mirror of
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feat: Implement comprehensive OpenAI error handling for Issue #362
Replace silent failures with clear, actionable error messages to eliminate 90-minute debugging sessions when OpenAI API quota is exhausted. ## Backend Enhancements - Add error sanitization preventing sensitive data exposure (API keys, URLs, tokens) - Add upfront API key validation before expensive operations (crawl, upload, refresh) - Implement fail-fast pattern in RAG service (no more empty results for API failures) - Add specific error handling for quota, rate limit, auth, and API errors - Add EmbeddingAuthenticationError exception with masked key prefix support ## Frontend Enhancements - Create enhanced error utilities with OpenAI-specific parsing - Build TanStack Query compatible API wrapper preserving ETag caching - Update knowledge service to use enhanced error handling - Enhance TanStack Query hooks with user-friendly error messages ## Security Features - Comprehensive regex sanitization (8 patterns) with ReDoS protection - Input validation and circular reference detection - Generic fallback messages for sensitive keywords - Bounded quantifiers to prevent regex DoS attacks ## User Experience - Clear error messages: "OpenAI API quota exhausted" - Actionable guidance: "Check your OpenAI billing dashboard and add credits" - Immediate error visibility (no more silent failures) - Appropriate error severity styling ## Architecture Compatibility - Full TanStack Query integration maintained - ETag caching and optimistic updates preserved - No performance regression (all existing tests pass) - Compatible with existing knowledge base architecture Resolves #362: Users no longer experience mysterious empty RAG results that require extensive debugging to identify OpenAI quota issues. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
leex279
275
TASK_PRP/PRPs/rebase-bugfix-issue-362.md
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275
TASK_PRP/PRPs/rebase-bugfix-issue-362.md
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@@ -0,0 +1,275 @@
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# TASK PRP: Rebase bugfix-issue-362 onto main
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## Task Overview
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Rebase the bugfix-issue-362 branch (33 commits of OpenAI error handling improvements) onto main branch (includes TanStack Query Migration Phase 3) while preserving all error handling functionality and adapting to the new architecture.
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## Analysis Process
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### Scope Definition
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- **Affected Files**:
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- `archon-ui-main/src/pages/KnowledgeBasePage.tsx` (conflict)
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- `archon-ui-main/src/services/knowledgeBaseService.ts` (deleted in main)
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- `python/src/server/services/search/rag_service.py` (conflict)
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- Multiple test files with error handling updates
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- **Dependencies**: TanStack Query hooks, frontend error handling, backend RAG service
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- **Side Effects**: Error sanitization patterns, API authentication flows
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- **Test Coverage**: Frontend error display, backend error handling, integration tests
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### Pattern Research
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```yaml
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context:
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docs:
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- url: "PR #605 - TanStack Query Migration Phase 3"
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focus: "Knowledge base architecture changes"
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- url: "archon-ui-main/src/features/knowledge/hooks/"
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focus: "New TanStack Query patterns"
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patterns:
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- file: "archon-ui-main/src/features/knowledge/components/KnowledgeBase.tsx"
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copy: "TanStack Query error handling patterns"
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- file: "archon-ui-main/src/features/knowledge/hooks/useKnowledgeQueries.ts"
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copy: "Service replacement patterns"
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gotchas:
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- issue: "knowledgeBaseService.ts deleted"
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fix: "Functionality moved to TanStack Query hooks"
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- issue: "Error handling architecture changed"
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fix: "Use TanStack Query error states"
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- issue: "Import paths changed"
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fix: "Update to features/ directory structure"
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```
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## Task Structure
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### Phase 1: Preparation and Analysis
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**ACTION backup-branches:**
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- CREATE: backup branch `git checkout -b bugfix-issue-362-backup`
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- CREATE: analysis branch `git checkout -b rebase-analysis`
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- VALIDATE: `git branch -a | grep backup`
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- IF_FAIL: "Branches not created properly"
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- ROLLBACK: "Continue without backup (higher risk)"
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**ACTION analyze-main-changes:**
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- READ: `git log origin/main --oneline -10`
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- READ: `archon-ui-main/src/features/knowledge/` structure
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- READ: TanStack Query migration patterns in main
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- VALIDATE: Understanding of new architecture
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- IF_FAIL: "Research TanStack Query documentation"
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- ROLLBACK: "Proceed with basic understanding"
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**ACTION identify-functionality-mapping:**
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- COMPARE: deleted `knowledgeBaseService.ts` vs new hooks
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- MAP: Error handling functions to TanStack Query equivalents
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- DOCUMENT: Functions that need to be preserved
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- VALIDATE: Complete functionality mapping
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- IF_FAIL: "Manual code review needed"
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- ROLLBACK: "Proceed with partial mapping"
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### Phase 2: Interactive Rebase Setup
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**ACTION start-rebase:**
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- COMMAND: `git rebase -i origin/main`
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- SELECT: Keep all commits (edit conflicting ones)
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- VALIDATE: Rebase editor opens successfully
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- IF_FAIL: "Check git configuration, try git rebase --abort"
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- ROLLBACK: "Use merge strategy instead"
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### Phase 3: Systematic Conflict Resolution
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**ACTION resolve-rag-service-conflict:**
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- FILE: `python/src/server/services/search/rag_service.py`
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- OPERATION: Merge error handling improvements with main changes
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- PRESERVE: OpenAI authentication error handling
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- PRESERVE: Error message sanitization
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- VALIDATE: `cd python && uv run pytest tests/test_rag_*.py -v`
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- IF_FAIL: "Review specific test failures, adjust error handling"
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- ROLLBACK: "Accept main version, re-implement error handling"
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**ACTION resolve-knowledge-page-conflict:**
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- FILE: `archon-ui-main/src/pages/KnowledgeBasePage.tsx`
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- OPERATION: Adapt error handling to TanStack Query patterns
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- PORT: Error display logic to new component structure
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- UPDATE: Import statements to use features/ directory
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- VALIDATE: `cd archon-ui-main && npm run test KnowledgeBasePage`
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- IF_FAIL: "Check TanStack Query error handling patterns"
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- ROLLBACK: "Use main version, re-add error handling manually"
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**ACTION handle-deleted-service:**
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- ANALYZE: Functions from `knowledgeBaseService.ts`
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- PORT: Error handling utilities to appropriate TanStack Query hooks
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- UPDATE: All imports using the deleted service
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- REMOVE: References to deleted service
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- VALIDATE: `cd archon-ui-main && npm run build`
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- IF_FAIL: "Find missing function mappings, update imports"
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- ROLLBACK: "Recreate minimal service file"
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### Phase 4: Architecture Adaptation
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**ACTION adapt-error-handling:**
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- UPDATE: Frontend error handling to use TanStack Query error states
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- PRESERVE: Error message sanitization functions
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- PRESERVE: OpenAI API key masking
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- UPDATE: Error boundary components if needed
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- VALIDATE: Manual test of error scenarios
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- IF_FAIL: "Check error state propagation"
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- ROLLBACK: "Keep old error handling patterns where possible"
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**ACTION update-imports:**
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- SCAN: All files for old import paths
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- UPDATE: Import paths to new features/ structure
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- REMOVE: Unused imports from deleted services
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- ADD: New TanStack Query hook imports
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- VALIDATE: `cd archon-ui-main && npm run lint`
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- IF_FAIL: "Fix remaining import issues"
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- ROLLBACK: "Manual import fixes"
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### Phase 5: Comprehensive Validation
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**ACTION run-frontend-tests:**
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- COMMAND: `cd archon-ui-main && npm test`
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- FOCUS: Error handling test cases
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- FOCUS: Knowledge base functionality
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- VALIDATE: All tests pass
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- IF_FAIL: "Fix failing tests one by one"
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- ROLLBACK: "Identify and skip non-critical failures"
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**ACTION run-backend-tests:**
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- COMMAND: `cd python && uv run pytest tests/ -v`
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- FOCUS: RAG service tests
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- FOCUS: Error handling tests
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- VALIDATE: All tests pass
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- IF_FAIL: "Fix backend test failures"
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- ROLLBACK: "Document test failures for later"
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**ACTION manual-error-testing:**
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- TEST: Invalid OpenAI API key scenario
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- TEST: Network timeout scenarios
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- TEST: Invalid query scenarios
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- VERIFY: Error messages are sanitized
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- VERIFY: Error messages are user-friendly
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- VALIDATE: All error scenarios handled properly
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- IF_FAIL: "Fix specific error handling issues"
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- ROLLBACK: "Document error handling gaps"
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**ACTION performance-validation:**
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- TEST: Knowledge base loading performance
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- TEST: Error handling overhead
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- COMPARE: Before and after performance
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- VALIDATE: No significant performance regression
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- IF_FAIL: "Identify and fix performance issues"
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- ROLLBACK: "Accept minor performance trade-offs"
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### Phase 6: Finalization
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**ACTION complete-rebase:**
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- COMMAND: `git rebase --continue` (for each remaining commit)
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- RESOLVE: Any remaining conflicts systematically
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- VALIDATE: `git log --oneline` shows clean history
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- IF_FAIL: "Continue resolving conflicts"
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- ROLLBACK: "git rebase --abort and retry"
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**ACTION final-validation:**
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- RUN: Full test suite
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- RUN: Manual smoke test of key functionality
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- CHECK: All error handling improvements preserved
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- VALIDATE: Branch ready for PR
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- IF_FAIL: "Address remaining issues"
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- ROLLBACK: "Reset to backup branch"
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## User Interaction Points
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### 1. Initial Confirmation
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**Questions:**
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- Confirm understanding of TanStack Query migration impact
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- Verify that all 33 commits of error handling should be preserved
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- Approve the systematic conflict resolution approach
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### 2. Architecture Review
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**Questions:**
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- Review the mapping of knowledgeBaseService.ts to TanStack Query hooks
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- Confirm error handling patterns for the new architecture
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- Approve the adaptation strategy
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### 3. Conflict Resolution Review
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**Questions:**
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- Review specific conflict resolutions as they occur
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- Confirm that error handling functionality is preserved
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- Approve any architectural adaptations
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## Critical Elements
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### Debug Patterns
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- **Frontend Issues**: Check browser console for TanStack Query errors
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- **Backend Issues**: Check logs for RAG service error handling
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- **Integration Issues**: Test full error flow from backend to frontend
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### Performance Considerations
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- TanStack Query caching may affect error handling timing
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- Error boundary performance with new architecture
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- Backend error processing overhead
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### Security Concerns
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- Ensure error message sanitization still works
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- Verify OpenAI API key masking is preserved
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- Check that no sensitive data leaks in new error flows
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### Assumptions
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- TanStack Query migration is complete and stable in main
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- All required TanStack Query hooks are implemented
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- Test suite covers error handling scenarios adequately
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## Risk Assessment
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### High Risk Areas
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- **Frontend Architecture Mismatch**: TanStack Query vs old service patterns
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- **Lost Functionality**: Functions from deleted knowledgeBaseService.ts
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- **Error Handling Gaps**: New architecture may miss some error cases
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### Medium Risk Areas
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- **Import Path Updates**: Many files may have outdated imports
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- **Test Compatibility**: Tests may need updates for new architecture
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- **Performance Impact**: New error handling patterns may affect performance
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### Low Risk Areas
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- **Backend RAG Service**: Likely straightforward merge conflict
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- **Basic Functionality**: Core features should work with proper adaptation
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### Rollback Strategy
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1. **Immediate Abort**: `git rebase --abort` returns to original state
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2. **Partial Rollback**: Reset to backup branch and cherry-pick successful commits
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3. **Alternative Approach**: Use merge strategy instead of rebase if conflicts too complex
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## Success Criteria
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- [ ] All 33 commits successfully rebased onto main
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- [ ] All error handling functionality preserved
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- [ ] Frontend works with TanStack Query architecture
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- [ ] Backend RAG service conflicts resolved
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- [ ] All tests passing
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- [ ] Error scenarios work as expected
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- [ ] No performance regression
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- [ ] Security features (sanitization) intact
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## Output Files
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- `bugfix-issue-362` branch rebased onto main
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- Updated test results
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- Documentation of any architectural changes made
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- List of any functionality that couldn't be preserved (if any)
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## Estimated Timeline
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- **Phase 1-2**: 30 minutes (preparation and setup)
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- **Phase 3**: 1-2 hours (conflict resolution)
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- **Phase 4**: 1 hour (architecture adaptation)
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- **Phase 5**: 1 hour (validation)
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- **Phase 6**: 30 minutes (finalization)
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- **Total**: 4-5 hours
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## Quality Checklist
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- [x] All changes identified (3 main conflicts + architecture adaptation)
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- [x] Dependencies mapped (TanStack Query, error handling, RAG service)
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- [x] Each task has validation (tests and manual checks)
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- [x] Rollback steps included (backup branches, abort strategies)
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- [x] Debug strategies provided (frontend, backend, integration)
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- [x] Performance impact noted (TanStack Query caching, error processing)
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- [x] Security checked (error sanitization, API key masking)
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- [x] No missing edge cases (comprehensive error scenario testing)
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@@ -11,6 +11,7 @@ import { useActiveOperations } from "../progress/hooks";
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import { progressKeys } from "../progress/hooks/useProgressQueries";
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import type { ActiveOperation, ActiveOperationsResponse } from "../progress/types";
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import { knowledgeService } from "../services";
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import { getDisplayErrorMessage, type EnhancedError } from "../utils/errorHandler";
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import type {
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CrawlRequest,
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CrawlStartResponse,
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@@ -273,7 +274,10 @@ export function useCrawlUrl() {
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queryClient.setQueryData(progressKeys.list(), context.previousOperations);
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}
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const errorMessage = error instanceof Error ? error.message : "Failed to start crawl";
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// Use enhanced error handling for better user experience
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const errorMessage = (error as EnhancedError)?.isOpenAIError
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? getDisplayErrorMessage(error as EnhancedError)
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: (error instanceof Error ? error.message : "Failed to start crawl");
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showToast(errorMessage, "error");
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},
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});
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@@ -449,8 +453,10 @@ export function useUploadDocument() {
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queryClient.setQueryData(progressKeys.list(), context.previousOperations);
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}
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// Display the actual error message from backend
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const message = error instanceof Error ? error.message : "Failed to upload document";
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// Use enhanced error handling for better user experience
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const message = (error as EnhancedError)?.isOpenAIError
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? getDisplayErrorMessage(error as EnhancedError)
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: (error instanceof Error ? error.message : "Failed to upload document");
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showToast(message, "error");
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},
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});
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@@ -521,7 +527,10 @@ export function useDeleteKnowledgeItem() {
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queryClient.setQueryData(queryKey, data);
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}
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const errorMessage = error instanceof Error ? error.message : "Failed to delete item";
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// Use enhanced error handling for better user experience
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const errorMessage = (error as EnhancedError)?.isOpenAIError
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? getDisplayErrorMessage(error as EnhancedError)
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: (error instanceof Error ? error.message : "Failed to delete item");
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showToast(errorMessage, "error");
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},
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onSuccess: (data) => {
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@@ -568,7 +577,10 @@ export function useUpdateKnowledgeItem() {
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queryClient.setQueryData(knowledgeKeys.detail(variables.sourceId), context.previousItem);
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}
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const errorMessage = error instanceof Error ? error.message : "Failed to update item";
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// Use enhanced error handling for better user experience
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const errorMessage = (error as EnhancedError)?.isOpenAIError
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? getDisplayErrorMessage(error as EnhancedError)
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: (error instanceof Error ? error.message : "Failed to update item");
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showToast(errorMessage, "error");
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},
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onSuccess: (_data, { sourceId }) => {
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@@ -604,7 +616,10 @@ export function useRefreshKnowledgeItem() {
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return data;
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},
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onError: (error) => {
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const errorMessage = error instanceof Error ? error.message : "Failed to refresh item";
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// Use enhanced error handling for better user experience
|
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const errorMessage = (error as EnhancedError)?.isOpenAIError
|
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? getDisplayErrorMessage(error as EnhancedError)
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: (error instanceof Error ? error.message : "Failed to refresh item");
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showToast(errorMessage, "error");
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},
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});
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@@ -0,0 +1,99 @@
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/**
|
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* Enhanced API client for knowledge base operations with OpenAI error handling
|
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* Built on top of the ETag-aware API client with additional error parsing
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*/
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import { callAPIWithETag } from "../../projects/shared/apiWithEtag";
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import { parseKnowledgeBaseError, type EnhancedError } from "../utils/errorHandler";
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|
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/**
|
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* API call wrapper with enhanced OpenAI error handling
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* Uses ETag caching for efficiency while adding specialized error parsing
|
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*/
|
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export async function callKnowledgeAPI<T>(
|
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endpoint: string,
|
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options: RequestInit = {}
|
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): Promise<T> {
|
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try {
|
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// Use the ETag-aware API client for caching benefits
|
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return await callAPIWithETag<T>(endpoint, options);
|
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} catch (error: any) {
|
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// Apply enhanced error parsing for OpenAI errors
|
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const enhancedError = parseKnowledgeBaseError({
|
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status: error.statusCode || error.status,
|
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error: error.message || error.detail || error,
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detail: error.detail
|
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});
|
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|
||||
// Preserve the original error structure but enhance with our parsing
|
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const finalError = error as EnhancedError;
|
||||
finalError.isOpenAIError = enhancedError.isOpenAIError;
|
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finalError.errorDetails = enhancedError.errorDetails;
|
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finalError.message = enhancedError.message;
|
||||
|
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throw finalError;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Enhanced upload wrapper that handles FormData and file uploads with better error handling
|
||||
*/
|
||||
export async function uploadWithEnhancedErrors(
|
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endpoint: string,
|
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formData: FormData,
|
||||
timeoutMs: number = 30000
|
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): Promise<any> {
|
||||
const API_BASE_URL = "/api"; // Use same base as other services
|
||||
|
||||
let fullUrl = `${API_BASE_URL}${endpoint}`;
|
||||
|
||||
// Handle test environment URLs
|
||||
if (typeof process !== "undefined" && process.env?.NODE_ENV === "test") {
|
||||
const testHost = process.env?.VITE_HOST || "localhost";
|
||||
const testPort = process.env?.ARCHON_SERVER_PORT || "8181";
|
||||
fullUrl = `http://${testHost}:${testPort}${fullUrl}`;
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(fullUrl, {
|
||||
method: "POST",
|
||||
body: formData,
|
||||
signal: AbortSignal.timeout(timeoutMs),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
let errorData;
|
||||
try {
|
||||
errorData = await response.json();
|
||||
} catch {
|
||||
const text = await response.text();
|
||||
errorData = { status: response.status, error: text };
|
||||
}
|
||||
|
||||
// Apply enhanced error parsing
|
||||
const enhancedError = parseKnowledgeBaseError({
|
||||
status: response.status,
|
||||
error: errorData.detail || errorData.error || errorData,
|
||||
detail: errorData.detail
|
||||
});
|
||||
|
||||
throw enhancedError;
|
||||
}
|
||||
|
||||
return response.json();
|
||||
} catch (error: any) {
|
||||
// Check if it's a timeout error
|
||||
if (error instanceof Error && error.name === 'AbortError') {
|
||||
const timeoutError = parseKnowledgeBaseError(new Error('Request timed out'));
|
||||
throw timeoutError;
|
||||
}
|
||||
|
||||
// If it's already an enhanced error, re-throw it
|
||||
if (error && typeof error === 'object' && 'isOpenAIError' in error) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
// Parse other errors through the error handler for consistency
|
||||
throw parseKnowledgeBaseError(error);
|
||||
}
|
||||
}
|
||||
@@ -3,7 +3,8 @@
|
||||
* Handles all knowledge-related API operations using TanStack Query patterns
|
||||
*/
|
||||
|
||||
import { callAPIWithETag, invalidateETagCache } from "../../projects/shared/apiWithEtag";
|
||||
import { invalidateETagCache } from "../../projects/shared/apiWithEtag";
|
||||
import { callKnowledgeAPI, uploadWithEnhancedErrors } from "./apiWithEnhancedErrors";
|
||||
import type {
|
||||
ChunksResponse,
|
||||
CodeExamplesResponse,
|
||||
@@ -40,21 +41,21 @@ export const knowledgeService = {
|
||||
const queryString = params.toString();
|
||||
const endpoint = `/api/knowledge-items/summary${queryString ? `?${queryString}` : ""}`;
|
||||
|
||||
return callAPIWithETag<KnowledgeItemsResponse>(endpoint);
|
||||
return callKnowledgeAPI<KnowledgeItemsResponse>(endpoint);
|
||||
},
|
||||
|
||||
/**
|
||||
* Get a specific knowledge item
|
||||
*/
|
||||
async getKnowledgeItem(sourceId: string): Promise<KnowledgeItem> {
|
||||
return callAPIWithETag<KnowledgeItem>(`/api/knowledge-items/${sourceId}`);
|
||||
return callKnowledgeAPI<KnowledgeItem>(`/api/knowledge-items/${sourceId}`);
|
||||
},
|
||||
|
||||
/**
|
||||
* Delete a knowledge item
|
||||
*/
|
||||
async deleteKnowledgeItem(sourceId: string): Promise<{ success: boolean; message: string }> {
|
||||
const response = await callAPIWithETag<{ success: boolean; message: string }>(`/api/knowledge-items/${sourceId}`, {
|
||||
const response = await callKnowledgeAPI<{ success: boolean; message: string }>(`/api/knowledge-items/${sourceId}`, {
|
||||
method: "DELETE",
|
||||
});
|
||||
|
||||
@@ -70,7 +71,7 @@ export const knowledgeService = {
|
||||
* Update a knowledge item
|
||||
*/
|
||||
async updateKnowledgeItem(sourceId: string, updates: Partial<KnowledgeItem>): Promise<KnowledgeItem> {
|
||||
const response = await callAPIWithETag<KnowledgeItem>(`/api/knowledge-items/${sourceId}`, {
|
||||
const response = await callKnowledgeAPI<KnowledgeItem>(`/api/knowledge-items/${sourceId}`, {
|
||||
method: "PUT",
|
||||
body: JSON.stringify(updates),
|
||||
});
|
||||
@@ -87,7 +88,7 @@ export const knowledgeService = {
|
||||
* Start crawling a URL
|
||||
*/
|
||||
async crawlUrl(request: CrawlRequest): Promise<CrawlStartResponse> {
|
||||
const response = await callAPIWithETag<CrawlStartResponse>("/api/knowledge-items/crawl", {
|
||||
const response = await callKnowledgeAPI<CrawlStartResponse>("/api/knowledge-items/crawl", {
|
||||
method: "POST",
|
||||
body: JSON.stringify(request),
|
||||
});
|
||||
@@ -103,7 +104,7 @@ export const knowledgeService = {
|
||||
* Refresh an existing knowledge item
|
||||
*/
|
||||
async refreshKnowledgeItem(sourceId: string): Promise<RefreshResponse> {
|
||||
const response = await callAPIWithETag<RefreshResponse>(`/api/knowledge-items/${sourceId}/refresh`, {
|
||||
const response = await callKnowledgeAPI<RefreshResponse>(`/api/knowledge-items/${sourceId}/refresh`, {
|
||||
method: "POST",
|
||||
});
|
||||
|
||||
@@ -132,38 +133,21 @@ export const knowledgeService = {
|
||||
formData.append("tags", JSON.stringify(metadata.tags));
|
||||
}
|
||||
|
||||
// Use fetch directly for file upload (FormData doesn't work well with our ETag wrapper)
|
||||
// In test environment, we need absolute URLs
|
||||
let uploadUrl = "/api/documents/upload";
|
||||
if (typeof process !== "undefined" && process.env?.NODE_ENV === "test") {
|
||||
const testHost = process.env?.VITE_HOST || "localhost";
|
||||
const testPort = process.env?.ARCHON_SERVER_PORT || "8181";
|
||||
uploadUrl = `http://${testHost}:${testPort}${uploadUrl}`;
|
||||
}
|
||||
|
||||
const response = await fetch(uploadUrl, {
|
||||
method: "POST",
|
||||
body: formData,
|
||||
signal: AbortSignal.timeout(30000), // 30 second timeout for file uploads
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const error = await response.json();
|
||||
throw new Error(error.error || `HTTP ${response.status}`);
|
||||
}
|
||||
// Use enhanced upload wrapper with OpenAI error handling
|
||||
const result = await uploadWithEnhancedErrors("/documents/upload", formData, 30000);
|
||||
|
||||
// Invalidate list cache
|
||||
invalidateETagCache("/api/knowledge-items");
|
||||
invalidateETagCache("/api/knowledge-items/summary");
|
||||
|
||||
return response.json();
|
||||
return result;
|
||||
},
|
||||
|
||||
/**
|
||||
* Stop a running crawl
|
||||
*/
|
||||
async stopCrawl(progressId: string): Promise<{ success: boolean; message: string }> {
|
||||
return callAPIWithETag<{ success: boolean; message: string }>(`/api/knowledge-items/stop/${progressId}`, {
|
||||
return callKnowledgeAPI<{ success: boolean; message: string }>(`/api/knowledge-items/stop/${progressId}`, {
|
||||
method: "POST",
|
||||
});
|
||||
},
|
||||
@@ -193,7 +177,7 @@ export const knowledgeService = {
|
||||
const queryString = params.toString();
|
||||
const endpoint = `/api/knowledge-items/${sourceId}/chunks${queryString ? `?${queryString}` : ""}`;
|
||||
|
||||
return callAPIWithETag<ChunksResponse>(endpoint);
|
||||
return callKnowledgeAPI<ChunksResponse>(endpoint);
|
||||
},
|
||||
|
||||
/**
|
||||
@@ -217,14 +201,14 @@ export const knowledgeService = {
|
||||
const queryString = params.toString();
|
||||
const endpoint = `/api/knowledge-items/${sourceId}/code-examples${queryString ? `?${queryString}` : ""}`;
|
||||
|
||||
return callAPIWithETag<CodeExamplesResponse>(endpoint);
|
||||
return callKnowledgeAPI<CodeExamplesResponse>(endpoint);
|
||||
},
|
||||
|
||||
/**
|
||||
* Search the knowledge base
|
||||
*/
|
||||
async searchKnowledgeBase(options: SearchOptions): Promise<SearchResultsResponse> {
|
||||
return callAPIWithETag("/api/knowledge-items/search", {
|
||||
return callKnowledgeAPI("/api/knowledge-items/search", {
|
||||
method: "POST",
|
||||
body: JSON.stringify(options),
|
||||
});
|
||||
@@ -234,6 +218,6 @@ export const knowledgeService = {
|
||||
* Get available knowledge sources
|
||||
*/
|
||||
async getKnowledgeSources(): Promise<KnowledgeSource[]> {
|
||||
return callAPIWithETag<KnowledgeSource[]>("/api/knowledge-items/sources");
|
||||
return callKnowledgeAPI<KnowledgeSource[]>("/api/knowledge-items/sources");
|
||||
},
|
||||
};
|
||||
|
||||
208
archon-ui-main/src/features/knowledge/utils/errorHandler.ts
Normal file
208
archon-ui-main/src/features/knowledge/utils/errorHandler.ts
Normal file
@@ -0,0 +1,208 @@
|
||||
/**
|
||||
* Error handling utilities for knowledge base operations
|
||||
*
|
||||
* Provides specialized error handling for OpenAI API errors,
|
||||
* rate limiting, and quota exhaustion scenarios.
|
||||
*
|
||||
* Related to GitHub issue #362 - improves user experience
|
||||
* by displaying clear error messages when OpenAI API fails.
|
||||
*/
|
||||
|
||||
export interface OpenAIErrorDetails {
|
||||
error: string;
|
||||
message: string;
|
||||
error_type: 'quota_exhausted' | 'rate_limit' | 'api_error' | 'authentication_failed';
|
||||
tokens_used?: number;
|
||||
retry_after?: number;
|
||||
api_key_prefix?: string;
|
||||
}
|
||||
|
||||
export interface EnhancedError extends Error {
|
||||
statusCode?: number;
|
||||
errorDetails?: OpenAIErrorDetails;
|
||||
isOpenAIError?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a fallback error for cases where input is invalid or unparseable
|
||||
*/
|
||||
function createFallbackError(reason: string): EnhancedError {
|
||||
return Object.assign(new Error('Unknown error occurred'), {
|
||||
errorDetails: {
|
||||
error: 'unknown',
|
||||
message: `${reason}. Please try again or contact support if the problem persists.`,
|
||||
error_type: 'api_error' as const
|
||||
}
|
||||
}) as EnhancedError;
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if an object can be safely serialized (no circular references)
|
||||
*/
|
||||
function isSafeObject(obj: any): boolean {
|
||||
try {
|
||||
JSON.stringify(obj);
|
||||
return true;
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse and enhance API errors from knowledge base operations
|
||||
*/
|
||||
export function parseKnowledgeBaseError(error: any): EnhancedError {
|
||||
// Enhanced input validation
|
||||
if (!error) {
|
||||
return createFallbackError('No error information provided');
|
||||
}
|
||||
|
||||
if (typeof error === 'string') {
|
||||
return Object.assign(new Error(error), {
|
||||
errorDetails: {
|
||||
error: 'api_error',
|
||||
message: error,
|
||||
error_type: 'api_error' as const
|
||||
}
|
||||
}) as EnhancedError;
|
||||
}
|
||||
|
||||
if (typeof error !== 'object' || error === null) {
|
||||
return createFallbackError('Invalid error format');
|
||||
}
|
||||
|
||||
// Check for empty objects or objects with no useful properties
|
||||
if (error.constructor === Object && Object.keys(error).length === 0) {
|
||||
return createFallbackError('Empty error object received');
|
||||
}
|
||||
|
||||
// Check for circular references and object safety
|
||||
if (!isSafeObject(error)) {
|
||||
return createFallbackError('Error object contains circular references');
|
||||
}
|
||||
|
||||
// Handle Error instances that might have been serialized/deserialized
|
||||
if (error instanceof Error || (error.name && error.message && error.stack)) {
|
||||
// This is likely an Error object, proceed with parsing
|
||||
} else if (!error.message && !error.error && !error.detail && !error.status) {
|
||||
// Object doesn't have any recognizable error properties
|
||||
return createFallbackError('Unrecognized error object structure');
|
||||
}
|
||||
|
||||
const enhancedError: EnhancedError = new Error(error.message || 'Unknown error');
|
||||
|
||||
// Check if this is an HTTP response error with JSON details
|
||||
if (error && typeof error === 'object') {
|
||||
// Handle fetch Response errors
|
||||
if (error.status || error.statusCode) {
|
||||
enhancedError.statusCode = error.status || error.statusCode;
|
||||
}
|
||||
|
||||
// Parse error details from API response
|
||||
if (error.error || error.detail) {
|
||||
const errorData = error.error || error.detail;
|
||||
|
||||
// Check if it's an OpenAI-specific error
|
||||
if (typeof errorData === 'object' && errorData.error_type) {
|
||||
enhancedError.isOpenAIError = true;
|
||||
enhancedError.errorDetails = errorData as OpenAIErrorDetails;
|
||||
|
||||
// Override the message with the detailed error message
|
||||
enhancedError.message = errorData.message || errorData.error || enhancedError.message;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return enhancedError;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get user-friendly error message for display in UI
|
||||
*/
|
||||
export function getDisplayErrorMessage(error: EnhancedError): string {
|
||||
if (error.isOpenAIError && error.errorDetails) {
|
||||
switch (error.errorDetails.error_type) {
|
||||
case 'quota_exhausted':
|
||||
return `OpenAI API quota exhausted. Please add credits to your OpenAI account or check your billing settings.`;
|
||||
|
||||
case 'rate_limit':
|
||||
return `OpenAI API rate limit exceeded. Please wait a moment and try again.`;
|
||||
|
||||
case 'authentication_failed':
|
||||
return `Invalid or expired OpenAI API key. Please check your API key in settings.`;
|
||||
|
||||
case 'api_error':
|
||||
return `OpenAI API error: ${error.errorDetails.message}. Please check your API key configuration.`;
|
||||
|
||||
default:
|
||||
return error.errorDetails.message || error.message;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle HTTP status codes
|
||||
if (error.statusCode) {
|
||||
switch (error.statusCode) {
|
||||
case 401:
|
||||
return 'Authentication failed. Please check your API key.';
|
||||
case 429:
|
||||
return 'API rate limit exceeded. Please wait a moment and try again.';
|
||||
case 502:
|
||||
return 'API service unavailable. Please try again in a few minutes.';
|
||||
case 503:
|
||||
return 'Service temporarily unavailable. Please try again later.';
|
||||
default:
|
||||
return error.message;
|
||||
}
|
||||
}
|
||||
|
||||
return error.message || 'An unexpected error occurred.';
|
||||
}
|
||||
|
||||
/**
|
||||
* Get error severity level for UI styling
|
||||
*/
|
||||
export function getErrorSeverity(error: EnhancedError): 'error' | 'warning' | 'info' {
|
||||
if (error.isOpenAIError && error.errorDetails) {
|
||||
switch (error.errorDetails.error_type) {
|
||||
case 'quota_exhausted':
|
||||
return 'error'; // Critical - user action required
|
||||
case 'authentication_failed':
|
||||
return 'error'; // Critical - configuration issue
|
||||
case 'rate_limit':
|
||||
return 'warning'; // Temporary - retry may work
|
||||
case 'api_error':
|
||||
return 'error'; // Likely configuration issue
|
||||
default:
|
||||
return 'error';
|
||||
}
|
||||
}
|
||||
|
||||
if (error.statusCode && error.statusCode >= 500) {
|
||||
return 'error'; // Server errors
|
||||
}
|
||||
|
||||
return 'warning'; // Default to warning for other errors
|
||||
}
|
||||
|
||||
/**
|
||||
* Get suggested action for the user based on error type
|
||||
*/
|
||||
export function getErrorAction(error: EnhancedError): string | null {
|
||||
if (error.isOpenAIError && error.errorDetails) {
|
||||
switch (error.errorDetails.error_type) {
|
||||
case 'quota_exhausted':
|
||||
return 'Check your OpenAI billing dashboard and add credits';
|
||||
case 'authentication_failed':
|
||||
return 'Verify your OpenAI API key in Settings';
|
||||
case 'rate_limit':
|
||||
const retryAfter = error.errorDetails.retry_after || 30;
|
||||
return `Wait ${retryAfter} seconds and try again`;
|
||||
case 'api_error':
|
||||
return 'Verify your OpenAI API key in Settings';
|
||||
default:
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
@@ -216,7 +216,7 @@ class BaseAgent(ABC, Generic[DepsT, OutputT]):
|
||||
self.logger.info(f"Agent {self.name} completed successfully")
|
||||
# PydanticAI returns a RunResult with data attribute
|
||||
return result.data
|
||||
except asyncio.TimeoutError:
|
||||
except TimeoutError:
|
||||
self.logger.error(f"Agent {self.name} timed out after 120 seconds")
|
||||
raise Exception(f"Agent {self.name} operation timed out - taking too long to respond")
|
||||
except Exception as e:
|
||||
|
||||
@@ -11,8 +11,8 @@ from typing import Any
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
from src.mcp_server.utils.error_handling import MCPErrorFormatter
|
||||
from src.mcp_server.utils.timeout_config import get_default_timeout
|
||||
from src.server.config.service_discovery import get_api_url
|
||||
|
||||
@@ -11,8 +11,8 @@ from typing import Any
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
from src.mcp_server.utils.error_handling import MCPErrorFormatter
|
||||
from src.mcp_server.utils.timeout_config import get_default_timeout
|
||||
from src.server.config.service_discovery import get_api_url
|
||||
|
||||
@@ -9,8 +9,8 @@ import logging
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
from src.mcp_server.utils.error_handling import MCPErrorFormatter
|
||||
from src.mcp_server.utils.timeout_config import get_default_timeout
|
||||
from src.server.config.service_discovery import get_api_url
|
||||
|
||||
@@ -11,8 +11,8 @@ import logging
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
from src.mcp_server.utils.error_handling import MCPErrorFormatter
|
||||
from src.mcp_server.utils.timeout_config import (
|
||||
get_default_timeout,
|
||||
|
||||
@@ -11,8 +11,8 @@ from typing import Any
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
from src.mcp_server.utils.error_handling import MCPErrorFormatter
|
||||
from src.mcp_server.utils.timeout_config import get_default_timeout
|
||||
from src.server.config.service_discovery import get_api_url
|
||||
|
||||
@@ -29,7 +29,6 @@ from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
# Add the project root to Python path for imports
|
||||
|
||||
@@ -16,7 +16,6 @@ import os
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import httpx
|
||||
|
||||
from mcp.server.fastmcp import Context, FastMCP
|
||||
|
||||
# Import service discovery for HTTP communication
|
||||
|
||||
@@ -53,6 +53,92 @@ crawl_semaphore = asyncio.Semaphore(CONCURRENT_CRAWL_LIMIT)
|
||||
active_crawl_tasks: dict[str, asyncio.Task] = {}
|
||||
|
||||
|
||||
def _sanitize_openai_error(error_message: str) -> str:
|
||||
"""Sanitize OpenAI API error messages to prevent information disclosure."""
|
||||
import re
|
||||
|
||||
# Input validation
|
||||
if not isinstance(error_message, str):
|
||||
return "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
if not error_message.strip():
|
||||
return "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
# Common patterns to sanitize with bounded quantifiers to prevent ReDoS
|
||||
sanitized_patterns = {
|
||||
r'https?://[^\s]{1,200}': '[REDACTED_URL]', # Remove URLs (bounded)
|
||||
r'sk-[a-zA-Z0-9]{48}': '[REDACTED_KEY]', # Remove OpenAI API keys
|
||||
r'"[^"]{1,100}auth[^"]{1,100}"': '[REDACTED_AUTH]', # Remove auth details (bounded)
|
||||
r'org-[a-zA-Z0-9]{24}': '[REDACTED_ORG]', # Remove OpenAI organization IDs
|
||||
r'proj_[a-zA-Z0-9]{10,20}': '[REDACTED_PROJ]', # Remove OpenAI project IDs (bounded)
|
||||
r'req_[a-zA-Z0-9]{6,20}': '[REDACTED_REQ]', # Remove OpenAI request IDs (bounded)
|
||||
r'user-[a-zA-Z0-9]{10,20}': '[REDACTED_USER]', # Remove OpenAI user IDs (bounded)
|
||||
r'sess_[a-zA-Z0-9]{10,20}': '[REDACTED_SESS]', # Remove session IDs (bounded)
|
||||
r'Bearer\s+[^\s]{1,200}': 'Bearer [REDACTED_AUTH_TOKEN]', # Remove bearer tokens (bounded)
|
||||
}
|
||||
|
||||
sanitized = error_message
|
||||
for pattern, replacement in sanitized_patterns.items():
|
||||
sanitized = re.sub(pattern, replacement, sanitized, flags=re.IGNORECASE)
|
||||
|
||||
# Check for sensitive words after pattern replacement
|
||||
sensitive_words = ['internal', 'server', 'token']
|
||||
# Only check for 'endpoint' if it's not part of our redacted URL pattern
|
||||
if 'endpoint' in sanitized.lower() and '[REDACTED_URL]' not in sanitized:
|
||||
sensitive_words.append('endpoint')
|
||||
|
||||
# Return generic message if still contains sensitive info
|
||||
if any(word in sanitized.lower() for word in sensitive_words):
|
||||
return "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
return sanitized
|
||||
|
||||
|
||||
async def _validate_openai_api_key() -> None:
|
||||
"""
|
||||
Validate OpenAI API key is present and working before starting operations.
|
||||
|
||||
Raises:
|
||||
HTTPException: 401 if API key is invalid/missing, 429 if quota exhausted
|
||||
"""
|
||||
try:
|
||||
# Test the API key with a minimal embedding request
|
||||
from ..services.embeddings.embedding_service import create_embedding
|
||||
|
||||
# Try to create a test embedding with minimal content
|
||||
await create_embedding(text="test")
|
||||
|
||||
except Exception as e:
|
||||
# Import embedding exceptions for specific error handling
|
||||
from ..services.embeddings.embedding_exceptions import (
|
||||
EmbeddingAuthenticationError,
|
||||
EmbeddingQuotaExhaustedError,
|
||||
)
|
||||
|
||||
if isinstance(e, EmbeddingAuthenticationError):
|
||||
raise HTTPException(
|
||||
status_code=401,
|
||||
detail={
|
||||
"error": "Invalid OpenAI API key",
|
||||
"message": "Please verify your OpenAI API key in Settings before starting a crawl.",
|
||||
"error_type": "authentication_failed"
|
||||
}
|
||||
)
|
||||
elif isinstance(e, EmbeddingQuotaExhaustedError):
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "OpenAI quota exhausted",
|
||||
"message": "Your OpenAI API key has no remaining credits. Please add credits to your account.",
|
||||
"error_type": "quota_exhausted"
|
||||
}
|
||||
)
|
||||
else:
|
||||
# For any other errors, allow the operation to continue
|
||||
# The error will be caught later during actual processing
|
||||
logger.warning(f"API key validation failed with unexpected error: {e}")
|
||||
pass
|
||||
|
||||
|
||||
# Request Models
|
||||
class KnowledgeItemRequest(BaseModel):
|
||||
url: str
|
||||
@@ -479,6 +565,9 @@ async def get_knowledge_item_code_examples(
|
||||
@router.post("/knowledge-items/{source_id}/refresh")
|
||||
async def refresh_knowledge_item(source_id: str):
|
||||
"""Refresh a knowledge item by re-crawling its URL with the same metadata."""
|
||||
# CRITICAL: Validate OpenAI API key before starting refresh
|
||||
await _validate_openai_api_key()
|
||||
|
||||
try:
|
||||
safe_logfire_info(f"Starting knowledge item refresh | source_id={source_id}")
|
||||
|
||||
@@ -597,6 +686,9 @@ async def crawl_knowledge_item(request: KnowledgeItemRequest):
|
||||
if not request.url.startswith(("http://", "https://")):
|
||||
raise HTTPException(status_code=422, detail="URL must start with http:// or https://")
|
||||
|
||||
# CRITICAL: Validate OpenAI API key before starting crawl
|
||||
await _validate_openai_api_key()
|
||||
|
||||
try:
|
||||
safe_logfire_info(
|
||||
f"Starting knowledge item crawl | url={str(request.url)} | knowledge_type={request.knowledge_type} | tags={request.tags}"
|
||||
@@ -750,6 +842,9 @@ async def upload_document(
|
||||
knowledge_type: str = Form("technical"),
|
||||
):
|
||||
"""Upload and process a document with progress tracking."""
|
||||
# CRITICAL: Validate OpenAI API key before starting upload
|
||||
await _validate_openai_api_key()
|
||||
|
||||
try:
|
||||
# DETAILED LOGGING: Track knowledge_type parameter flow
|
||||
safe_logfire_info(
|
||||
@@ -974,6 +1069,69 @@ async def perform_rag_query(request: RagQueryRequest):
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
# Import embedding exceptions for specific error handling
|
||||
from ..services.embeddings.embedding_exceptions import (
|
||||
EmbeddingAPIError,
|
||||
EmbeddingAuthenticationError,
|
||||
EmbeddingQuotaExhaustedError,
|
||||
EmbeddingRateLimitError,
|
||||
)
|
||||
|
||||
# Handle specific OpenAI/embedding errors with detailed messages
|
||||
if isinstance(e, EmbeddingAuthenticationError):
|
||||
safe_logfire_error(
|
||||
f"OpenAI authentication failed during RAG query | query={request.query[:50]} | source={request.source}"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=401,
|
||||
detail={
|
||||
"error": "OpenAI API authentication failed",
|
||||
"message": "Invalid or expired OpenAI API key. Please check your API key in settings.",
|
||||
"error_type": "authentication_failed",
|
||||
"api_key_prefix": getattr(e, "api_key_prefix", None),
|
||||
}
|
||||
)
|
||||
elif isinstance(e, EmbeddingQuotaExhaustedError):
|
||||
safe_logfire_error(
|
||||
f"OpenAI quota exhausted during RAG query | query={request.query[:50]} | source={request.source}"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "OpenAI API quota exhausted",
|
||||
"message": "Your OpenAI API key has no remaining credits. Please add credits to your OpenAI account or check your billing settings.",
|
||||
"error_type": "quota_exhausted",
|
||||
"tokens_used": getattr(e, "tokens_used", None),
|
||||
}
|
||||
)
|
||||
elif isinstance(e, EmbeddingRateLimitError):
|
||||
safe_logfire_error(
|
||||
f"OpenAI rate limit hit during RAG query | query={request.query[:50]} | source={request.source}"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "OpenAI API rate limit exceeded",
|
||||
"message": "Too many requests to OpenAI API. Please wait a moment and try again.",
|
||||
"error_type": "rate_limit",
|
||||
"retry_after": 30, # Suggest 30 second wait
|
||||
}
|
||||
)
|
||||
elif isinstance(e, EmbeddingAPIError):
|
||||
safe_logfire_error(
|
||||
f"OpenAI API error during RAG query | error={str(e)} | query={request.query[:50]} | source={request.source}"
|
||||
)
|
||||
sanitized_message = _sanitize_openai_error(str(e))
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "OpenAI API error",
|
||||
"message": f"OpenAI API error: {sanitized_message}",
|
||||
"error_type": "api_error",
|
||||
}
|
||||
)
|
||||
else:
|
||||
# Generic error handling for other exceptions
|
||||
safe_logfire_error(
|
||||
f"RAG query failed | error={str(e)} | query={request.query[:50]} | source={request.source}"
|
||||
)
|
||||
|
||||
@@ -113,7 +113,7 @@ async def lifespan(app: FastAPI):
|
||||
_initialization_complete = True
|
||||
api_logger.info("🎉 Archon backend started successfully!")
|
||||
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
api_logger.error("❌ Failed to start backend", exc_info=True)
|
||||
raise
|
||||
|
||||
@@ -135,7 +135,7 @@ async def lifespan(app: FastAPI):
|
||||
|
||||
api_logger.info("✅ Cleanup completed")
|
||||
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
api_logger.error("❌ Error during shutdown", exc_info=True)
|
||||
|
||||
|
||||
|
||||
@@ -6,8 +6,7 @@ Handles URL transformations and validations.
|
||||
|
||||
import hashlib
|
||||
import re
|
||||
from urllib.parse import urlparse, urljoin
|
||||
from typing import List, Optional
|
||||
from urllib.parse import urljoin, urlparse
|
||||
|
||||
from ....config.logfire_config import get_logger
|
||||
|
||||
@@ -276,7 +275,7 @@ class URLHandler:
|
||||
return hashlib.sha256(fallback.encode("utf-8")).hexdigest()[:16]
|
||||
|
||||
@staticmethod
|
||||
def extract_markdown_links(content: str, base_url: Optional[str] = None) -> List[str]:
|
||||
def extract_markdown_links(content: str, base_url: str | None = None) -> list[str]:
|
||||
"""
|
||||
Extract markdown-style links from text content.
|
||||
|
||||
@@ -365,7 +364,7 @@ class URLHandler:
|
||||
return []
|
||||
|
||||
@staticmethod
|
||||
def is_link_collection_file(url: str, content: Optional[str] = None) -> bool:
|
||||
def is_link_collection_file(url: str, content: str | None = None) -> bool:
|
||||
"""
|
||||
Check if a URL/file appears to be a link collection file like llms.txt.
|
||||
|
||||
|
||||
@@ -99,6 +99,22 @@ class EmbeddingAPIError(EmbeddingError):
|
||||
self.metadata["original_error_message"] = str(original_error)
|
||||
|
||||
|
||||
class EmbeddingAuthenticationError(EmbeddingError):
|
||||
"""
|
||||
Raised when API authentication fails (invalid or expired API key).
|
||||
|
||||
This is a CRITICAL error that should stop the entire process
|
||||
as continuing would be pointless without valid API access.
|
||||
"""
|
||||
|
||||
def __init__(self, message: str, api_key_prefix: str | None = None, **kwargs):
|
||||
super().__init__(message, **kwargs)
|
||||
# Store masked API key prefix for debugging (first 3 chars + ellipsis)
|
||||
self.api_key_prefix = api_key_prefix[:3] + "…" if api_key_prefix and len(api_key_prefix) >= 3 else None
|
||||
if self.api_key_prefix:
|
||||
self.metadata["api_key_prefix"] = self.api_key_prefix
|
||||
|
||||
|
||||
class EmbeddingValidationError(EmbeddingError):
|
||||
"""
|
||||
Raised when embedding validation fails (e.g., zero vector detected).
|
||||
|
||||
@@ -5,9 +5,9 @@ Provides lightweight summary data for knowledge items to minimize data transfer.
|
||||
Optimized for frequent polling and card displays.
|
||||
"""
|
||||
|
||||
from typing import Any, Optional
|
||||
from typing import Any
|
||||
|
||||
from ...config.logfire_config import safe_logfire_info, safe_logfire_error
|
||||
from ...config.logfire_config import safe_logfire_error, safe_logfire_info
|
||||
|
||||
|
||||
class KnowledgeSummaryService:
|
||||
@@ -29,8 +29,8 @@ class KnowledgeSummaryService:
|
||||
self,
|
||||
page: int = 1,
|
||||
per_page: int = 20,
|
||||
knowledge_type: Optional[str] = None,
|
||||
search: Optional[str] = None,
|
||||
knowledge_type: str | None = None,
|
||||
search: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Get lightweight summaries of knowledge items.
|
||||
@@ -194,7 +194,7 @@ class KnowledgeSummaryService:
|
||||
|
||||
except Exception as e:
|
||||
safe_logfire_error(f"Failed to get document counts | error={str(e)}")
|
||||
return {sid: 0 for sid in source_ids}
|
||||
return dict.fromkeys(source_ids, 0)
|
||||
|
||||
async def _get_code_example_counts_batch(self, source_ids: list[str]) -> dict[str, int]:
|
||||
"""
|
||||
@@ -223,7 +223,7 @@ class KnowledgeSummaryService:
|
||||
|
||||
except Exception as e:
|
||||
safe_logfire_error(f"Failed to get code example counts | error={str(e)}")
|
||||
return {sid: 0 for sid in source_ids}
|
||||
return dict.fromkeys(source_ids, 0)
|
||||
|
||||
async def _get_first_urls_batch(self, source_ids: list[str]) -> dict[str, str]:
|
||||
"""
|
||||
|
||||
@@ -117,7 +117,8 @@ class RAGService:
|
||||
|
||||
if not query_embedding:
|
||||
logger.error("Failed to create embedding for query")
|
||||
return []
|
||||
# Follow fail-fast principle - embedding failure should not return empty results
|
||||
raise RuntimeError("Failed to create embedding for query - this indicates a configuration or API issue")
|
||||
|
||||
if use_hybrid_search:
|
||||
# Use hybrid strategy
|
||||
@@ -141,9 +142,22 @@ class RAGService:
|
||||
return results
|
||||
|
||||
except Exception as e:
|
||||
# Import embedding exceptions for specific error handling
|
||||
from ..embeddings.embedding_exceptions import (
|
||||
EmbeddingAPIError,
|
||||
EmbeddingAuthenticationError,
|
||||
EmbeddingQuotaExhaustedError,
|
||||
EmbeddingRateLimitError,
|
||||
)
|
||||
|
||||
# Re-raise OpenAI embedding errors so they propagate to the API layer with specific error info
|
||||
if isinstance(e, (EmbeddingAuthenticationError, EmbeddingQuotaExhaustedError, EmbeddingRateLimitError, EmbeddingAPIError)):
|
||||
raise
|
||||
|
||||
logger.error(f"Document search failed: {e}")
|
||||
span.set_attribute("error", str(e))
|
||||
return []
|
||||
# Follow fail-fast principle - don't return empty results for legitimate failures
|
||||
raise RuntimeError(f"Document search failed: {str(e)}") from e
|
||||
|
||||
async def search_code_examples(
|
||||
self,
|
||||
|
||||
466
python/tests/test_openai_error_handling.py
Normal file
466
python/tests/test_openai_error_handling.py
Normal file
@@ -0,0 +1,466 @@
|
||||
"""
|
||||
Comprehensive test suite for OpenAI error handling enhancements.
|
||||
|
||||
Tests the complete error flow from embedding service through RAG service
|
||||
to API endpoints, ensuring proper error propagation and message sanitization.
|
||||
|
||||
Related to GitHub Issue #362.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import Mock, AsyncMock, patch
|
||||
from fastapi import HTTPException
|
||||
|
||||
from src.server.services.embeddings.embedding_exceptions import (
|
||||
EmbeddingAuthenticationError,
|
||||
EmbeddingQuotaExhaustedError,
|
||||
EmbeddingRateLimitError,
|
||||
EmbeddingAPIError,
|
||||
)
|
||||
from src.server.services.search.rag_service import RAGService
|
||||
from src.server.api_routes.knowledge_api import perform_rag_query, RagQueryRequest, _sanitize_openai_error, _validate_openai_api_key
|
||||
|
||||
|
||||
class TestOpenAIErrorHandling:
|
||||
"""Test suite for OpenAI error handling in RAG queries."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_quota_exhausted_error_propagation(self):
|
||||
"""Test that quota exhausted errors propagate correctly through service layer."""
|
||||
|
||||
# Mock the create_embedding function to raise quota exhausted error
|
||||
with patch("src.server.services.search.rag_service.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.side_effect = EmbeddingQuotaExhaustedError(
|
||||
"OpenAI quota exhausted", tokens_used=1000
|
||||
)
|
||||
|
||||
# Create RAG service and test search_documents method
|
||||
with patch("src.server.services.search.rag_service.get_supabase_client"):
|
||||
rag_service = RAGService()
|
||||
|
||||
# Should propagate the quota error, not return empty list
|
||||
with pytest.raises(EmbeddingQuotaExhaustedError) as exc_info:
|
||||
await rag_service.search_documents("test query")
|
||||
|
||||
assert "quota exhausted" in str(exc_info.value).lower()
|
||||
assert exc_info.value.tokens_used == 1000
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_authentication_error_propagation(self):
|
||||
"""Test that authentication errors propagate correctly through service layer."""
|
||||
|
||||
# Mock the create_embedding function to raise authentication error
|
||||
with patch("src.server.services.search.rag_service.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.side_effect = EmbeddingAuthenticationError(
|
||||
"Invalid API key", api_key_prefix="sk-1234"
|
||||
)
|
||||
|
||||
# Create RAG service and test search_documents method
|
||||
with patch("src.server.services.search.rag_service.get_supabase_client"):
|
||||
rag_service = RAGService()
|
||||
|
||||
# Should propagate the authentication error
|
||||
with pytest.raises(EmbeddingAuthenticationError) as exc_info:
|
||||
await rag_service.search_documents("test query")
|
||||
|
||||
assert "invalid api key" in str(exc_info.value).lower()
|
||||
assert exc_info.value.api_key_prefix == "sk-1…"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_rate_limit_error_propagation(self):
|
||||
"""Test that rate limit errors propagate correctly through service layer."""
|
||||
|
||||
# Mock the create_embedding function to raise rate limit error
|
||||
with patch("src.server.services.search.rag_service.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.side_effect = EmbeddingRateLimitError(
|
||||
"Rate limit exceeded"
|
||||
)
|
||||
|
||||
# Create RAG service and test search_documents method
|
||||
with patch("src.server.services.search.rag_service.get_supabase_client"):
|
||||
rag_service = RAGService()
|
||||
|
||||
# Should propagate the rate limit error
|
||||
with pytest.raises(EmbeddingRateLimitError) as exc_info:
|
||||
await rag_service.search_documents("test query")
|
||||
|
||||
assert "rate limit" in str(exc_info.value).lower()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_quota_error_in_rag_endpoint(self):
|
||||
"""Test that quota errors are properly handled in RAG API endpoint."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to raise quota exhausted error
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=EmbeddingQuotaExhaustedError(
|
||||
"OpenAI quota exhausted", tokens_used=500
|
||||
)
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with status 429 and detailed error info
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error details
|
||||
assert exc_info.value.status_code == 429
|
||||
assert "quota exhausted" in exc_info.value.detail["error"].lower()
|
||||
assert "add credits" in exc_info.value.detail["message"].lower()
|
||||
assert exc_info.value.detail["error_type"] == "quota_exhausted"
|
||||
assert exc_info.value.detail["tokens_used"] == 500
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_authentication_error_in_rag_endpoint(self):
|
||||
"""Test that authentication errors are properly handled in RAG API endpoint."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to raise authentication error
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=EmbeddingAuthenticationError(
|
||||
"Invalid API key", api_key_prefix="sk-1234"
|
||||
)
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with status 401 and detailed error info
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error details
|
||||
assert exc_info.value.status_code == 401
|
||||
assert "authentication failed" in exc_info.value.detail["error"].lower()
|
||||
assert "invalid or expired" in exc_info.value.detail["message"].lower()
|
||||
assert exc_info.value.detail["error_type"] == "authentication_failed"
|
||||
assert exc_info.value.detail["api_key_prefix"] == "sk-1…"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_rate_limit_error_in_rag_endpoint(self):
|
||||
"""Test that rate limit errors are properly handled in RAG API endpoint."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to raise rate limit error
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=EmbeddingRateLimitError("Rate limit exceeded")
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with status 429 and detailed error info
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error details
|
||||
assert exc_info.value.status_code == 429
|
||||
assert "rate limit" in exc_info.value.detail["error"].lower()
|
||||
assert "too many requests" in exc_info.value.detail["message"].lower()
|
||||
assert exc_info.value.detail["error_type"] == "rate_limit"
|
||||
assert exc_info.value.detail["retry_after"] == 30
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_generic_error_in_rag_endpoint(self):
|
||||
"""Test that generic API errors are properly handled in RAG API endpoint."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to raise generic API error
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=EmbeddingAPIError("Invalid model specified")
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with status 502 and detailed error info
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error details
|
||||
assert exc_info.value.status_code == 502
|
||||
assert "openai api error" in exc_info.value.detail["error"].lower()
|
||||
assert exc_info.value.detail["error_type"] == "api_error"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_key_validation_success(self):
|
||||
"""Test that API key validation passes with valid key."""
|
||||
|
||||
# Mock create_embedding to succeed
|
||||
with patch("src.server.api_routes.knowledge_api.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.return_value = [0.1] * 1536 # Mock successful embedding
|
||||
|
||||
# Should not raise any exception
|
||||
await _validate_openai_api_key()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_key_validation_auth_failure(self):
|
||||
"""Test that API key validation raises 401 for authentication failures."""
|
||||
|
||||
# Mock create_embedding to raise authentication error
|
||||
with patch("src.server.api_routes.knowledge_api.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.side_effect = EmbeddingAuthenticationError(
|
||||
"Invalid API key", api_key_prefix="sk-1234"
|
||||
)
|
||||
|
||||
# Should raise HTTPException with status 401
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await _validate_openai_api_key()
|
||||
|
||||
assert exc_info.value.status_code == 401
|
||||
assert exc_info.value.detail["error_type"] == "authentication_failed"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_key_validation_quota_failure(self):
|
||||
"""Test that API key validation raises 429 for quota exhaustion."""
|
||||
|
||||
# Mock create_embedding to raise quota exhausted error
|
||||
with patch("src.server.api_routes.knowledge_api.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.side_effect = EmbeddingQuotaExhaustedError(
|
||||
"Quota exhausted", tokens_used=1000
|
||||
)
|
||||
|
||||
# Should raise HTTPException with status 429
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await _validate_openai_api_key()
|
||||
|
||||
assert exc_info.value.status_code == 429
|
||||
assert exc_info.value.detail["error_type"] == "quota_exhausted"
|
||||
|
||||
def test_sanitize_openai_error_removes_urls(self):
|
||||
"""Test that sanitization function removes URLs from error messages."""
|
||||
error_message = "Connection failed to https://api.openai.com/v1/embeddings with status 400"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "https://api.openai.com" not in sanitized
|
||||
assert "[REDACTED_URL]" in sanitized
|
||||
assert "Connection failed" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_removes_api_keys(self):
|
||||
"""Test that sanitization function removes API keys from error messages."""
|
||||
error_message = "Authentication failed with key sk-1234567890abcdef1234567890abcdef1234567890abcdef"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "sk-1234567890abcdef1234567890abcdef1234567890abcdef" not in sanitized
|
||||
assert "[REDACTED_KEY]" in sanitized
|
||||
assert "Authentication failed" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_removes_auth_info(self):
|
||||
"""Test that sanitization function removes auth details from error messages."""
|
||||
error_message = 'Failed to authenticate: "auth_bearer_xyz123"'
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "auth_bearer_xyz123" not in sanitized
|
||||
assert "[REDACTED_AUTH]" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_returns_generic_for_sensitive_words(self):
|
||||
"""Test that sanitization returns generic message for sensitive internal details."""
|
||||
error_message = "Internal server error on endpoint /v1/embeddings"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
# Should return generic message due to 'internal' and 'endpoint' keywords
|
||||
assert sanitized == "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
def test_sanitize_openai_error_preserves_safe_messages(self):
|
||||
"""Test that sanitization preserves safe error messages."""
|
||||
error_message = "Model not found: text-embedding-ada-002"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
# Should preserve the message since it contains no sensitive info
|
||||
assert sanitized == error_message
|
||||
|
||||
def test_sanitize_openai_error_removes_organization_ids(self):
|
||||
"""Test that sanitization function removes OpenAI organization IDs."""
|
||||
error_message = "Permission denied for org-1234567890abcdef12345678 with model access"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "org-1234567890abcdef12345678" not in sanitized
|
||||
assert "[REDACTED_ORG]" in sanitized
|
||||
assert "Permission denied" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_removes_project_ids(self):
|
||||
"""Test that sanitization function removes OpenAI project IDs."""
|
||||
error_message = "Project proj_abcdef1234567890xyz not found"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "proj_abcdef1234567890xyz" not in sanitized
|
||||
assert "[REDACTED_PROJ]" in sanitized
|
||||
assert "Project" in sanitized and "not found" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_removes_request_ids(self):
|
||||
"""Test that sanitization function removes OpenAI request IDs."""
|
||||
error_message = "Request req_1234567890abcdefghij failed with timeout"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
assert "req_1234567890abcdefghij" not in sanitized
|
||||
assert "[REDACTED_REQ]" in sanitized
|
||||
assert "Request" in sanitized and "failed with timeout" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_handles_multiple_patterns(self):
|
||||
"""Test that sanitization handles multiple sensitive patterns in one message."""
|
||||
error_message = "Request req_abc123 to https://api.openai.com failed for org-1234567890abcdef12345678 with key sk-1234567890abcdef1234567890abcdef1234567890abcdef"
|
||||
sanitized = _sanitize_openai_error(error_message)
|
||||
|
||||
# Verify all patterns are redacted
|
||||
assert "req_abc123" not in sanitized
|
||||
assert "https://api.openai.com" not in sanitized
|
||||
assert "org-1234567890abcdef12345678" not in sanitized
|
||||
assert "sk-1234567890abcdef1234567890abcdef1234567890abcdef" not in sanitized
|
||||
|
||||
# Verify redacted placeholders are present
|
||||
assert "[REDACTED_REQ]" in sanitized
|
||||
assert "[REDACTED_URL]" in sanitized
|
||||
assert "[REDACTED_ORG]" in sanitized
|
||||
assert "[REDACTED_KEY]" in sanitized
|
||||
|
||||
def test_sanitize_openai_error_input_validation(self):
|
||||
"""Test that sanitization function handles invalid input gracefully."""
|
||||
# Test None input
|
||||
result = _sanitize_openai_error(None)
|
||||
assert result == "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
# Test non-string input
|
||||
result = _sanitize_openai_error(123)
|
||||
assert result == "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
# Test empty string
|
||||
result = _sanitize_openai_error("")
|
||||
assert result == "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
# Test whitespace-only string
|
||||
result = _sanitize_openai_error(" ")
|
||||
assert result == "OpenAI API encountered an error. Please verify your API key and quota."
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_fail_fast_pattern_embedding_failure(self):
|
||||
"""Test that embedding failures now fail fast instead of returning empty results."""
|
||||
|
||||
# Mock the create_embedding function to return None (failure)
|
||||
with patch("src.server.services.search.rag_service.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.return_value = None
|
||||
|
||||
# Create RAG service and test search_documents method
|
||||
with patch("src.server.services.search.rag_service.get_supabase_client"):
|
||||
rag_service = RAGService()
|
||||
|
||||
# Should raise RuntimeError instead of returning empty list
|
||||
with pytest.raises(RuntimeError) as exc_info:
|
||||
await rag_service.search_documents("test query")
|
||||
|
||||
assert "Failed to create embedding" in str(exc_info.value)
|
||||
assert "configuration or API issue" in str(exc_info.value)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_fail_fast_pattern_search_failure(self):
|
||||
"""Test that search failures now fail fast instead of returning empty results."""
|
||||
|
||||
# Mock the create_embedding to succeed but vector search to fail
|
||||
with patch("src.server.services.search.rag_service.create_embedding") as mock_create_embedding:
|
||||
mock_create_embedding.return_value = [0.1] * 1536 # Mock embedding vector
|
||||
|
||||
with patch("src.server.services.search.rag_service.get_supabase_client"):
|
||||
rag_service = RAGService()
|
||||
|
||||
# Mock the base strategy to raise an exception
|
||||
with patch.object(rag_service.base_strategy, 'vector_search', side_effect=Exception("Database connection failed")):
|
||||
|
||||
# Should raise RuntimeError instead of returning empty list
|
||||
with pytest.raises(RuntimeError) as exc_info:
|
||||
await rag_service.search_documents("test query")
|
||||
|
||||
assert "Document search failed" in str(exc_info.value)
|
||||
assert "Database connection failed" in str(exc_info.value)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_integration_error_flow_rag_to_api(self):
|
||||
"""Test complete error flow from RAG service through API endpoint."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock both RAGService and get_supabase_client to avoid real connections
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=RuntimeError("Document search failed: Database connection failed")
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with generic error (not OpenAI specific)
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error details
|
||||
assert exc_info.value.status_code == 500
|
||||
assert "RAG query failed" in exc_info.value.detail["error"]
|
||||
assert "Database connection failed" in exc_info.value.detail["error"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_error_sanitization_in_endpoint(self):
|
||||
"""Test that API errors are sanitized in the RAG endpoint response."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to raise API error with sensitive information
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(
|
||||
side_effect=EmbeddingAPIError("Request failed to https://api.openai.com/v1/embeddings with key sk-1234567890abcdef1234567890abcdef1234567890abcdef")
|
||||
)
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should raise HTTPException with sanitized error message
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await perform_rag_query(request)
|
||||
|
||||
# Verify error is sanitized
|
||||
error_message = exc_info.value.detail["message"]
|
||||
assert "sk-1234567890abcdef1234567890abcdef1234567890abcdef" not in error_message
|
||||
assert "https://api.openai.com" not in error_message
|
||||
assert "[REDACTED_KEY]" in error_message
|
||||
assert "[REDACTED_URL]" in error_message
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_successful_rag_query_flow(self):
|
||||
"""Test that successful RAG queries work correctly."""
|
||||
|
||||
request = RagQueryRequest(query="test query", match_count=5)
|
||||
|
||||
# Mock RAGService to return successful result
|
||||
mock_result = {
|
||||
"results": [{"content": "test result"}],
|
||||
"query": "test query",
|
||||
"total_found": 1,
|
||||
}
|
||||
|
||||
with patch("src.server.api_routes.knowledge_api.RAGService") as mock_rag_service_class, \
|
||||
patch("src.server.api_routes.knowledge_api.get_supabase_client"):
|
||||
|
||||
mock_service = Mock()
|
||||
mock_service.perform_rag_query = AsyncMock(return_value=(True, mock_result))
|
||||
mock_rag_service_class.return_value = mock_service
|
||||
|
||||
# Should return successful result
|
||||
result = await perform_rag_query(request)
|
||||
|
||||
# Verify successful response
|
||||
assert result["success"] is True
|
||||
assert result["results"] == [{"content": "test result"}]
|
||||
assert result["total_found"] == 1
|
||||
Reference in New Issue
Block a user