacity=0.8),\n",
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(senders, documents)],\n",
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" hoverinfo='text'\n",
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")])\n",
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"\n",
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"fig.update_layout(\n",
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" title='2D Chroma Vector Store Visualization',\n",
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" scene=dict(xaxis_title='x',yaxis_title='y'),\n",
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" width=800,\n",
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" height=600,\n",
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" margin=dict(r=20, b=10, l=10, t=40)\n",
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")\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||||
|
"id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Let's try 3D!\n",
|
||||||
|
"\n",
|
||||||
|
"tsne = TSNE(n_components=3, random_state=42)\n",
|
||||||
|
"reduced_vectors = tsne.fit_transform(vectors)\n",
|
||||||
|
"\n",
|
||||||
|
"# Create the 3D scatter plot\n",
|
||||||
|
"fig = go.Figure(data=[go.Scatter3d(\n",
|
||||||
|
" x=reduced_vectors[:, 0],\n",
|
||||||
|
" y=reduced_vectors[:, 1],\n",
|
||||||
|
" z=reduced_vectors[:, 2],\n",
|
||||||
|
" mode='markers',\n",
|
||||||
|
" marker=dict(size=5, color=colors, opacity=0.8),\n",
|
||||||
|
" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(senders, documents)],\n",
|
||||||
|
" hoverinfo='text'\n",
|
||||||
|
")])\n",
|
||||||
|
"\n",
|
||||||
|
"fig.update_layout(\n",
|
||||||
|
" title='3D Chroma Vector Store Visualization',\n",
|
||||||
|
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
|
||||||
|
" width=900,\n",
|
||||||
|
" height=700,\n",
|
||||||
|
" margin=dict(r=20, b=10, l=10, t=40)\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"fig.show()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "bbbcb659-13ce-47ab-8a5e-01b930494964",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Langchain and Gradio to prototype a chat with the LLM\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "d72567e8-f891-4797-944b-4612dc6613b1",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"\n",
|
||||||
|
"from langchain.prompts import PromptTemplate\n",
|
||||||
|
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
|
||||||
|
"from langchain.chains import create_retrieval_chain\n",
|
||||||
|
"\n",
|
||||||
|
"# create a new Chat with OpenAI\n",
|
||||||
|
"llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
|
||||||
|
"\n",
|
||||||
|
"# Alternative - if you'd like to use Ollama locally, uncomment this line instead\n",
|
||||||
|
"# llm = ChatOpenAI(temperature=0.7, model_name='llama3.2', base_url='http://localhost:11434/v1', api_key='ollama')\n",
|
||||||
|
"\n",
|
||||||
|
"# change LLM standard prompt (standard prompt defaults the answer to be 'I don't know' too often, especially when using a small LLM\n",
|
||||||
|
"\n",
|
||||||
|
"qa_prompt=PromptTemplate.from_template(\"Use the following pieces of context to answer the user's question. Answer as best you can given the information you have;\\\n",
|
||||||
|
" if you have a reasonable idea of the answer,/then explain it and mention that you're unsure. \\\n",
|
||||||
|
" But if you don't know the answer, don't make it up. \\\n",
|
||||||
|
" {context} \\\n",
|
||||||
|
" Question: {question} \\\n",
|
||||||
|
" Helpful Answer:\"\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# Wrap into a StuffDocumentsChain, matching the variable name 'context'\n",
|
||||||
|
"combine_docs_chain = create_stuff_documents_chain(\n",
|
||||||
|
" llm=llm,\n",
|
||||||
|
" prompt=qa_prompt,\n",
|
||||||
|
" document_variable_name=\"context\"\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# set up the conversation memory for the chat\n",
|
||||||
|
"#memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
|
||||||
|
"memory = ConversationBufferMemory(\n",
|
||||||
|
" memory_key='chat_history', \n",
|
||||||
|
" return_messages=True,\n",
|
||||||
|
" output_key='answer' \n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
|
||||||
|
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n",
|
||||||
|
"\n",
|
||||||
|
"# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
|
||||||
|
"# conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
|
||||||
|
"\n",
|
||||||
|
"conversation_chain = ConversationalRetrievalChain.from_llm(\n",
|
||||||
|
" llm=llm,\n",
|
||||||
|
" retriever=retriever,\n",
|
||||||
|
" memory=memory,\n",
|
||||||
|
" combine_docs_chain_kwargs={\"prompt\": qa_prompt},\n",
|
||||||
|
" return_source_documents=True\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"def chat(question, history):\n",
|
||||||
|
" result = conversation_chain.invoke({\"question\": question})\n",
|
||||||
|
" return result[\"answer\"]\n",
|
||||||
|
"\n",
|
||||||
|
"view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=False)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "fe4229aa-6afe-4592-93a4-71a47ab69846",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.0"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user