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2025-09-25 07:54:36 Kaushal Lamkhade: Updated format
Projects/CRM System.md ..
@@ 1,66 1,41 @@
- 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.
+ # 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
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