The rag_agent is a graph-based Retrieval-Augmented Generation (RAG) agent built with LangGraph and Ollama. It offers a streamlined way to perform query rewriting, vector search, relevance checking, and answer generation in a fully automated pipeline.
This application is designed for users who want to make the most of their data without the need for programming skills. With rag_agent, you can efficiently extract valuable insights and answers from your text data.
To run rag_agent, you need:
- A computer running Windows, macOS, or Linux.
- An internet connection for downloading and updates.
- At least 4 GB of RAM for optimal performance.
Follow these steps to download and install rag_agent:
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Visit the Download Page Head over to the Releases page to find the latest version of rag_agent.
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Choose Your Version Look for the most recent release. You will find options for different operating systems.
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Download the File Click on the appropriate download link for your system. This will save a compressed file to your computer.
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Extract the File Locate the downloaded compressed file in your Downloads folder. Right-click on it and select "Extract All" to unzip it.
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Run the Application Open the extracted folder and double-click on the rag_agent executable file. This will launch the application.
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Query Rewriting Input your question naturally. The agent will rephrase it to get better answers.
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Vector Search The agent can search your text data in a way that finds the most relevant pieces of information.
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Relevance Checking With built-in checks, rag_agent evaluates how well the information answers your question.
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Answer Generation Need a direct response? The agent generates clear and concise answers based on your input.
The interface is designed for ease of use. You will see a main text box to enter your queries. Below it, results will appear, showing you relevant answers in real-time.
Here’s how you can interact with rag_agent:
- Type a query, such as "What is the purpose of RAG?"
- Press the enter key.
- View the results returned by the application, and follow any suggested links for further information.
rag_agent supports:
- Windows 10 and later
- macOS 10.15 and later
- Most modern Linux distributions (Ubuntu, Fedora, etc.)
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Application won’t start
Ensure your operating system is up-to-date. Check your RAM and close any unnecessary applications to free up memory. -
Slow Performance Restart your computer and ensure no other heavy tasks are running in the background.
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Error Messages If you encounter error messages while using rag_agent, consult the FAQ or submit an issue on the GitHub page.
Q: What are embeddings?
A: Embeddings convert text into numerical formats that capture meanings and relationships, making searches more efficient.
Q: How does the vector search work?
A: The vector search finds answers by comparing the numerical formats of your query with stored data, ensuring relevancy.
Q: Can I run rag_agent offline?
A: Yes, you can use rag_agent offline after the initial download and installation.
For any questions or issues, please reach out through the GitHub Issues page. You can also join the community discussion for tips and tricks.
To get started, click this link: Download rag_agent. Follow the steps outlined above to install and start using the application effectively.
Take control of your data and gain insights effortlessly with rag_agent.
