Merge pull request #59 from Madhuvod/rag-doc-router

Added new demo

ai_agent_tutorials/ai_recruitment_agent_team/README.md

@@ -35,8 +35,8 @@ A Streamlit application that simulates a full-service recruitment team using mul
 1. **Setup Environment**
    ```bash
    # Clone the repository
-   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
-   cd ai_agent_tutorials/ai_recruitment_agent_team
+    git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+    cd ai_agent_tutorials/ai_recruitment_agent_team
 
    # Install dependencies
    pip install -r requirements.txt

ai_agent_tutorials/multimodal_design_agent_team/requirements.txt

@@ -1,5 +1,5 @@
 google-generativeai==0.8.3
-streamlit==1.30.0
+streamlit==1.41.1
 phidata==2.7.2
 Pillow==11.0.0
 duckduckgo-search==6.3.7

rag_tutorials/rag_database_routing/README.md

@@ -0,0 +1,56 @@
+# RAG Agent with Database Routing
+
+This project showcases the RAG with database routing capabilities - which is a very efficient way to retrieve information from a large set of documents. The application allows users to:
+
+1. Upload documents to three different databases:
+   - Product Information
+   - Customer Support & FAQ
+   - Financial Information
+
+2. Query information using natural language, with automatic routing to the most relevant database.
+
+## Features
+
+- **Document Upload**: Users can upload multiple PDF documents related to a particular company. These documents are processed and stored in one of the three databases: Product Information, Customer Support & FAQ, or Financial Information.
+
+- **Natural Language Querying**: Users can ask questions in natural language. The system automatically routes the query to the most relevant database using a phidata agent as the router.
+
+- **RAG Orchestration**: Utilizes Langchain for orchestrating the retrieval augmented generation process, ensuring that the most relevant information is retrieved and presented to the user.
+
+- **Fallback Mechanism**: If no relevant documents are found in the databases, a LangGraph agent with a DuckDuckGo search tool is used to perform web research and provide an answer.
+
+- **User Interface**: Built with Streamlit, providing an intuitive and interactive user experience.
+
+## How to Run?
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+   cd rag_tutorials/rag_database_routing
+   ```
+
+2. **Install Dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Run the Application**:
+   ```bash
+   streamlit run rag_database_routing.py
+   ```
+
+4. **Configure API Key**: Obtain an OpenAI API key and set it in the application. This is required for initializing the language models used in the application.
+
+5. **Upload Documents**: Use the document upload section to add PDF documents to the desired database.
+
+6. **Ask Questions**: Enter your questions in the query section. The application will route your question to the appropriate database and provide an answer.
+
+## Technologies Used
+
+- **Langchain**: For RAG orchestration, ensuring efficient retrieval and generation of information.
+- **Phidata Agent**: Used as the router agent to determine the most relevant database for a given query.
+- **LangGraph Agent**: Acts as a fallback mechanism, utilizing DuckDuckGo for web research when necessary.
+- **Streamlit**: Provides a user-friendly interface for document upload and querying.
+- **ChromaDB**: Used for managing the databases, storing and retrieving document embeddings efficiently.
+
+This application is designed to streamline the process of retrieving information from large sets of documents, making it easier for users to find the answers they need quickly and efficiently.