updated the readme

Author: codejutsu1

.env.example

@@ -63,3 +63,17 @@ AWS_BUCKET=
 AWS_USE_PATH_STYLE_ENDPOINT=false
 
 VITE_APP_NAME="${APP_NAME}"
+
+GEMINI_API_KEY=
+
+QDRANT_URL=
+QDRANT_API_KEY=
+QDRANT_COLLECTION_NAME=
+
+BROADCAST_CONNECTION=reverb
+
+
+VITE_REVERB_APP_KEY="${REVERB_APP_KEY}"
+VITE_REVERB_HOST="${REVERB_HOST}"
+VITE_REVERB_PORT="${REVERB_PORT}"
+VITE_REVERB_SCHEME="${REVERB_SCHEME}"

README.md

@@ -0,0 +1,62 @@
+
+# Document Analyzer 
+An AI-powered application that analyzes PDFs using the Retrieval-Augmented Generation (RAG) technique to deliver accurate, context-aware answers from your documents.
+
+It simply works by uploading a PDF and prompting the LLM questions about the document. 
+
+Built with:
+- [Laravel 12](https://laravel.com/docs/12.x)
+- [VueJS](https://vuejs.org/guide/introduction.html)
+- [InertiaJS](https://inertiajs.com/)
+- [PrismPHP](https://prismphp.com/)
+
+## How It works (Client Side)
+- User uploads a document (PDF)
+- The system processes the document
+- Once processing is complete, the user can prompt the LLM for queries related to the document.
+- Users can start new conversations or continue from previous sessions.
+
+## How It Works (Server Side)
+- User uploads a PDF
+- Text is extracted from the PDF using ... package.
+- The extracted text is segmented into smaller chunks for efficient processing and to meet the embedding model context.
+- Each chunk is sent to an embedding model to generate vector representations.
+- The resulting vectors are stored in a vector database for retrieval.
+- When the user submits a query, the system performs a similarity search in the vector database using the RAG (Retrieval-Augmented Generation) technique to find relevant context.
+- The retrieved context is passed to the LLM, which generates a context-aware response.
+- The LLM’s response is returned and displayed to the user through a chatbot interface.
+
+## Notable Features
+- `Manager Design Pattern` - The system uses a Manager pattern, allowing easy configuration and switching between different Vector Databases and LLMs.
+    - Vector Database: [Qdrant](https://qdrant.tech/) 
+    - LLM: [Gemini](https://gemini.google.com/app)
+
+- `Queues` - Each text chunk is enqueued as an individual job. Batchable jobs are grouped together and processed in parallel to optimize throughput and performance.
+- `Job Middleware` - To comply with the LLM’s rate limits, a middleware enforces a rate limit of 9 requests per minute (RPM) for job execution.
+- `Reverb` - A real-time communication layer that enables live tracking of job progress and interactive chatbot updates. It uses event-driven architecture with WebSockets to broadcast job state changes, logs, and chatbot messages as they occur.
+
+## Installation
+
+- Copy this repository
+- Copy the `.env.example` to `.env`
+- Setup the database connection
+- Install `Composer`
+
+    ```bash
+    composer install
+    ```
+- Generate the application key
+
+    ```bash
+    php artisan key:generate
+    ```
+- Migrate and seed the database 
+
+    ```bash
+    php artisan migrate:fresh --seed
+    ```
+- Run the project
+
+    ```bash
+    php artisan serve
+    ```