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Plan in-app AI chat and Fix It assistant

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Tranquilo
Stack tecnológico
csharp
Área
api, backend

Línea de trabajo

This is a broad foundation plan and does not name implementation files. Start by reading the recommended first implementation slice and checking the existing plan, usage-tracking, and backend API patterns; the proposed conversation endpoints are listed in Phase 3. Done means the first read-only foundation slice has plan limits, persistent usage tracking, limit enforcement, a configurable provider abstraction, and minimal admin/system visibility.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Goal

Add an in-app AI assistant for Exceptionless that lets users ask project questions in plain English and powers a Fix It / Explain button on events and stacks.

The in-app assistant should reuse the same core capabilities as the hosted MCP server, but it should be implemented as a first-party backend feature rather than calling MCP over HTTP from the app.

Product rules

  • AI is available only on selected paid plans.
  • Usage is token-based because provider billing is token-based.
  • Start with included monthly token budgets only.
  • Do not support overage billing in v1.
  • Track provider cost internally from day one.
  • Start read-only; any write actions must require explicit user confirmation.

Initial plan fields:

  • ai_enabled
  • ai_monthly_token_limit
  • ai_max_tokens_per_request
  • ai_max_tool_calls_per_request
  • ai_max_context_bytes
  • ai_allowed_models
  • ai_overage_enabled = false

Phase 1: Shared AI tool layer

Extract reusable backend tool logic so MCP and in-app AI use the same permission checks, validation, result shaping, and truncation behavior.

Suggested services:

  • AiToolCatalog
  • AiToolExecutor
  • AiContextBuilder
  • AiUsageTracker
  • AiPlanLimitService

The in-app chat should call shared internal services directly. It should not call /mcp over HTTP.

Phase 2: Usage metering and limits

Add persistent usage tracking per organization.

Track:

  • organization id
  • user id
  • operation: chat, fix_it, explain_event, explain_stack
  • model
  • input tokens
  • output tokens
  • total tokens
  • estimated provider cost
  • created date / month bucket

Enforce before and during requests:

  • plan allows AI
  • monthly token budget remaining
  • per-request max token cap
  • max tool calls
  • max context size

Use a reservation or capped request budget so a single request cannot materially overspend the remaining monthly allowance.

Phase 3: Backend chat API

Add API endpoints such as:

  • POST /api/v2/ai/conversations
  • GET /api/v2/ai/conversations
  • GET /api/v2/ai/conversations/{id}
  • POST /api/v2/ai/conversations/{id}/messages
  • optional streaming endpoint for assistant responses

The backend owns:

  • permission checks
  • plan checks
  • usage reservation
  • model calls
  • tool calls
  • persisted conversation history

Phase 4: Chat UI

Add a project-scoped AI chat surface in the app.

Potential entry points:

  • Account AI Tools page
  • project settings
  • client setup page
  • profile menu
  • later: sidebar/global command menu

UI should show:

  • current project / organization context
  • token usage this month
  • remaining token budget
  • limit reached state
  • conversation history

Phase 5: Fix It / Explain buttons

Add Fix It / Explain buttons on stack and event detail pages.

Clicking starts a conversation with preloaded context:

  • project
  • stack/event id
  • stack trace
  • request info
  • environment
  • recent occurrences
  • version/release info if available
  • related stacks/events if useful

Assistant response should be structured:

  • likely cause
  • evidence
  • suggested fix
  • assumptions
  • next debugging steps
  • related Exceptionless links

Phase 6: Confirmed write actions later

After read-only chat is useful, add confirmed write actions:

  • mark fixed in version
  • snooze
  • mark future events critical
  • add reference link

The assistant may propose an action, but the UI should execute it only after explicit user confirmation. All write actions should be audited.

Phase 7: Overage billing later

After real usage data exists, add pay-as-you-go.

Add:

  • ai_overage_enabled
  • ai_monthly_spend_limit_cents
  • ai_overage_price_per_million_tokens
  • internal provider cost tracking
  • markup calculation

User options:

  • stop at included token limit
  • allow overage up to a monthly cap

Recommended first implementation slice

Start with foundation work only:

  1. Add plan AI limit fields.
  2. Add AI usage tracking.
  3. Add backend limit enforcement service.
  4. Add provider abstraction, with model calls behind config/feature flag.
  5. Keep the first slice read-only.
  6. Add minimal admin/system visibility for usage and limits.

A later PR can add the first user-facing read-only chat and Fix It experience.

Lenguaje dominante
C#
Estrellas
2.5k
Forks
508
Merge medio
2 d 17 h
PR fusionados (30 d)
40

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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