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Chapter 11. Troubleshooting playground issues

If you encounter issues while using the playground, refer to the following scenarios and solutions.

11.1. The chatbot thinks indefinitely
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Problem After sending a query, the chatbot shows a thinking indicator but never returns a response.

Cause This issue often occurs when the query or the accumulated context exceeds the maximum context length (sequence length) configured for the model.

Solution

  1. In the OpenShift AI dashboard, click the Applications menu and select OpenShift Console.
  2. Navigate to your project’s namespace.

  3. Check the logs for the following pods:

    • The playground pod: lsd-genai-playground-<id>
    • The model serving pod: <model-name>-predictor-<id>
  4. Look for errors related to context length limits or memory (OOM) constraints.

11.2. The model does not use RAG data
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Problem The model answers questions using its training data instead of searching the uploaded RAG documents.

Solution

Update the System instructions in the playground to explicitly force the use of the search tool.

  • Example: "You MUST use the knowledge_search tool to obtain updated information."
  • Example: "Always search the knowledge base before answering questions about company policies."

11.3. MCP servers are missing from the UI
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Problem The MCP servers section is empty or not visible in the playground configuration.

Cause MCP servers must be configured at the cluster level by an administrator.

Solution

Contact your OpenShift AI administrator to configure the required MCP servers. Administrators can find a list of available servers in the Red Hat OpenShift AI documentation.

11.4. The model fails to call MCP tools
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Problem The model attempts to use a tool but fails, or outputs raw XML tags (e.g., <tool_call>).

Cause

  • The model does not support tool calling.
  • The vLLM runtime arguments are missing or incorrect.
  • Known Issue: Some models (e.g., Qwen3-4B-Instruct) may output raw tags if the correct reasoning parser is not available in the current vLLM version.

Solution

  1. Verify the model supports tool calling on its Hugging Face model card.

  2. In the model’s deployment settings, ensure the following Custom Runtime Arguments are present:

    • --enable-auto-tool-choice
    • --tool-call-parser
  3. If the model outputs <think> tags, you can hide them by adding /no_think to your prompt.
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