AskOracle Blog

Building a lead-capturing Telegram bot is a high-ROI task. You automate the qualification process, store data structured, and reduce friction for the client. We will use aiogram 3.x for its asynchronous performance and gspread for Google Sheets integration. Prerequisites and Tech Stack Language: Python 3.10+ Library: aiogram (Bot API) Database: Google Sheets API (Free, easy for clients to view) Hosting: Ubuntu 22.04 VPS Process Manager: systemd Step 1: Project Setup and Google Sheets API Go to Google Cloud Console . Create a project, enable "Google Sheets API" and "Google Drive API". Create a Service Account, download the JSON key, and share your target Google Sheet with the client_email found in that JSON. Step 2: Bot Code Skeleton Install dependencies: pip install aiogram gspread oauth2client import asyncio import logging import gspread from aiogram import Bot, Dispatcher, types, F from aiogram.filters import Command from aiogram.fsm.cont...

Integrating an AI chatbot into a website is no longer about building from scratch; it is about orchestrating existing APIs and RAG (Retrieval-Augmented Generation) frameworks. Here is the technical roadmap to deploying a production-grade AI agent. The Tech Stack To minimize overhead and maximize reliability, use this stack: * Brain: OpenAI API (GPT-4o) or Anthropic API (Claude 3.5 Sonnet). * Orchestration: LangChain or Flowise (for visual flow building). * Knowledge Base: Pinecone (Vector Database) for RAG. * Frontend: Custom React widget or a managed service like Voiceflow/Stack AI. * Backend: Vercel Functions or Supabase Edge Functions. Step 1: Knowledge Base and RAG Pipeline You cannot rely on the LLM’s base training. You need RAG to ground the bot in your specific data. Ingestion: Convert your documentation (PDFs, Notion pages, website URLs) into text. Chunking: Split text into 500-character segments with 50-character overlaps. Embedding: Use text-embeddin...