feat(dashboard): add Model Selection History & Contextual Routing Visualization #8323
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…ting visualization - Integrated fetch_model_history to display recent task outcomes. - Updated dashboard layout with a dedicated history panel. - Enabled dynamic path resolution for metrics databases. - Added seed script for benchmarking and contextual bandit training.
Contributor
|
Thanks for your contribution! This PR doesn't have a linked issue. All PRs must reference an existing issue. Please:
See CONTRIBUTING.md for details. |
Contributor
|
The following comment was made by an LLM, it may be inaccurate: No duplicate PRs found |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
This PR refactors the
swarm_dashboard.pyto include a Model Selection History panel, enabling real-time visualization of the Contextual Bandit's routing decisions.Changes
routing_decisionstable.swarm/path_resolver.pyto dynamically locate metrics databases, fixing hardcoded path issues and ensuring consistency across the orchestrator.swarm/benchmark/seed_metrics.pyto bootstrap the routing intelligence with historical performance data.Live Test Results
The refactor was verified in a live test where the Swarm correctly routed:
deepseek-reasoner-r1(100% reasoning success).gemini-2.5-flash(high speed/low cost).grok-code(free-tier optimization).The dashboard successfully displayed these decisions, providing transparency into the agentic workflow.