Update README.md

README.md

@@ -14,11 +14,20 @@ pipeline_tag: text-generation
 
 # HyperCache AI — Unified KV-Cache & Vector DB Acceleration SDK
 
+[![PyPI - Version](https://img.shields.io/pypi/v/hypercache-ai?color=blue&label=PyPI%20Package)](https://pypi.org/project/hypercache-ai/)
+[![Python Versions](https://img.shields.io/badge/python-3.8%20%E2%80%93%203.13%2B-blue)](https://pypi.org/project/hypercache-ai/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HyperCache--AI-yellow)](https://huggingface.co/Walid-Kotb-1/HyperCache-AI)
+[![Commercial License](https://img.shields.io/badge/License-Commercial%20PRO-green)](https://kotbster7.gumroad.com/l/hypercache-ai)
+
 **Cut LLM KV-Cache VRAM by 58.2% (`+30%` faster decoding) AND compress Vector Database / RAG Embeddings by 57.3% (`2.34x` faster search) with zero model retraining.**
 
-👉 **[Get Your Instant Annual PRO License Key on Gumroad (\$99/yr Launch Offer)](https://www.google.com/search?q=https://kotbster7.gumroad.com/l/hypercache-ai)**
+```bash
+pip install hypercache-ai
+```
 
+👉 **[Get Your Instant Annual PRO License Key on Gumroad ($99/yr Launch Offer)](https://kotbster7.gumroad.com/l/hypercache-ai)**
 
+![HyperCache AI Benchmark](./Screenshot_Qwen2.5-3B-Instruct.png)
 
 HyperCache AI is a dual-engine runtime acceleration SDK for **PyTorch / Hugging Face LLMs** (`Qwen2.5`, `Llama-3`, `Mistral`, `Gemma`) and **Enterprise Vector Databases** (`Pinecone`, `Milvus`, `Qdrant`, `FAISS`, `pgvector`):
 1. **Engine 1 — LLM KV-Cache Runtime Compressor:** Preserves full-resolution semantic blocks while condensing historical background tokens on the fly, achieving **100% verbatim Needle-in-a-Haystack recall** while cutting GPU KV-Cache memory in half.
@@ -40,14 +49,14 @@ HyperCache AI is a dual-engine runtime acceleration SDK for **PyTorch / Hugging
 | **3. Standard Sliding Window (`-58.2% RAM`)** | 1,156 | 40.64 MB | 19.1 tok/s | **Failed (0%)** | `The primary vault authorization key is the exact primary vault authorization key...` *(Hallucinates & loops)* |
 
 #### B) `Qwen/Qwen2.5-7B-Instruct` (Large-Scale VRAM Reduction)
-* **Uncompressed Baseline:** `2,764 tokens` (**`151.16 MB VRAM`**) $\to$ Retrieves `'HC-ENTERPRISE-9940-X'` and `90.37 million USD`.
-* **HyperCache AI SDK (`-58.2% RAM`):** **`1,156 tokens`** (**`63.22 MB VRAM`**) $\to$ Retrieves the **exact same verbatim output** while saving **`87.94 MB VRAM`** per single request!
+* **Uncompressed Baseline:** `2,764 tokens` (**`151.16 MB VRAM`**) → Retrieves `'HC-ENTERPRISE-9940-X'` and `90.37 million USD`.
+* **HyperCache AI SDK (`-58.2% RAM`):** **`1,156 tokens`** (**`63.22 MB VRAM`**) → Retrieves the **exact same verbatim output** while saving **`87.94 MB VRAM`** per single request!
 
 ---
 
-### 2. Vector Database & RAG Embedding Benchmark (`5,000` Docs, `1536-D` $\to$ `656-D`)
+### 2. Vector Database & RAG Embedding Benchmark (`5,000` Docs, `1536-D` → `656-D`)
 
-| Vector DB Compression Method (At 42.7% RAM Budget) | Compressed Dim | RAM Saved | Top-1 Match ($\uparrow$) | Recall@10 ($\uparrow$) | Cosine Score Correlation ($\uparrow$) |
+| Vector DB Compression Method (At 42.7% RAM Budget) | Compressed Dim | RAM Saved | Top-1 Match (↑) | Recall@10 (↑) | Cosine Score Correlation (↑) |
 | :--- | :---: | :---: | :---: | :---: | :---: |
 | **1. Standard Dimension Truncation** | 656-D | 57.29% | 99.0% | 22.90% | 66.28% |
 | **2. HyperCache Vector DB Engine (Ours)** | **656-D** | **57.29%** | **100.0%** | **95.20%** | **99.92%** |
@@ -56,7 +65,7 @@ HyperCache AI is a dual-engine runtime acceleration SDK for **PyTorch / Hugging
 
 ### 3. Multi-Head Long-Context Attention Fidelity (`N = 4,096` Tokens, 16 Heads)
 
-| Compression Method (At Identical 42.5% RAM Budget) | Output Cosine Similarity ($\uparrow$) | Relative L2 Error ($\downarrow$) | Fact Recall ($\uparrow$) | Theoretical Speedup |
+| Compression Method (At Identical 42.5% RAM Budget) | Output Cosine Similarity (↑) | Relative L2 Error (↓) | Fact Recall (↑) | Theoretical Speedup |
 | :--- | :---: | :---: | :---: | :---: |
 | **1. Standard Sliding Window** | 66.73% | 115.84% | 40.0% | 2.35x |
 | **2. Uniform Strided Subsampling** | 63.47% | 114.45% | 20.0% | 2.35x |
@@ -64,26 +73,20 @@ HyperCache AI is a dual-engine runtime acceleration SDK for **PyTorch / Hugging
 
 ---
 
-## Instant 30-Second Evaluation (Free Trial Key Included)
+## Instant 30-Second Installation & Evaluation (Free Trial Key Included)
 
-You can evaluate both engines right now on your own machine or in Google Colab using our public evaluation key:
+You can install HyperCache AI directly from **Official PyPI** and evaluate both engines right now on your own machine or in Google Colab using our public evaluation key:
 * **Public Evaluation Key:** `HYPERCACHE-FREE-TRIAL-2026`
   *(Hardware-locked to **15 free evaluation compressions** per machine, up to **4,096 tokens/dimensions** per call).*
 
-### Step 1: Python Environment & Requirements
-> **Universal Python Version Support (3.8 – 3.13+):** `hypercache_sdk_client.py` decrypts and compiles the runtime engine in volatile RAM directly on your target Python interpreter, ensuring seamless compatibility across **Python 3.10, 3.11, 3.12, and 3.13** (including Google Colab, Kaggle, and production Linux/Windows servers).
+### Step 1: One-Line Installation via `pip`
+**Universal Python Version Support (3.8 – 3.13+):** `hypercache-ai` decrypts and compiles the runtime engine in volatile RAM directly on your target Python interpreter, ensuring seamless compatibility across **Python 3.8 through 3.13+** (including Google Colab, Kaggle, Linux, macOS, and Windows).
 
 ```bash
-pip install numpy cryptography torch transformers accelerate
+pip install hypercache-ai torch transformers accelerate
 ```
 
-### Step 2: Run the Unified Quickstart Benchmark (KV-Cache + Vector DB)
-Download `hypercache_sdk_client.py` and `quickstart_demo.py` into the same directory and run:
-```bash
-python3 quickstart_demo.py
-```
-
-### Step 3: Use Both Engines in Python (3 Lines of Code)
+### Step 2: Use Both Engines in Python (3 Lines of Code)
 ```python
 import hypercache_sdk_client
 
@@ -102,22 +105,27 @@ compressed_docs    = engine.fit_compress_embeddings(doc_embeddings_1536d)
 compressed_queries = engine.compress_query_embeddings(query_embeddings_1536d)
 ```
 
+### Step 3: Or Run the Full Benchmark Suite
+```bash
+python quickstart_demo.py
+```
+
 ---
 
 ## Annual Licensing, Early-Bird Pricing & Seat Management
 
-HyperCache AI is distributed as an **AES-256-GCM authenticated commercial vault** (`hypercache_sdk_client.py`) that decrypts strictly in volatile RAM upon license verification.
+HyperCache AI is distributed as an **AES-256-GCM authenticated commercial vault** (`hypercache-ai`) that decrypts strictly in volatile RAM upon license verification.
 
 | Feature | Free Evaluation Trial | Developer / Startup PRO | Enterprise Cluster PRO | Enterprise Unlimited (K8s) |
 | :--- | :---: | :---: | :---: | :---: |
 | **Annual Price (Early-Bird Launch)** | **Free** | ~~$199/yr~~ **$99 / year** | ~~$999/yr~~ **$399 / year** | ~~$2,999/yr~~ **$1,499 / year** |
-| **Purchase Instant License Key** | `HYPERCACHE-FREE-TRIAL-2026` | **[Buy Developer PRO (\$99/yr)](https://www.google.com/search?q=https://kotbster7.gumroad.com/l/hypercache-ai)** | **[Buy Cluster PRO (\$399/yr)](https://www.google.com/search?q=https://kotbster7.gumroad.com/l/hypercache-ai)** | **[Buy Unlimited K8s (\$1,499/yr)](https://www.google.com/search?q=https://kotbster7.gumroad.com/l/hypercache-ai)** |
+| **Purchase Instant License Key** | `HYPERCACHE-FREE-TRIAL-2026` | **[Buy Developer PRO ($99/yr)](https://kotbster7.gumroad.com/l/hypercache-ai)** | **[Buy Cluster PRO ($399/yr)](https://kotbster7.gumroad.com/l/hypercache-ai)** | **[Buy Unlimited K8s ($1,499/yr)](https://kotbster7.gumroad.com/l/hypercache-ai)** |
 | **Active Machine Seats** | 1 Machine (Non-Transferable) | **2 Transferable Seats** *(Dev Laptop + 1 Prod Server)* | **10 Transferable Server Seats** | **Unlimited Nodes / Containers** *(Kubernetes Auto-Scaling)* |
 | **Machine Transfer (`deactivate`)** | No | **Yes (`deactivate_current_machine()`)** | **Yes (`deactivate_current_machine()`)** | **Automatic (Zero Seat Lock)** |
 | **Total Runs Allowed** | **15 Runs** (Hardware-Locked) | **Unlimited (24/7 Production)** | **Unlimited (24/7 Production)** | **Unlimited (24/7 Production)** |
 | **Max Context / Vector DB Size** | Up to **4,096 Tokens / Dims** | **Unlimited (32k–1M+ Tokens)** | **Unlimited (32k–1M+ Tokens)** | **Unlimited (Cluster-Wide)** |
 
-👉 **[Click Here to Purchase Your Instant Annual PRO License Key on Gumroad](https://www.google.com/search?q=https://kotbster7.gumroad.com/l/hypercache-ai)**
+👉 **[Click Here to Purchase Your Instant Annual PRO License Key on Gumroad](https://kotbster7.gumroad.com/l/hypercache-ai)**
 
 ### Moving Your PRO License Between Machines
 Both **Developer PRO (2 Seats)** and **Enterprise Cluster PRO (10 Seats)** allow you to transfer active seats between workstations and cloud servers at any time with a single line of Python: