🧠 AI Company Brain

An Enterprise GraphRAG Knowledge Platform for Explainable Organizational Intelligence

📖 Overview

AI Company Brain is an enterprise-grade GraphRAG (Graph Retrieval-Augmented Generation) knowledge platform designed to transform fragmented organizational information into an intelligent, explainable, and queryable knowledge system.

Traditional enterprise search systems fail because they treat documents as isolated pieces of information. AI Company Brain goes beyond keyword search by combining:

Semantic vector retrieval
Knowledge graph reasoning
Large language models
Source citations
Explainable AI workflows

The result is a system capable of answering complex organizational questions while providing transparent reasoning paths and verifiable sources.

🚨 The Problem

Modern organizations store information across dozens of disconnected systems:

Slack conversations
Internal documentation
PDFs
Incident reports
Notion pages
GitHub repositories
Knowledge bases
Engineering runbooks

Existing solutions suffer from several limitations:

Problem	Existing Systems
Keyword-based search	❌
No semantic understanding	❌
No relationship reasoning	❌
Hallucinated answers	❌
No source attribution	❌
No explainability	❌

This creates a significant knowledge retrieval bottleneck where employees spend more time searching for information than using it.

💡 Our Solution

AI Company Brain combines:

Semantic Search
        +
Knowledge Graphs
        +
Graph Traversal
        +
LLM Reasoning
        +
Citation Generation

to create an explainable enterprise intelligence system.

Example query:

"What caused the payment outage?"

Instead of returning documents, the system:

Question
    ↓
Embedding Generation
    ↓
Semantic Search
    ↓
Graph Expansion
    ↓
Knowledge Retrieval
    ↓
LLM Reasoning
    ↓
Source Attribution
    ↓
Final Answer

Result:

Answer
Reasoning steps
Supporting entities
Supporting documents
Knowledge graph paths
Source citations

🏗️ System Architecture

                    ┌──────────────────┐
                    │ React Frontend   │
                    └────────┬─────────┘
                             │
                             ▼
                    ┌──────────────────┐
                    │ FastAPI Backend  │
                    └────────┬─────────┘
                             │
         ┌───────────────────┼───────────────────┐
         ▼                   ▼                   ▼

 ┌──────────────┐    ┌──────────────┐    ┌──────────────┐
 │ Embeddings   │    │ Qdrant       │    │ Neo4j        │
 │ Generation   │───▶│ Vector DB    │    │ Knowledge DB │
 └──────────────┘    └──────────────┘    └──────────────┘
                             │                   │
                             ▼                   ▼
                      ┌───────────────────────────┐
                      │ Graph Expansion Engine    │
                      └─────────────┬─────────────┘
                                    ▼
                      ┌───────────────────────────┐
                      │ Gemini 2.5 Flash          │
                      │ Reasoning Engine          │
                      └─────────────┬─────────────┘
                                    ▼
                      ┌───────────────────────────┐
                      │ Citation Engine           │
                      └───────────────────────────┘

🚀 Features

🔍 Semantic Search

Sentence-transformer embeddings
Dense vector retrieval
Similarity search
Context-aware retrieval
Semantic ranking

🕸️ Knowledge Graph

Entity extraction
Relationship mapping
Multi-hop graph traversal
Explainable graph reasoning
Interactive graph visualization

🧠 GraphRAG

Semantic retrieval
Graph expansion
Knowledge enrichment
Multi-source reasoning
Structured context generation

🤖 LLM Reasoning

Gemini 2.5 Flash integration
Context-aware synthesis
Multi-document reasoning
Graph-assisted generation

📑 Citation Engine

Every generated answer includes:

Source documents
Supporting entities
Supporting relationships
Evidence tracing
Provenance mapping

📊 Enterprise Dashboard

Knowledge statistics
Database health
Retrieval analytics
Graph metrics
System monitoring

🎨 Interactive Frontend

Real-time chat
Knowledge graph explorer
Architecture visualization
Source inspection
Responsive UI

🧩 Tech Stack

Frontend

Technology	Purpose
React	User Interface
Vite	Build Tool
Tailwind CSS	Styling
JavaScript	Frontend Logic

Backend

Technology	Purpose
Python	Core Language
FastAPI	API Framework
Pydantic	Validation
Uvicorn	ASGI Server

AI & ML

Technology	Purpose
Sentence Transformers	Embeddings
all-MiniLM-L6-v2	Embedding Model
Gemini 2.5 Flash	LLM Reasoning
GraphRAG	Retrieval Architecture

Databases

Technology	Purpose
Qdrant	Vector Database
Neo4j	Graph Database

Infrastructure

Technology	Purpose
Docker	Containerization
Docker Desktop	Local Runtime
WSL2	Linux Compatibility

⭐ What Makes This Different?

Traditional RAG

Question
    ↓
Vector Search
    ↓
LLM
    ↓
Answer

Problems:

Limited context
Weak reasoning
Hallucinations
No relationships
Poor explainability

AI Company Brain

Question
     ↓
Embedding Search
     ↓
Vector Retrieval
     ↓
Knowledge Graph
     ↓
Graph Expansion
     ↓
LLM Reasoning
     ↓
Citation Engine
     ↓
Explainable Answer

Advantages:

✅ Semantic understanding

✅ Relationship reasoning

✅ Multi-hop retrieval

✅ Explainable outputs

✅ Source citations

✅ Knowledge graph exploration

📷 Demo Screenshots

Login

Dashboard

AI Assistant

Knowledge Graph

📂 Project Structure

CodeSmiths/

├── src/                     # React frontend
├── backend/
│   ├── app/
│   ├── embeddings/
│   ├── retrieval/
│   ├── graph/
│   ├── rag/
│   ├── citations/
│   ├── evaluation/
│   └── brain/
│
├── data/
├── agent_orchestration/
│
├── test_embeddings.py
├── test_qdrant.py
├── test_neo4j.py
├── test_graph_expansion.py
├── test_graph_rag.py
└── test_citations.py

⚙️ System Requirements

Minimum:

Windows 10/11
Linux
macOS
8 GB RAM
15 GB free storage

Recommended:

16 GB RAM
Quad-core CPU
SSD
Docker Desktop

🛠️ Prerequisites

Install:

Python 3.12+
Node.js 20+
npm
Git
Docker Desktop
WSL2 (Windows)

🐳 Start Databases

Qdrant

docker run -d \
--name qdrant \
-p 6333:6333 \
-p 6334:6334 \
qdrant/qdrant

Neo4j

docker run -d \
--name neo4j \
-p 7474:7474 \
-p 7687:7687 \
-e NEO4J_AUTH=neo4j/password123 \
neo4j

📥 Installation

Clone repository:

git clone https://github.com/YOUR_USERNAME/CodeSmiths.git

cd CodeSmiths

Backend Setup

pip install -r requirements.txt

or

pip install \
fastapi \
uvicorn \
sentence-transformers \
qdrant-client \
neo4j \
google-generativeai

Create:

.env

Add:

GEMINI_API_KEY=YOUR_GEMINI_API_KEY

Frontend Setup

npm install

🚀 Running the Project

Start Backend

python demo_server.py

Backend:

http://localhost:8000

API Docs:

http://localhost:8000/docs

Start Frontend

npm run dev

Frontend:

http://localhost:5173

🧪 Testing

Run individual subsystem tests:

python test_qdrant.py
python test_neo4j.py
python test_graph_expansion.py
python test_graph_rag.py
python test_citations.py

📈 Example Query

Input:

What caused the payment outage?

Output:

Answer:
The payment outage was caused by the Redis Cluster outage.

Sources:
[1] payment_incident.md
[2] redis_outage.md
[3] slack_thread.md

Graph Entities:
• Payment Service
• Incident #1001
• Slack Thread
• Redis Cluster

🔮 Future Improvements

Multi-user authentication
Role-based access control
Real document connectors
Slack integration
Notion integration
GitHub integration
Hybrid retrieval
Agentic workflows
Real-time graph updates
Fine-tuned retrieval models
Observability dashboard
Production deployment

👥 Team

Built by Team CodeSmiths.

Contributors:

Backend Ingestion
Frontend Dashboard
Agent Orchestration
AI Company Brain / GraphRAG

📜 License

This project is intended for educational, research, and demonstration purposes.

Name		Name	Last commit message	Last commit date
Latest commit History 59 Commits
agent_orchestration		agent_orchestration
assets		assets
backend		backend
data/demo_documents		data/demo_documents
dist		dist
node_modules		node_modules
src		src
.gitignore		.gitignore
Project Completion Report.pdf		Project Completion Report.pdf
README.md		README.md
agents.md		agents.md
context.md		context.md
demo_server.py		demo_server.py
index.html		index.html
package-lock.json		package-lock.json
package.json		package.json
plan.md		plan.md
postcss.config.js		postcss.config.js
tailwind.config.js		tailwind.config.js
test_citations.py		test_citations.py
test_embeddings.py		test_embeddings.py
test_graph_expansion.py		test_graph_expansion.py
test_graph_rag.py		test_graph_rag.py
test_neo4j.py		test_neo4j.py
test_qdrant.py		test_qdrant.py
vite.config.js		vite.config.js

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