rsp2k
c655949bbb
EmbeddingSink queues text from each batch on its own bounded queue and
upserts vectors into a new {prefix}embeddings table (pgvector on Postgres,
JSON elsewhere). mw.similar() and mw.similar_feedback() search it.
submit_feedback records a feedback.submitted event; record_llm_call keeps
prompt and completion only with capture_llm_text=True.
123 lines
4.7 KiB
Python
123 lines
4.7 KiB
Python
"""Submission mixin for feedback collection."""
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from datetime import UTC, datetime
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from typing import TYPE_CHECKING, Any
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from ..insights import record_feedback_submission
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from ..models import SubmitFeedbackRequest, create_feedback_from_request
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from .base import (
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BaseFeedbackMixin,
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FeedbackDatabase,
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FeedbackInsights,
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logger,
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mcp_tool,
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)
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if TYPE_CHECKING:
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from ..instrumentation import InstrumentationMiddleware
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class SubmissionMixin(BaseFeedbackMixin):
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"""Mixin for feedback submission functionality.
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Pass an InstrumentationMiddleware to link each submission to the tool
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calls that led up to it and record a ``feedback.submitted`` event for it.
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"""
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def __init__(
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self,
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database: FeedbackDatabase,
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insights: FeedbackInsights | None = None,
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instrumentation: "InstrumentationMiddleware | None" = None,
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):
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super().__init__(database, insights)
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self.instrumentation = instrumentation
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@mcp_tool(
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description="Submit feedback about your MCP experience - Use this tool when you encounter issues, have suggestions for improvement, or want to report positive experiences. AI models should proactively use this to help improve future MCP sessions. PRIVACY: Only include information you've already shared via previous MCP calls. Ask human permission before including any new sensitive data."
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)
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async def submit_feedback(self, request: SubmitFeedbackRequest) -> dict[str, Any]:
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"""Submit feedback about the MCP server, tools, or interaction experience.
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AI models should use this tool to:
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- Report when tools don't work as expected (type='bug')
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- Suggest improvements to tool functionality (type='improvement')
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- Request new capabilities that would be helpful (type='feature')
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- Ask questions about unclear tool behavior (type='question')
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PRIVACY GUIDELINES:
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- Only include information already shared in this MCP session
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- Avoid including sensitive data not previously disclosed
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- When in doubt, ask the human for permission first
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- Focus on the specific technical issue, not broader context
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This helps improve the MCP server and tools for future sessions.
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Args:
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request: Feedback submission request with type, title, description, submitter.
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Returns:
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Dictionary with submission result including feedback_id if successful.
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"""
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start_time = datetime.now(UTC)
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try:
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# Create feedback from request
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feedback = create_feedback_from_request(request)
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# Save to database
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async with self.database.session() as session:
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session.add(feedback)
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session.commit()
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session.refresh(feedback)
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feedback_id = str(feedback.id)
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# Record analytics (without PII)
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if self.insights.enabled:
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record_feedback_submission(
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self.insights,
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feedback_type=request.type,
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has_contact=bool(request.contact_info),
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title_length=len(request.title),
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description_length=len(request.description),
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source="mcp_tool",
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)
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linked_calls = 0
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if self.instrumentation is not None:
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# Feeds an EmbeddingSink, so similar reports can be found.
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self.instrumentation.record_event(
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"feedback.submitted",
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key=feedback_id,
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attrs={
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"type": request.type,
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"title": request.title,
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"description": request.description,
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"submitter": request.submitter,
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},
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)
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links = await self.instrumentation.capture_recent_calls()
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if await self.instrumentation.link_feedback(feedback_id, links):
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linked_calls = len(links)
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self._record_tool_usage("submit_feedback", start_time, True)
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logger.info(f"Feedback submitted successfully: {feedback_id}")
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response = {
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"success": True,
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"feedback_id": feedback_id,
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"message": "Feedback submitted successfully",
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}
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if self.instrumentation is not None:
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response["linked_calls"] = linked_calls
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return response
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except Exception as e:
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logger.error(f"Failed to submit feedback: {e}")
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self._record_tool_usage(
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"submit_feedback", start_time, False, type(e).__name__
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)
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return {"success": False, "error": str(e)}
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