201 lines
201 KiB
Plaintext
201 lines
201 KiB
Plaintext
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy to ensure it only applies to compliant devices. Since contractors use personal laptops, consider allowing conditional access based on Azure AD app registration or use of a specific app. Consult Okta or Azure portal policies for this scenario.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy for external contractors. Ensure it does not enforce compliant devices. Consider adding an exception for external contractors using personal laptops. Check for any misconfigured settings that may be causing the policy to block them.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue seems to be related to MFA reset procedures. Refer to KB-00011 for Okta, Google Workspace, and Azure AD/Entra MFA reset procedures. Since the users are locked out, follow the procedure to reset their MFA. Ensure the user is verified via video call or badge scan before proceeding.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy to ensure it's not unintentionally blocking devices that meet all other criteria (such as compliant devices). Check if there are any misconfigurations or outdated policies that might be causing the issue. Reach out to the users directly for their laptops and understand if they can use a different device that is compliant with the new policy. If necessary, escalate the issue to the appropriate support team.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00011\", \"KB-00008\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and update the Conditional Access policy to allow personal laptops for EU contractors. Ensure the policy is configured to allow MFA but does not enforce device compliance for external users.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy to ensure it includes an exception for external contractors using personal laptops. Update the policy to allow for this scenario without MFA. Reach out to the relevant Okta or Entra admin for policy modification.\", \"cited_artifacts\": [\"KB-00011\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy to ensure it allows access to external contractors. Consult the runbook provided in KB-00011 for MFA reset procedures. If the issue persists, check Active Directory authentication failures by reviewing Event ID logs and following the steps in KB-00008.\", \"cited_artifacts\": [\"KB-00011\", \"KB-00008\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00043", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Conditional Access policy to ensure it does not mistakenly block external contractors. The policy likely requires a compliant device. Since contractors use personal laptops, these devices do not need to be enrolled in Intune. Investigate if the policy includes specific device compliance requirements and adjust accordingly. Reach out to your security team for guidance on configuring the policy to allow personal devices for contractors.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Helm chart and rollback the billing-service to the previous version using the 'helm rollback <release-name> 0' command. Verify the rollback by checking the status of the deployment and ensuring all pods are ready. Monitor the service metrics to ensure the rollback was successful and error rates have returned to normal.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Rollback the billing-service deployment using Helm commands: helm rollback <release-name> 0. Verify the rollback using `helm history <release-name>` and check the status with `helm status <release-name>`. If needed, manually verify the rollback by checking the service logs and traffic.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Roll back the billing-service deployment immediately. Verify that the rollback is successful by checking the pods' status and ensuring all consumers are using the new version of the service. Re-enable the service if needed.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Roll back the billing-service deployment using the Helm rollback command. Verify the rollback by checking the status of the deployment and ensuring all pods are ready. If there are issues, revert manually or escalate the issue.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Rollback the deployment of billing-service to version v2.7.0 by using the Helm rollback command. Check the Kubernetes history to ensure the correct revision is selected. Verify the rollback was successful by checking the service status and logs.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Roll back the billing-service deployment using the Helm rollback command. Run `helm rollback billing-service 0` to revert to the previous version. Ensure all dependent services are updated in the correct order before and after the rollback.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Rollback the billing-service deployment to the previous version using the Helm rollback command. Ensure all necessary configurations are checked and verified post-rollback.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00018", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Helm chart for the billing-service and roll back to the previous version. Verify the rollback is successful and redeploy the application as needed.\", \"cited_artifacts\": [\"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the API gateway access logs to identify the slow upstream service. Test the upstream directly to verify if it's responding. Check for common causes like DB query regressions, downstream dependency issues, or thread pool exhaustion. Adjust the timeout configuration on the API gateway if needed.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Examine the API gateway access logs to identify the upstream service experiencing the 504 Gateway Timeout. Use curl to test the upstream directly and bypass the gateway if necessary. Check for common issues such as DB query regression, downstream dependency slow, or thread pool exhaustion. Adjust timeout configuration in the API gateway as needed.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the API gateway access logs for 504 errors. Identify the slow upstream service. Test the upstream directly and adjust the timeout configuration if necessary. Verify the database connection and query performance. If the upstream service is overloaded, investigate the thread pool usage and adjust accordingly.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The API gateway 504 timeouts could be due to a slow downstream dependency. Verify TCP/179 reachability and check BGP timers on the upstream service. If the issue persists, test the upstream directly using curl to bypass the gateway and ensure it's not the gateway causing the problem. Adjust the timeout configuration on the Kong/Nginx if necessary.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the recent deployment to billing-service v3.1.0. Check the access logs for 504 Gateway Timeout errors. Use API Gateway access logs to identify the slow upstream service. Test the upstream directly using curl to confirm the issue is not with the API Gateway itself. Investigate the slow upstream service for common issues such as DB query regression or thread pool exhaustion. Adjust the proxy_read_timeout configuration if necessary. If the upstream service is not the issue, revert to the previous version of billing-service.\", \"cited_artifacts\": [\"KB-00020\", \"KB-00013\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the API gateway access logs for slow upstreams. Identify and test any upstream services that may be experiencing issues. Adjust the proxy_read_timeout configuration in the API gateway to a higher value if needed.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Investigate the new API Gateway configuration and any changes made with the deployment. Check for any misconfigurations or missing dependencies. Verify that the billing-service is not causing any thread pool exhaustion. If the upstream service is still slow, test it directly outside of the API Gateway. If the issue persists, consider increasing the timeout configuration in the API Gateway.\", \"cited_artifacts\": [\"KB-00020\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00031", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the API Gateway access logs for 504 timeouts and identify the slow upstream service. Test the upstream service directly using cURL to bypass the gateway. Review the retrieved context for common upstream causes like DB query regressions, downstream dependency issues, and thread pool exhaustion. Adjust the timeout configuration in the API Gateway as needed.\", \"cited_artifacts\": [\"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review API Gateway access logs for 504 errors, identify upstream services. Test each in isolation to determine root cause. Adjust Kong proxy_read_timeout and enable Redis eviction if necessary. Ensure Redis maxmemory-policy is set to 'allkeys-lru'. Check JVM heap usage on the affected services and enable heap dumps on OOM for analysis.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00018\", \"KB-00019\", \"KB-00015\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and adjust Kubernetes Pod CrashLoopBackOff, API Gateway 504 timeouts, Redis eviction, and JVM OOM settings. Investigate and correct configurations that are causing memory issues and pod restarts. Ensure that the Redis eviction policy is set to 'allkeys-lru' and configure Redis to handle memory pressure by setting a maximum memory limit. For the API Gateway, troubleshoot the slow upstream service and adjust the timeout configuration if necessary. For the JVM, ensure heap dumps are enabled and analyze them with Eclipse MAT to find and resolve the root cause of the OutOfMemoryError.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00017\", \"KB-00018\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. Review API Gateway access logs for 504s and upstream timeouts. Check Redis eviction policy and memory pressure. Ensure no TTL is set on session keys in Spring Session with Redis. Analyze heap dumps and memory usage on the affected pods.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00020\", \"KB-00018\", \"KB-00019\", \"KB-00015\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and update resource limits for the affected pods. Check the Redis configuration for memory limits and eviction settings. Investigate and adjust the API gateway's timeout configuration. Ensure correct Redis eviction policy and memory settings. Evaluate and possibly modify the Java heap size for the JVM-based services. Monitor and adjust the database connection pool settings.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00018\", \"KB-00019\", \"KB-00015\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability to identify potential routing issues causing 504s. Check Redis eviction policies and TTL settings to address OOM and eviction issues. Investigate Kubernetes pod events and CrashLoopBackOff conditions for pod crashes. Review API gateway logs for upstream service timeouts and probe times.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00018\", \"KB-00019\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Identify the upstream service causing the 504 errors by filtering the API gateway access logs for 504s. Test the identified upstream service directly to isolate issues not within the gateway. Check Redis eviction policy and memory pressure, and ensure session keys in Spring Session have TTLs. For memory issues, consider increasing Redis memory or heap settings.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00020\", \"KB-00018\", \"KB-00019\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify the Redis eviction policy is set to 'allkeys-lru'. Check Redis memory pressure metrics. If evictions are active, scale up Redis memory. If keys without TTL are filling memory, consider setting a Redis expiry policy for session keys. Check API Gateway access logs for upstream timeouts and test the upstream service directly. Review Kubernetes Pod crash logs and events to diagnose pod crashes. Consider rolling back the application deployment if the issue started 5 minutes ago.\", \"cited_artifacts\": [\"KB-00018\", \"KB-00016\", \"KB-00019\", \"KB-00015\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00035", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the Redis eviction policy and memory pressure. If eviction is active, scale up Redis memory. Review API Gateway and upstream service configurations for 504 errors. Verify TCP/179 reachability and BGP settings. Check for missing secrets or misconfigured secrets in the deployment process.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\", \"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency issues, ensure MTU between the new router and existing routers are the same. For missing BGP routes, verify TCP/179 reachability and correct BGP state and configuration. Check for authentication mismatches and ensure BGP timers are correctly set. For example, if the BGP peer session is down, use 'debug ip bgp <peer-ip> events' to look for 'BGP notification: hold time expired' logs.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency issues, check MTU and BGP settings. For BGP route missing in OSPF, ensure AS and MD5 configurations match. Verify TCP/179 reachability and correct BGP timers.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability and correct BGP timers. Check for any AS or MD5 mismatches. Ensure OSPF MTU matches and correct authentication configuration. For DPD, ensure aggressive timers are set appropriately. Adjust firewall rules if necessary to allow necessary ports.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review OSPF neighbor configurations, ensure MTU mismatches are corrected, check for authentication issues, and verify BGP peer configurations including hold-timer settings. Ensure both OSPF and BGP are configured to use the same AS. Correct any AS mismatches or MD5 password errors.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check TCP/179 reachability between routers. Ensure BGP timers are correctly configured. Verify OSPF authentication settings. If necessary, align MTUs and adjust AS numbers.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OSPF adjacency issue, check MTU consistency and ensure authentication settings match. For the BGP route missing issue, verify TCP/179 reachability and correct any AS or MD5 mismatches. Additionally, review BGP timers to ensure they are configured correctly.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OSPF adjacency issue, verify MTU matches between the new router and existing routers. For the BGP route issue, ensure TCP/179 reachability and check BGP timers. Correct any AS or MD5 mismatch configurations.\", \"cited_artifacts\": [\"KB-00002\", \"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00021", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review OSPF adjacency and BGP configuration. Verify TCP/179 reachability for BGP sessions. Check BGP timers and correct any configuration mismatches. For OSPF, align MTU, area-type, and authentication settings.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Identify the upstream service causing the 504 timeouts by checking the API gateway access logs and bypassing the gateway directly to test the upstream. For Redis memory pressure, check the eviction policy and memory usage, and set a TTL for session keys in Spring Session. Increase Redis memory if needed.\", \"cited_artifacts\": [\"KB-00018\", \"KB-00001\", \"KB-00019\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. For Redis, ensure eviction policy is set to 'allkeys-lru' and there are no keys without TTLs. For the API gateway, test the upstream service directly and adjust timeout configuration if necessary.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check Redis eviction policy, ensure it's set to 'allkeys-lru'. Scale up Redis memory if needed. Verify TCP/179 reachability and BGP hold-time configuration. Restart the API gateway service to clear any cached timeouts.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review Redis eviction policy and check memory pressure. If evictions are active, consider scaling up Redis memory. Ensure no session keys lack TTL by setting a timeout. For immediate relief, clear evicted keys.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. Check Redis eviction policy and memory pressure. Identify keys without TTL in Redis. If Redis memory pressure is high, scale up Redis memory. Adjust Redis eviction policy if necessary.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Identify and test the Redis connections for TCP/179 reachability, then check BGP timers and validate AS and MD5 configurations. If issues persist, review Redis eviction policy and key TTLs.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. For the Redis issue, ensure eviction policy is set to 'allkeys-lru'. Monitor memory usage and key expiry. If eviction is not configured, set `spring.session.redis.cleanup-cron=0 * * * * *` to enable cleanup.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00026", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. For Redis eviction, ensure maxmemory-policy is set to 'allkeys-lru' and check for keys without TTL. Adjust Redis memory limits if necessary.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00001\", \"KB-00019\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability by pinging the BGP peer. Check BGP timers and ensure AS and MD5 passwords match. If not, adjust the timers or correct the passwords.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, and ensure MD5 passwords match. If not, configure correct MD5 passwords or disable authentication.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. Ensure the peer AS number and password configuration match on both routers.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, and correct any AS or MD5 mismatches. Ensure BGP hold-timer mismatch with `neighbor <ip> timers <keepalive> <hold>` or clear ACL blocking TCP/179.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00001", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches.\", \"cited_artifacts\": [\"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review Salesforce configuration for Kerberos ticket issues. Ensure the clock skew is within acceptable limits. Check Event ID 4771 for password-related errors.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check Okta SCIM 2.0 configuration and verify the necessary attributes are mapped correctly. Ensure the SCIM connector base URL is correct and test the connection. Additionally, verify that the user attributes (e.g., email, givenName, familyName) are properly mapped in Okta.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review Okta SCIM 2.0 configuration for the Salesforce integration. Check the attribute mappings and ensure they match Salesforce's required fields. Additionally, verify the SCIM endpoint is enabled in Salesforce's app settings. If issues persist, contact Salesforce support.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify Okta SCIM 2.0 provisioning configuration, check authentication and attribute mapping settings. Ensure the new employee's user details are correctly synced from Okta to Salesforce.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify that the Salesforce app in Okta is correctly configured for SCIM 2.0 and that the attribute mappings are set up properly. Check for any SCIM failures or errors in the Okta logs. Ensure that the Salesforce app allows updates to user profiles via SCIM. If issues persist, contact Salesforce support.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the Okta SCIM provisioning settings and ensure the attribute mapping is correct. Check for any errors during the provisioning process. If there are silent failures, retry the provisioning and verify the user's profile in Salesforce is being updated correctly.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review Okta SCIM 2.0 configuration for new employee Sarah Williams. Ensure the required SCIM attributes are correctly mapped (userName, givenName, familyName) and the SCIM connector is enabled in Okta. Test the connector to confirm it is working properly. Check for any 401, 404, or attribute mismatch issues. If needed, reconfigure the connector settings.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00009", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Contact IT Helpdesk to verify Okta SCIM 2.0 integration settings. Ensure the SCIM connector base URL, auth method, and test configuration are correct. Reach out to Salesforce support to confirm their SCIM endpoint is reachable and working properly.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability between the branch office and the corporate network. Check BGP timers and validate the AS numbers match. Ensure MD5 passwords are configured correctly. For DNS, ensure the correct DNS server is being used and the zone delegation is properly set. For VPN, align the IKE proposal parameters and correct any NAT-T configuration. Update configuration on both ends and test connectivity.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. For DNS, isolate DNS vs network, check forwarder chain, and flush DNS cache on client machines.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability between the branch office and the corporate network. Check BGP state and AS number configuration. Ensure MD5 passwords match. Resolve DNS resolution issues by verifying the correct DNS server is being used for internal names, and check forwarder chain connectivity. For the VPN tunnel, capture IKEv2 NOTIFY payloads to identify algorithm mismatches and adjust DPD timers. Enable NAT-T if necessary.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. Confirm DNS server assignment on branch office clients. For VPN tunnel issues, capture IKEv2 NOTIFY payloads, verify Phase 1 and Phase 2 proposals, and adjust DPD timers if necessary.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. Ensure DNS server assignment and correct configuration for corporate DNS resolution. For VPN tunnel issues, capture IKEv2 NOTIFY payload, align IKE proposals, and configure aggressive DPD timers. Enable NAT-T if necessary.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability for the BGP peers, check BGP hold timers, ensure MD5 passwords match, and configure DPD timers. For DNS resolution failures, check the forwarder chain and zone delegation issues. Ensure NAT-T is enabled on the branch office's routers if they are behind NAT.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability, check BGP timers, correct any AS or MD5 mismatches. For DNS resolution, isolate DNS vs network, check forwarder chain, and flush the DNS cache. For IKEv2 Phase 1, verify identical IKE proposals and NAT-T configuration. Correct any issues found.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\", \"KB-00005\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00037", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify TCP/179 reachability for the VPN, check BGP timers and correct AS/MD5 mismatches, ensure DNS forwarder connectivity, and check for NAT-T issues for the branch office.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the SAML 2.0 SSO configuration on both Okta and Jira. Ensure the SP Entity ID on Jira matches the Audience in Okta's metadata. Check the ACS URL and NameID format settings. If clock skew is suspected, verify the SAML Tracer extension is correctly configured and record the SAMLResponse for troubleshooting. If assertion attribute mismatches are identified, adjust the attribute mapping in the SAML metadata. If errors persist, escalate to the Jira and Okta administrators for further investigation.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00010\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify SAML SSO configuration on both IdP (Okta) and SP (Jira) sides. Ensure Entity ID matches. Check clock skew by comparing IdP's NotBefore and NotOnOrAfter in SAML tracer. If mismatch, correct in SAML configuration. Check internal DNS resolution for corp.example.com and svc.example.com to ensure there are no zone delegation issues.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00010\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the SAML configuration on both the IdP (Okta) and the SP (Jira). Ensure the Entity ID matches and that the ACS URL points to the correct SAML endpoint in Jira. If issues persist, verify the clock skew by checking the NotBefore and NotOnOrAfter values in the SAML response. If necessary, rotate the service account password in AD and update dependent services in order.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify that the JIRA service's SAML SSO configuration matches Okta's Entity ID. Check the SAML response for clock skew errors. If attribute mismatch, update the SAML attribute mappings in the JIRA IdP setup. Ensure that the SAML ACS URL in JIRA points to the correct Okta SSO URL.\", \"cited_artifacts\": [\"KB-00010\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and update the SAML configuration on both the Identity Provider (Okta) and the Service Provider (Jira). Verify the SP Entity ID matches Okta's Audience value. Check the ACS URL and SAML NameID format configuration. If necessary, consult the SAML Tracer extension to troubleshoot further. Ensure the internal DNS servers are correctly configured and that there are no forwarder chain issues. If the problem persists, consider the network team for additional diagnostics.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00010\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Examine Jira’s SAML SSO configuration to ensure the IdP Entity ID matches Jira's Audience claim. Verify that the SAML Response's NotBefore and NotOnOrAfter values are not expired. If issues persist, check for clock skew. Ensure the SAML ACS URL matches Jira's expected URL. Update the Service Account password according to the Service Account Password Rotation Procedure and update dependent services in the specified order. If the problem still persists, escalate the issue to the appropriate team.\", \"cited_artifacts\": [\"KB-00010\", \"KB-00012\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review SAML SSO configuration settings on both the IdP (Okta) and SP (Jira). Ensure the Entity ID, ACS URL, and NameID format match exactly. Also, check for any clock skew issues by comparing NotBefore and NotOnOrAfter times in SAMLResponses. If configuration is correct, test the SSO flow directly from Jira to Okta using a proxy tool to capture and decode the SAMLResponse.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00010\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00019", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and verify the SAML SP and IdP Entity IDs match. Check the IdP's SSO URL and SP's ACS URL are correctly configured. If issues persist, enable SAML Tracer for more detailed troubleshooting. If external certificate issues are suspected, renew the SAML certificate from KB-00014.\", \"cited_artifacts\": [\"KB-00010\", \"KB-00014\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Investigate DHCP pool exhaustion and high CRC errors on the switch. For DHCP, use KB-00006 for exhaustion and KB-00007 for CRC errors. Verify TCP/179 reachability and check BGP timers as per KB-00001. Correct any AS or MD5 mismatches.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the DHCP pool exhaustion in IoT VLAN 60 by running `show ip dhcp pool CORP_WIFI | include Utilization` and verify the utilization. If it's >95%, clear the DHCP bindings with `clear ip dhcp binding *` to free up leases. Ensure the switch's CRC error rate is below 0.01% by running `show interface <int> | include CRC` and resolving any duplex mismatch issues. Adjust the OSPF timers for adjacency and ensure MTU compatibility between the IoT VLAN and the switch.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Investigate DHCP scope exhaustion by clearing stale DHCP bindings. Check for high CRC error rates on the switch interface. For DHCP, verify the DHCP scope exhaustion and clear any stale bindings. For the switch, ensure the interface is configured correctly and check for duplex mismatch. Additionally, align the MD5 authentication keys on both OSPF routers if there are any issues with adjacency.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Investigate the DHCP pool exhaustion on VLAN 60 and high interface errors on the switch. Check the DHCP scope utilization with `show ip dhcp pool` and clear stale leases if necessary. For interface errors, check for mismatched duplex settings using `show interface <int> | include duplex`. If duplex mismatch is found, set the speed and duplex on both sides to `speed 1000 / duplex full`. Verify the MTU settings on the interface and align them if necessary. Also, ensure there are no misconfigured IPsec IKEv2 parameters causing tunnel flapping.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Examine the DHCP pool exhaustion and interface errors. Investigate the DHCP pool utilization and perform the recommended immediate mitigation steps. For the high CRC error rate, check the physical layer and duplex settings on the switch. Correct any MTU and duplex mismatches. For the DHCP pool exhaustion, clear the stale DHCP bindings. Identify and handle any rogue DHCP servers or uncleanly deleted DHCP servers. Finally, investigate the OSPF adjacency failures, especially focusing on MTU and area-type mismatches.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\", \"KB-00016\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Investigate DHCP pool exhaustion and high interface errors. For DHCP pool exhaustion, clear the DHCP bindings to free up addresses. For interface errors, check for CRC errors and ensure correct duplex settings. Additionally, verify OSPF neighbor adjacency to address any stuck states. Clear OSPF adjacencies and check MTU settings if necessary.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00007\", \"KB-00002\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check DHCP pool utilization and clear stale leases if >95%. Investigate and align IKE proposal parameters if DHCP pool exhaustion is not the cause. Verify and correct MTU settings on interfaces showing high interface errors. Debug OSPF neighbors to address stuck adjacency issues. Ensure proper configuration and authentication for OSPF, particularly if authentication parameters are mismatched.\", \"cited_artifacts\": [\"KB-00006\", \"KB-00005\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00029", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the DHCP pool exhaustion, clear the DHCP bindings to force a reassignment: `clear ip dhcp binding *`. Identify and clear any stale leases using: `show ip dhcp binding | include Expiry` and `clear ip dhcp binding <specific-ip>` if necessary. For the interface errors on the switch, check the CRC error rate and ensure duplex settings match on both ends: `show interface <int> | include duplex`. If not, hard-set the speed and duplex: `speed 1000 / duplex full`. Address potential VLAN trunk issues by ensuring native VLANs are consistent on both ends.\", \"cited_artifacts\": [\"KB-00007\", \"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the built-in fingerprint reader on the Lenovo T16 laptops to ensure it is recognized as a FIDO2 authenticator. If it is not recognized, it could be a software issue or a driver problem. Update the BIOS and Windows drivers to the latest versions. If the problem persists, consult the Lenovo support documentation for the T16 series.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Contact Lenovo support for hardware-specific issues related to FIDO2 support on Lenovo T16 laptops. Check if there's an update or patch available for the fingerprint reader driver. Ensure the system BIOS is up to date as well.\", \"cited_artifacts\": [], \"confidence\": 0.7, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with Lenovo T16 laptops not recognizing FIDO2 passkeys might be related to the FIDO2 support in the built-in fingerprint reader driver. Check for updated drivers on the Lenovo website or try enabling the FIDO2 option in the BIOS settings. Additionally, ensure that the fingerprint reader is properly configured in the Lenovo security settings. If the problem persists, consider reaching out to Lenovo support for further assistance.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review Lenovo T16 laptop configuration for FIDO2 support. Ensure the device is running the latest BIOS and drivers. Check the device's Windows Security settings to confirm FIDO2 is enabled and recognized. If not, update the device's Windows Security settings accordingly. If the issue persists, contact Lenovo support for further assistance.\", \"cited_artifacts\": [\"KB-00009\", \"KB-00012\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue might be related to the built-in fingerprint reader not being recognized as a FIDO2 authenticator on new Lenovo T16 laptops. Check the touchID and FIDO2 settings in Windows Security > Fingerprint access > Fingerprint settings. Ensure that FIDO2 is selected and fingerprint scanning is enabled. If the fingerprint reader is not supported, consider using an external FIDO2 authenticator.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue might be related to the FIDO2 support on Lenovo T16 laptops. Ensure that the FIDO2 driver is installed and up-to-date. Additionally, check the FIDO2 configuration in the Windows security settings. If the problem persists, contact Lenovo support for further assistance.\", \"cited_artifacts\": [\"KB-00012\"], \"confidence\": 0.7, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check if the Lenovo T16 laptops support FIDO2 or WebAuthn. Update the Windows Group Policy to include FIDO2 as an authentication method. If the laptops still cannot recognize the fingerprint reader as a FIDO2 authenticator, check the fingerprint driver for the laptop. Ensure that the fingerprint reader is properly configured and recognized by the system.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00040", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with Lenovo T16 laptops not being able to use FIDO2 passkeys may be related to the compatibility or configuration settings. Check the fingerprint reader driver on the laptops to ensure it is up-to-date and correctly installed. If the driver is missing or outdated, update it. Also, verify that the fingerprint reader is enabled and set as an authentication method in the device settings. If the problem persists, consult the Lenovo support documentation for additional troubleshooting steps.\", \"cited_artifacts\": [\"KB-00009\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on GigabitEthernet0/1 of SW-FLOOR2-01. Use `show interface GigabitEthernet0/1 | include duplex` to verify if both sides are configured as full-duplex. If not, set them to full-duplex on both ends using `speed 1000 / duplex full`. Recheck the CRC error rate after making the change.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify the MTU between GigabitEthernet0/1 and the workstation. If mismatch, align MTUs. Check for duplex mismatch. If necessary, hard-set speed and duplex on both ends. For SFP, check Rx power. For copper, ensure cable length is within limits.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on the port. Verify that both the access switch and the workstation are configured for full-duplex operation. If the issue persists, isolate the port and verify the cable length. If the cable length is over 100 meters, replace it with a shorter cable. If the issue still persists, consider checking for physical layer issues such as SFP or copper cable problems.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on GigabitEthernet0/1. If one side is configured for half-duplex while the other is full-duplex, this can cause CRC errors. Use `show interface GigabitEthernet0/1 | include duplex` to identify the current configuration and configure both interfaces to be in the same mode (e.g., both half-duplex or both full-duplex). If the duplex setting is correct, check the MTU settings and cable length, as CRC errors can also occur due to these factors.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex settings on GigabitEthernet0/1. Verify that both the access switch and the workstation are set to full-duplex. If not, set the workstation to full-duplex as well. Additionally, recheck the MTU settings between the switch and the workstation to ensure they are consistent. If necessary, align the MTUs by adjusting either the switch or the workstation settings. If the problem persists, consider checking for physical layer issues such as cable length or cable quality. Ensure that the VLAN settings on the trunk are correct. If the issue still exists, escalate the ticket to network operations for further investigation.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on GigabitEthernet0/1. Use the command `show interface GigabitEthernet0/1 | i Duplex` to determine if one side is set to full-duplex while the other is set to half-duplex or auto. If so, manually set the speed and duplex to full on both interfaces: `speed 1000 / duplex full`. If the issue persists, recheck the interface MTU and ensure OSPF is configured correctly. Refer to KB-00007 and KB-00002 for further troubleshooting.\", \"cited_artifacts\": [\"KB-00007\", \"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on the GigabitEthernet0/1 interface. If one side is set to full-duplex and the other to half-duplex, it can cause CRC errors. Use `show interface GigabitEthernet0/1 | include duplex` to verify. If mismatched, set both sides to the same duplex mode (e.g., `speed 1000 / duplex full`). Recheck the CRC error rate with `show interface GigabitEthernet0/1 | include CRC`.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00004", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the duplex setting on GigabitEthernet0/1. Use `show interface GigabitEthernet0/1 | include duplex` to verify. If one side is set to half-duplex and the other is full-duplex, this can cause CRC errors. Set both interfaces to full-duplex: `interface GigabitEthernet0/1; switchport mode trunk; duplex full; speed 1000; exit`.\", \"cited_artifacts\": [\"KB-00007\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Perform an nslookup test by directly targeting your internal DNS servers (10.10.1.53, 10.10.2.53) for corp-sharepoint.corp.example.com. If nslookup fails, check forwarder connectivity by running `dig @10.10.1.53 google.com +time=2`. If external resolution fails but internal succeeds, this indicates a DNS forwarder issue. Flush your DNS cache with `ipconfig /flushdns` on Windows or `service network restart` on Linux, then try nslookup again.\", \"cited_artifacts\": [\"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Perform nslookup tests targeting the corp DNS servers (10.10.1.53, 10.10.2.53) to isolate DNS vs network issues. If nslookup succeeds but the application fails to resolve the hostname, the client is likely using the wrong DNS server. Check forwarder connectivity by testing external names using dig command. If forwarder connectivity is blocked, resolve the issue with the ISP or network provider. Ensure the correct DNS server is assigned to the client.\", \"cited_artifacts\": [\"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue may be related to DNS resolution. Run `nslookup corp-sharepoint.corp.example.com` to check the DNS resolution. If the IP lookup fails, try resolving the IP directly: `nslookup 10.10.1.53 corp-sharepoint.corp.example.com` to verify connectivity to the internal DNS server. If external DNS resolution is failing, test it using `dig @10.10.1.53 google.com +time=2` to ensure forwarders are working correctly. If the external resolution fails, check the DNS server assignment on the client machine.\", \"cited_artifacts\": [\"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Run `nslookup corp-sharepoint.corp.example.com 10.10.1.53` to check DNS resolution from your corporate DNS server. If this fails, it may indicate a DNS issue. Otherwise, ensure network connectivity by pinging the IP address of the external server: `ping 8.8.8.8` and `ping corp-sharepoint.corp.example.com`.\", \"cited_artifacts\": [\"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Isolate the DNS issue by running `nslookup corp-sharepoint.corp.example.com 10.10.1.53` and `nslookup corp-sharepoint.corp.example.com 10.10.2.53` to check internal and external DNS resolution. If external resolution fails, ensure connectivity to the ISP resolvers. Check for any ACLs blocking TCP/179 on the router VRF. Verify BGP session state and configurations. If BGP issues are found, correct hold-time mismatch or clear any blocking ACLs.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00001\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the DNS settings on your laptop. Ensure it is using the corporate DNS servers (10.10.1.53, 10.10.2.53). If it's not, update the DNS server settings to point to the corporate DNS servers. Additionally, verify TCP/179 reachability between the corporate network and the router via ping from the router's VRF. If TCP reachability is confirmed, check the BGP state and configuration for any errors or mismatches.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Perform an nslookup against the corp DNS servers to check if DNS resolution is failing. Confirm the issue is not a network problem. If DNS resolution fails, try flushing the DNS cache on the client machine: `ipconfig /flushdns` on Windows or `sysctl -w net.inet.tcp.tw_probes=0 && kill -SIGUSR1 $PPID` on macOS/Linux. If the problem persists, investigate BGP connectivity between the client and the DNS server.\", \"cited_artifacts\": [\"KB-00003\", \"KB-00001\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00003", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Run `nslookup corp-sharepoint.corp.example.com 10.10.1.53` to confirm the internal DNS is working correctly. If external resolution fails, check `dig @10.10.1.53 google.com +time=2` to see if there are network issues between the internal DNS server and the ISP's DNS servers. If both internal and external resolution fail, it might indicate a DNS server issue or split-DNS routing causing the queries to be sent to external resolvers. Flush the DNS cache on the client by running `ipconfig /flushdns` on Windows or `sudo ipcache flush` on Linux. If the issue persists, escalate the ticket.\", \"cited_artifacts\": [\"KB-00003\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Contact Aruba support for assistance with ClearPass NAC blocking new IoT devices as this seems to be an edge case not covered by the provided runbooks. Additionally, ensure that the device fingerprint and any policies associated with it are correctly configured in ClearPass. Check if the device is compliant with ClearPass' policies and consider creating a new policy for this device category if necessary. Reach out to Aruba ClearPass experts for further guidance.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00008\"], \"confidence\": 0.7, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue seems to be related to Active Directory authentication failures. Please check if the new Bosch wearable sensors require a specific domain account. Ensure that the account credentials are correct and not locked out. If necessary, unlock the account and reset the password. Additionally, verify that the account is not subject to a password expiry rule. Troubleshoot Kerberos ticket issues if the sensor uses Kerberos for authentication. If the problem persists, consider checking if the sensor's MAC address is present in the Active Directory. For further investigation, consult KB-00008 for detailed troubleshooting steps.\", \"cited_artifacts\": [\"KB-00008\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and align IKE proposals on both ClearPass NAC and the IoT devices. Ensure that the encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) settings are identical. If the issue persists, check for NAT-T configuration and enable it if necessary. Additionally, ensure that the device fingerprint is correctly registered in ClearPass.\", \"cited_artifacts\": [\"KB-00005\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check Aruba ClearPass NAC settings for any policy or configuration issues related to new IoT device categories. Ensure that the new wearable sensors from Bosch are included in the approved device list or create a new policy to allow access. If the device fingerprint is not present, you may need to upload the device fingerprint to ClearPass. Verify that there are no AS or MD5 mismatches in the ClearPass settings. If the problem persists, consult the runbook for additional troubleshooting steps.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00005\", \"KB-00008\", \"KB-00006\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review and correct ClearPass NAC device fingerprinting and policies. Verify IKEv2 proposal settings and DPD timers to ensure compatibility and avoid flapping. Check and adjust DHCP scope settings if necessary to prevent exhaustion. Address any authentication issues in Active Directory if necessary.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00008\", \"KB-00006\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify that the new wearable sensors from Bosch are not being quarantined due to misconfigured IKE proposals. Check the IKE proposal settings on both ClearPass and the Aruba ClearPass NAC devices. Ensure that the encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s) are aligned on both ends. If necessary, align these settings and reconfigure the IKE proposal. If the problem persists, escalate to the vendor or support team for further assistance.\", \"cited_artifacts\": [\"KB-00005\"], \"confidence\": 0.80, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify that the device fingerprint for the new Bosch wearable sensor matches any of the configured known good device fingerprints in Aruba ClearPass NAC. If the fingerprint is not recognized, it may be necessary to add a new device category in ClearPass and configure appropriate policies. Ensure that the AD authentication is working correctly by checking lockout status, password expiry, and Kerberos ticket issues. For the DHCP exhaustion issue, ensure that the DHCP pool settings are adequate and that the DHCP scope is not exhausting. If the DHCP scope is full, consider extending the scope, reducing the lease time, or adding a secondary pool. If issues persist, escalate to network operations for further investigation.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00008\", \"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00042", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the ClearPass configuration for any policies or device categories that might be preventing the new Bosch wearable sensors from being added. Ensure that the device fingerprint is correctly registered in ClearPass. If there are policies blocking the new device class, modify or remove them. If the problem persists, check the Aruba ClearPass logs for any related error messages. Ensure that there are no misconfigurations in the ClearPass policies that could be causing the quarantine. Check for any AS or MD5 mismatches and correct them as needed.\", \"cited_artifacts\": [\"KB-00005\", \"KB-00008\", \"KB-00006\"], \"confidence\": 0.75, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review DHCP scope utilization for VLAN 40. Use `show ip dhcp pool` to check utilization. If it's above 95%, clear stale DHCP bindings with `clear ip dhcp binding *`. Identify and clear any specific stale entries with `clear ip dhcp binding <specific-ip>`. Ensure DHCP snooping is enabled to prevent rogue DHCP servers. If necessary, expand the DHCP scope or reduce the lease time.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify DHCP scopes on floor 4 switches. Clear stale DHCP bindings and check for any rogue DHCP servers. If necessary, adjust lease times or expand DHCP scopes. Ensure no rogue devices are using DHCP inappropriately. If the issue persists, investigate for potential BGP configuration issues.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.1, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review DHCP scope exhaustion on VLAN 40. Use the `show ip dhcp pool` command to confirm utilization. If utilization is above 95%, follow the emergency recovery steps in KB-00006. Clear IP DHCP bindings for expired leases and check for any stale entries using the `show ip dhcp binding | include Expiry` command. If necessary, expand the DHCP scope or reduce lease times to prevent exhaustion.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check DHCP scope exhaustion on VLAN 40. Run `show ip dhcp pool` to confirm utilization. If utilization is >95%, clear DHCP bindings using `clear ip dhcp binding *` to force clients to renew their leases. Identify and clear stale leases with `show ip dhcp binding | include Expiry` and cross-reference with ARP table. Ensure DHCP snooping is enabled to prevent rogue DHCP servers from consuming addresses.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Review the DHCP scope on the network switch for floor 4. Check the utilization and ensure there are no issues causing exhaustion. If necessary, implement the immediate mitigation steps such as clearing stale DHCP bindings. Additionally, check the configuration of the DHCP server to ensure it's not incorrectly configured to assign APIPA addresses. Verify that the DHCP server is properly configured to allocate addresses from the correct subnet (10.40.0.0/24). If the issue persists, escalate the ticket to the network team for further investigation.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify if the DHCP scope on VLAN 40 is exhausted. Run `show ip dhcp pool VLAN40` to check utilization. If utilization is >95%, clear stale DHCP bindings using `clear ip dhcp binding *` command. This should resolve the issue with users getting APIPA addresses. If the scope is full, consider expanding the scope or enabling DHCP snooping to prevent address exhaustion.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check DHCP scope exhaustion on the 10.40.0.0/24 VLAN. Use the command `show ip dhcp pool` to confirm utilization. If utilization is >95%, clear stale leases with `clear ip dhcp binding *` or `clear ip dhcp binding <specific-ip>` to allow clients to renew addresses. Additionally, consider expanding the DHCP scope or reducing the lease time to prevent exhaustion.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00002", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with users on floor 4 getting APIPA addresses could be due to a DHCP scope exhaustion. Run the following commands to address the problem: `show ip dhcp pool` to confirm utilization. If >95%, proceed with `clear ip dhcp binding *` to force all clients to renew their leases. Additionally, check for and clear any stale leases using `show ip dhcp binding | include Expiry` and `show ip arp | include 10.40.0.0` to identify and remove orphaned entries. If necessary, expand the DHCP scope or reduce the lease time.\", \"cited_artifacts\": [\"KB-00006\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes and DB pool exhaustion issues, review the logs from the user-profile and orders-service pods. Use the steps outlined in KB-00017 to identify and address memory leaks. For the connection pool exhaustion, follow the steps in KB-00000 to find and close any long-running queries or unclosed ResultSets. To proceed with the rollback and DB migration, ensure that the new v5.0.0 deployment did not inadvertently apply the schema migration. Refer to KB-00020 for the rollback procedure, and check the corresponding rollback migration scripts in the `/db/migrations/` directory. Finally, verify the rollback was successful by checking the database activity and connection pool metrics. If the rollback is safe, apply it; otherwise, contact the database administrator for further assistance.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00020\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes in the user-profile service and orders-service DB connections exhaustion, start by checking the JVM heap dumps. Verify that the automatic heap dump is enabled and capture one when the crash occurs. Review the heap dump with Eclipse MAT to identify any String internation or cache bloat issues. For the rollback concern, since v5.0.0 includes a DB schema migration, first check if there is a corresponding rollback migration script in the `/db/migrations/` directory. If no rollback is found, it is safer to proceed with caution, as manual intervention may be required to reverse the changes. Monitor the connection pool exhaustion in the application logs and check the database side with SQL queries. Restart the application service to flush the connection pool. If the issue persists, escalate the ticket for further investigation.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes and DB pool exhaustion, follow these steps: For OOM crashes, enable automatic heap dump on OOM as described in KB-00015. Increase or fix the memory limit in your Kubernetes deployment if the `resources.limits.memory` is too low. For DB pool exhaustion, confirm the connection pool saturation by checking the Prometheus/Grafana metric `hikaricp_connections_active`. If the pool is saturated, check for long-running queries or unclosed ResultSets in the application thread dump. To recover, restart the application service to flush the connection pool. For the rollback concern, since v5.0.0 included a DB migration, you need to determine if the rollback is safe by reviewing the `/db/migrations/` directory for a corresponding `V<n>__down migration file`. If available, manually execute the downgrade migration. If the rollback is not safe, a new DB migration will be required to correct the issues.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\", \"KB-00020\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes, set the JVM flags for automatic heap dump and enable `ExitOnOutOfMemoryError`. For the orders-service DB connections exhaustion, check Prometheus/Grafana for the connection pool status and find any long-running queries. For the DB migration rollback, review the migrations directory for a corresponding down migration script. Confirm there are no active user profiles with large byte[] or char[] arrays causing string internation issues. If there are any common failures in SCIM provisioning, ensure the required attributes are correctly mapped. After addressing the issues, roll back the deployment if the rollback is safe based on the migration scripts, and verify the rollback process. Monitor the service health post-deployment.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00009\", \"KB-00020\", \"KB-00017\", \"KB-00016\"], \"confidence\": 0.90, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes, verify that the JVM heap dump settings are enabled and correct. Analyze the heap dump using Eclipse MAT to identify large byte or char arrays. For the DB connections exhaustion, check Prometheus metrics for the connection pool status and find idle connections. Restart the application service to flush the connection pool. For the rollback concern, follow the procedure outlined in KB-00017 for Kubernetes/Helm rollback, and use KB-00016 to diagnose and recover from DB connection pool exhaustion. Ensure the database migration rollback is handled properly as described in KB-00020. Contact the database administrator if the rollback is needed to avoid data loss or corruption.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\", \"KB-00017\", \"KB-00020\"], \"confidence\": 0.95, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes, verify the JVM heap settings and heap dump on the OOM process. Adjust the heap size if necessary. For the DB pool exhaustion, identify the threads holding onto connections by examining the thread dumps. Check the database side using SQL queries to understand the state of the connections. To rollback safely, ensure no conflicting migration scripts were included in the v5.0.0 release and verify that the rollback script is compatible with the current schema. For the rollback, use the Kubernetes/Helm rollback method (`helm rollback <release-name> 0` for Helm deployments or `kubectl rollout undo deployment/<name>` for non-Helm deployments). After rollback, check the health of the services and DB connections.\", \"cited_artifacts\": [\"KB-00009\", \"KB-00015\", \"KB-00017\", \"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes, enable automatic heap dump on OOM with the provided JVM flags to capture heap dumps. Analyze the heap dumps using Eclipse MAT to identify the large byte[] or char[] arrays, which usually indicate String interning or cache bloat. For the DB connections exhausted issue, confirm pool exhaustion by checking Prometheus/Grafana metrics and confirm the pool is saturated. Find connection holders in the thread dumps and investigate long-running queries or unclosed ResultSets on the database side. To rollback, follow the Kubernetes/Helm rollback procedure if the deployment used Helm, or the VM-based rollback procedure if it's a legacy deployment. Ensure that the DB migration is rolled back by checking the corresponding 'down' migration scripts if available. Once the issues are resolved, monitor the system and services to ensure they are stable.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\", \"KB-00020\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00038", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the OOM crashes, verify TCP/179 reachability and check BGP timers. For the DB pool exhaustion, immediately restart the application service to flush the connection pool. As for the rollback and DB migration, since a new migration was included in the v5.0.0 release, you should first confirm if a corresponding `V<n>__down` migration script exists in the `/db/migrations/` directory. If such a script is found, you can proceed with the rollback process. Ensure to monitor the application and database health post-rollback, and consider checking the SCIM 2.0 provisioning setup if there were issues with user lifecycle synchronization post-deployment.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00009\", \"KB-00020\", \"KB-00017\", \"KB-00016\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Examine the MTU settings on the interfaces connecting dist-01 and dist-02. Ensure they match for OSPF to proceed. If the MTUs are different, disable OSPF MTU checking on both interfaces using `ip ospf mtu-ignore`. Check for authentication issues by using `debug ip ospf adj` to look for 'Invalid authentication' messages. Investigate if there are any area-type mismatches using `show ip ospf` and ensure both routers agree on the stub/nssa flags.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Check the MTU settings on the interfaces connecting dist-01 and dist-02. Ensure they match or consider using the `ip ospf mtu-ignore` command to ignore the MTU differences. Verify that the OSPF area types are consistent between the routers. Check for any authentication issues by enabling debugging with `debug ip ospf adj` and looking for 'Invalid authentication' messages. If the issue persists, consider the possibility of other OSPF configuration mismatches or issues like interface down time during the maintenance window that may have affected the OSPF adjacency.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency stuck in EXSTART, check the MTU settings on both routers. Ensure the physical MTU values and OSPF MTU-ignore commands are aligned on interfaces jumbo frames were introduced on dist-01. If the MTU settings are already correct, verify there are no area-type mismatches or authentication issues. Use `show ip ospf neighbor` to identify stuck states and debug tools to inspect OSPF adjacency details.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Verify that the MTU mismatch is not causing the EXSTART state between the routers. Check the MTU settings on interfaces dist-01 and dist-02. If MTUs differ, set `ip ospf mtu-ignore` on both interfaces to ignore the MTU during OSPF communication. Ensure the OSPF area types are consistent between the two routers. If authentication is enabled, check for MD5 key mismatches. Use `debug ip ospf adj` to monitor the adjacency and look for authentication issues.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency stuck in EXSTART, check the MTU of the interfaces on both routers. Since interface MTU was changed during a maintenance window, ensure it matches the MTU of the other router. Additionally, verify that authentication settings, such as MD5 keys, are consistent on both routers. If these steps do not resolve the issue, consider aligning the area types or checking the OSPF configuration for any area-type mismatch.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.5, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency stuck in EXSTART between dist-01 and dist-02, the most common cause is a mismatch in MTU sizes between the interfaces of the two routers. Ensure that the MTU sizes are the same on both routers. If this is not the case, you can try using the `ip ospf mtu-ignore` command on both interfaces to ignore the MTU settings temporarily to see if it resolves the issue. If the MTU sizes are correct, check for any authentication issues, such as MD5 key mismatches, by using `debug ip ospf adj` and looking for 'Invalid authentication' messages. Additionally, verify that the OSPF areas are configured correctly between the two routers.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with OSPF neighbors being stuck in EXSTART state is likely due to an MTU mismatch between the two routers. Check the MTU settings on both dist-01 and dist-02 interfaces. Ensure that the physical MTU matches and that the MTU on both routers is the same. If changing the MTU setting is not an option, you can use the `ip ospf mtu-ignore` command on both interfaces to ignore MTU mismatches. Additionally, ensure that there are no authentication issues causing the neighbors to drop hellos. Use `debug ip ospf adj` to check for any invalid authentication messages.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00007", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For OSPF adjacency stuck in EXSTART between dist-01 and dist-02, check and align the MTU settings on both routers. Ensure the subnets are on the same network and the hello/dead intervals match. If MTU settings differ, set `ip ospf mtu-ignore` on both interfaces. If area-type mismatch is suspected, verify the area flags in `show ip ospf area <id>` output for both routers. Check for MD5 authentication mismatches using `debug ip ospf adj` to ensure MD5 keys are aligned. If MTU settings do not resolve the issue, there may be a mismatch between the routers' OSPF configurations for stub/nssa areas, which needs to be corrected by ensuring both routers agree on the correct area flags.\", \"cited_artifacts\": [\"KB-00002\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The analytics-service is experiencing OOM crashes due to excessive memory consumption, likely caused by the newly added ML model feature that pre-loads embeddings. To resolve this, we need to analyze the heap dumps generated during the OOM crashes. Enable automatic heap dump on OOM by adding the following JVM flags:\\n\\n-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/\\n-XX:+ExitOnOutOfMemoryError\\n\\nCapture the heap dump on the running process using the following command:\\n\\njmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>\\n\\nUse Eclipse MAT to analyze the heap dump. Common causes of heap exhaustion include unbounded caches like Guava Cache or Caffeine without `maximumSize` configured. Additionally, check for large byte[] or char[] arrays in the dominator tree, which indicate String interning or cache bloat. To diagnose further, review the application thread dumps generated by killing the process with `kill -3 <pid>`. Look for threads in `RUNNABLE` or `WAITING` state with JDBC/database stack frames. Address any long-running queries or unclosed ResultSets. Ensure that the Kubernetes pod is not crashing due to liveness probe issues. Check the pod events using `kubectl describe pod <pod> --show-filters=true` to identify any error messages related to OOM or missing secrets. Adjust the resource limits in the Kubernetes deployment YAML if necessary to avoid OOMKilled issues. Finally, monitor the heap usage and connection pool metrics using Prometheus/Grafana and PostgreSQL/MYSQL commands to ensure the service is not being degraded by high connection counts. Adjust the configuration as needed to prevent the service from crashing due to memory exhaustion and connection pool exhaustion.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The analytics-service intermittently OOM crashing can be investigated by enabling automatic heap dump on OOM and capturing heap dumps. This will help analyze the large arrays under ModelCacheManager. Ensure the memory limits for the service are set correctly and review the heap dump for memory leaks or excessive use of byte arrays. Additionally, check the environment variables and secrets in the Kubernetes deployment to confirm they are correctly defined. Investigate the liveness probe configuration to ensure it does not cause the service to crash prematurely. Address any identified memory issues and reconfigure the service appropriately. If the issue persists, it may be beneficial to increase the heap size or implement a more robust caching mechanism.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00017\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with analytics-service intermittently OOM crashing every 4-6 hours can be resolved by increasing the JVM heap size. Add the following JVM flags to the analytics-service startup configuration:\\n-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/\\n-XX:+ExitOnOutOfMemoryError\\n-XX:+UseG1GC\\n-XX:MaxHeapSize=4G\\n-XX:InitiatingHeapOccupancyPercent=30\\nAlso, ensure the heap dump is captured and analyzed using Eclipse MAT to identify the large arrays under ModelCacheManager. This should help in reducing the heap exhaustion and preventing OOM crashes. Monitor the heap size and adjust the settings as needed.\\n\\nAdditionally, for the new ML model feature that pre-loads embeddings, review the cache configurations (e.g., Guava Cache, Caffeine) and ensure that they have proper maximumSize settings to prevent bloat in the ModelCacheManager.\\n\\nFor any underlying database connection pool exhaustion, ensure that the pool size is appropriately configured and monitor the Prometheus metrics for the database connections. If necessary, adjust the pool size and configuration.\\n\\nLastly, for Kubernetes pod crashes, verify the last crash logs and check the Events section for more details. Correct any configuration or resource limits that might be causing the pod to crash and retry the application service to flush the pool if needed.\\n\\nTo prevent future occurrences, set up automated monitoring and alerting for OOM crashes and connection pool exhaustion to proactively handle these issues.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\", \"KB-00017\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The analytics-service intermittently OOM crashing can be resolved by enabling automatic heap dump on OOM and configuring a larger heap size in the JVM. Additionally, ensure that the JVM heap configuration aligns with the size of the arrays causing the issue in ModelCacheManager. Here are the steps:\n\n1. **Enable Automatic Heap Dump on OOM**: Add JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/` to generate heap dumps automatically when an OOM error occurs.\n\n2. **Increase JVM Heap Size**: Update the `-Xmx` and `-Xms` JVM flags to a higher value than the current settings to avoid frequent OOM errors. For example, `-Xmx2g -Xms2g`.\n\n3. **Prevent Zombie State**: Add `-XX:+ExitOnOutOfMemoryError` to force the JVM to exit when an OOM error occurs, avoiding a zombie state.\n\n4. **Capture Heap Dump on Running Process**: Use `jmap` to capture heap dumps on the running analytics-service process: `jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>`. This will help in analyzing the heap dump with Eclipse MAT to identify the large byte[] or char[] arrays causing the issue.\n\n5. **Analyze with Eclipse MAT**: Open the heap dump in Eclipse MAT and run the 'Leak Suspects' report. Focus on the 'Problem Suspect' with the largest retained heap. Drill into the dominator tree to find out where the large arrays are created and if they are part of a larger issue like String interning or cache bloat.\n\n6. **Optimize Large Arrays**: If the issue is due to large arrays in ModelCacheManager, consider optimizing the caching mechanism to limit the size of the arrays or use a different caching strategy that doesn't create such large arrays.\n\n7. **Review and Adjust Configuration**: After making changes, review the application logs and metrics (heap dumps, JMX metrics, Prometheus/Grafana) to ensure that the new configuration is working as expected. Monitor the application's performance and resource usage to ensure that the service can handle the increased heap size and larger arrays.\n\nBy following these steps, you should be able to reduce the frequency of OOM crashes in the analytics-service and improve the overall stability of the service.\", \"cited_artifacts\": [\"KB", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with analytics-service intermittently OOM crashing can be resolved by increasing the JVM heap size and optimizing the ModelCacheManager. Enable automatic heap dump on OOM as described in KB-00015. To do this, add the following JVM flags: `-Xms1g -Xmx1g -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError`. Additionally, configure the ModelCacheManager to use a bounded cache, for example, using Guava Cache with a maximum size. Analyze heap dumps with Eclipse MAT to identify large byte[] or char[] arrays in the ModelCacheManager and optimize them. Ensure that there are no long-running queries or unclosed ResultSets that could be holding onto database connections, as described in KB-00016. Finally, check Kubernetes logs for any issues such as OOMKilled or missing secrets, as described in KB-00017. Restart the service to apply the changes and monitor the heap usage and connection pool status.\", \"cited_artifacts\": [\"KB-00001\", \"KB-00015\", \"KB-00016\", \"KB-00017\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The intermittent OutOfMemoryError in the analytics-service might be due to excessive allocation of large arrays in ModelCacheManager. Ensure that the maximum size is configured for any in-memory caches like Guava Cache or Caffeine. Additionally, analyze heap dumps to identify problematic objects. To prevent the service from restarting due to OOM, enable automatic heap dump collection and add JVM flags `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/` with `-XX:+ExitOnOutOfMemoryError` to force a clean restart. Monitor the connection pool in the database and ensure it is not being exhausted, as this could lead to pods crashing. Check Kubernetes pod logs for any errors related to resource limits or configuration issues. Adjust resources limits and ensure proper configuration of liveness probes. Restart the analytics-service service to apply changes.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00017\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The analytics-service OOM crashes appear to be related to the newly added ML model feature that pre-loads embeddings. The large arrays under ModelCacheManager in heap dumps suggest issues with memory allocation and caching. Enable automatic heap dump on OOM as described in KB-00015 and capture the heap dump for analysis. Common causes of OOM in this scenario include unbounded caches like Guava Cache or Caffeine without `maximumSize` configured. Analyze the heap dump with Eclipse MAT to identify the largest retained heap and the problematic byte[] or char[] arrays. Fix the issue by configuring a maximum size for the cache, or by optimizing the cache to reduce the number of entries. Additionally, ensure that the JVM is configured to handle large heaps, possibly by increasing the heap size. Check the database side for connection pool exhaustion and handle accordingly, as described in KB-00016. Monitor the application's logs and database metrics to ensure that the issue is resolved and prevent future occurrences. Restart the application service to flush the connection pool and monitor that connections are being released properly. Restart the service to apply changes and clean up resources if necessary.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00015", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The analytics-service intermittently crashing with OOMKilled might be due to a large allocation of memory by the ModelCacheManager. To address this, verify that the heap dump on OOM is enabled and the heap dump path is correctly set. Use `jmap -dump:format=b,file=/tmp/heap_$(date +%s).hprof <pid>` to capture the heap dump when the OOM occurs. Analyze the heap dump with Eclipse MAT to identify large byte[] or char[] arrays which indicate potential issues with string interning or cache bloat. Ensure that the ModelCacheManager configuration is optimized to avoid large memory usage. Additionally, check for unbounded caches like Guava Cache or Caffeine without proper maximumSize settings. Update the JVM configuration to include `-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/heapdumps/ -XX:+ExitOnOutOfMemoryError` to force a clean restart after an OOM event. Review the logs for any error messages or stack traces that might indicate the root cause. Monitor the heap usage and connections in the database and Kubernetes pod to ensure they are within acceptable limits. If the issue persists, consider increasing the memory limits for the application service and the Kubernetes pod.\", \"cited_artifacts\": [\"KB-00015\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It appears that the new deployment of notification-service v1.5.2 resulted in pods being in a CrashLoopBackOff state. To diagnose and resolve this issue, follow these steps:\n\n1. Use `kubectl logs <pod> --previous` to retrieve the logs from the previous container run, which often provides more detailed information about what went wrong.\n\n2. Check the `Events` section in `kubectl describe pod <pod>` to look for common messages such as 'OOMKilled', 'Error', 'CreateContainerConfigError', and 'CreateContainerConfigError'. These can indicate a memory issue, missing secrets, misconfigured environment variables, or bad ConfigMap injection.\n\n3. If an OOMKilled error is found, increase the memory limit for the pod in the deployment or address any memory leaks in the application.\n\n4. Ensure that the required SCIM attributes are correctly mapped and that there are no 404 errors on the SCIM endpoint in the target application.\n\n5. Review the application's logs and configurations to ensure that there are no misconfigurations that could lead to a crash loop.\n\n6. To prevent future incidents, consider implementing liveness probes with shorter intervals to catch issues early.\n\n7. Verify that the API tokens are correctly managed and rotated to prevent unauthorized access.\n\nBy following these steps, you should be able to diagnose and resolve the issue with the notification-service pods in the CrashLoopBackOff state.\n\nFor further assistance, refer to KB-00017 for Kubernetes/CrashLoopBackOff diagnosis and KB-00009 for Okta SCIM 2.0 provisioning troubleshooting.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00009\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It appears that the notification-service pods are experiencing a CrashLoopBackOff after the new deployment. The recommended steps to diagnose this issue are as follows:\n\n1. First, gather the last crash logs by running `kubectl logs <pod> --previous`. This will provide the logs from the previous container run, which is often more detailed and can help identify the root cause.\n\n2. Next, check the events associated with the pod to identify any common messages such as 'OOMKilled' (memory limit hit), 'Error' (non-zero exit), 'CreateContainerConfigError' (bad env var or secret reference), or 'CreateContainerConfigError' (bad container image). \n\n3. Common root causes for CrashLoopBackOff include low memory limits, missing secrets, configuration errors, or liveness probes that are too aggressive. Review the resources limits in the pod definition, check for any missing secrets or environment variables, and ensure that the liveness probe is not set to be too aggressive.\n\n4. If memory is the issue, you may need to increase the memory limit for the pods. If it's a configuration error, correct the YAML or ConfigMap. If the liveness probe is too aggressive, adjust the interval or timeout for the probe.\n\n5. To ensure the issue does not reoccur, make sure to check the new deployment for any changes in the configuration or settings that might have introduced the problem. Verify that all the required attributes are correctly mapped in the SCIM setup if applicable.\n\n6. Monitor the system and pods post-deployment to ensure they are healthy and functioning as expected.\n\nMake sure to apply the necessary changes and then wait a few minutes to see if the pods come back up. If the issue persists, further investigation may be required, such as comparing the staging environment with the production environment to identify any differences in configurations or settings.\", \"cited_artifacts\": [\"KB-00017\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The notification-service pods are experiencing a CrashLoopBackOff issue after a new deployment. To diagnose, follow these steps:\n\n1. Check the last crash logs for the pod by using `kubectl logs <pod> --previous`. This will show logs from the previous container run, not the current one which might be in the backoff state. The current run's logs are often empty if the container crashes within 1 second.\n\n2. Use `kubectl describe pod <pod>` to view the Events section. Common messages include 'OOMKilled' (memory limit hit), 'Error' (container exited with non-zero status), 'CreateContainerConfigError' (issues with environment variables or secrets), and 'CreateContainerError' (problems with the container definition).\n\n3. Identify the root cause of the issue based on the Events:\n - If the container is OOMKilled, increase the memory limits for the pod.\n - Ensure there is no missing secret in the pod's namespace.\n - Check for configuration errors in the pod's deployment YAML.\n - Verify that the liveness probe is not set too aggressively.\n\n4. Once the root cause is identified, correct the issue and redeploy the notification-service pods.\n\n5. Monitor the pods to ensure they are running without issues. If the problem persists, consider rolling back the deployment using the rollback procedure in KB-00020.\n\n6. If the issue is related to configuration, make sure the required SCIM attributes (userName, name.givenName, name.familyName) are correctly mapped in the SCIM integration setup in KB-00013.\n\n7. If the problem is related to API tokens, ensure they are rotated following the zero-downtime rotation pattern described in KB-00013.\n\n8. Verify the database migration rollback if necessary, and make sure it's working correctly by checking the `/db/migrations/` directory.\n\n9. Finally, if the issue persists, review the Kubernetes documentation and community forums for additional insights.\n\nMake sure to increase the resources and checks to avoid memory or other resource issues, and ensure all configuration files are correct.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00020\", \"KB-00013\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The pods are crashing in a CrashLoopBackOff state after the deployment of notification-service v1.5.2. First, let's gather the crash logs for the problematic pods to understand the root cause. Use the command `kubectl logs <pod> --previous` to retrieve the logs from the previous container run. This can be done for each of the two pods that are crashing. Additionally, check the events for these pods using `kubectl describe pod <pod>` to see if any error messages are present. Common messages include 'OOMKilled' for memory issues, 'Error' for non-zero exit statuses, and 'CreateContainerConfigError' for configuration errors. If the issue is related to memory, increase the limits specified in the deployment YAML for the notification-service. Ensure that all required configuration files and secrets are correctly referenced in the deployment. Finally, verify the liveness probe configuration to make sure it is not set too aggressively. Address any detected issues and redeploy the notification-service. Monitor the status of the pods after the deployment to ensure they are now stable and running without crashes.\", \"cited_artifacts\": [\"KB-00017\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"To diagnose the issue with the notification-service pods crashing, follow these steps:\n\n1. **Check the last crash logs**: Use the command `kubectl logs <pod> --previous` to get logs from the previous container run. This is often where you'll find the actual error log.\n\n2. **Inspect events**: Use `kubectl describe pod <pod>` to look at the Events section. Common messages include 'OOMKilled' (memory limit hit), 'Error' (non-zero exit), and 'CreateContainerConfigError' (bad environment variables or secret references).\n\n3. **Common root causes**:\n - **OOMKilled**: Ensure that the memory limit for the container is sufficient. If necessary, increase the limits or resolve any memory leaks.\n - **Missing secret**: Verify that the required secret exists in the correct namespace and that it's referenced correctly in the deployment configuration.\n - **Config error**: Check the deployment YAML for any misconfigurations in the ConfigMap or environment variables.\n - **Liveness probe too aggressive**: If the liveness probe is failing before the application starts, consider adjusting the liveness probe interval or threshold.\n\n4. **Verify the deployment**: If the issue persists, you may want to rollback the deployment to a previous revision. For Kubernetes deployments, you can use `helm rollback <release-name> 0` or `kubectl rollout undo deployment/<name>`.\n\n5. **Check events and logs for any misconfigurations**: Ensure that all required attributes are correctly mapped in the SCIM 2.0 provisioning setup, especially the `userName` attribute mapping, which should map to the email address.\n\n6. **Monitor the application**: After resolving the immediate issues, monitor the pods and pods' statuses to ensure they are stable. Verify that the application is functioning correctly with both new and old API tokens or SCIM provisioned users.\n\n7. **Document and communicate**: Document the steps taken and the outcomes of the troubleshooting process. Communicate with the team to ensure that the deployment issues are resolved and that there are no ongoing issues that require further action.\n\nTo resolve the issue with the notification-service pods crashing, ensure that you have the latest logs and events to diagnose and correct the root cause. If the issue persists, consider reaching out to the team for further assistance.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00009\"], \"confidence\": ", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The notification-service pods are experiencing a CrashLoopBackOff after the new deployment. To diagnose, follow these steps:\n\n**Step 1 — Get the last crash logs**\nUse the command `kubectl logs <pod> --previous` to retrieve the logs from the previous container run, which often contains more detailed information about the issue.\n\n**Step 2 — Check events**\nUse the command `kubectl describe pod <pod>` to examine the Events section for common messages such as 'OOMKilled', 'Error', or 'CreateContainerConfigError'. This will help identify the root cause of the problem.\n\n**Step 3 — Common root causes**\n- **OOMKilled**: Increase the `resources.limits.memory` setting in the deployment configuration.\n- **Missing secret**: Verify that the secret referenced in the ConfigMap exists in the correct namespace.\n- **Config error**: Review the ConfigMap for any issues with environment variables or secret references.\n- **Liveness probe too aggressive**: Adjust the liveness probe configuration if necessary.\n\n**Step 4 — Kubernetes-specific considerations**\n- Ensure that the new deployment includes the correct resources and that there are no changes in the pod definition or resource limits that could cause the pods to crash.\n- Verify that the environment is consistent between staging and production. Differences in configurations or dependencies can cause unexpected behavior.\n\n**Step 5 — Monitoring and Logging**\nMonitor the system to ensure that the deployment is stable and that no new issues arise. Use Kubernetes monitoring tools to track pod status and resource usage.\n\nAfter diagnosing and resolving the issue, perform a rolling deployment to ensure that the changes are applied correctly to all affected pods. Finally, verify the deployment by checking the logs and events again to confirm that the pods are running without issues.\n\nIf the problem persists, consider rolling back the deployment and reapplying the changes, ensuring to update all necessary configurations and dependencies.\", \"cited_artifacts\": [\"KB-00017\"], \"confidence\": 0.9, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The CrashLoopBackOff issue with the notification-service pods after the deployment could be due to a resource limit issue or misconfiguration. Check the resource limits specified in the deployment YAML. If memory limits are too low, increase them. Also, ensure that there are no missing secrets or config errors in the deployment. Use kubectl describe pod <pod> to check the events for any error messages. If the issue persists, verify that the liveness probe is not too aggressive. For OOMKilled errors, ensure there are no memory leaks in the application. If the issue is related to SCIM provisioning or API token management, refer to the relevant KB articles for troubleshooting steps. If the problem persists despite these checks, escalate the issue to the development or ops team.\", \"cited_artifacts\": [\"KB-00017\", \"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00013", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The notification-service pods are crashing due to a resource limit issue. The current deployment limits the memory resource too low, causing the containers to exit non-zero. To resolve this, increase the memory limit in the deployment configuration. Ensure the deployment YAML file specifies a higher memory limit than the current 2Gi. Additionally, check for any memory leaks in the application code. Verify the updated deployment, then manually scale down and scale up the affected pods to ensure the new configuration is applied correctly. If the memory limit still seems too low, consider increasing the overall resource limits for the namespace to ensure there is enough resources for all applications running in it.\", \"cited_artifacts\": [], \"confidence\": 0.8, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GitHub integration CI pipeline failing with HTTP 401 errors suggests an issue with the API token used by the github-integration-bot. Based on the context, this could be related to token rotation or expiration. To resolve this issue, follow these steps:\n\n1. Generate a new API token in the GitHub Developer Settings.\n2. Set the token's expiry to 90 days or less.\n3. In the Jenkinsfile, update the credentials binding to use the new token.\n4. Verify that all consumers of the token are using the new token by checking the Jenkins access logs.\n5. After all consumers are using the new token, revoke the old token.\n\nIf the issue persists, check the GitHub Personal Access Token settings for any mismatches or incorrect configurations.\n\nIf the problem continues, escalate the issue to the IT team for further investigation into any upstream service timeouts or connectivity issues. If the CI pipeline is still failing, review the Jenkins logs and network access logs for any additional clues.\n\nFor future prevention, set up rotation reminders for these tokens and ensure that the CI pipeline is configured to check for and handle token revocation gracefully.\", \"cited_artifacts\": [\"KB-00013\", \"KB-00009\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the GitHub integration CI pipeline failing due to a 401 HTTP response code for the github-integration-bot token can be resolved by revoking and rotating the token. Follow these steps:\n\n1. **Generate a new token:** Navigate to the Developer Settings > Personal access tokens > Fine-grained tokens in the GitHub account. Generate a new token, set it to expire in 90 days, and apply a rotation reminder at -14 days.\n\n2. **Update all consumers of the token:** In your Jenkins environment, update the credentials binding for the github-integration-bot token to use the new token. Ensure that the Jenkinsfile has the correct API token bound to the `github-integration-bot` user.\n\n3. **Verify all consumers are using the new token:** Check the Jenkins access logs to confirm that the new token is being used for the GitHub API requests.\n\n4. **Revoke the old token:** In GitHub Developer Settings, find the old token and revoke it.\n\n5. **Push the new token to all consumers:** Ensure that the new token is pushed to all Jenkins jobs that use it.\n\n6. **Test the pipeline:** After applying these changes, test the CI pipeline to ensure that it can successfully push build statuses to GitHub.\n\nMake sure to review the context about token rotation and revocation in KB-00013 for more detailed instructions and best practices.\n\nIf the issue persists after following these steps, escalate the ticket.\", \"cited_artifacts\": [\"KB-00013\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The 401 HTTP status code indicates an authentication issue with the API token used by the github-integration-bot. To resolve this, follow these steps:\\n\\n1. Confirm that the github-integration-bot's personal access token is still valid and has not expired.\\n2. Check the token settings in the Jenkins configuration to ensure it is being used correctly.\\n3. If the token is older than 90 days, consider generating a new token. Follow the instructions in KB-00013 for zero-downtime rotation.\\n4. Update the Jenkinsfile to bind the new token.\\n5. Verify that all consumers of the token are now using the new token by checking access logs.\\n6. Revoke the old token to prevent unauthorized use.\\n7. Test the CI pipeline to ensure it now functions correctly with the new token.\\n\\nIf the token is still valid but still causing issues, it may be due to the API gateway setting a timeout. Review the API Gateway access logs for 504 Gateway Timeout errors and test the upstream service directly to identify any performance issues. Adjust the timeout configuration in the API Gateway if necessary.\",\n \"cited_artifacts\": [\"KB-00013\", \"KB-00014\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GitHub integration CI pipeline failure due to the 401 HTTP status code for the github-integration-bot token suggests that the token might be either expired or revoked. Given that the token was working fine last month, it's likely that it has expired. To resolve this issue, you should revoke the current token and regenerate a new one. Here are the steps to revoke and regenerate the token:\n\n1. Navigate to your Jenkins instance and go to Manage Jenkins > Configure System > API Tokens.\n2. Locate the token for the github-integration-bot and click the 'Revise' button to revoke it.\n3. Generate a new API token for the github-integration-bot.\n4. Update the Jenkinsfile to use the newly generated token for pushing build status to GitHub.\n5. Verify that the new token is being used by checking the Jenkins job logs and the GitHub API logs to ensure the 401 errors are resolved.\n6. If the token issue persists, check for any potential network or upstream service issues as described in the KB-00018 troubleshooting guide for API Gateway 504 Timeout Troubleshooting.\n\nEnsure that the token expiration is set to at least 90 days to avoid future issues. Set the token's expiry to -14 days as a reminder to regenerate the token when it nears expiration.\n\nIf the issue with the token is resolved, ensure that there are no other network issues that might be causing the 401 errors. Review the access logs for the GitHub integration and the upstream service (if applicable) to identify any other potential issues.\n\nCited artifacts: [KB-00013, KB-00018], Confidence: 0.85, Escalate: false\", \"cited_artifacts\": [\"KB-00013\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It appears that the GitHub Personal Access Token (PAT) used by the github-integration-bot to push build status to GitHub has expired or is no longer valid, causing the 401 HTTP status code. The GitHub PAT should be set to expire in 90 days and it's recommended to set rotation reminders to -14 days. To resolve this issue, please follow these steps:\n\n1. Navigate to the Developer Settings in your GitHub account.\n2. Go to Personal access tokens and generate a new fine-grained token with an expiry of 90 days or set a rotation reminder for -14 days.\n3. Update the Jenkinsfile to use the newly generated token for the github-integration-bot. For example, if you are using a Bash script step in the Jenkinsfile to set the token, replace the existing token with the new one:\n ```yaml\n sh \"\"\"\n export GITHUB_TOKEN=<new-generated-token>\n ```\n4. Verify that the Jenkinsfile has been updated with the new token and save the changes.\n5. Push the updated Jenkinsfile to the repository and trigger a build to test the new token.\n\nIf the issue persists, it might be worth checking the Jenkins API Token settings as well. Ensure that the token used in the Jenkinsfile matches the one configured in the Jenkins UI and that it has the necessary permissions to access the GitHub API.\n\nAdditionally, if the problem still exists, consider enabling and testing the SCIM provisioning to sync user lifecycle data from Okta to Jenkins. This might help identify any misconfigurations or issues with the SCIM integration. Please refer to KB-00018 for troubleshooting guidance on API Gateway 504 Timeout Troubleshooting if this does not resolve the issue.\n\nTo prevent future occurrences, set up email alerts for token rotation and regularly review and update tokens when necessary. You may also want to implement a webhook to notify your team of any token expiration or revocation events.\n\nCited Artifacts: [KB-00013, KB-00018, KB-00009]\",\n \"confidence\": 0.85,\n \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GitHub integration CI pipeline failing with HTTP 401 indicates that the github-integration-bot token might have expired or been revoked. Follow the zero-downtime rotation pattern outlined in the KB-00013 article to rotate the token. First, generate a new token while the old one is still valid. Then, update all consumers of the token one by one or via a rolling deployment. Verify all consumers are using the new token by checking access logs. Finally, revoke the old token. Ensure that the new token has the appropriate permissions for the GitHub integration CI pipeline. Additionally, check the expiration settings for the GitHub Personal Access Token (PAT) in the Developer Settings > Personal access tokens. Set the expiration to 90 days and set rotation reminders at -14 days.\", \"cited_artifacts\": [\"KB-00013\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the GitHub integration CI pipeline failing could be due to the API token used by the `github-integration-bot` expiring or being revoked. Please ensure the API token is renewed before it expires. Refer to KB-00013 for detailed steps on API token rotation and revocation. Additionally, check that the token has the correct permissions required by the Jenkinsfile and that it is correctly bound to the Jenkins user. Ensure that the Jenkins `github-api` plugin is up to date to support the SCIM provisioning. If the issue persists, investigate any changes in the downstream services or dependencies that might be causing timeouts, as mentioned in KB-00014 and KB-00018. Finally, check if the TLS certificate for the internal services is up to date, as mentioned in KB-00014, to avoid any TLS certificate-related issues affecting the pipeline. Please escalate if the problem continues after these steps are taken.\", \"cited_artifacts\": [\"KB-00013\", \"KB-00014\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00012", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It appears that the GitHub integration CI pipeline is failing due to a 401 HTTP status code. This might indicate that the API token used by the github-integration-bot has expired or been revoked. To resolve this issue, follow these steps:\n\n1. Generate a new API token in the GitHub Developer settings > Personal access tokens > Fine-grained tokens.\n2. Set the token's expiry to 90 days max per policy.\n3. Set rotation reminders at -14 days.\n4. Update the Jenkinsfile credentials binding to use the new token.\n5. Verify all consumers are using the new token by checking Jenkins access logs.\n6. Revoke the old API token to ensure there is no overlap.\n\nAdditionally, check the environment where the GitHub integration bot is running to ensure that it is using the correct credentials and that there are no network issues affecting the API call. If the issue persists, escalate the problem to the IT team as the problem might be related to the gateway or upstream services.\n\nTo further troubleshoot, you can review the access logs of the Jenkins and the GitHub integration bot to see any errors or failures related to the API call. Additionally, you can test the API call directly to the GitHub service to see if the token is indeed causing issues.\n\nPlease ensure to follow the zero-downtime rotation pattern as described in KB-00013 and KB-00018 to avoid any downtime during the token revocation process.\n\nPlease escalate the issue to the IT team if the problem persists after trying these steps and if further troubleshooting is required.\", \"cited_artifacts\": [\"KB-00013\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"After the datacenter fiber maintenance, it's crucial to systematically investigate and address the network issues affecting BGP, OSPF, and DHCP. Here’s a comprehensive plan to resolve the issue:\n\n1. **BGP Issues:**\n - Verify TCP/179 reachability: Use `ping vrf MGMT <peer-ip> source <local-ip>` from the router to the peer. If ping fails, check ACLs on both ends.\n - Check BGP state: Use `show bgp neighbors <peer-ip>` to identify 'BGP state = Active' or 'Idle (Admin)' states and ensure proper hold times and keepalive intervals.\n - Validate AS and peer configuration: Ensure `neighbor <ip> remote-as <AS>` on your router matches the peer's local AS. MD5 password mismatch causes BGP notifications.\n\n2. **OSPF Issues:**\n - Identify stuck state: Use `show ip ospf neighbor` to check for stuck states like INIT or 2-WAY.\n - MTU mismatch: Use `show interfaces <int> | include mtu` to align MTUs between the interfaces on both sides.\n - Area-type mismatch: Check `show ip ospf` for area flags to ensure non-stub areas agree on the same configuration.\n - Authentication mismatch: Use `debug ip ospf adj` and look for 'Invalid authentication' messages to identify MD5 key issues.\n\n3. **DHCP Issues:**\n - DHCP Scope Exhaustion: If utilization exceeds 95%, use `show ip dhcp pool` to confirm and clear binding entries: `clear ip dhcp binding *`. Identify and clear specific stale leases if necessary.\n - Identify and fix stale leases: Use `show ip dhcp binding | include Expiry` to find leases with expiry >7 days. Cross-reference with the ARP table for orphaned entries.\n\n4. **DNS Issues:**\n - Confirm DNS resolution: Use `nslookup <hostname> <dns-server-ip>` to isolate DNS vs network issues. Check forwarder chain and ensure proper resolution from internal servers.\n - Clear DNS cache: On Windows: `ipconfig /flushdns`. On Linux: `sudo ip restart`.\n\n5. **Physical Layer and VLAN Trunk Configurations:**\n - Check physical layer conditions: Use `show interfaces <int> transceiver detail` to check SFP power levels and reseat any faulty S", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Based on the retrieved context, the issues you're experiencing appear to be related to network connectivity and configuration problems post-fiber maintenance. Here's a step-by-step resolution for the three issues:\n\n1. **BGP Session Drops**: Perform a TCP reachability test to ensure that the BGP session isn't being blocked by ACLs. Use the following command to ping the BGP peer from the router:\n ```\n ping vrf MGMT <peer-ip> source <local-ip>\n ```\n Check the BGP state to confirm the session is down:\n ```\n show bgp neighbors <peer-ip>\n ```\n Correct any configuration mismatch such as AS numbers or MD5 passwords:\n ```\n neighbor <peer-ip> remote-as <correct-AS>\n neighbor <peer-ip> password <correct-MD5-password>\n ```\n\n2. **OSPF Neighbor Issues**: Investigate for stuck states and mismatched parameters. Check the OSPF state to find where the adjacency is failing:\n ```\n show ip ospf neighbor\n ```\n Ensure MTU compatibility and authentication settings are correct:\n ```\n ip ospf mtu-ignore\n debug ip ospf adj\n ```\n\n3. **DHCP Scope Exhaustion**: Identify the reason for the DHCP pool exhaustion. First, confirm if there are stale leases:\n ```\n show ip dhcp pool\n show ip dhcp binding | include Expiry\n show ip arp | include <subnet>\n ```\n Resolve the exhaustion by either reducing lease time, expanding the scope, or enabling DHCP snooping.\n\nReview the logs to find any errors or notifications related to BGP, OSPF, and DHCP, and correct them accordingly. Additionally, ensure that the physical interfaces and VLAN configurations are correctly set up to avoid native VLAN mismatch issues. \n\nLastly, after making these changes, flush the DNS cache and perform a DNS resolution test to ensure DNS is functioning correctly. If the issues persist, retest all configurations and perform a full network sweep to identify any other potential connectivity issues.\n\nTo mitigate the impact of downtime, monitor the network closely and be prepared to perform additional troubleshooting steps as necessary.\n\nEnsure all configurations are saved and backed up before making any changes to prevent potential rollback issues.\n\nConsider reaching out to network administrators or contractors if the issue persists, as they may have additional insights or the tools needed to resolve the issue.\",\n \"cited_artifacts\": [\"KB-0", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Given the multiple network failures reported after datacenter fiber maintenance, it's important to systematically investigate the root causes of BGP, OSPF, and DHCP issues. Here's a step-by-step approach for each:\n\n1. **BGP Failure**: Verify TCP/179 reachability between the affected routers and the ISP. Check BGP state and configuration details to ensure there are no hold timer mismatches. Use `show bgp neighbors <peer-ip>` to verify the BGP state and `debug ip bgp <peer-ip> events` for notifications. If TCP/179 is blocked by ACLs, clear them. Correct hold timer settings with `neighbor <ip> timers <keepalive> <hold>` or clear ACLs blocking TCP 179. After fixing, run `clear ip bgp <peer-ip> events`.\n\n2. **OSPF Failure**: Check OSPF neighbor adjacency between the core and distribution segments. Use `show ip ospf neighbor` to identify any stuck states (e.g., INIT, 2-WAY). For stuck INIT states, check the subnet mask and hello/dead intervals to ensure they match. Address MTU mismatch with `ip ospf mtu-ignore` on both interfaces or align MTUs. Resolve area-type mismatch by ensuring both routers agree on the stub/nssa flags in the `area <id>` statement. For authentication issues, ensure MD5 key mismatches are resolved.\n\n3. **DHCP Pool Exhaustion**: Mitigate DHCP pool exhaustion alerts by identifying and clearing expired or stale leases. Use `show ip dhcp pool` to confirm utilization. If utilization is high (>95%), clear all DHCP bindings with `clear ip dhcp binding *`. For specific entries, use `clear ip dhcp binding <specific-ip>`. Implement preventative measures by reducing lease time or expanding the scope.\n\nFor physical layer issues, such as CRC errors, check for duplex mismatch and physical layer health. Use `show interface <int> | include CRC|input|output` to baseline counters and check duplex settings. Re-seat SFP modules for SFP interfaces and ensure cable length is within acceptable limits (e.g., Cat5e max 100m at 1G). \n\nOnce these issues are resolved, ensure DNS server configurations are correct and that the correct DNS servers are being used. Verify the forwarder chain and check for zone delegation issues. Clean the DNS cache on client machines with", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The multiple network failures after the datacenter fiber maintenance can be attributed to several issues stemming from the BGP, OSPF, and DHCP. Below is a step-by-step approach to identify and resolve these issues:\n\n1. **BGP Session Issues:** BGP session drops can occur due to TCP reachability issues, hold-timer expiry, or configuration mismatch. To verify TCP reachability, use the `ping vrf MGMT <peer-ip> source <local-ip>` command from the router VRF to the peer IP address. If the ping fails, check the access control lists (ACLs) on both ends for blocking TCP/179. For the BGP state, use `show bgp neighbors <peer-ip>` to check for 'BGP state = Active' (indicating a connectivity issue) or 'Idle (Admin)' (indicating a local configuration issue). The hold time and keepalive interval should be confirmed as well.\n\n2. **OSPF Adjacency Issues:** OSPF adjacency failures may be due to MTU mismatch, area-type mismatch, or authentication issues. Check the subnet mask and hello/dead intervals to align them. MTU mismatches can be resolved by either aligning the physical MTUs or using `ip ospf mtu-ignore` on both interfaces. For area-type mismatches, ensure both routers agree on the stub/nssa flags in `area <id> st` for the respective areas. For authentication issues, use `debug ip ospf adj` to look for `Invalid authentication` messages and ensure MD5 keys match.\n\n3. **DHCP Scope Exhaustion:** If a DHCP scope is showing exhaustion alerts, first check the utilization with `show ip dhcp pool`. If it's >95%, clear the DHCP bindings with `clear ip dhcp binding *` within the maintenance window to force a renewal, or clear specific bindings with `clear ip dhcp binding <specific-ip>`. Identify stale leases with `show ip dhcp binding | include Expiry` and cross-reference with the ARP table to find orphaned entries. For permanent fixes, reduce the lease time or expand the scope if necessary, and enable DHCP snooping to prevent rogue DHCP servers. \n\n4. **DNS Issues:** Ensure that DNS resolution failures are not causing network errors. Isolate DNS vs. network with `nslookup <hostname> <dns-server-ip>`. If the external resolution fails but internal succeeds, check forwarder connectivity", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"Based on the issues described, it appears that the network failures after the datacenter fiber maintenance are related to BGP, OSPF, and DHCP issues. The first step should be to verify the BGP peer session status and reachability. Use the following commands to check the BGP status and verify TCP/179 reachability:\n\n1. Verify TCP reachability: `ping vrf MGMT <peer-ip> source <local-ip>` from the router VRF to the ISP peer IP.\n2. Check BGP state: `show bgp neighbors <peer-ip>` to check if the BGP session is active or idle.\n3. Validate AS and peer configuration: Ensure that the BGP neighbor configuration matches between the two routers (including AS numbers and MD5 passwords).\n\nFor OSPF, since adjacency is not forming, check for MTU mismatches and other configuration issues. Ensure that the OSPF areas and configurations are correct, and the `ip ospf mtu-ignore` command is not being used inadvertently.\n\nFor DHCP, check for exhaustion and stale lease issues. Use the following commands to monitor and mitigate DHCP exhaustion:\n1. Check utilization: `show ip dhcp pool` to confirm if the pool is fully utilized.\n2. Clear bindings: `clear ip dhcp binding *` to force clients to re-acquire leases.\n3. Identify and clear stale leases: `show ip dhcp binding | include Expiry` to find entries with expiry >7 days, and clear them with `clear ip dhcp binding <specific-ip>`.\n4. Permanent fix options: Reduce lease time, expand the scope, or enable DHCP snooping.\n\nAdditionally, flush the DNS resolver cache on clients: `ipconfig /flushdns` on Windows and `sudo ipcache flush` on Linux.\n\nEnsure physical layer and VLAN trunk configurations are correctly set. Reseat SFP modules and check cable lengths. For native VLAN mismatch, ensure the native VLAN is consistent between interfaces.\n\nThe immediate next steps involve fixing BGP and OSPF issues, monitoring DHCP for exhaustion, and flushing DNS. If issues persist, escalate to a network administrator or follow up with additional diagnostic steps based on the root causes identified.\n\nTo summarize, the solution involves verifying TCP/179 reachability for BGP peers, checking and configuring OSPF, mitigating DHCP exhaustion, and troubleshooting physical layer issues as outlined above.\", \"cited_artifacts\": [\"KB-00001\",", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issues observed after the datacenter fiber maintenance are multifaceted, and they need to be addressed in a systematic manner. First, let's investigate the BGP session issues. Ensure that there is TCP connectivity to the ISP. Run the following commands to check reachability:\n\n1. Ping the ISP's peer address from the router VRF to verify connectivity.\n ```sh\n ping vrf MGMT <peer-ip> source <local-ip>\n ```\n2. Check the BGP state of the peer:\n ```sh\n show bgp neighbors <peer-ip>\n ```\n Look for 'BGP state = Active' (cannot connect) or 'Idle (Admin)' (locally shut). Check the 'Hold time' and 'Keepalive interval'.\n\nNext, validate the AS and peer configuration:\n- Ensure `neighbor <ip> remote-as <AS>` in your configuration matches the peer's local AS.\n- Check for MD5 password mismatches, which can cause BGP notifications like 'BGP notification: hold time expired'. Use:\n ```sh\n debug ip bgp <peer-ip> events\n ```\n Look for NOTIFICATION messages indicating 'cease/hold-timer/open-message-error' subtypes.\n\nFor OSPF adjacency issues, it's possible that there is a mismatch in the MTU between the interfaces, especially if one is set to a larger MTU than the other. Ensure that the MTU settings are consistent between the interfaces.\n\n1. Check the MTU settings:\n ```sh\n show interfaces\n ```\n2. Verify the MTU settings and align them if necessary. For example, use:\n ```sh\n ip ospf mtu-ignore\n ```\n This command disables the MTU check on the OSPF interface.\n\nFor the DHCP pool exhaustion, clear the DHCP bindings to force clients to renew their leases:\n1. Run the following command to clear all DHCP bindings:\n ```sh\n clear ip dhcp binding *\n ```\n However, this should only be done in a maintenance window to avoid disrupting service.\n\nFinally, for any DNS resolution issues, ensure that the client is not using the wrong DNS server and that the forwarder chain is correctly set up. Run the following commands to check the DNS configuration:\n1. Verify the DNS forwarders:\n ```sh\n show ip name-server\n ```\n2. Ensure the forwarder chain is correctly set up", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"First, investigate the BGP session status by running `show ip bgp neighbors <peer-ip>` to check the BGP state. Look for 'BGP state = Active' or 'Idle (Admin)' to determine the issue. Ensure that the `neighbor <ip> remote-as <AS>` configuration matches the peer's local AS. Verify TCP reachability by pinging the peer address from the router VRF using `ping vrf MGMT <peer-ip> source <local-ip>`. Check for ACLs blocking TCP/179 by running `ping vrf MGMT <peer-ip> source <local-ip>`. If TCP/179 is being blocked, the issue lies in the ACLs and needs to be adjusted.\n\nFor the OSPF adjacency issues, run `show ip ospf neighbor` to identify stuck states such as INIT, 2-WAY, and EXSTART/EXCHANGE. Check for MTU mismatch by ensuring both interfaces have the same MTU size and use `ip ospf mtu-ignore` on both sides if necessary. Address area-type mismatch by ensuring both routers agree on stub/nssa flags in `area <id> st`. Check MD5 password mismatch by using `debug ip ospf adj` and looking for 'Invalid authentication' messages.\n\nFor the DHCP scope exhaustion, immediately mitigate by clearing DHCP bindings with `clear ip dhcp binding *`. Confirm which entries are stale by running `show ip dhcp binding | include Expiry`. For clients on APIPA (169.254.x.x), there are recovery actions such as flushing the DNS cache and adjusting the DHCP lease time. If the issue persists, consider expanding the DHCP scope or adding a new secondary pool.\n\nFor DNS resolution failures, isolate DNS vs. network by testing directly to the corp DNS servers. Check for a forwarder chain issue by testing `dig @10.10.1.53 google.com +time=2` if the internal resolution fails. Address zone delegation issues by cross-referencing with the ARP table and ensuring client DNS server assignment is correct. For prevention, consider setting a shorter DHCP lease time or enabling DHCP snooping.\n\nOnce each of these components are addressed, revalidate the BGP, OSPF, and DHCP configurations to ensure all settings are correct and the network is stable. If any of the components fail to resolve, escalate the issue.\", \"cited_artifacts\": [\"KB", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00033", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The network failures observed after the datacenter fiber maintenance can be attributed to a combination of issues: BGP session failures, OSPF adjacency failures, and DHCP scope exhaustion. Let's address each issue systematically.\n\nFor the BGP session failures, ensure that TCP/179 is not blocked by ACLs on either end. Ping the peer address from the router VRF to verify TCP reachability: `ping vrf MGMT <peer-ip> source <local-ip>`. If ping fails, check ACLs for blocking TCP/179.\n\nTo check BGP state, use `show bgp neighbors <peer-ip>`. Look for 'BGP state = Active' (can't connect) or 'Idle (Admin)' (locally shut). Check 'Hold time' and 'Keepalive interval'.\n\nFor validation, confirm the AS number and peer configuration: `neighbor <ip> remote-as <AS>` should match the peer's local AS. MD5 password mismatch causes 'BGP notification: hold time expired' logs. Ensure that MD5 passwords are correctly configured on both sides.\n\nAs for OSPF adjacency failures, inspect `show ip ospf neighbor` to identify stuck states such as INIT, 2-WAY, or EXSTART/EXCHANGE. Common causes include mismatched MTU or area types between peers. Check `show ip ospf` for area flags and ensure that stub/nssa flags are set correctly.\n\nTo address DHCP exhaustion alerts, immediately mitigate by reducing lease time from 8 days to 1 day with the command: `ip dhcp pool CORP_WIFI / lease 1`. If the scope is full, consider expanding the scope or adding a new secondary pool. Disable DHCP snooping to prevent rogue DHCP servers consuming addresses.\n\nFor the DNS issue, confirm that DNS is the problem before escalating network connectivity issues. Use `nslookup <hostname> <dns-server-ip>` to target the corp DNS. If external resolution fails but internal succeeds, check forwarder connectivity: `dig @10.10.1.53 google.com +time=2`.\n\nFinally, if the issue persists, escalate the ticket and involve the network operations team to conduct a comprehensive network scan and perform physical layer checks on the re-patched cables. Ensure that all interfaces and cables are properly connected and powered.\n\nPlease ensure to cross-reference the findings with the relevant KB articles for detailed troubleshooting steps and configuration examples.\n\nNote: The escalation can", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GPU-accelerated ML inference service running out of VRAM is likely due to the service's high memory usage and no existing runbook or precedent. The A10G GPU is a powerful but memory-intensive device, and the error `CUDA_ERROR_OUT_OF_MEMORY` suggests that the service is exceeding the GPU's VRAM limit. Here are some steps to address the issue:\n\n1. **Analyze and Optimize the Service:**\n - **Memory Usage:** Use profiling tools like `nvidia-smi` to monitor the GPU's memory usage. Identify which parts of the service are consuming the most VRAM.\n - **Optimize Algorithm and Model:** Check if the model being used can be optimized. Techniques like quantization, pruning, and knowledge distillation can reduce the model size without significantly impacting accuracy.\n - **Data Preprocessing:** Ensure that data preprocessing steps are optimized to minimize memory usage. For instance, reduce image dimensions, use efficient data types, and apply data augmentation techniques judiciously.\n\n2. **Configuration Adjustments:**\n - **TTL Configuration:** For the A10G GPU, consider setting appropriate TTLs for the image classification data in your Redis or any other data storage. Without TTLs, data can fill up the GPU's VRAM indefinitely.\n - **Redis Optimization:** Refer to KB-00019 for best practices. Ensure that Redis is configured correctly to avoid unexpected key expiry and memory pressure. This can prevent the service from running out of VRAM.\n\n3. **JVM Memory Management:**\n - **Heap Dump Analysis:** For JVM OOM errors, use the methods described in KB-00015. Ensure that heap dumps are generated and analyzed to identify any misconfigurations or memory leaks.\n - **Code Review:** Examine the service's codebase for any significant memory leaks. Look for large byte[] or char[] arrays that are not properly managed.\n\n4. **Database Connection Pool Management:**\n - **Monitor and Adjust:** Use Prometheus/Grafana metrics to monitor the connection pool health. Identify and resolve any issues that prevent connections from being released.\n - **Increase Pool Size:** If necessary, increase the connection pool size in the application configuration to handle more concurrent connections.\n\n5. **Scaling and Resource Allocation:**\n - **Resource Allocation:** If the service continues to run out of VRAM, consider scaling up the GPU", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It seems the GPU-accelerated ML inference service is running out of VRAM due to the limitations of the A10G GPU, which is a new service type with no precedents. The error code (CUDA_ERROR_OUT_OF_MEMORY) suggests there is not enough GPU VRAM allocated for the service to perform the ML inference tasks. Given that this is a new service type, there is no existing runbook or past ticket that directly addresses this issue. However, the recommended steps to manage GPU memory in a new service are as follows:\n\n1. Review the maximum VRAM allocation for the A10G GPU: Check the GPU's specifications to confirm the maximum VRAM allocation. This can typically be found in the hardware documentation.\n\n2. Optimize the model: Ensure the model being used in the inference service is optimized for the GPU. This includes using quantization, pruning, and other model optimization techniques that reduce the model size and improve performance.\n\n3. Adjust model configuration: If the model is too large for the GPU, you may need to adjust the model configuration. This could include downgrading the model or using a smaller version of the model that fits within the available GPU VRAM.\n\n4. Use tensor cores efficiently: The A10G GPU has tensor cores that can be utilized more efficiently for certain types of operations, such as matrix multiplication. Ensure that these cores are being used effectively in your model.\n\n5. Manage memory footprint: For the new service, consider implementing features to manage memory usage. This might include caching results of computations that can be reused, using a cache eviction policy like `allkeys-lru` (as per KB-00019), or setting appropriate TTLs for session keys (as per KB-00015).\n\n6. Monitor and adjust: Continuously monitor the GPU usage and memory footprint of the service. Make adjustments as necessary to ensure that the service is running smoothly within the available VRAM limits.\n\nNo specific artifacts or solutions from the retrieved context are directly applicable to managing GPU memory for this new service, so no artifacts need to be cited. The resolution is based on general best practices for managing GPU memory in machine learning inference services.\n\n\"cited_artifacts\": [], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"It seems that the GPU-accelerated ML inference service on the A10G GPU is encountering a CUDA out-of-memory error (CUDA_ERROR_OUT_OF_MEMORY). This is a new service and there is no existing runbook or past ticket for GPU memory management. For GPU memory issues, ensure the GPU driver is up-to-date and compatible with the CUDA version being used. Additionally, verify the GPU usage in the application through profiling tools to identify whether there are any unused or non-optimized GPU operations. Check the CUDA memory usage by the application with tools like `nvidia-smi` to monitor GPU memory usage. Ensure that the GPU memory allocation and management is optimized, possibly by reducing the batch size or number of images processed per batch if possible. It is also important to check the network traffic since large datasets can put a strain on GPU memory. If the service involves large model inference, consider reducing the model complexity or increasing the GPU memory. Monitor the service's performance and logs to detect any other patterns or potential causes for the out-of-memory errors. Adjust the service configuration accordingly to handle the GPU memory requirements. If the service is using a large number of images or data, consider expanding the GPU memory by using a more powerful GPU or increasing the GPU memory in the existing configuration. If the service is using persistent memory like NVMe, ensure that it is properly configured to handle the GPU memory allocation. If the problem persists, consult the NVIDIA forums or support for additional assistance.\", \"cited_artifacts\": [], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.4, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GPU-accelerated ML inference service running out of VRAM is likely due to insufficient GPU memory allocation or high memory pressure. Given that this is a new service type without a runbook, the following steps can be taken to diagnose and mitigate the issue:\n\n1. **Memory Pressure Check:**\n - For the inference service, it's crucial to monitor the GPU memory usage. Use tools like nvidia-smi to check the GPU memory utilization. Ensure that the service is configured with an appropriate GPU memory allocation based on the model being run and its parameters.\n\n2. **Increase GPU Memory Allocation:**\n - If the GPU memory usage is consistently high, increase the GPU memory allocation for the service. This can be done by adjusting the batch size, downscaling the model complexity, or using techniques like quantization and pruning to reduce the memory footprint without compromising the model's performance.\n\n3. **Check for Memory Leaks:**\n - Since the GPU memory issues are GPU-specific, focus on ensuring there are no memory leaks in the CUDA/C++ code. Use tools like nvidia-smi to track GPU memory usage over time and look for spikes that could indicate a leak.\n\n4. **Optimize Memory Usage:**\n - Optimize the inference service to minimize unnecessary memory usage. This includes releasing unused memory and ensuring that all allocated buffers are properly managed and deallocated.\n\n5. **Consider Asynchronous Processing:**\n - To handle high GPU memory demands, consider implementing asynchronous processing for inference tasks. This can help manage memory usage more efficiently by offloading some tasks to a separate thread or process.\n\n6. **Monitor and Tune:**\n - Continuously monitor the inference service's performance and memory usage. Use log analysis tools to identify patterns and outliers. Adjust settings like the number of concurrent inferences and memory limits based on the observed performance.\n\n7. **Documentation and Runbook:**\n - Document the process for managing GPU memory in the future. This should include steps for increasing memory allocation, monitoring GPU usage, and troubleshooting common issues. Create a runbook that includes these steps to help manage GPU memory for new and existing services.\n\nIn case of persistent issues, escalate to the GPU administrator or hardware support team for hardware-specific optimizations, such as increasing the GPU memory capacity or investigating the hardware for potential failures.\n\nBy following these steps, you can diagnose and resolve the GPU memory issues for the new inference service. If the problem persists,", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GPU-accelerated ML inference service running out of VRAM is an unexpected scenario since there is no precedent. The issue seems to be related to memory usage and potential misconfigurations within the service or its environment. Ensure that the GPU inference service is using an appropriate amount of VRAM. If the service is not configured to limit its memory usage, it could be consuming more VRAM than expected.\n\n1. Review the service's configuration for any settings that might be causing it to allocate more VRAM than intended. Look into any settings related to GPU memory usage or allocation. \n\n2. Check for any misconfigurations in the GPU driver or the operating system's kernel parameters that might be affecting the memory management.\n\n3. For the GPU itself, ensure it is correctly configured to handle the workload. Incorrect settings can lead to memory leaks or incorrect memory management, causing out of memory errors.\n\n4. Consider using tools like nvidia-smi to monitor GPU memory usage and ensure it is within safe limits. This will help you identify if the issue is with the service itself or the GPU configuration.\n\n5. If the service relies on third-party libraries or frameworks for GPU acceleration, check their documentation and configuration for any memory management settings. Ensure they are optimized for your specific use case.\n\n6. Evaluate if there are any caching mechanisms in place that might be causing excessive memory usage. This could be in the form of session management or other types of caching that are not configured properly.\n\n7. If the service is running on a cluster, check the memory usage of other nodes to ensure they are not contributing to the memory pressure.\n\n8. Consider scaling the service horizontally by adding more GPU nodes if the issue is consistently affecting the same node. This should help distribute the workload and reduce the likelihood of hitting the memory limit.\n\n9. Regularly monitor the service and its environment for any changes that might affect memory usage. Implement automated checks and alerts to quickly identify and address issues before they become critical.\n\n10. Document the findings and any changes made to address the issue. This will help in creating a runbook for future reference and ensure that the issue does not recur.\n\ncited_artifacts\": [\"KB-00015\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU-accelerated ML inference service running out of VRAM and hitting CUDA out of memory errors, ensure that the GPU memory allocation is optimized and the inference workload is well-managed. Given the lack of precedent, it's important to start with a basic review and tuning process. First, confirm that the inference workload is configured to utilize GPU memory efficiently. Check the allocation size and buffer size settings in your inference service’s configuration to ensure they are not set too high. Additionally, validate that the inference workload is not holding onto unnecessary data or objects, especially large ones that could contribute to out-of-memory errors. \n\nFor GPU memory management, the relevant context from the KB-00015 article on JVM Out of Memory Error — Heap Dump Analysis is not directly applicable since the error is related to VRAM (Virtual Memory) on the GPU rather than the heap memory on the CPU. However, the heap dump analysis techniques and tools mentioned could still be useful in diagnosing any issues related to memory bloat or inefficient use of GPU resources.\n\nCommon causes of out-of-VRAM errors on GPUs include:\n1. Unbounded cache usage: Ensure that your caching mechanisms, such as using a cache or memcached, are configured correctly. For example, in a Java environment, consider using a cache like Guava or Caffeine with appropriate configuration parameters like `maximumSize`.\n2. Holding onto unnecessary objects: Check for any objects that are not properly released or are holding onto large data structures that could cause out-of-VRAM errors.\n3. Long-running inference tasks: Ensure that inference tasks are not running for excessively long periods, which can exhaust GPU memory. This might involve optimizing the inference algorithm or the input data.\n\nImmediate recovery steps:\n1. Scale up the GPU memory allocation in the inference service’s configuration if necessary.\n2. Increase the allocated GPU memory if possible, but be cautious to avoid over-allocation which could cause system instability.\n\nMonitor the GPU memory usage and inference workload to ensure that the service is running efficiently. Consider using profiling tools to monitor memory usage and cache behavior during inference tasks.\n\nIf the problem persists after applying these optimizations, escalate to a higher level support or technical team for further investigation. Reach out to the GPU and ML experts within your organization for potential architectural changes or different hardware configurations that might better suit the workload requirements.\",\n\"cited_artifacts\": [], \"confidence\": 0.85, \"escalate\":", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The GPU-accelerated ML inference service running out of VRAM can be addressed by increasing the GPU memory or optimizing the service to use less VRAM. To start, ensure that the GPU inference service is correctly configured to utilize the A10G GPU. Check the CUDA configuration to ensure that the service is not set to use more VRAM than the GPU has available. If scaling the GPU memory is not an option, consider optimizing the service by reducing the batch size or using quantization techniques that reduce the amount of data processed on the GPU. Additionally, check if there are any memory leaks within the service, which could be indicated by an increasing used_memory_rss value in Redis, as described in KB-00019. For database connection pool exhaustion, ensure that the connection pool settings are appropriate and monitor for long-running queries or unclosed ResultSets, as detailed in KB-00016. Implement auto heap dump on OOM with JVM flags as described in KB-00015 to analyze heap dumps for root cause. Address any misconfigurations such as unbounded caches (e.g., Guava Cache or Caffeine without `maximumSize` configured) and session object bloat. To handle out-of-memory errors in the inference service, also consider increasing the available GPU memory or using tools like cuDNN to optimize tensor operations.\", \"cited_artifacts\": [\"KB-00015\", \"KB-00001\", \"KB-00019\", \"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00044", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"For the GPU-accelerated ML inference service running out of VRAM on the A10G GPU, the issue appears to stem from insufficient memory management due to the use of a GPU that is not optimized for high memory usage, especially in the context of GPU memory fragmentation and resource allocation. Ensure that the GPU driver and CUDA toolkit are up-to-date, and configure the memory usage settings correctly. Check the memory usage through the GPU's control panel to see if there are any settings that can be adjusted to allow more VRAM to be used. If the issue persists, consider using a different GPU model that is more suitable for high VRAM usage in machine learning tasks. Additionally, ensure that the inference service itself is configured to manage memory properly, such as by using appropriate batch sizes and model configurations to reduce the amount of VRAM required per inference run. For instance, increasing the batch size can reduce the number of times the model needs to be loaded, thereby reducing VRAM usage. If the service is memory-intensive, it might be beneficial to offload some of the processing to the CPU to reduce GPU memory pressure. Review the TensorFlow or PyTorch documentation for best practices on managing GPU memory in ML inference services.\", \"cited_artifacts\": [], \"confidence\": 0.7, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The error 'Connection is not available, request timed out after 30000ms' in the inventory-service is indicative of connection pool exhaustion in the application. Given that the database server is healthy, the issue likely lies within the application's connection handling, specifically the connection pool configuration. First, confirm if the connection pool is saturated by checking Prometheus/Grafana metrics for `hikaricp_connections_active` and `hikaricp_connections_max`. If the pool is indeed saturated, this suggests that the pool is being exhausted and not being managed efficiently. The current number of connections should ideally be less than or equal to `hikaricp_connections_max` to prevent timeouts. To resolve this, you should increase the `hikaricp_connections_max` setting to allow for more connections in the pool. Additionally, monitor the number of active connections (`hikaricp_connections_active`) and `hikaricp_connections_pending` to ensure that the pool is not holding too many idle connections. If the issue persists, review the thread dumps to identify any threads that are consuming excessive resources or remaining in a long-running state, which could indicate a resource leak. Ensure that the application is configured correctly to handle the number of connections it is managing. Clearing the connection pool by restarting the application service can also help release idle connections and free up the pool. If the problem continues, review the application logs for any exceptions or errors that might be causing connections to be left open longer than they should. Finally, ensure that the application's thread pools are properly configured and that there are enough threads available for new work requests.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.4, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The error messages indicate that the inventory-service is experiencing connection pool exhaustion, causing it to time out after 30000ms. First, let's confirm if the connection pool is saturated by checking the Prometheus/Grafana metric 'hikaricp_connections_active'. If it equals 'hikaricp_connections_max', the pool is indeed saturated. You can also check for high 'idle' connections in the database using the `SHOW PROCESSLIST;` command (for MySQL) or `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` (for PostgreSQL). This will help identify idle connections that are not being used but are still holding onto database resources.\n\nNext, find the connection holders by examining the application's thread dumps using `kill -3 <pid>` in the container. Look for threads in a `RUNNABLE` or `WAITING` state with JDBC/database stack frames. Long-running queries or unclosed ResultSets are common culprits for this issue.\n\nOnce we've identified the problematic connections, ensure that the application is properly closing these connections. This involves checking for any unclosed connections or result sets that could be holding onto database resources.\n\nAdditionally, review the application code for potential memory leaks, especially in the connection handling and pooling logic. Tools like JProfiler can help identify and debug memory usage patterns within the application.\n\nTo mitigate the issue, consider increasing the connection pool size, especially during peak hours, or fine-tune the connection pool settings to better match application demands. Ensure that there are no unnecessary threads or processes holding onto database resources.\n\nTo prevent future occurrences, implement a robust connection pooling mechanism that includes connection validation and idle connection eviction. This can help reclaim resources and prevent the pool from becoming saturated.\n\nIt is also recommended to regularly monitor the database connection metrics and application logs to proactively address any issues before they affect service availability.\n\nAfter implementing these changes, monitor the application logs and database statistics to ensure that the connection pool exhaustion is resolved. Restart the application service if needed to flush the pool.\n\nIf the issue persists, investigate further by looking at the application crash logs and events using `kubectl logs <pod> --previous` and `kubectl describe pod <pod>`, and check for any root causes such as configuration errors or memory issues.\n\nFor further assistance, consider consulting the application's codebase and documentation to ensure that the connection handling and pooling logic are optimized for the expected workload.\", \"cited", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The connection pool timeout errors in the inventory-service can be traced back to the connection pool exhaustion. First, ensure the maximum pool size (`hikaricp_connections_max`) is adequate to handle the load. Examine the active connections (`hikaricp_connections_active`) to identify if connections are being held by long-running queries. Check the database side for idle connections (`SELECT count(*), state FROM pg_stat_activity GROUP BY state;` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL), which might indicate a connection pool issue.\n\nTo diagnose the issue, retrieve the thread dump from the application to find threads in `RUNNABLE` or `WAITING` state with JDBC/database stack frames, which are indicative of long-running queries or unclosed ResultSets. Review the slow queries using the `EXPLAIN` command and consider adding indexes if necessary.\n\nFor the database side, ensure there are no unclosed connections by checking the state of `pg_stat_activity` or `SHOW PROCESSLIST` and close any idle connections. Monitor the connection pool metrics using Prometheus/Grafana and adjust the connection pool size as needed.\n\nAdditionally, verify the upstream service's health. Use the API gateway access logs to identify slow upstream services by filtering for 504 errors. Test the upstream directly bypassing the API gateway to confirm the issue is not with the upstream service. If the issue persists, increase the `proxy_read_timeout` in Kong to 60000 seconds to accommodate the increased latency.\n\nTo resolve the CrashLoopBackOff issue in Kubernetes, get the last crash logs with `kubectl logs <pod> --previous` and check the events with `kubectl describe pod <pod>` to identify the root cause. Common issues include memory limits being too low, missing secrets, misconfiguration, or a liveness probe that is too aggressive.\n\nRestart the application service to flush the connection pool and ensure the application is healthy. If Kubernetes restarts the pod, check the pod events and logs to troubleshoot the root cause of the CrashLoopBackOff.\n\nEnsure all configurations, timeouts, and memory limits are appropriately set to avoid connection pool exhaustion and CrashLoopBackOff issues.\n\nEnsure to monitor the system and make adjustments as necessary to maintain optimal performance and reliability.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\", \"KB-00001\", \"KB-00", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The 'Connection is not available, request timed out after 30000ms' errors in inventory-service during peak hours (18:00-19:00) could be due to connection pool exhaustion on the database side. Check the Prometheus/Grafana metric `hikaricp_connections_active` to confirm the pool is saturated. Additionally, check `hikaricp_connections_pending` to see if there are any connections waiting in the pool. Look for long-running queries or unclosed ResultSets in the application thread dumps, which can cause the pool to exhaust. On the database side, review the `pg_stat_activity` or `SHOW PROCESSLIST` query to see if there are idle connections not being used and queries running for an extended period. To recover, restart the application service to flush the connection pool. Ensure that the `hikaricp_connections_max` setting is adequate for the expected load, and consider increasing the `hikaricp_ping_interval` if necessary to reduce ping overhead. Investigate if the cause is external to the service, such as network latency affecting the connection pool. If the issue persists, consider adding a new connection pool to distribute load or increasing the maximum number of connections to prevent saturation. Adjust the connection timeout settings in the database configuration if applicable. Finally, monitor the connection pool usage and make necessary adjustments based on the observed behavior.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The connection pool timeout errors in the inventory-service can likely be attributed to a lack of connections available due to exhaustion, as indicated by the connection pool metrics being saturated. First, confirm that the connection pool exhaustion is occurring by checking the Prometheus/Grafana metric `hikaricp_connections_active` against `hikaricp_connections_max`. This indicates that the pool is at its maximum capacity and cannot accommodate new requests. In cases of connection pool exhaustion, you should monitor the `hikaricp_connections_pending` metric to identify which connections are stuck and not being used.\n\nTo address this issue, you should immediately attempt to flush the connection pool by restarting the application service. This will clear any stale connections and allow the pool to release them back into the available pool. Monitor the connection pool metrics after restarting the service to ensure they are normalized, and check the application logs for any clues about the cause of the exhaustion, such as frequent long-running queries or unclosed ResultSets.\n\nAlso, it's important to look at the application threads for any long-running tasks or queries. Using a thread dump command like `kill -3 <pid>` can help identify threads in a RUNNABLE or WAITING state, which might indicate a problem in long-running queries or unclosed database connections. Check for any suspicious database activity or queries in the thread dump that are causing the application to hang.\n\nAdditionally, ensure that the application is not holding onto too many database connections. Review the database connections to see if there are any idle connections that are not being properly released. In PostgreSQL, you can use `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` to see the counts of idle and active connections.\n\nFor the API Gateway, which is causing the 504 Gateway Timeout errors, it's important to investigate the upstream service directly, as the gateway's logs (access.log) might not provide the full picture. Use `curl -w '%{time_total}' -o /dev/null http://<upstream-host>:<port>/endpoint` to test the upstream service directly. If the timeout occurs on the upstream service, look for regression in database queries, dependency slowness, or thread pool exhaustion at the upstream level.\n\nLastly, check the Kubernetes pod events for any errors or warnings that may provide additional insights into why the pod is crashing. Look for messages like 'OOMKilled', 'Error' (in the container logs),", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The connection pool timeout errors in inventory-service are likely due to database connection exhaustion. Since the database server is healthy, the issue is likely within the application's connection pool. To resolve this, follow these steps:\n\n1. **Monitor the Connection Pool**: Use Prometheus and Grafana to monitor the `hikaricp_connections_active` and `hikaricp_connections_pending` metrics. If these metrics are both high, it indicates the pool is saturated.\n \n2. **Identify Long-Running Queries**: Examine thread dumps for any threads in the `RUNNABLE` or `WAITING` state with JDBC/database stack frames. This can help identify long-running queries that are holding onto connections. Additionally, check `SHOW PROCESSLIST;` in PostgreSQL or `psql -c \"SELECT count(*), state FROM pg_stat_activity GROUP BY state;\"` in PostgreSQL to find idle connections that are not being freed.\n\n3. **Restart the Application Service**: To flush the connection pool, restart the inventory-service application service. Monitor the `hikaricp_connections_active` metric post-restart to ensure the pool is being cleared.\n\n4. **Investigate Slow Queries**: Use the explain plan to identify slow queries. Optimize queries by adding indexes, rewriting them, or improving their performance.\n\n5. **Adjust JDBC Settings**: Increase the `maxPoolSize` of the HikariCP configuration if the pool is often reaching its maximum capacity. Ensure the `connectionTimeout` and `idleTimeout` settings are set appropriately.\n\n6. **Check Database Configuration**: Verify the database configuration such as query timeout, connection settings, and maintenance activities.\n\nBy following these steps, you should be able to resolve the connection pool timeout errors and ensure the service can handle load effectively. If the issue persists, consider investigating the upstream service causing the 504 Gateway Timeout errors as mentioned in KB-00018.\n\nCited Artifacts: [KB-00016, KB-00018]\n\nConfidence: 0.85\n\nEscalate: false\", \"cited_artifacts\": [\"KB-00016\", \"KB-00018\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The connection pool timeout errors in inventory-service could be related to the database connection pool exhaustion rather than an API gateway timeout issue. First, confirm that the connection pool is indeed exhausted by checking the Prometheus/Grafana metrics for `hikaricp_connections_active` and `hikaricp_connections_max`. If `hikaricp_connections_active` equals `hikaricp_connections_max`, it indicates the pool is saturated. Next, find the connection holders by examining the application thread dump with `kill -3 <pid>` and searching for threads in `RUNNABLE` or `WAITING` states with JDBC/database stack frames. This could indicate long-running queries or unclosed ResultSets that are holding onto connections. On the database side, use commands like `SELECT count(*), state FROM pg_stat_activity GROUP BY state;` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL to check the status of connections. High 'idle' connection counts indicate the pool holding connections not in use, while high 'active' counts point to queries running long. To immediately recover, restart the application service to flush the connection pool. Monitor that connections are being released properly. If the issue persists, check the Kubernetes logs for the inventory-service pod to see if there are any specific reasons for the CrashLoopBackOff. Look for common root causes like OOMKilled (memory limit hit), missing secrets, config errors, or overly aggressive liveness probes. If the root cause is determined to be a misconfiguration or an external dependency slow, adjust the respective configurations accordingly.\", \"cited_artifacts\": [\"KB-00016\", \"KB-00017\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00017", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The 'Connection is not available, request timed out after 30000ms' errors in inventory-service during peak hour could be due to connection pool exhaustion. Check the application logs for a high 'idle' connection count in the Prometheus/Grafana metric `hikaricp_connections_idle`. This indicates the connection pool may be holding idle connections that are not being released and are waiting for a new request, leading to timeout errors. To resolve this, the application service should be restarted to flush the connection pool. Monitor that the `hikaricp_connections_idle` metric decreases and the `hikaricp_connections_pending` metric returns to zero. Additionally, review the database side for any queries running long ('active' state in `pg_stat_activity` for PostgreSQL or `SHOW PROCESSLIST;` for MySQL) that are holding onto connections. Address any long-running queries and optimize the database query plan if necessary. Ensure the application and database configurations are correctly aligned to prevent connection pool exhaustion. Restart the application service to flush the connection pool and monitor the issue.\", \"cited_artifacts\": [\"KB-00016\"], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 1.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the site-to-site VPN tunnel to the Singapore branch office dropping every 20-40 minutes can likely be attributed to the Phase 1 (IKEv2) parameter mismatch, causing the IKE protocol to fail and the tunnel to flap. To troubleshoot, follow these steps:\n\n1. Capture the NOTIFY payload from the Cisco IPSec IKEv2 debug logs. Ensure both ends are using identical IKE proposals, including encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400 seconds). Even the slightest mismatch in these parameters can cause the tunnel to immediately drop.\n\n2. Check the Dead Peer Detection (DPD) configuration. If the WAN latency spikes above 20 seconds, the DPD timers might be too aggressive, causing the tunnel to drop. Adjust the DPD timers on both ends by setting them to `dpd 60 retry 5` to better handle high latency.\n\n3. Ensure both ends support NAT-T (Network Address Translation Traversal) by adding the following commands on both routers:\n ```\n ip ipsec ike nat-traversal\n ip ipsec ike peer nat-traversal\n ```\n Verify that the NAT-T flag is present in the IKE SA (Security Association) show command on both sides.\n\n4. Examine the `debug ip bgp <peer-ip>` output for BGP state transitions. Look for the 'BGP state = Active' or 'Idle (Admin)' status. Ensure that the `neighbor <ip> remote-as <AS>` command on both ends matches the remote AS number. Any configuration mismatch can cause BGP sessions to fail.\n\n5. Check for MTU mismatch, which can prevent the BGP adjacency from forming. Ensure that the MTU values on both interfaces are equal.\n\n6. Review the logs to identify any 'NOTIFICATION' messages that indicate subtypes like 'CEASE' or 'HOPLIMIT'. These logs can provide information on the cause of the session drop.\n\n7. For TCP connectivity issues, use `ping vrf MGMT <peer-ip> source <local-ip>` to verify that TCP/179 is not blocked by ACLs on the local router.\n\nBy aligning these parameters and ensuring proper configuration, the tunnel should stabilize and no longer flap. Additionally, monitor the API Gateway's 504", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the site-to-site VPN tunnel to your Singapore branch office is likely due to a combination of causes related to both the BGP peer session and the IKEv2 phase. Let's troubleshoot and resolve these two aspects separately.\n\n**BGP Troubleshooting:**\n1. **Verify TCP reachability:** Ensure that TCP/179 is allowed through the appropriate ACLs on both the branch office and your main site routers. Verify reachability using the command `ping vrf MGMT <peer-ip> source <local-ip>`.\n2. **Check BGP state:** Use the command `show bgp neighbors <peer-ip>` to identify the BGP state of the peer. It should be in 'Established' state if the connection is up. Look for any 'BGP Notification' messages indicating a hold-time expiration, which may point to hold-timer mismatch or configuration mismatch.\n3. **Validate AS and peer configuration:** Confirm that `neighbor <ip> remote-as <AS>` matches the peer's local AS. Check for MD5 mismatch between the encryption parameters of both ends.\n4. **Review logs:** Enable debugging for IP BGP events using `debug ip bgp <peer-ip> events` on both sides. Look for 'NOTIFICATION' messages with 'BGP notification: hold time expired' or 'NOTIFICATION' messages indicating other types of errors.\n\n**IKEv2 Troubleshooting:**\n1. **Capture NOTIFY payload:** Use `debug crypto ikev2` on both ends to capture the NOTIFY payload. Look for `NO_PROPOSAL_CHOSEN` in the error logs indicating mismatch in IKE proposals, such as algorithm (e.g., AES-256, SHA-256), PRF (e.g., SHA-256), Diffie-Hellman group (e.g., 14 or 19), and lifetime (e.g., 86400s).\n2. **Algorithm mismatch:** Ensure that both peers use identical IKE proposal details.\n3. **Dead Peer Detection (DPD):** Check `dpd` timers on both ends. Default DPD timers should be `dpd 30 retry 5`. If WAN latency spikes over 20 seconds, increase the timers to `dpd 60 retry 5`.\n4. **NAT-T configuration:** If one end is behind NAT, NAT-T must", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The site-to-site VPN to your Singapore branch office is dropping due to a combination of factors. First, investigate the VPN tunnel flapping by capturing the NOTIFY payload using `debug crypto ikev2` on both the client and the remote gateway. This will help identify the cause of the phase 1 or phase 2 parameter mismatch. Look for 'NO_PROPOSAL_CHOSEN' in the logs, which indicates an algorithm mismatch. Ensure both ends use identical IKE proposals: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Aligning these will prevent the VPN tunnel from dropping immediately.\n\nFor the BGP peer session drops, first ensure TCP connectivity by pinging the peer address from the router VRF with the `ping vrf MGMT <peer-ip> source <local-ip>` command. If pinging fails, check for ACLs blocking TCP/179 on both ends.\n\nNext, verify the BGP state. Use `show bgp neighbors <peer-ip>` to look for 'BGP state = Active' or 'Idle (Admin)', indicating a dropped connection. Check 'Hold time' and 'Keepalive interval' to ensure they are aligned. The AS and peer configurations must match between both ends. Validate that the MD5 password is correct, as a mismatch will cause a 'BGP notification: hold time expired' log.\n\nReview the BGP logs for any 'BGP notification' messages indicating a 'NOTIFICATION' subtype. After identifying the cause, apply the necessary configuration changes. Ensure that DPD timers are properly configured: use `neighbor <ip> timers <keepalive> <hold>` to adjust the timers or clear the ACL blocking TCP/179.\n\nFinally, address any potential issues with the BGP AS number or MD5 password mismatch by correcting them in the BGP configuration. Once resolved, the BGP session should stabilize and prevent the connection drops.\n\nAdditionally, since the WAN latency to the Singapore branch averages 180ms, the IKE aggressive DPD timers should be set to 60 seconds with a retry of 5 seconds, which aligns with the typical latency window. Set this with the command `neighbor <ip> timers 60 5`.\n\nLastly, ensure that the MTU of both interfaces is the same and align with the full MT", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Site-to-Site VPN to the Singapore branch office dropping and reestablishing every 20-40 minutes can likely be traced to several potential causes. Let's focus on common ones. First, ensure the MTU between both sides of the router matches. Use `show ip ospf neighbor` to identify stuck states, and check if they are due to different subnet masks or different MTUs. If MTU differences are found, resolve them by either aligning the physical MTU or setting `ip ospf mtu-ignore` on both interfaces. Secondly, check the BGP state between the routers. Run `show bgp neighbors 198.51.100.22` to see if there are any BGP state issues. Any 'BGP state = Active' might indicate a connection issue. Ensure the 'Hold time' and 'Keepalive interval' match between both routers. Also, validate that the `neighbor <ip> remote-as <AS>` on both routers match the peer's local AS. MD5 password mismatches are also known to cause 'BGP notification: hold time expired' logs. Check logs for any such messages. For NAT-T, if one end is behind NAT, ensure NAT-T is enabled on both sides. Check `show crypto ikev2 sa` for NAT-T flags. For IPSec IKEv2, verify that both ends use identical IKE proposal: encryption (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Even one mismatch causes immediate teardown. Lastly, check DPD (Dead Peer Detection) timers. If the WAN latency spikes above 20 seconds, increase the DPD timers to `dpd 60 retry 5`. If there are no NAT-T and IKE mismatches, and DPD settings are correct, the issue might be due to high latency causing the tunnel to drop. In such cases, consider checking the WAN link conditions and potentially improving the network infrastructure between the headquarters and Singapore. Ensure that the site-to-site tunnel is not congested or experiencing high packet loss. Analyze the access logs for the API gateway to check for timeout issues, but since this is not relevant to the VPN issue, address the identified BGP and IPsec issues first. Once these are fixed, reestablish the connection", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Site-to-site VPN tunnel dropping and reestablishing every 20-40 minutes is likely due to a misconfigured BGP hold timer mismatch between the branch office and the main office. Check the BGP hold timers on both routers and ensure they match. You can verify this by running `show ip bgp neighbors` on both ends and checking the 'Hold time' and 'Keepalive interval.' For example, you might see a message like 'BGP neighbor is up and running, hold time is 180s,' indicating the hold timer is set correctly. Additionally, ensure that the authentication settings (MD5 keys) match on both routers to prevent silent BGP sessions drops. You can check the MD5 keys with `show ip bgp neighbors <peer-ip>` and look for 'bgp peer remote-as' and 'BGP notification.' If there are any mismatches, correct them by setting the MD5 password on both routers using `neighbor <ip> password <new-md5-key>` and then clearing the BGP adjacency with `clear ip bgp <peer-ip> neighbors`. Also, consider adjusting the BGP timers if necessary, but be cautious as overly aggressive timers can lead to frequent drops. For example, if the branch office has higher latency, you might need to increase the hold timers on the branch router to accommodate the longer responses, like `neighbor <ip> timers <keepalive> <hold>` to `neighbor <ip> timers 60 180` to increase the interval between BGP keepalives and hold timers, respectively. If the problem persists, consider checking for MTU mismatches on the interfaces connected to the branch office, which can be done by running `show ip ospf neighbor` to ensure both routers have matching MTU values and setting `ip ospf mtu-ignore` on the interfaces if needed. Lastly, ensure that there are no ACLs blocking TCP/179 between the routers, as this can prevent BGP sessions from coming up, as per KB-00002. Once BGP settings are corrected, the VPN tunnel should stabilize and experience fewer interruptions.\", \"cited_artifacts\": [], \"confidence\": 0.85, \"escalate\": false}}\n```", "parsed": true, "r_parsimony": 0.4, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the Site-to-site VPN tunnel dropping and reestablishing on its own every 20-40 minutes to the Singapore branch office could be due to a mismatch in the Phase 1 and Phase 2 parameters of the IKEv2 protocol. Specifically, the negotiation of encryption algorithms, PRF, and DH group versions must be identical on both ends to avoid 'NO_PROPOSAL_CHOSEN' notifications. Additionally, ensure that the Dead Peer Detection (DPD) timers are configured correctly. Check both sides for the following parameters and align them:\n\nFor IKEv2 Phase 1:\n- Encryption algorithms: Ensure both sides use AES-256 for both encryption and PRF (SHA-256).\n- DH group: Ensure both sides use the same DH group (e.g., 14 or 19).\n- Lifetime: Both sides should have the same IKE proposal lifetime (86400s).\n\nFor IKEv2 Phase 2:\n- Encryption algorithms: Ensure both sides use AES-256 for both encryption and PRF (SHA-256).\n- Lifetime: Ensure the proposal lifetime for Phase 2 matches the Phase 1.\n\nFor DPD:\n- Aggressive DPD timer: Use `dpd 30 retry 5`. Increase the timers if WAN latency spikes more than 20 seconds.\n\nAfter configuring these parameters, verify the tunnel health by checking the tunnel status and neighbor statistics. Use `debug crypto ikev2` to capture and analyze IKEv2 messages for any errors, and ensure the tunnel reestablishes without dropping again. If the issue persists, check the NAT-T configuration on both sides to confirm it is enabled (UDP 4500). If NAT-T is not enabled on one side, the tunnel may not be able to establish a phase 1 connection.\n\nRegarding the 180ms WAN latency, the high latency could cause packets to be lost, which can lead to phase 2 retransmissions. Ensure that the retransmission timer on the Phase 2 is set appropriately to handle the higher latency. If the WAN latency is high, consider increasing the retransmission timer to handle the network conditions. Adjust the retransmission timers using the following commands on Cisco IOS:\n- For Phase 2 retransmit timer: `crypto isakmp policy 1 retransmit-initial 3 retransmit-period ", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The issue with the site-to-site VPN tunnel dropping every 20-40 minutes and reestablishing on its own appears to be related to incorrect IKE proposal parameters in the IPSec phase 1, causing the tunnel to flap. Here's how to address this:\n\n1. Verify the IKE proposal parameters on both ends. The most common reason for flap is mismatch in the encryption algorithm (AES-256), PRF (SHA-256), DH group (14 or 19), and lifetime (86400s). Use the debug command to capture the NOTIFY payload and check for 'NO_PROPOSAL_CHOSEN' messages to identify the mismatch. Ensure both ends use identical IKE proposals.\n\n2. For NAT-T if one end is behind NAT, enable NAT-T on both ends by setting `crypto ikev2 nat-traversal` and `crypto ikev2 ipsec-proposal xxx` in your configuration.\n\n3. To correct the IKE proposal mismatch, modify the proposal and restart the IKE process:\n On the tunnel interface:\n ```\n crypto ikev2 ipsec-proposal P0\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n\n crypto ikev2 ipsec-proposal P1\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n\n crypto ikev2 ipsec-proposal P2\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n\n crypto ikev2 ipsec-proposal P3\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n\n crypto ikev2 ipsec-proposal P4\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n\n crypto ikev2 ipsec-proposal P5\n encryption AES-256\n integrity SHA-256\n group 14\n lifetime 86400\n ```\n\n Then restart the IKE process", "parsed": false, "r_parsimony": 0.0, "step": 25}
|
|||
|
|
{"ticket_id": "TRAIN-00006", "completion": "```json\n{\"tool_name\": \"submit_resolution\", \"arguments\": {\"resolution\": \"The site-to-site VPN tunnel between the headquarters and the Singapore branch keeps dropping due to issues with the Phase 1 (IKE) configuration, specifically with the IKE proposal mismatch. Both ends should be configured to use the same encryption (AES-256), PRF (SHA-256), DH group (19), and lifetime (86400s) in the IKE proposal. Misconfigured MD5 passwords can also cause the tunnel to drop. Additionally, verify that the MTU settings on both ends of the tunnel are consistent, as mismatched MTUs can lead to EXSTART/EXCHANGE stuck states in OSPF, although this is less likely in a site-to-site VPN context.\n\nFor the ping test, it should be performed from the router VRF, not directly from the user's workstation. Ensure the subnet mask and hello/dead intervals match on both ends. If the tunnel is intermittently dropping, it's also important to check the DPD timers, as aggressive DPD may cause the tunnel to drop under high latency. Ensure that the dead-peer-detection timers are set correctly on both sides, or consider increasing them if WAN latency spikes. Verify that NAT-T is enabled if one end is behind NAT, and ensure it's present on both sides with UDP 4500.\n\nTo ensure TCP/179 reachability, run `ping vrf MGMT <peer-ip> source <local-ip>` from the router to confirm the connectivity. Check the BGP state on the router for the peer: `show bgp neighbors <peer-ip>` to look for 'BGP state = Active'. Validate the remote AS number in `neighbor <peer-ip> remote-as <remote-as-number>` matches the peer's local AS. If you see 'BGP notification: hold time expired', it means the peer's hold timer has expired due to configuration issues, which can be addressed by adjusting the hold-timer values with `neighbor <peer-ip> timers <keepalive> <hold>`.\n\nFor the WAN latency to Singapore of 180ms, ensure the DPD timers are set appropriately to handle this latency without causing tunnel drops. The default DPD settings should be sufficient unless latency spikes significantly. Adjust DPD timers if necessary. Finally, test the upstream service for the 504 timeout issue, as described in KB-00018, and make any necessary adjustments to the", "parsed": false, "r_parsimony": 0.0, "step": 25}
|