Update README.md

README.md

@@ -1,43 +1,207 @@
-
+---
+license: apache-2.0
+language:
+- en
+tags:
+- rag
+- retrieval
+- semantic-search
+- faiss
+- bm25
+- reranker
+- cross-encoder
+- sentence-transformers
+- flan-t5
+- hybrid-search
+- dense-retrieval
+- ai
+- llm
+- search
+- question-answering
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
 # ApexRetriever-Pro
 
-A 5-stage hybrid retrieval pipeline.
+A powerful 5-stage hybrid retrieval system combining sparse retrieval, dense semantic search, diversity optimization, reranking, and generative refinement.
+
+Built for:
+- RAG pipelines
+- AI agents
+- semantic search
+- document QA
+- memory systems
+- knowledge retrieval
+- research assistants
+
+---
+
+# Architecture
+
+ApexRetriever-Pro uses a multi-stage retrieval pipeline:
+
+## Stage ① — BM25 Sparse Retrieval
+Fast keyword-based retrieval using BM25.
+
+## Stage ② — Dense Semantic Retrieval
+Semantic vector search powered by:
+
+- `BAAI/bge-small-en-v1.5`
+
+Uses FAISS for high-speed similarity search.
+
+## Stage ③ — MMR Diversity Filtering
+Maximal Marginal Relevance (MMR) improves result diversity and reduces duplicate-style retrieval.
+
+## Stage ④ — CrossEncoder Reranking
+High-quality neural reranking using:
+
+- `cross-encoder/ms-marco-MiniLM-L-6-v2`
+
+Improves relevance precision significantly.
 
-## Stages
+## Stage ⑤ — FLAN-T5 Refinement
+Final answer refinement using:
 
-1. BM25 sparse retrieval
-2. Dense semantic retrieval
-3. MMR diversity filtering
-4. CrossEncoder reranking
-5. Final optimized retrieval
+- `google/flan-t5-base`
 
-## Components
+Generates concise refined outputs from retrieved context.
 
-- BAAI/bge-small-en-v1.5
-- cross-encoder/ms-marco-MiniLM-L-6-v2
-- FAISS
-- BM25
-- MMR
+---
 
-## Usage
+# Features
+
+- Hybrid sparse+dense retrieval
+- FAISS accelerated search
+- MMR diversity optimization
+- Neural reranking
+- Generative refinement
+- GPU acceleration
+- Plug-and-play pipeline
+- Lightweight deployment
+- Kaggle compatible
+- HuggingFace compatible
+
+---
+
+# Repository Structure
+
+```text
+ApexRetriever-Pro/
+│
+├── bi_encoder/
+├── reranker/
+├── flan_t5/
+├── pipeline.py
+└── README.md
+````
+
+---
+
+# Installation
+
+```bash
+pip install -U \
+    sentence-transformers \
+    transformers \
+    faiss-cpu \
+    rank-bm25 \
+    torch
+```
+
+---
+
+# Quick Start
 
 ```python
 from pipeline import ApexRetrieverPro
 
 retriever = ApexRetrieverPro(model_dir=".")
 
+# Example documents
+
 docs = [
     "Python was created by Guido van Rossum.",
-    "Paris is the capital of France."
+    "Paris is the capital of France.",
+    "Transformers power modern LLMs."
 ]
 
+# Build index
+
 retriever.index_documents(docs)
 
+# Retrieve
+
 results = retriever.retrieve(
     "Who created Python?",
     top_k=3
 )
 
 print(results)
-````
+```
+
+---
+
+# Example Output
+
+```python
+[
+    'Python was created by Guido van Rossum.'
+]
+```
+
+---
+
+# Use Cases
+
+* Retrieval-Augmented Generation (RAG)
+* AI chatbots
+* Local document search
+* Agent memory systems
+* Knowledge bases
+* Research copilots
+* Semantic indexing
+* QA systems
+* Enterprise search
+
+---
+
+# Performance Notes
+
+Recommended:
+
+* CUDA GPU
+* 16GB+ RAM
+* Python 3.10+
+
+Works on:
+
+* Kaggle
+* Colab
+* Local GPU systems
+* Linux
+* Windows
+
+---
+
+# Model Components
+
+| Component     | Model                                |
+| ------------- | ------------------------------------ |
+| Dense Encoder | BAAI/bge-small-en-v1.5               |
+| Reranker      | cross-encoder/ms-marco-MiniLM-L-6-v2 |
+| Refiner       | google/flan-t5-base                  |
+| Vector Engine | FAISS                                |
+| Sparse Search | BM25                                 |
+
+---
+
+# License
+
+Apache 2.0
+
+---
+
+# Author
 
+QuantaSparkLabs