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* Refine docs based on internal feedback * update the zh and ja docs --------- Co-authored-by: Riskey <riskey47@dify.ai>
83 lines
3.3 KiB
Plaintext
83 lines
3.3 KiB
Plaintext
---
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title: "Logs"
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description: "Monitor real-time conversations, debug issues, and collect user feedback"
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icon: "memo"
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---
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Conversation logs provide detailed visibility into every interaction with your AI application. Use them to debug specific issues, understand user behavior patterns, and collect feedback for continuous improvement.
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## What Gets Logged
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**All User Interactions**
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Every conversation through your web app or API is logged with complete input/output history, timing data, and system metadata.
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**User Feedback**
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Thumbs up/down ratings and user comments are captured alongside the conversations they reference.
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**System Context**
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Model used, token consumption, response times, and any errors or warnings during processing.
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**Exclusions:** Debugging sessions and prompt testing are not included in logs.
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## Using the Logs Console
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Access logs from your application's navigation menu. The interface shows:
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- **Conversation Timeline:** Chronological list of user interactions
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- **Message Details:** Full conversation context with AI responses
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- **Performance Data:** Response times and token usage per interaction
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- **User Feedback:** Ratings and comments from users and team members
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## Debugging with Logs
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**Failed Interactions**
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Quickly identify conversations where the AI provided poor responses, failed to understand user intent, or encountered errors.
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**Performance Issues**
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Spot slow responses, high token usage, or system errors that affect user experience.
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**User Journey Analysis**
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Follow individual users through multiple conversations to understand usage patterns and pain points.
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## Feedback Collection
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**User Ratings**
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Users can provide thumbs up/down feedback on AI responses. Track satisfaction trends over time.
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**Team Annotations**
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Team members can add internal notes and improved responses directly in the log interface.
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**Feedback Analysis**
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Identify common complaint patterns, successful interaction types, and areas needing improvement.
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## Log Retention
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<Warning>
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Ensure your application complies with local data privacy regulations. Publish a privacy policy and obtain user consent where required.
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</Warning>
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- **Sandbox**: Logs are retained for 30 days.
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- **Professional & Team**: Unlimited log retention during active subscription.
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- **Self-hosted**: Unlimited by default; configurable via environment variables `WORKFLOW_LOG_CLEANUP_ENABLED`, `WORKFLOW_LOG_RETENTION_DAYS`, and `WORKFLOW_LOG_CLEANUP_BATCH_SIZE`.
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## Improving Applications with Logs
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**Pattern Recognition**
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Look for recurring user questions that your application handles poorly. These indicate opportunities for prompt improvements or knowledge base updates.
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**Response Quality**
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Use feedback patterns to identify which types of responses work well and which need refinement.
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**Performance Optimization**
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Track response times and token usage to identify inefficient prompts or model configurations.
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**Content Gaps**
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Spot topics or question types where your application consistently struggles, indicating areas for knowledge base expansion.
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## Privacy Considerations
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Logs contain complete user conversations and may include sensitive information. Implement appropriate access controls and ensure compliance with applicable data protection regulations.
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Consider configuring shorter retention periods for applications handling sensitive data or implement log anonymization where appropriate. |