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.

Backend (Python)

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.

Database

PostgreSQL/MySQL: for structured data (users, projects, configs, logs).

Vector DB: Pinecone, Weaviate, or open-source FAISS for knowledge base indexing.

AI / NLP

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.

Communication Integrations

Telegram Bot API: customer chats.

Email Gateway: SMTP + IMAP, or services like SendGrid.

Optional: WhatsApp Business API, Slack, Web widget.

File Handling & 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.

Infrastructure

Cloud deployment: AWS, GCP, Azure, or DigitalOcean.

Containerization: Docker.

Background task runner: Celery / RQ for async jobs (e.g., doc parsing).

Caching: Redis for messages and sessions.