The Summarize App is a sophisticated document analysis and summarization tool that leverages advanced AI models and frameworks to provide comprehensive insights from text documents. It utilizes Swarms, a multi-agent system, and LightRAG, a Retrieval-Augmented Generation (RAG) framework, to enhance document retrieval, summarization, and contextual analysis.

• app.py: The main application file that initializes the system, loads documents, and sets up the agents and router.
• concurrent_workflow_async.py: Contains the AsyncConcurrentWorkflow class, which manages the concurrent execution of agents using asyncio.
• gradio_app.py: Provides a Gradio interface for interacting with the app, allowing users to input queries and view results.
• lightrag/: A directory containing the LightRAG framework, which includes modules for language model interaction, storage, and operations.
• .env and .env.example: Environment configuration files for setting API keys and other necessary configurations.

The Summarize App uses a multi-agent system to perform various tasks related to document analysis and summarization. The agents are coordinated by a SwarmRouter, which manages their execution and communication. Here's how the workflow is structured:

1. Initialization:

   • The system is initialized by loading text documents and setting up the LightRAG framework.
   • Agents are instantiated with specific roles and system prompts.

2. Agent Execution:

   • The AsyncSwarmRouter manages the execution of agents using the AsyncConcurrentWorkflow class.
   • Agents perform their tasks concurrently, with each agent focusing on a specific aspect of the document analysis process.

3. Task Handling:

   • Agents are designed to handle specific tasks such as document retrieval, summarization, citation tracing, contextual relevance analysis, output formatting, and quality checking.
   • Each agent uses a specialized system prompt to guide its operations and ensure task-specific outputs.

4. Result Aggregation:

   • The results from all agents are aggregated and processed to provide a comprehensive response to the user's query.
   • Logs are maintained for each agent's activity, allowing for transparency and debugging.

The Swarm agents are specialized components that perform distinct tasks within the system. Each agent is initialized with a specific role and system prompt, guiding its operations. The key agents include:

• Document Retriever Agent: Utilizes LightRAG and ChromaDB for advanced document retrieval, combining results from both systems for comprehensive information retrieval.
• Summarizer Agent: Extracts key insights from documents and summarizes them into concise bullet points.
• Citation Tracer Agent: Extracts citations from retrieved documents and traces them back to their original sources.
• Contextual Relevance Agent: Analyzes user context to tailor responses based on specific business functions or areas of interest.
• Output Formatter Agent: Formats responses in a structured, magazine-style format, ensuring clarity and readability.
• Quality Checker Agent: Evaluates the accuracy and relevance of the information, providing confidence scores and identifying potential inaccuracies.

LightRAG is a Retrieval-Augmented Generation framework that enhances document retrieval and analysis. It operates in multiple modes to provide accurate and context-aware responses:

• Naive Mode: Basic retrieval using vector embeddings.
• Local Mode: Focuses on local context and relationships within documents.
• Global Mode: Considers global context and relationships across documents.
• Hybrid Mode: Combines local and global contexts for comprehensive analysis.

LightRAG uses embeddings to represent text documents and queries, allowing for efficient and accurate retrieval. It also maintains a knowledge graph to store and manage relationships between entities and concepts.

1. Clone the repository:

   git clone https://github.com/yourusername/summarize-app.git
   cd summarize-app

2. Install the required dependencies:

   pip install -r requirements.txt

3. Set up the environment variables:

   • Copy .env.example to .env and fill in the necessary API keys and configurations.

1. Start the Gradio app:

   python swarms-master/summarize/gradio_app.py

2. Access the app in your browser at http://localhost:7860.

3. Enter your query and let the app analyze and summarize the documents.