RAG-App is a powerful open-source tool that allows you to build and deploy customized chatbots and AI agents without writing any code. It provides a user-friendly interface for configuring and integrating various large language models, including OpenAI, Gemini, and AURA, to power your conversational agents.

One of the key features of RAG-App is its ability to work with a wide range of file types, including PDFs, documents, and other media. You can easily upload your data sources and configure the chatbot to reference and summarize the information, providing users with tailored responses based on the content.

The platform also offers flexibility in terms of integrations, allowing you to connect custom tools, CRM systems, and email clients to your chatbot. This makes it an ideal solution for enterprises and developers who need to create specialized AI-powered applications for their specific needs.

Setting up RAG-App is straightforward, with the ability to deploy it using Docker containers on any cloud or on-premises infrastructure. The platform's open-source nature also allows for easy customization and extension, enabling you to build more intricate and feature-rich chatbots and AI agents.

Overall, RAG-App stands out as a comprehensive and user-friendly solution for creating powerful conversational interfaces, making it a valuable tool for businesses and developers alike.

Ragat, the open-source tool for building conversational AI agents, offers a range of powerful features that make it a compelling choice for enterprises and developers alike:

1. Semantic Search: Ragat's advanced natural language processing capabilities enable users to search their knowledge base using natural language queries, rather than relying on exact keyword matching. This allows for more intuitive and contextual information retrieval.

2. AI Agents: Ragat seamlessly integrates AI agents into the conversational interface, allowing users to interact with intelligent assistants that can provide tailored responses, draw insights, and even perform tasks based on the user's input and the available knowledge.

3. Embeddings and Vector Search: Ragat leverages state-of-the-art language models to generate semantic embeddings of the content in the knowledge base. This enables powerful vector search capabilities, allowing users to find relevant information based on conceptual similarity, rather than just lexical matching.

4. Extensibility: Ragat is designed as an open and extensible framework, allowing developers to integrate custom plugins, models, and integrations to further enhance the capabilities of the conversational AI agents. This makes it a highly flexible and adaptable solution for a wide range of use cases.

5. Privacy and Security: Ragat's architecture is designed with a focus on privacy and security, allowing users to host and manage their knowledge base and AI agents entirely within their own infrastructure, without the need for third-party cloud services.

By leveraging these powerful features, Ragat empowers enterprises and developers to build highly customized and effective conversational AI solutions that can seamlessly integrate with their existing workflows and data sources, providing users with a more intuitive and intelligent way to interact with information.

To install and configure the Ragat tool, you'll need to have Docker installed on your system, whether it's Mac, Windows, or Linux. Once you have Docker set up, follow these steps:

1. Open Docker and keep it running in the background.
2. Copy the provided Docker command from the repository and paste it into your command prompt or terminal.
3. Press Enter, and Docker will start building the image.
4. Once the container is up and running, you'll see the endpoints for the chat UI, API, and admin UI.
5. Open the chat UI endpoint in your browser, and you'll be greeted with a message saying you've successfully installed the Ragat app.
6. Configure the model you want to use, such as OpenAI's GPT-4 model, by providing your API key.
7. Customize the system prompt and conversation question as needed.
8. Configure the agents to reference sources like Wikipedia or Duckduckgo.
9. Upload your own data, such as research papers or other file types, and the tool will parse them efficiently using the provided file loader configuration.
10. Start chatting with your uploaded files, and the tool will provide responses based on the information in the files.
11. You can export the API for your configured Ragat app to use it in other applications or share it with others.

This straightforward installation and configuration process with Docker makes Ragat a user-friendly and accessible tool for building custom AI agents to interact with your files, without the need for extensive coding.

Ragab is a comprehensive open-source project that enables users to set up customizable AI agents and integrate them with a knowledge base. This no-code interface allows users to configure chatbots that are entirely private and locally hosted, providing a flexible solution for various use cases.

Key features:

• AI Agent Configuration: Users can easily create and configure AI agents within the Ragab interface. This includes setting system prompts, conversation questions, and integrating various plugins and custom tools.

• Knowledge Base Integration: Ragab allows users to upload their own data, including documents, PDFs, and other file types. The platform utilizes efficient parsing techniques, such as LLaMa-Parsers, to process the content and make it accessible to the AI agents.

• Seamless Interaction: Users can chat directly with the AI agents, which can reference the integrated knowledge base to provide relevant and informative responses. The agents can leverage online sources like Duckduckgo or Wikipedia to enhance their capabilities.

• Extensibility and Deployment: Ragab is designed as an open-source, extendable framework. Users can incorporate additional plugins and custom integrations to further enhance the functionality of their AI agents. The platform can be easily deployed using Docker containers, allowing for flexible infrastructure adaptation.

By leveraging Ragab, users can create personalized AI assistants that are tailored to their specific needs, without the need for extensive coding. This makes it an attractive solution for enterprises, developers, and individuals looking to harness the power of AI for their internal use cases.

Ragab is an open-source tool that allows you to build a conversational AI agent to interact with your PDF files and other file types. It provides a no-code interface to configure chatbots that are completely private and locally hosted.

With Ragab, you can easily upload your PDF files and start chatting with them. The tool uses large language models, such as OpenAI's GPT, to understand the content of the files and provide relevant responses.

Once you have uploaded your PDF file, you can ask Ragab to summarize the main points of the document or emphasize specific sections. Ragab will reference the file and provide a concise summary or highlight the key information from the selected section.

The flexibility of Ragab allows you to integrate custom tools, CRM systems, or email workflows, making it a powerful solution for various use cases. Additionally, Ragab's open-source nature and extensibility enable developers to tailor the tool to their specific needs.

Overall, Ragab offers a seamless way to interact with your PDF files, leveraging the power of AI to provide summarization and emphasis on the most important points, without the need for extensive coding.

Once you have configured your RAG-app to your liking, you can easily export it for use in other applications or share it with others. The RAG-app provides a few options for this:

1. Export API: You can export the API for your RAG-app, which allows you to integrate it into other applications or services. This gives you the flexibility to use your customized AI agent in various contexts.

2. Start Fresh App: You can start a fresh instance of your RAG-app, which creates a new, exportable version of your configured agent. This allows you to share your RAG-app with others, who can then use it within the RAG-app interface.

To export your API, simply click the "Export API" button in the RAG-app interface. This will provide you with the necessary information, such as the API endpoint and authentication details, that you can use to integrate your RAG-app into other applications.

To start a fresh instance of your RAG-app, click the "Start Fresh App" button. This will create a new version of your configured agent, which you can then share with others. They can access your RAG-app by visiting the provided URL and using the same interface you've set up.

By leveraging these export and sharing features, you can easily distribute your customized RAG-app to colleagues, clients, or anyone who could benefit from your tailored AI agent. This makes the RAG-app a versatile and collaborative tool for working with your data and files.

The Ragat open-source tool provides a powerful and user-friendly interface for building AI-powered chatbots that can interact with various file types, including PDFs. With its no-code approach, Ragat allows users to easily configure and customize their chatbots without the need for extensive coding knowledge.

One of the key features of Ragat is its ability to integrate with different large language models, such as OpenAI, Gemini, and AURA, giving users the flexibility to choose the most suitable model for their specific use cases. This integration allows the chatbots to provide accurate and contextual responses, leveraging the capabilities of these advanced AI models.

The ability to upload and reference multiple files within the Ragat application is another notable aspect. Users can seamlessly integrate their own data, including research papers and other documents, allowing the chatbot to draw insights and information from these sources. This feature makes Ragat particularly useful for enterprises and developers who need to create custom AI assistants for internal use cases.

Overall, Ragat stands out as a comprehensive and user-friendly open-source solution for building AI-powered chatbots. Its ease of use, flexibility, and integration with powerful language models make it an attractive option for those looking to leverage the benefits of AI in their applications and workflows.