Troubleshooting RAG Playground
Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud. Availability This functionality is not available in Elastic Cloud Serverless. The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats. Availability This functionality is planned to be removed in an upcoming Elastic Stack update. Subject to changes. This functionality is deprecated. You can still use it, but it'll be removed in a future Elastic Stack update. This functionality is in beta and is not ready for production usage. For beta features, the design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features. Specific Support terms apply. This functionality is in technical preview and is ready for evaluation. Use with caution in production; it is not recommended for mission-critical workloads. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. Specific Support terms apply. Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments. This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Serverless
Unavailable
Unavailable
Stack
Deprecated
9.4-9.5
…
Planned for removal
Deprecated from 9.4 to 9.5
Beta from 9.1 to 9.3
Preview in 9.0
Playground was deprecated in Elastic Stack 9.4 and removed in 9.6. It is unavailable in Elastic Serverless. Use Agent Builder (generally available since 9.3) instead.
With Agent Builder you can:
- Interact with your Elasticsearch data using a chat interface in full-screen and sidebar modes
- Use built-in agents, tools, and skills out of the box
- Create custom agents, custom tools, and custom skills
- Integrate via REST API and MCP
- Combine LLM reasoning with Elastic Workflows to automate complex processes
- Dense vectors are not searchable
- Embeddings must be generated using the inference processor with an ML node.
- Context length error
- You’ll need to adjust the size of the context you’re sending to the model. Refer to Optimize model context.
- LLM credentials not working
- Under LLM settings, use the button to edit your GenAI connector settings.
- Poor answer quality
-
Check the retrieved documents to verify if they are valid. Adjust your Elasticsearch queries to improve the relevance of the documents retrieved. Refer to View and modify queries.
You can update the initial instructions to be more detailed. This is called prompt engineering. Refer to this OpenAI guide for more information.
You might need to click ⟳ Clear chat to clear chat history and start a new conversation. If you mix topics, the model will find it harder to generate relevant responses.