ENTERPRISE RAG BOT
llm
Enterprise Knowledge RAG & LLM Assistant
A secure internal Enterprise Retrieval-Augmented Generation (RAG) assistant allowing employees to query confidential company wikis, technical manuals, and legal contracts in natural language with source citation.
# GenAI RAG# Claude 3.5 Sonnet# Gemini 1.5 Pro# OpenAI Codex# Vector DB
Contract formatContract development
Development system5 people
Development period4 months
Key Features
Core System Capabilities
Optimized modular design for both End-Users and Enterprise Management Admin
01
User side
- Natural language QA search
- Source document citation & page preview
- Multi-turn conversational context retention
- Document upload & private notebook
- Feedback & answer rating
5 features
02
LLM & RAG Engine side
- Vector embedding indexing (Milvus / Qdrant)
- Hybrid keyword & semantic search reranking
- Strict hallucination guardrails & prompt filtering
- Local LLM fallback (Llama 3 70B / Mistral)
4 features
03
Management side
- Knowledge base collection management
- Document ingestion pipeline & sync
- User token usage & latency analytics
- Enterprise SSO & data encryption
4 features
Development Scope
Project Life Cycle & Phases
Phase 01
Requirement definition
Phase 02
UI/UX design
Phase 03
RAG pipeline tuning
Phase 04
Development
Phase 05
Unit / Integration testing
Phase 06
Operation and Maintenance
Tech Stack Architecture
Technologies Used in Project
Languages & Core Frameworks
Python, TypeScript, Next.js 14
AI Tools & GenAI Models
Claude 3.5 Sonnet, Gemini 1.5 Pro, OpenAI Codex, Antigravity RAG Pipeline, LangChain
Databases & Vector Storage
Milvus Vector DB, PostgreSQL, Redis
Infrastructure & Cloud Services
AWS Bedrock, Azure OpenAI, vLLM, Docker
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