fix(llm): handle empty content with length finish reason for reasoning models

[marevol]

Summary

Handle the edge case where LLM reasoning models return empty/blank content with finish_reason=length, which occurs when the model exhausts its token budget entirely on internal chain-of-thought reasoning, leaving no tokens for the actual output.

Changes Made

• AbstractLlmClient: Added isEmptyContentWithLengthFinish() utility method to detect the empty-content-with-length-finish pattern
• AbstractLlmClient: Applied fallback to intent detection paths — returns IntentDetectionResult.fallbackSearch(userMessage) when token exhaustion is detected
• AbstractLlmClient: Applied fallback to relevance evaluation — returns RelevanceEvaluationResult.fallbackAllRelevant(allDocIds) when token exhaustion is detected
• ChatAction: Added Javadoc to getUserId() for clarity

Testing

• The fallbacks degrade gracefully: intent detection falls back to a standard search, and relevance evaluation treats all documents as relevant rather than failing

Breaking Changes

None. The changes add fallback behavior for an edge case that previously would have led to empty/null content being parsed.

Additional Notes

This issue is specific to reasoning models (e.g., o1, o3, DeepSeek R1) that use tokens for internal reasoning steps. When finish_reason=length is combined with blank content, it's a reliable signal that the reasoning consumed all available tokens with no room for the final answer.