AIGA1 — Enterprise AI Knowledge Platform

AI Generalist Accelerator (AIGA) Portfolio Project

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Overview

Enterprise AI Knowledge Platform is an enterprise-grade Retrieval-Augmented Generation (RAG) system designed to ingest organizational knowledge, generate embeddings, perform semantic retrieval, assemble contextual information, and generate grounded AI responses using local Large Language Models (LLMs).

The platform demonstrates modern AI infrastructure patterns including:

• Knowledge ingestion pipelines
• Vector databases
• Embedding generation
• Semantic search
• Context assembly
• Retrieval-Augmented Generation (RAG)
• Local LLM execution
• Streamlit-based AI applications

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Key Features

Knowledge Ingestion

• Document loading
• Document normalization
• Smart chunking
• Embedding generation
• ChromaDB vector storage

Semantic Retrieval

• Similarity search
• Vector retrieval
• Context assembly
• Knowledge grounding

AI Response Generation

• Local LLM execution
• Grounded response generation
• Retrieval-Augmented Generation (RAG)
• Enterprise knowledge question answering

User Experience

• Streamlit web application
• Interactive question interface
• Grounded answer presentation

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Architecture Diagram

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Implemented Features

Knowledge Pipeline

• Document Loader
• Document Normalizer
• Smart Chunker
• Embedding Generator
• ChromaDB Integration

Retrieval Pipeline

• Semantic Search
• Retrieval Engine
• Context Assembly

Response Pipeline

• Grounded Response Generator
• Ollama Integration
• Llama 3.2 Integration

User Interface

• Streamlit Application
• End-to-End Question Answering Workflow

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Business Outcomes

• Faster enterprise knowledge discovery
• Semantic retrieval of organizational knowledge
• Grounded AI responses
• Reduced information search effort
• Reusable AI knowledge infrastructure
• Foundation for enterprise AI assistants
• Local AI deployment capability

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Portfolio Focus

This project demonstrates:

• Retrieval-Augmented Generation (RAG)
• Semantic Search
• Vector Databases
• ChromaDB
• Embeddings
• Ollama
• Local LLM Deployment
• Prompt Engineering
• Streamlit Development
• AI Infrastructure Engineering

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Technology Stack

Layer	Technology
UI	Streamlit
Retrieval	Semantic Search
Vector Database	ChromaDB
Embeddings	nomic-embed-text
LLM Runtime	Ollama
Model	llama3.2:1b
Language	Python

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Screenshots

Home Page

Question and Answer

End-to-End Demo

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Installation

Create Virtual Environment

python -m venv venv

Activate Environment

venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Start Ollama

ollama serve

Pull Required Models

ollama pull llama3.2:1b
ollama pull nomic-embed-text

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Run Application

streamlit run src/ui/app.py

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Sample Question

What does the Enterprise AI Knowledge Platform support?

Sample Answer

Semantic retrieval.

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Project Structure

enterprise-ai-knowledge-platform/

├── architecture/
├── data/
├── docs/
├── screenshots/
├── src/
├── requirements.txt
├── README.md
└── .gitignore

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Portfolio Skills Demonstrated

• Python
• ChromaDB
• Ollama
• Embeddings
• Semantic Search
• Vector Databases
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Streamlit
• Enterprise AI Platforms

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MVP Status

Version: 1.0

Status: MVP COMPLETE ✅

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AI Generalist Accelerator (AIGA) Portfolio Project