High-Level Architecture

Frontend

Client dashboard: onboarding, project setup, knowledge base upload, channel configuration
Customer-facing AI bot interface: chat page embedded in client’s site, Telegram, email, etc.
Admin dashboard: monitor conversations, override responses

Core app framework: Django / FastAPI (FastAPI recommended for async, API-first approach)
Authentication & Accounts: Multi-tenant user management (each client = tenant)
Project & Channel Management: Store client configs (Telegram bot keys, email SMTP creds, AI model choice)
Message Orchestration: Routes queries → preprocess → AI model → knowledge base → return response
Knowledge Base (MCP integration): Ingest docs, PDFs, Google Drive/OneDrive, index content, semantic search
AI Connector Layer: Wrappers for ChatGPT, Perplexity, and future LLMs
Monitoring & Logging: Store all conversations, admin can review & edit

PostgreSQL/MySQL: for structured data (users, projects, configs, logs)
Vector DB: Pinecone, Weaviate, or open-source FAISS for knowledge base indexing

Connector: OpenAI API (ChatGPT), Perplexity API, and pluggable LLMs
Pre-/Post-processing pipeline: inject context, system prompts, etc.
Retrieval Augmented Generation (RAG): option to fine-tune or use RAG with knowledge base

File parser: PDF, DOCX, TXT
Cloud storage integration: Google Drive/Dropbox API for linked knowledge sources
Embeddings generator: OpenAI embeddings, HuggingFace models
Indexing service: store embeddings into Vector DB