SkillShift

<center><h1>🚀 SkillShift</h1></center>

# Project: **SkillShift**

**Tagline:** _Ensuring that when employees leave, change roles, or new hires join, no expertise is lost._  

👥 **Team:** [[Teams/Team Orbit]]
  
📌 **Status:** 🟡 Planning  


# 🔎 **Overview**

**SkillShift** is an AI-powered organizational knowledge assistant that helps companies capture, organize, and retrieve insights from internal communication platforms like **Google Chat** and **Telegram**.
It allows **admins** to manage employees, data sources, and topic-based tags, while **employees** can query information using an AI chatbot powered by **Retrieval-Augmented Generation (RAG)**.
Messages are sanitized, embedded, and stored for semantic search, ensuring **accurate and context-aware answers**.
With **secure role-based access**, SkillShift enables teams to find relevant knowledge quickly, reducing dependency on manual information transfer.
The tool serves as a **smart, centralized knowledge hub** that enhances **transparency, collaboration, and productivity** across the organization.
  

#### ✨ **Key Characteristics**
1. 📝 Always-On Knowledge Capture  
2. 🤖 AI-Powered Summarization & Q&A  
3. 🔗 Role-Based Access Control (RBAC)  
4. 📦 Tag-Based Organization  

---

# ❌ **The Problem**

1. Huge knowledge loss when employees resign or switch roles.  
2. Scattered and unstructured information across multiple chat platforms.  
3. Single points of failure: only one person knows a critical system.    
4. Manual documentation is outdated and inconsistent.  
5. Experts are overloaded answering repetitive questions.  

---

# 💡 **Our AI Solution**

#### ✅ **What does it do?**
- **For Employees:** Smooth onboarding, faster learning, less frustration.  
- **For Teams:** Reduced dependency on single experts, better collaboration.  

#### ⚙️ **How does it work?**

#### 🏗️ Architecture
- **Frontend (React):** Employee and Admin dashboards with AI chatbot interface.  
- **Backend (FastAPI):** REST APIs for authentication, data management, and AI query handling.  
- **Database (PostgreSQL + pgvector):** Stores employees, tags, messages, and embeddings for semantic search.  
- **NLP Worker:** Cleans and embeds chat messages for AI retrieval.  
- **Docker Compose:** Orchestrates all services for seamless deployment.

#### 🔄 **Data Flow**
1. **Ingestion:** Messages are fetched from Google Chat or Telegram data sources.  
2. **Sanitization:** Text is cleaned to remove PII, links, and noise.  
3. **Embedding:** Cleaned text is converted into vector embeddings using NLP models.  
4. **Storage:** Messages and embeddings are saved in PostgreSQL (pgvector).  
5. **Retrieval:** When a user queries, embeddings are generated and similar messages are retrieved for AI-generated answers.

---

## 🚀 **What makes it innovative?**

1. **AI-Driven Retrieval:** Uses RAG to deliver context-aware, factual answers.  
2. **Tag-Based Access:** Organizes knowledge dynamically by topics or projects.  
3. **Automated Knowledge Capture:** Extracts insights directly from internal chats.  
4. **Privacy-First Design:** Sanitizes and anonymizes all data before storage.  
5. **Seamless Integration:** Connects with existing chat tools like Google Chat and Telegram.

---

## 🛠️ **Technology Stack**

- **NLP/LLM:** gemini-2.0-flash
- **Framework & Libraries:** LangChain  
- **Frontend:** React.js (dashboards, chatbot UI)  
- **Backend:** FastAPI (Python) + JWT auth  
- **Database:** PostgreSQL (pgvector)
- **Integrations:** Telegram Bot API, Google Chat API  
- **Deployment:** Docker 

---

## 🏗️ **Project Architecture**  

```mermaid
graph LR
    A[📂 Data Sources → Chats, Docs, Code ] --> B[⚙️ Data Ingestion Pipelines → Remove PII, links, and noise]
    B --> C[🗄️ Knowledge Store → Postgres + pgvector]
    C --> D[🤖 AI Services Layer, LLM Q&A - LangChain]
    D --> E[💻 Client Interfaces, Web Dashboards - React, Chatbot ]
```
## 🧩 **Challenges & Learnings**  

To be added during development phase  

---

## 📅 **Future Roadmap**  

- **Short-term (MVP):**  
    1. **Employee & Admin Roles:** Secure login with role-based access (RBAC).  
    2. **Tag Management:** Admins can create, update, and assign tags to employees.  
    3. **Data Source Integration:** Configure Telegram or Google Chat groups as data sources.  
    4. **Message Ingestion:** NLP worker cleans and stores messages with embeddings.  
    5. **AI Chatbot (RAG):** Employees query data and get contextual, AI-generated answers.  
    6. **Dashboards:**  
       - **Employee Dashboard:** Profile and chatbot view.  
       - **Admin Dashboard:** Manage users, tags, and data sources.  
    7. **Dockerized Setup:** Complete stack deployable via Docker Compose. 

- **Medium-term:** To be added  
- **Long-term:** To be added  

---

## 📂 **Repository & Demo**  

- **GitHub Repository:**  [https://gitlab.enovate-it.com/pk278/skillshift.git/](https://gitlab.enovate-it.com/pk278/skillshift.git/) 
- **Live Demo:** 🔜 Coming Soon  

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## 📸 **Screenshots / Demo Video**  

🔜 Will be shared in next milestone  

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## 🏷️ **Categories**  

#project #ai-hackathon-2025