Delete README.md

---
title: Rag-System-with- Gemini
sdk: streamlit
sdk_version: 1.50.0
app_file: app.py
pinned: false
license: mit
---

README.md

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-# Agentic RAG Streamlit Application
-
-This project implements an Retrieval-Augmented Generation (RAG) system using **Gemini** and **Streamlit**. It allows users to ingest data from PDF files and web URLs, ask questions, and receive answers generated by a **Large Language Model (LLM)** leveraging the ingested context and optional web search results.
-![architecture ](./assets/architecture.jpg)
-
-### How it works
-
-* The user uploads PDF documents or provides web URLs, these documents are processed and stored in **Chroma** Vector Database.
-* The user submits a query, the query is first sent to a **Rewrite Agent**. This agent analyzes and reformulates the original query, aiming to improve its clarity and effectiveness for retrieval.
-* The rewritten query is forwarded to the LLM. The LLM searches the Vector DB (**Chroma**), retrieving relevant text chunks based on semantic similarity. Simultaneously or based on configuration, it can leverage Web Search (**DuckDuckGo**) to gather information not present in the uploaded documents. If no specific context found, the LLM answers based on its general knowledge.
-* The generated Response is sent back to the Streamlit interface, where it is displayed to the user.
-
-## Features
-
-* **Data Ingestion:** Upload PDF files or enter web URLs to populate the knowledge base.
-* **Persistent Vector Store:** Uses **ChromaDB** to store and retrieve text embeddings locally.
-* **Query Rewriting:** Employs an agent with **Agno** to reformulate user questions for potentially better retrieval results.
-* **Retrieval-Augmented Generation (RAG):**
-    * Retrieves relevant text chunks from the **ChromaDB** vector store based on the (rewritten) query.
-    * Uses a RAG agent (**Gemini**) to synthesize an answer based on the retrieved context.
-* **Web Search:** Optionally performs a web search via **DuckDuckGo** if:
-    * No relevant documents are found in the local vector store.
-    * Web search is explicitly forced via the UI.
-* **Configuration:** Allows users to configure:
-    * Enabling/disabling web search.
-    * Forcing web search.
-    * Adjusting the similarity score threshold for document retrieval.
-* **Database Management:** Options to clear chat history and the vector database.
-* **Dockerized:** Includes a `Dockerfile` for easy containerization and deployment.
-
-## Tech Stack
-
-* **Web Framework:** Streamlit
-* **Vector Database:** ChromaDB
-* **LLM & Embeddings:** Gemini
-* **Core Logic:** Langchain (for document processing, vector store integration), Agno (for agents)
-* **Containerization:** Docker
-
-## Prerequisites
-
-* **Python:** Version 3.11 or higher recommended.
-* **pip:** Python package installer.
-* **Git:** For cloning the repository.
-* **Docker:** Required for running the application using Docker (recommended for easy setup and persistence).
-* **Google API Key:** You need an API key for Google Generative AI (e.g., Gemini API). You can obtain one from [Google AI Studio](https://aistudio.google.com/app/apikey).
-
-## How to use
-### Without Docker
-
-1.  **Clone the Repository:**
-    ```bash
-    git clone https://github.com/luanntd/RAG-System-with-Gemini.git
-    cd RAG-System-with-Gemini
-    ```
-
-2.  **Create a Virtual Environment (Recommended):**
-    ```bash
-    python -m venv venv
-    # Activate it (Linux/macOS)
-    source venv/bin/activate
-    # Activate it (Windows)
-    .\venv\Scripts\activate
-    ```
-
-3.  **Install Dependencies:**
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-4. **Create Directory for Vector Store**
-    ```bash
-    mkdir chroma_db
-    ```
-
-5.  **Set Up Environment Variables:**
-    * Create a file named `.env` in the project's root directory.
-    * Add the following variables:
-
-        ```dotenv
-        GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY
-        COLLECTION_NAME=rag_system
-        DB_PATH=chroma_db
-        ```
-    * Replace `"YOUR_GOOGLE_API_KEY"` with your actual Google API key.
-
-6. **Running the Application**
-    ```bash
-    streamlit run main.py
-    ```
-
-### With Docker (Recommended)
-
-You need to do steps 1 and 5 above before this.
-
-1.  **Build the Docker Image:**
-    ```bash
-    docker build -t rag-system .
-    ```
-
-2.  **Run the Docker Container:**
-    * Create the volume:
-        ```bash
-        docker volume create chroma_data
-        ```
-    * Run the container:
-        ```bash
-        docker run -d \
-            -p 8501:8501 \
-            --env-file ./.env \
-            -v chroma_data:/chroma_db \
-            --name rag-system-container \
-            rag-system
-        ```
-
-    * **Explanation of `docker run` flags:**
-        * `-d`: Run the container in detached mode (in the background).
-        * `-p 8501:8501`: Map port 8501 on your host machine to port 8501 inside the container.
-        * `--env-file ./.env`: Load environment variables from your local `.env` file into the container.
-        * `-v rag_chroma_data:/app/chroma_db`: Mounts persistent storage. It links the named volume `chroma_data` to the `/chroma_db` directory *inside* the container. This path (`/chroma_db`) is where ChromaDB will store its data.
-        * `--name rag-system-container`: Assigns a name to your running container.
-        * `rag-system`: The name of the Docker image you built.
-
-3.  **Access the Application:**
-    * Open your web browser and navigate to `http://localhost:8501`.
-
-## Demo
-![demo](./assets/demo.gif)