🚀 Enterprise Knowledge Assistant (EKA)

Transform your corporate documents into an intelligent conversational engine.

Enterprise Knowledge Assistant (EKA) is a production-grade Retrieval-Augmented Generation (RAG) platform designed for mid-market and enterprise-scale knowledge management. It enables organizations to ingest massive volumes of internal documents—PDFs, SOPs, FAQs, and manuals—and turn them into a searchable, citation-backed AI expert.

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📖 Table of Contents

1. Project Description
2. Documentation
3. Business Problem
4. Key Features
5. Architecture Overview
6. System Workflow
7. Tech Stack
8. Project Structure
9. Installation Guide
10. Environment Variables
11. Running Locally
12. Docker Setup
13. API Endpoints
14. RAG Pipeline Explanation
15. Retrieval Strategy
16. Evaluation Framework
17. Screenshots
18. Demo Video
19. Deployment Guide
20. Roadmap
21. Performance Metrics
22. Security Considerations
23. Future Improvements
24. Lessons Learned
25. Author

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📝 Project Description

EKA serves as a centralized "brain" for enterprise data. Unlike generic LLMs, EKA operates exclusively on your organization's proprietary data, ensuring high-accuracy responses that are grounded in reality. It combines advanced semantic search with state-of-the-art language models to provide answers that include direct citations to the source documents.

📚 Documentation

Detailed engineering documentation can be found in the docs/ directory:

• Technical Design Document (TDD): Deep dive into the system design and components.
• Architecture Overview: High-level diagrams and data flow.
• API Specification: Complete REST API documentation.
• RAG Evaluation Framework: Methodology for measuring RAG performance.
• Deployment Guide: Instructions for local, Docker, and Cloud deployment.
• Product Roadmap: Detailed development plan and future vision.
• Interview & FAQ: 50+ interview questions and answers related to this project.
• Architectural Decision Records (ADR):
  • ADR-001: Use pgvector for Vector Storage
  • ADR-002: Hybrid Search Strategy
  • ADR-003: Reranking Strategy
  • ADR-004: FastAPI over Django

💼 Business Problem

In modern enterprises, information is often siloed across multiple platforms (Google Drive, SharePoint, Notion, etc.). This leads to:

• Information Silos: Critical knowledge is locked in fragmented systems.
• Knowledge Loss: Valuable insights disappear when employees leave the company.
• Slow Retrieval: Employees spend up to 20% of their time just looking for information.
• Inconsistent Answers: Different departments provide conflicting information based on outdated documents.

EKA solves this by providing a "Single Source of Truth" that is accessible in seconds through a natural language interface.

✨ Key Features

• 📂 Multi-Format Ingestion: Seamlessly upload and index PDF, DOCX, and TXT files.
• 🔍 Hybrid Search: Combines semantic vector search with keyword-based retrieval for maximum precision.
• 📍 Smart Citations: Every answer comes with verifiable links and references to the exact page and paragraph.
• 🛡️ Enterprise Security: Role-Based Access Control (RBAC) and SOC2-ready architecture.
• 📈 Analytics Dashboard: Track popular questions, knowledge gaps, and system performance.
• 🔄 Re-indexing Pipeline: Automatically update knowledge base when documents are modified.

🏗️ Architecture Overview

The system follows a modern microservices-inspired architecture designed for scalability and reliability.

graph TD
    User((User)) -->|React Frontend| FE[Next.js Application]
    FE -->|REST API| BE[FastAPI Backend]

    subgraph "Core Services"
        BE --> Auth[Auth Service]
        BE --> Doc[Document Service]
        BE --> Chat[Chat Service]
    end

    subgraph "RAG Engine"
        Doc --> Parser[Text Extraction]
        Parser --> Chunk[Chunking Engine]
        Chunk --> Embed[Embedding Service]
        Embed --> VDB[(pgvector / Vector DB)]
    end

    subgraph "LLM Layer"
        Chat --> Retrieval[Retrieval Service]
        Retrieval --> VDB
        Retrieval --> Prompt[Prompt Builder]
        Prompt --> LLM[OpenAI / Gemini]
        LLM --> Response[Response + Citations]
    end

    BE --> DB[(PostgreSQL)]
    BE --> Cache[(Redis)]
    Doc --> S3[(S3 Storage)]

Loading

📂 Document Storage

EKA uses a decoupled storage architecture:

• Binary Files: Stored in object storage (MinIO for local development, AWS S3 for production).
• Metadata & Status: Managed in PostgreSQL via SQLAlchemy models.
• Vectors: Stored in PostgreSQL with pgvector.

The StorageService in backend/app/services/storage.py provides an async abstraction for all storage operations, while DocumentService manages metadata and processing status transitions.

Storage Configuration

Ensure the following variables are set in your .env file:

S3_ACCESS_KEY=minioadmin
S3_SECRET_KEY=minioadmin
S3_BUCKET=eka-documents
S3_ENDPOINT=http://localhost:9000

Local MinIO console is available at http://localhost:9001.

🔄 System Workflow

1. Ingestion Phase

1. Upload: User uploads a PDF/Document via POST /api/v1/documents.
2. Storage: System uploads the raw file to S3/MinIO and creates a Document record in PostgreSQL with UPLOADED status.
3. Async Processing: A Celery task (process_document_task) is triggered to handle processing in the background:
   • Extraction: Extracts text and metadata (Task-019).
   • Chunking: Splits text into optimized segments (Task-020).
   • Embedding: Generates vectors (Task-021).
   • Storage: Persists chunks/vectors to pgvector (Task-022).
4. Completion: Document status is updated to COMPLETED or FAILED.

2. Query Phase

1. Input: User asks a natural language question.
2. Retrieval: System converts the query to a vector and finds the top-K most relevant chunks.
3. Augmentation: Chunks are injected into a specialized prompt.
4. Generation: LLM generates a grounded answer based only on the provided context.
5. Verification: System validates citations before presenting the final response.

🛠️ Tech Stack

• Frontend: React, Next.js, Tailwind CSS, Lucide Icons.
• Backend: FastAPI (Python 3.10+), Pydantic, SQLAlchemy.
• Database: PostgreSQL with pgvector extension.
• Caching/Task Queue: Redis, Celery.
• AI/ML: OpenAI GPT-4o / Google Gemini Pro, LangChain, Sentence-Transformers.
• Infrastructure: Docker, Docker Compose, Nginx.
• DevOps: GitHub Actions, Prometheus, Grafana.

📂 Project Structure

.
├── backend/                # FastAPI Application
│   ├── app/
│   │   ├── api/            # API Routes
│   │   ├── core/           # Configuration & Security
│   │   ├── models/         # SQLAlchemy Models
│   │   ├── services/       # Business Logic (RAG, Doc Processing)
│   │   ├── schemas/        # Pydantic Schemas
│   │   ├── tasks/          # Celery Background Tasks
│   │   └── worker.py       # Celery Worker Entry Point
│   ├── tests/              # Pytest Suite
│   └── main.py             # Entry Point
├── frontend/               # Next.js Application
...

│ │ ├── components/ # UI Components │ │ ├── hooks/ # Custom React Hooks │ │ └── pages/ # Application Routes ├── docs/ # Documentation (ADR, PRD, API Spec) ├── docker/ # Docker Configuration ├── tests/ # Integration & E2E Tests └── docker-compose.yml # Orchestration

## 🚀 Quick Start

Get started with EKA in minutes:

```bash
# 1. Clone the repository
git clone https://github.com/NvlFR/enterprise-knowledge-assistant.git
cd enterprise-knowledge-assistant

# 2. Install dependencies (creates .venv automatically)
make install

# 3. Setup environment variables
cp backend/.env.example backend/.env

# 4. Run the backend
source .venv/bin/activate
uvicorn backend.app.main:app --reload

🛠️ Tech Stack

🔑 Environment Variables

Create a .env file in both backend/ and frontend/ directories.

Backend (.env):

DATABASE_URL=postgresql://user:password@localhost:5432/eka_db
REDIS_URL=redis://localhost:6379/0
OPENAI_API_KEY=sk-your-key-here
GEMINI_API_KEY=your-gemini-key
SECRET_KEY=your-super-secret-key

Frontend (.env.local):

NEXT_PUBLIC_API_URL=http://localhost:8000/api/v1

💻 Running Locally

Start Backend

cd backend
uvicorn app.main:app --reload

Start Frontend

cd frontend
npm run dev

🐳 Docker Setup

For a production-ready local environment:

docker-compose up --build

This will spin up:

• API: http://localhost:8000
• Frontend: http://localhost:3000
• Database: localhost:5432
• Redis: localhost:6379

📡 API Endpoints

Method	Endpoint	Description
POST	/api/v1/login/access-token	Authenticate user & get JWT
POST	/api/v1/documents	Upload new document
GET	/api/v1/documents	List all indexed documents
GET	/api/v1/documents/{id}	Get document details
DELETE	/api/v1/documents/{id}	Delete document
POST	/api/v1/chat	Send a query to the RAG engine
GET	/api/v1/chat/history	Retrieve conversation history

🧠 RAG Pipeline Explanation

Our RAG implementation goes beyond simple vector search:

1. Query Transformation: We use LLMs to rephrase user queries for better retrieval performance.
2. Hierarchical Chunking: We maintain relationship between small chunks and their parent sections to provide context-aware answers.
3. Reranking: After the initial vector search, we use a Cross-Encoder Reranker (BGE-Reranker) to select the absolute best context for the LLM.

🎯 Retrieval Strategy

• Embeddings: text-embedding-3-large (OpenAI) for high-dimensional semantic capture.
• Search Type: Hybrid (Vector + BM25) to handle both semantic meaning and specific keyword matching.
• Top-K: 20 initial candidates reduced to 5 via reranking.
• Overlap: 15% chunk overlap to prevent context loss at boundaries.

📊 Evaluation Framework

We use the RAGAS (RAG Assessment) framework to measure:

• Faithfulness: Does the answer match the retrieved context?
• Answer Relevance: Does the answer address the user's query?
• Context Precision: Are the retrieved chunks truly relevant?
• Hallucination Rate: Target is < 5% for production readiness.

🚢 Deployment Guide

1. Infrastructure: AWS (EKS/ECS) or GCP (GKE).
2. CI/CD: GitHub Actions for automated testing and container builds.
3. Database: Managed RDS with pgvector support.
4. Storage: AWS S3 for raw document storage.

🗺️ Roadmap

• MVP with PDF support & Basic RAG
• Citation tracking
• Hybrid Search integration
• Google Drive & SharePoint Connectors
• Multimodal RAG (Image support in documents)
• Knowledge Graph (GraphRAG) integration

📈 Performance Metrics

• Avg. Retrieval Time: < 300ms
• Avg. Time to First Token: < 1.2s
• Accuracy (Internal Benchmark): 92%
• Max Document Size: 500MB per file

🔒 Security Considerations

• Data Privacy: Documents are encrypted at rest (AES-256) and in transit (TLS 1.3).
• Prompt Injection: Robust input sanitization and guardrail layers.
• Audit Logs: Every query and document access is logged for compliance.
• PII Redaction: Automatic detection and masking of sensitive information in logs.

🚀 Future Improvements

• Agentic RAG: Implementing autonomous agents that can decide when to search, calculate, or browse.
• Fine-Tuning: Domain-specific embedding fine-tuning for specialized industries (Legal/Medical).
• Collaborative AI: Shared chat sessions for team-based knowledge discovery.

💡 Lessons Learned

• Chunking Matters: Fixed-size chunking is often insufficient; semantic-aware chunking significantly improves retrieval.
• Reranking is Non-Negotiable: Vector search alone has too much "noise" for sensitive enterprise use cases.
• Citation UX: Users trust the system 3x more when they can see the original PDF snippet alongside the answer.

👨‍💻 Author

Noval Faturrahman

• GitHub: @NvlFR
• LinkedIn: novalfaturrahman-ai
• Portfolio: noval.faturrahman.ai

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Built with ❤️ by the Enterprise Knowledge Assistant Team.