Hi @nitindhiman,
To achieve real-time data interaction on the Grafana Open Source front end using the LLM plugin, you need to ensure a few key configurations and understand the capabilities of the plugin:
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Real-Time Streaming Interactions: The LLM plugin supports live streaming of responses from LLM providers. This feature allows you to interact with the LLM in real-time, which can be particularly useful for dynamic data analysis and monitoring. Ensure that your LLM provider supports streaming responses.
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Plugin Configuration: Make sure that the LLM plugin is correctly configured in your Grafana instance. This involves:
- Setting up API access with your chosen LLM provider. You can use providers like OpenAI, Azure OpenAI, or custom APIs with OpenAI-like signatures.
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Ensuring that your API keys are correctly set up and that the plugin is enabled.
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Integration with Grafana MCP: The Model Context Protocol (MCP) allows for more complex interactions and actions as part of agent-like conversations with the LLM. This can enhance the real-time capabilities of your Grafana setup by allowing the LLM to perform actions and gather information dynamically.
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Exploring Other Features: Besides real-time interactions, the LLM plugin offers functionalities like incident auto-summary, AI-powered flamegraph interpretation, and more. These features can provide additional value to your Grafana setup.
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Developing Custom Plugins: If you're developing plugins, you can use the
@grafana/llmnpm package to integrate LLM functionality into your custom Grafana plugins. This allows you to make secure requests to the LLM and handle responses within your plugin's logic.
For more detailed information on setting up and using these features, you can refer to the Grafana LLM plugin documentation.
If you have any specific questions or need further assistance, feel free to ask!
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