Update README with vortexa core API and Indian examples for nano tier

README.md

@@ -14,6 +14,7 @@ tags:
 - ultra-lightweight
 - code-search
 - retrieval
+- vortexa
 pipeline_tag: feature-extraction
 metrics:
 - spearman_cosine
@@ -78,12 +79,13 @@ model-index:
 
 <div align="center">
 
-# 🚀 vtx-embed-1M
+# 🚀 vtx-embed-1M (`nano`)
 
-**The world's most compact 100% data-free distilled embedding model.**
-Native 4-Bit quantization · 0.57 MB RAM · 1.05M Parameters · 1.14M Tokens/sec CPU Latency
+**The world's most memory-efficient static embedding model powering [vortexa](https://github.com/OEvortex/vortexa).**
+Native 4-Bit quantization · 0.57 MB RAM · 1.05M Parameters · Matryoshka MRL · Sub-millisecond CPU latency
 
-[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-VTXAI%2Fvtx--embed--1M-blue)](https://huggingface.co/VTXAI/vtx-embed-1M)
+[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-VTXAI%2Fvtx-embed-1M-blue)](https://huggingface.co/VTXAI/vtx-embed-1M)
+[![GitHub Vortexa](https://img.shields.io/badge/GitHub-OEvortex%2Fvortexa-black?logo=github)](https://github.com/OEvortex/vortexa)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 [![Python 3.8+](https://img.shields.io/badge/Python-3.8%2B-blue)](https://python.org)
 
@@ -91,87 +93,86 @@ Native 4-Bit quantization · 0.57 MB RAM · 1.05M Parameters · 1.14M Tokens/sec
 
 ---
 
+## ⚡ Integration with Vortexa
+
+This model powers **[vortexa](https://github.com/OEvortex/vortexa)** — a standalone codebase indexing and semantic search engine designed for AI agents and developers.
+
+`vortexa` builds a persistent, hybrid search index over source code using:
+- **Dense Retrieval**: Driven natively by `vtx-embed-1M` (on-the-fly LF4 4-bit dequantization, SIF+PC pooling, Matryoshka truncation).
+- **Sparse Retrieval**: BM25 keyword scoring for exact symbol matches.
+- **AST-Aware Chunking**: Tree-sitter powered chunking respecting function and class boundaries.
+- **LMDB Storage**: Fast, persistent vector and document chunk storage.
+
+---
+
 ## 📄 Model Details
 
 | Property | Value |
 | :--- | :--- |
-| **Model Type** | Native 4-Bit Data-Less Distilled Embedding (LF4) |
-| **Total Parameters** | **1.05M (1,048,576 parameters)** |
+| **Model Name / Tier** | **vtx-embed-1M** (`"nano"`) |
+| **Total Parameters** | **1.05M** |
 | **Tensor Storage Format** | `lf4` — 4-bit per-block with FP16 scale + zero |
-| **In-RAM Memory** | **0.57 MB (570 KB)** |
-| **On-Disk Size** | **0.57 MB (570 KB)** |
+| **In-RAM Memory** | **0.57 MB** |
+| **On-Disk Size** | **0.57 MB** |
 | **Vocabulary Size** | 16,384 |
 | **Max Sequence Length** | 512 tokens |
-| **Output Dimensions** | 64 *(Matryoshka: also 32)* |
+| **Output Dimensions** | 64 *(Matryoshka supported)* |
 | **Pooling** | SIF IDF-weighted + PC-1 removal |
-| **Distillation Type** | 100% Data-Free SVD Subspace Reconstruction |
+| **Primary Engine Integration** | [OEvortex/vortexa](https://github.com/OEvortex/vortexa) |
 | **License** | MIT |
 
 ---
 
-## 📊 MTEB Benchmark Results (Exact Empirical Scores)
-
-All scores below were produced by running evaluation across all benchmark tasks directly against `vtx-embed-1M`:
+## 📊 Official Benchmark Results
 
-### Semantic Textual Similarity (STS)
-
-| Dataset | Metric | **vtx-embed-1M (0.57 MB)** | **vtx-embed-7M (4.72 MB)** | MiniLM-L6-v2 (90 MB) | bge-small-en-v1.5 (134 MB) |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **STSBenchmark** | Spearman ρ | **0.7149** | **0.7918** | 0.8284 | 0.8278 |
-| **SICK-R** | Spearman ρ | **0.5916** | **0.6294** | 0.7572 | 0.7460 |
-
-### Classification
-
-| Dataset | Metric | **vtx-embed-1M (0.57 MB)** | **vtx-embed-7M (4.72 MB)** | MiniLM-L6-v2 (90 MB) | bge-small-en-v1.5 (134 MB) |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **Banking77Classification** | Accuracy | **0.8384** | **0.7043** | 0.7451 | 0.7884 |
-| **AmazonCounterfactualClassification** | Accuracy | **0.7731** | **0.6679** | 0.7371 | 0.7279 |
-
-### Clustering
-
-| Dataset | Metric | **vtx-embed-1M (0.57 MB)** | **vtx-embed-7M (4.72 MB)** | MiniLM-L6-v2 (90 MB) | bge-small-en-v1.5 (134 MB) |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **TwentyNewsgroupsClustering** | V-Measure | **0.2511** | **0.2936** | 0.3529 | 0.4419 |
-| **RedditClustering** | V-Measure | **0.3762** | **0.3880** | 0.4342 | 0.5376 |
+| Dataset | Metric | **vtx-embed-1M (0.57 MB)** | MiniLM-L6-v2 (90 MB) | bge-small-en-v1.5 (134 MB) |
+| :--- | :---: | :---: | :---: | :---: |
+| **STSBenchmark** | Spearman ρ | **0.7149** | 0.8284 | 0.8278 |
+| **SICK-R** | Spearman ρ | **0.5916** | 0.7572 | 0.7460 |
+| **Banking77Classification** | Accuracy | **0.8384** | 0.7451 | 0.7884 |
+| **AmazonCounterfactual** | Accuracy | **0.7731** | 0.7371 | 0.7279 |
+| **TwentyNewsgroups** | V-Measure | **0.2511** | 0.3529 | 0.4419 |
+| **RedditClustering** | V-Measure | **0.3762** | 0.4342 | 0.5376 |
 
 ---
 
-## ⚡ Efficiency & Throughput Benchmark
-
-*Evaluated directly on CPU execution over 1,158 codebase files (900k+ tokens):*
+## 💻 Quickstart Usage
 
-| Model | RAM Size | Throughput | Single File Latency | Tool Routing Accuracy |
-| :--- | :-: | :-: | :-: | :-: |
-| **vtx-embed-1M (64-dim)** | **0.57 MB** | **1,139,267 Tokens/sec** | **0.682 ms** | **100.0% (20/20)** |
-| **vtx-embed-7M (256-dim)** | **4.72 MB** | 878,578 Tokens/sec | 0.841 ms | **100.0% (20/20)** |
-| LiquidAI/LFM2.5-Embedding-350M | ~700 MB | ~137 texts/sec | 7.30 ms | — |
-| sentence-transformers/all-MiniLM-L6-v2 | 90 MB | ~80 texts/sec | 12.4 ms | — |
-
-> **vtx-embed-1M is 157× smaller than MiniLM-L6-v2 while achieving 0.8384 Banking77 Accuracy and 100% Agent Tool Search Accuracy.**
-
----
-
-## 💻 Quickstart
-
-### Installation
-
-```bash
-pip install numpy tokenizers safetensors huggingface_hub
-```
-
-### Python Inference
+### Native Vortexa Core API
 
 ```python
-from vortex_embed.src.lf4_v4_5 import VortexEmbedV4_5
-
-# Load 1M model from Hugging Face
-model = VortexEmbedV4_5.from_pretrained("VTXAI/vtx-embed-1M")
-
-sentences = ["Execute terminal command", "Run bash shell process in background"]
-embeddings = model.encode(sentences, normalize=True)  # (2, 64) float32
-
-sim = (embeddings[0] * embeddings[1]).sum()
-print(f"Similarity: {sim:.4f}")
+from vortexa.core.inference import VortexEmbedInference, similarity
+
+# Load model using the "nano" model alias
+model = VortexEmbedInference("nano")
+
+queries = [
+    "What is the capital of India?",
+    "Explain gravity and general relativity",
+]
+documents = [
+    "The capital of India is New Delhi.",
+    "Gravity is a fundamental interaction that causes mutual attraction between all things with mass or energy.",
+]
+
+# 1. Encode queries and documents
+query_embeddings = model.encode(queries)
+document_embeddings = model.encode(documents)
+
+# 2. Compute similarity matrix directly
+similarity_matrix = query_embeddings @ document_embeddings.T
+print("Similarity Matrix:")
+print(similarity_matrix)
+# Example output:
+# [[0.82, 0.12],
+#  [0.11, 0.74]]
+
+# 3. Use built-in model similarity method
+scores = model.similarity(query_embeddings, document_embeddings)
+
+# 4. Single query against document list lookup
+scores_single = model.similarity("What is the capital of India?", documents)
+print("Single query scores:", scores_single)
 ```
 
 ---
@@ -179,8 +180,8 @@ print(f"Similarity: {sim:.4f}")
 ## 📜 Citation
 
 ```bibtex
-@misc{vtx-embed-1M,
-  title  = {vtx-embed-1M: Data-Free Matrix Distillation for Sub-Megabyte Embeddings},
+@misc{vtx-embed-1m},
+  title  = {vtx-embed-1M: Native 4-Bit Embeddings for Standalone Codebase Indexing},
   author = {VTXAI},
   year   = {2026},
   url    = {https://huggingface.co/VTXAI/vtx-embed-1M}
@@ -191,4 +192,4 @@ print(f"Similarity: {sim:.4f}")
 
 ## 📄 License
 
-MIT License
+MIT License — free for commercial and research use.