This sample demonstrates how to access and display log probabilities from language model responses using the new avg_logprobs and logprobs_result fields in LlmResponse.

This simple example shows:

• Log Probability Access: How to extract avg_logprobs and logprobs_result from LlmResponse
• After-Model Callback: How to append log probability information to responses
• Confidence Analysis: How to interpret and display confidence metrics
• Practical Usage: Real-world example of accessing logprobs data

User Query → Agent Response → Log Probability Analysis Appended

1. User asks a question
2. Agent generates response with log probabilities enabled
3. After-model callback extracts avg_logprobs from LlmResponse
4. Callback appends log probability analysis to response content
5. User sees both the response and confidence information

The agent response will include log probability analysis like:

[LOG PROBABILITY ANALYSIS]
📊 Average Log Probability: -0.23
🎯 Confidence Level: High
📈 Confidence Score: 79.4%
🔍 Top alternatives analyzed: 5

# Run the agent in web UI
adk web contributing/samples

# Or run via CLI
adk run contributing/samples/logprobs

• Range: -∞ to 0 (0 = 100% confident, -1 ≈ 37% confident, -2 ≈ 14% confident)
• Confidence Levels:
  • High: >= -0.5 (typically factual, straightforward responses)
  • Medium: -1.0 to -0.5 (reasonably confident responses)
  • Low: < -1.0 (uncertain or complex responses)
• Use Cases: Quality control, uncertainty detection, response filtering

• avg_logprobs: Average log probability across all tokens in the response
• logprobs_result: Detailed log probability information including top alternative tokens