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

Client Communication Management Application – High-Level Overview

1. Tech Stack

• Backend (Python)
  • FastAPI (recommended for async, lightweight, API-first approach) or Django (if you want batteries-included features).
  • SQLAlchemy or Django ORM (for database interactions).
• Database
  • PostgreSQL (yes, perfectly suited for multi-tenant, relational data storage).
• Frontend
  • React / Next.js (or Vue/Angular depending on preference).
  • Admin dashboard + Client dashboard.
• AI/ML Integrations
  • OpenAI API / Perplexity API / Custom AI connectors.
  • Knowledge Base Indexing (e.g., LangChain + Vector DB like Postgres pgvector, Weaviate, or Pinecone).
  • MCP Server (for tool orchestration + context passing).
• Communication Channels
  • Telegram Bot API integration.
  • Email integration (IMAP/SMTP or services like SendGrid/Mailgun).
• Infrastructure
  • Docker for containerization.
  • Nginx / Traefik for reverse proxy.
  • Optional: Kubernetes for scaling.

2. Core Modules & Features

🔹 Client Onboarding

• Form to capture client + project details.
• Generate dedicated project page for embedding AI bot.
• Multi-tenant user management (each client = tenant).

🔹 Communication Channels

• Configurable options for each client:
  • Telegram bot setup.
  • Email inbox setup.
  • Future: WhatsApp, Slack, etc.

🔹 AI Model Connector

• Ability to select which AI model:
  • ChatGPT (OpenAI).
  • Perplexity.
  • Others (pluggable architecture).
• Standardized response pipeline regardless of model.

🔹 Knowledge Base

• Upload project docs (PDF, DOCX, TXT).
• Import from Drive links / Notion / Confluence.
• Store embeddings in pgvector (Postgres extension).
• Retrieval-Augmented Generation (RAG) for query responses.

🔹 Query Processing

• Customer submits query → AI pipeline:
  1. Normalize input.
  2. Retrieve context from Knowledge Base.
  3. Feed context + query to chosen AI model.
  4. Return AI response.
• Option for admin override & monitoring.

🔹 Monitoring & Admin Portal

• View all conversations per client/project.
• Override/edit AI responses.
• Track usage per channel/model.
• Analytics & logs.

3. Database Design (Postgres)

Tables (example):

• clients → Client info (name, domain, etc.)
• projects → Linked to clients (stores project metadata).
• channels → Configured communication channels.
• ai_models → Available model connectors.
• knowledge_base → Documents & embeddings.
• queries → User queries & responses.
• users → Client admins & internal admins.

Use pgvector for embeddings storage and similarity search.

4. External Integrations

• Telegram Bot API → Webhook for messages.
• Email API → IMAP/SMTP or SendGrid.
• AI APIs → OpenAI, Perplexity, etc.
• Storage → AWS S3 / GCP Storage for uploaded files.
• Drive APIs → Google Drive API (for fetching docs).
• Authentication → JWT or OAuth2 (multi-tenant).

5. Deployment & Scaling

• Containerize with Docker.
• CI/CD (GitHub Actions / GitLab CI).
• Cloud hosting (AWS/GCP/Azure/DigitalOcean).
• Use Postgres + pgvector for persistence + semantic search.
• Caching layer (Redis) for session storage & rate limiting.