updates readme

README.md

@@ -42,7 +42,6 @@ This repository contains a **PDF-based product query assistant** built using **L
    ```bash
    pip install -r requirements.txt
    ```
-
 ---
 
 ## **Configuration**
@@ -70,7 +69,6 @@ Replace the placeholder API key or use environment variables for production:
    CREATE_FAISS_INDEX = True  # To create a new index
    CREATE_FAISS_INDEX = False # To load an existing index
    ```
- On initial start set this to TRUE
 ---
 
 ## **Usage**
@@ -91,7 +89,7 @@ Replace the placeholder API key or use environment variables for production:
 ## **Folder Structure**
 
 ```
-xbo-product-query/
+CodeChallenge/
 │
 ├── app/
 │   ├── main.py                # Application entry point
@@ -115,9 +113,8 @@ When scaling up following optimization can be applied:
   
    - Allow appending new documents to the existing FAISS index without rebuilding it entirely.
 
-2. **Memory and Disk Management**  
-   - Use a persistent FAISS index stored on disk, which can be loaded as needed.  
-   - Enable FAISS's `diskann` mode to load only the necessary portions of the index into memory, reducing RAM consumption.
+2. **Use a Vector Store DB**  
+   - Use a Vector DB for scalibility
 
 3. **Batch Processing of Queries**  
    - Break large queries into smaller batches to improve response times.