Update README.md

README.md

@@ -29,4 +29,48 @@ This directory contains tools and utilities for working with multilingual embedd
 - **Vector Database**: ChromaDB
 - **Integration Framework**: LlamaIndex
 
-## Directory Structure
+## Directory Structure
+
+embeddings/  
+├── setup.py           # Setup script for the embeddings model and vector store  
+├── demo.py            # Demo application with Gradio web UI  
+├── indexer.py         # Utility for indexing new documents  
+├── requirements.txt   # Dependencies for the embeddings components  
+└── chroma_db/         # Directory for the vector database (created on first run)
+
+Getting Started
+
+1. Install the dependencies:
+
+pip install -r models/embeddings/requirements.txt
+
+
+2. Add documents to index:
+
+# Place your text files in the data/text_corpus directory  
+python models/embeddings/indexer.py --corpus data/text_corpus/
+
+
+3. Run the demo application:
+
+python models/embeddings/demo.py
+
+
+
+Using the Embeddings in Your Code
+
+from models.embeddings.setup import setup_embedding_model  
+  
+# Initialize the model and related components  
+embedding_components = setup_embedding_model()  
+  
+# Get the query engine  
+query_engine = embedding_components["query_engine"]  
+  
+# Query in any language  
+result = query_engine.query("What is the capital of Afghanistan?")  
+# Or in Pashto  
+result = query_engine.query("د افغانستان پلازمېنه څه ده؟")  
+  
+print(result)
+