Update README.md

README.md

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 Production-grade Retrieval-Augmented Generation (RAG) backend built with FastAPI, FAISS (ANN), and Google Gemini — featuring hybrid retrieval, HNSW indexing, cross-encoder reranking, evaluation logging, and analytics.
 
-This repository demonstrates how modern AI backend systems are actually built in industry, not toy demos.
+This repository demonstrates how modern AI backend systems are actually built in industry.
 
 🚀 What This Project Is
 
@@ -96,30 +96,44 @@ Persistent vector store
 
 🏗️ System Architecture
 
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 Frontend (HTML / JS)
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         ↓
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 FastAPI Backend
         ↓
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 Document Ingestion (PDF / TXT)
         ↓
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 Sentence Chunking + Metadata
         ↓
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 Embeddings (SentenceTransformers)
         ↓
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 FAISS ANN Index (HNSW)
         ↓
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 Hybrid Retrieval (Vector + Keyword)
         ↓
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 Cross-Encoder Reranking
         ↓
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 Prompt Assembly
         ↓
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 Google Gemini LLM
         ↓
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 Answer + Confidence + Citations
         ↓
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 Evaluation Logging + Analytics
 
+
 🧠 Core Concepts Demonstrated
 Retrieval-Augmented Generation (RAG)
 
@@ -168,6 +182,7 @@ Clean Git hygiene
 Incremental system evolution
 
 🛠️ Tech Stack
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 Backend
 
 Python
@@ -209,14 +224,30 @@ Virtual Environments (venv)
 ⚙️ Setup & Run Locally
 
 1️⃣ Clone Repository
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 git clone https://github.com/LVVignesh/gemini-rag-fastapi.git
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 cd gemini-rag-fastapi
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+2️⃣ Create Virtual Environment
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 python -m venv venv
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 venv\Scripts\activate
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+3️⃣ Install Dependencies
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 pip install -r requirements.txt
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+4️⃣ Configure Environment Variables
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 GEMINI_API_KEY=your_api_key_here
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+5️⃣ Run Server
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 uvicorn main:app --reload
 
+
 ⚠️ Known Limitations
 
 Scanned/image-only PDFs require OCR (not included)