Revert "Enhance README with architecture and system flow details (#18)"

This reverts commit 629d4d3.

Author: QuantumByte-01

README.md

@@ -2,38 +2,6 @@
 
 A web application that provides an AI-powered chatbot interface for dataset discovery, using Google Gemini API on the backend and a React-based frontend.
 
-## Architecture Overview
-
-The KnowledgeSpace AI Agent is a Retrieval-Augmented Generation (RAG) system designed for neuroscience dataset discovery. It combines keyword search, semantic retrieval, and large language model reasoning to surface relevant datasets across heterogeneous sources.
-
-The system consists of the following core components:
-
-- **React Frontend**  
-  - Provides a chat-based interface for interacting with the dataset discovery agent.
-
-- **FastAPI Backend**  
-  - Orchestrates LLM-based reasoning (Gemini), keyword search (Elasticsearch), and semantic search (Vertex AI Matching Engine).
-
-- **Data Processing Pipeline**  
-  - Scrapes and normalizes neuroscience metadata, stores structured records in BigQuery, and indexes vector embeddings in Vertex AI for retrieval.
-
-## System Flow (High-Level)
-
-The following diagram shows the high-level request and data flow through the system:
-
-```mermaid
-flowchart LR
-    User --> Frontend
-    Frontend --> Backend
-    Backend -->|Keyword Search| Elasticsearch
-    Backend -->|Semantic Search| VertexAI
-    Backend -->|LLM Reasoning| Gemini
-    Elasticsearch --> Backend
-    VertexAI --> Backend
-    Gemini --> Backend
-    Backend --> Frontend
-```
-
 ## Table of Contents
 
 - [Prerequisites](#prerequisites)
@@ -97,8 +65,6 @@ Create a file named `.env` in the project root based on `.env.template`. You can
 **Option 1: Google API Key (Recommended for development)**
 
 - Set `GOOGLE_API_KEY` in your `.env` file
-> You can generate a Gemini API key from **Google AI Studio**:  
-> https://aistudio.google.com/app/apikey
 
 **Option 2: Vertex AI (Recommended for production)**
 
@@ -173,10 +139,6 @@ The backend requires specific environment variables to connect to **Google Cloud
 
 
 ## Running the Application
-> ⚠️ **Note on Port Configuration**
->
-> - Local development: The React development server runs on port **5000**
-> - Docker / Nginx deployment: The containerized frontend is exposed on port **3000**
 
 #### Backend (port 8000)