update README

.ipynb_checkpoints/README-checkpoint.md

@@ -1,21 +1,28 @@
+---
+title: Amazon E-commerce Visual Assistant
+emoji: 🛍️
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: "1.28.0"
+app_file: amazon_app.py
+pinned: false
+---
+
 # Amazon E-commerce Visual Assistant
 
-A multimodal AI assistant that helps users search and explore Amazon products through natural language and image-based interactions.
+A multimodal AI assistant leveraging the Amazon Product Dataset 2020 to provide comprehensive product search and recommendations through natural language and image-based interactions[1].
 
-## Features
+## Project Overview
 
-- Text and image-based product search
-- Product comparisons and recommendations
-- Visual product recognition
-- Detailed product information retrieval
-- Price analysis and comparison
+This conversational AI system combines advanced language and vision models to enhance e-commerce customer support, enabling accurate, context-aware responses to product-related queries[1].
 
-## Technologies Used
+## Project Structure
 
-- FashionCLIP for visual understanding
-- Mistral-7B Language Model for text generation
-- FAISS for efficient similarity search
-- Streamlit for the user interface
+- `amazon_app.py`: Main Streamlit application
+- `model.py`: Core AI model implementations
+- `Vision_AI.ipynb`: EDA, Embedding Model, LLM
+- `requirements.txt`: Project dependencies
 
 ## Setup and Installation
 
@@ -35,11 +42,55 @@ pip install -r requirements.txt
 streamlit run amazon_app.py
 ```
 
-## Project Structure
+## Technical Architecture
 
-- `amazon_app.py`: Main Streamlit application
-- `model.py`: Core AI model implementations
-- `requirements.txt`: Project dependencies
+### Data Processing & Storage
+- Standardized text fields and normalized numeric attributes
+- Enhanced metadata indices for categories, price ranges, keywords, brands
+- Validated image quality and managed duplicates
+- Structured data storage in Parquet format[1]
+
+### Model Components
+- **Vision-Language Integration**: FashionCLIP for multimodal embedding generation
+- **Vector Search**: FAISS with hybrid retrieval combining embedding similarity and metadata filtering
+- **Language Model**: Mistral-7B with 4-bit quantization
+- **RAG Framework**: Context-enhanced response generation[1]
+
+### Performance Metrics
+- Recall@1: 0.6385
+- Recall@10: 0.9008
+- Precision@1: 0.6385
+- NDCG@10: 0.7725[1]
+
+## Implementation Details
+
+### Core Features
+- Text and image-based product search
+- Product comparisons and recommendations
+- Visual product recognition
+- Detailed product information retrieval
+- Price analysis and comparison[1]
+
+### Technologies Used
+- FashionCLIP for visual understanding
+- Mistral-7B Language Model (4-bit quantized)
+- FAISS for similarity search
+- Google Vertex AI for vector storage
+- Streamlit for user interface[1]
+
+## Challenges & Solutions
+
+### Technical Challenges Addressed
+- Image processing with varying quality
+- GPU memory optimization
+- Efficient embedding storage
+- Query response accuracy[1]
+
+### Implemented Solutions
+- Robust image validation pipeline
+- 4-bit model quantization
+- Optimized batch processing
+- Enhanced metadata enrichment[1]
 
 ## Future Directions
 

README.md

@@ -11,22 +11,18 @@ pinned: false
 
 # Amazon E-commerce Visual Assistant
 
-A multimodal AI assistant that helps users search and explore Amazon products through natural language and image-based interactions.
+A multimodal AI assistant leveraging the Amazon Product Dataset 2020 to provide comprehensive product search and recommendations through natural language and image-based interactions[1].
 
-## Features
+## Project Overview
 
-- Text and image-based product search
-- Product comparisons and recommendations
-- Visual product recognition
-- Detailed product information retrieval
-- Price analysis and comparison
+This conversational AI system combines advanced language and vision models to enhance e-commerce customer support, enabling accurate, context-aware responses to product-related queries[1].
 
-## Technologies Used
+## Project Structure
 
-- FashionCLIP for visual understanding
-- Mistral-7B Language Model for text generation
-- FAISS for efficient similarity search
-- Streamlit for the user interface
+- `amazon_app.py`: Main Streamlit application
+- `model.py`: Core AI model implementations
+- `Vision_AI.ipynb`: EDA, Embedding Model, LLM
+- `requirements.txt`: Project dependencies
 
 ## Setup and Installation
 
@@ -46,14 +42,58 @@ pip install -r requirements.txt
 streamlit run amazon_app.py
 ```
 
-## Project Structure
+## Technical Architecture
 
-- `amazon_app.py`: Main Streamlit application
-- `model.py`: Core AI model implementations
-- `requirements.txt`: Project dependencies
+### Data Processing & Storage
+- Standardized text fields and normalized numeric attributes
+- Enhanced metadata indices for categories, price ranges, keywords, brands
+- Validated image quality and managed duplicates
+- Structured data storage in Parquet format[1]
+
+### Model Components
+- **Vision-Language Integration**: FashionCLIP for multimodal embedding generation
+- **Vector Search**: FAISS with hybrid retrieval combining embedding similarity and metadata filtering
+- **Language Model**: Mistral-7B with 4-bit quantization
+- **RAG Framework**: Context-enhanced response generation[1]
+
+### Performance Metrics
+- Recall@1: 0.6385
+- Recall@10: 0.9008
+- Precision@1: 0.6385
+- NDCG@10: 0.7725[1]
+
+## Implementation Details
+
+### Core Features
+- Text and image-based product search
+- Product comparisons and recommendations
+- Visual product recognition
+- Detailed product information retrieval
+- Price analysis and comparison[1]
+
+### Technologies Used
+- FashionCLIP for visual understanding
+- Mistral-7B Language Model (4-bit quantized)
+- FAISS for similarity search
+- Google Vertex AI for vector storage
+- Streamlit for user interface[1]
+
+## Challenges & Solutions
+
+### Technical Challenges Addressed
+- Image processing with varying quality
+- GPU memory optimization
+- Efficient embedding storage
+- Query response accuracy[1]
+
+### Implemented Solutions
+- Robust image validation pipeline
+- 4-bit model quantization
+- Optimized batch processing
+- Enhanced metadata enrichment[1]
 
 ## Future Directions
 
 - [ ] Fine-Tune FashionClip embedding model based on the specific domain data
 - [ ] Fine-Tune large language model to improve its generalization capabilities
-- [ ] Develop feedback loops for continuous improvement
+- [ ] Develop feedback loops for continuous improvement