commit 06d021

Commit `06d021`

2025-12-08 09:10:22 Vaishnavi Shinde: updated

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`Projects/CommNet.md` ..
@@ 18,18 18,18 @@
	- A customer-facing AI interface (chat page, Telegram bot, or email integration) where end-users can interact with the AI assistant.
	- An admin dashboard to monitor, review, and override AI responses when necessary.

-	Uploaded project documents and external resources are indexed in a knowledge base powered by vector embeddings (Postgres `pgvector` or external vector DB). This enables retrieval-augmented generation (RAG), ensuring AI responses are accurate and project-specific.
+	Uploaded project documents and external resources are indexed in a knowledge base powered by vector embeddings (Postgres `pgvector`). This enables retrieval-augmented generation (RAG), ensuring AI responses are accurate and project-specific.

	The platform supports multiple AI connectors, enabling flexibility for businesses to choose the best model for their needs. Clients can seamlessly manage communication, while administrators maintain oversight and control over all conversations.

-	The system is built on a Python backend (FastAPI), PostgreSQL database, and integrates with external APIs for AI, messaging, email, and storage services.
+	The system is built on a Go lang backend, PostgreSQL database, and integrates with external APIs for AI, messaging, email, and storage services.


	# System Architecture

	## Frontend

-	- UI: ShadcnUI (TailwindCSS + RadixUI)
+	- UI: React

	- Build Tool: Vite

@@ 43,11 43,11 @@

	- 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 (Go)
-	- Core framework: Go (Golang) using a modular, clean architecture pattern (e.g., layered or hexagonal).
-	- API layer: Standard Go HTTP server with chi or Echo for routing.
+	- Core framework: Go (Golang) using a modular, clean architecture pattern.
+	- API layer: Standard Go HTTP server with Gin for routing.
	- Authentication & Accounts: JWT-based multi-tenant authentication.
	- Project & Channel Management: Business logic for workspaces, channels, and configurations.
	- Message Orchestration: Handles incoming messages, context injection, AI processing pipeline, and logging.
@@ 59,25 59,23 @@
	- Vector DB: pgvector

	## AI / NLP
-	- Connector: OpenAI API (Gemini), Perplexity API, and pluggable LLMs
+	- Connector: OpenAI API (Gemini)
	- 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
+	- Email Gateway: SMTP + IMAP, or services like SendGrid

	## 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
+	- Embeddings generator: OLLAMA_MODEL(bge-m3)
	- Indexing service: store embeddings into Vector DB

	## Infrastructure
	- Cloud deployment: AWS
	- Containerization: Docker
-	- Background task runner: Celery / RQ for async jobs (e.g., doc parsing)
	- Caching: Redis for messages and sessions
	- CI/CD: GITLAB

@@ 90,10 88,10 @@
	- Database
	- PostgreSQL (perfectly suited for multi-tenant, relational data storage).
	- Frontend
-	- ShadcnUI , vite
+	- React , vite
	- Admin dashboard + Client dashboard.
	- AI/ML Integrations
-	- OpenAI API / Perplexity API / Custom AI connectors.
+	- OpenAI API.
	- Knowledge Base Indexing (e.g., LangChain + Vector DB like Postgres pgvector).
	- MCP Server (for tool orchestration + context passing).
	- Communication Channels
@@ 118,13 116,13 @@
	- Configurable options for each client:
	- Telegram bot setup.
	- Email inbox setup.
-	- Future: WhatsApp, Slack, etc.
+	- Future: Slack.

	### 🔹 AI Model Connector
	- Ability to select which AI model:
	- ChatGPT (OpenAI).
-	- Perplexity.
-	- Others (pluggable architecture).
+	- Gemini
+	- Ollama.
	- Standardized response pipeline regardless of model.

	### 🔹 Knowledge Base
@@ 133,12 131,13 @@
	- Store embeddings in pgvector (Postgres extension).
	- Retrieval-Augmented Generation (RAG) for query responses.
	-
-	### 🔹 MCP (Model Connection Protocol)
+	### 🔹 MCP (Model Context Protocol)
	- Centralized system for managing connections between AI agents and models.
	- Handles authentication, routing, and configuration for both local and remote models.
	- Decouples application logic from model APIs, enabling dynamic model switching.
	- Standardizes inference requests and responses for consistent performance.
	- Forms the foundation for future multi-model orchestration and context-aware AI pipelines.
+	-

	### 🔹 Query Processing
	- Customer submits query → AI pipeline:
@@ 375,9 374,8 @@

	## 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.
+	- Email API → SendGrid.
+	- AI APIs → OpenAI, Gemini, etc.
	- Drive APIs → Google Drive API (for fetching docs).
	- Authentication → JWT or OAuth2 (multi-tenant).

@@ 479,16 477,13 @@
	The platform is designed with scalability in mind, allowing integration of new features and channels to enhance client communication and management. Planned future improvements include:

	## 1. Additional Communication Channels
-	- Meeting Integrations
-	- Integration with platforms like Zoom, Google Meet, and Microsoft Teams.
-	- AI-powered meeting notes, summaries, and action items.
-
-	- Phone Call Channels
-	- Integration with telephony services (e.g., Twilio, Vonage).
+	- Channels
+	- Integration with email
+	- Integration with telephony services.
	- AI-powered call transcription, sentiment analysis, and automated follow-up responses.

	## 2. Ticketing & Workflow Management
-	- Jira / Trello / Asana Integrations
+	- Jira Integrations
	- Automatic ticket creation from client queries.
	- Linking AI conversations to tasks and workflows.
	- Syncing client issues directly into project management tools.
@@ 509,11 504,6 @@
	- Predictive analytics for identifying recurring issues or potential churn risks.
	- Exportable insights for stakeholders.

-	## 5. Enterprise Readiness
-	- Multi-language support for global clients.
-	- Role-based access control with fine-grained permissions.
-	- Audit logging for compliance and security.
-
	## 🔸 Scope of Demo 1

	The first demo of CommNet establishes the core multi-tenant communication foundation and essential management modules.

Commit 06d021

Commit `06d021`