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2025-09-25 07:53:06 Kaushal Lamkhade: High Level Architecutre
Projects/CRM System.md ..
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- # CRM System
+ 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
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+ Connector: OpenAI API (ChatGPT), Perplexity API, and pluggable LLMs.
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+ Pre-/Post-processing pipeline: inject context, system prompts, etc.
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+ Retrieval Augmented Generation (RAG): option to fine-tune or use RAG with knowledge base.
+
+ Communication Integrations
+
+ Telegram Bot API: customer chats.
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+ Email Gateway: SMTP + IMAP, or services like SendGrid.
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+ Optional: WhatsApp Business API, Slack, Web widget.
+
+ File Handling & Knowledge Base
+
+ File parser: PDF, DOCX, TXT.
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+ Cloud storage integration: Google Drive/Dropbox API for linked knowledge sources.
+
+ Embeddings generator: OpenAI embeddings, HuggingFace models.
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+ 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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