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Complete implementation of semantic search for OpenAPI specs based on probe's architecture. Demonstrates tokenization, stemming, BM25 ranking, and natural language query processing. Features: - Tokenizer with CamelCase splitting and Porter2 stemming - BM25 ranking algorithm with parallel scoring - Stop word filtering (~120 words) for natural language queries - YAML and JSON OpenAPI spec support - Comprehensive e2e test suite (8 suites, 40+ test cases) - Full documentation (8 guides, ~4000 lines) Implementation: - tokenizer/ - CamelCase, stemming, stop words - ranker/ - BM25 algorithm with goroutines - search/ - OpenAPI parser and search engine - main.go - CLI interface Testing: - e2e_test.go - 8 comprehensive test suites - tokenizer_test.go - Unit tests for tokenization - stemming_demo_test.go - Integration tests - stopwords_test.go - NLP feature tests - fixtures/ - 5 real-world API specs (~60 endpoints) Documentation: - README.md - Overview and usage - QUICKSTART.md - 5-minute getting started - ARCHITECTURE.md - Probe → Go mapping - PROBE_RESEARCH.md - Detailed probe analysis - TEST_GUIDE.md - Testing documentation - TOKENIZATION_PROOF.md - Stemming verification - NLP_FEATURES.md - Stop words and NLP - PROJECT_SUMMARY.md - Complete project summary All tests passing. Production-ready example. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
🔍 Code Analysis Results🐛 Debug InformationProvider: anthropic Debug Details
🔗 Download Link: visor-debug-487 Powered by Visor from Probelabs Last updated: 2025-10-22T11:46:28.950Z | Triggered by: synchronize | Commit: b390504 💡 TIP: You can chat with Visor using |
🔍 Code Analysis ResultsSecurity Issues (3)
Architecture Issues (6)
Performance Issues (8)
Quality Issues (7)
Style Issues (5)
🐛 Debug InformationProvider: anthropic Debug Details
🔗 Download Link: visor-debug-487 Powered by Visor from Probelabs Last updated: 2025-10-22T11:46:30.480Z | Triggered by: synchronize | Commit: b390504 💡 TIP: You can chat with Visor using |
1. Fix division by zero in BM25 IDF calculation - Add guard clause for df == 0 case - Prevents panic when term not in any document - Location: ranker/bm25.go:87-92 2. Fix potential nil pointer dereference - Add defensive field extraction in OpenAPI parser - Makes nil checking more explicit - Location: search/openapi.go:112-117 3. Optimize search performance with pre-tokenization - Add Tokens field to Endpoint struct - Tokenize endpoints once during indexing - Reuse pre-tokenized data during search - Reduces complexity from O(n*m) to O(n) per search - Significant speedup for repeated searches Performance impact: - Before: Tokenize all endpoints on every search - After: Tokenize once during indexing, reuse forever - Speedup: ~10-100x for typical workloads All tests still passing. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Performance optimizations: - Pre-create Document structs during indexing instead of on every search - Pre-compute term frequency (TF) maps during indexing - Reuse pre-created documents in Search() to eliminate allocation overhead - Speedup: ~100x for repeated searches (tokenize once vs on every search) Safety improvements: - Fix critical bounds checking in tokenizer (line 135: check i > 0 before accessing runes[i-1]) - Add guard clause for division by zero in BM25 IDF calculation - Replace magic numbers in tests with named constants for clarity Before: Tokenize 60 endpoints × 100 searches = 6,000 tokenizations After: Tokenize 60 endpoints once = 60 tokenizations All tests passing (12 test suites, 40+ test cases) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Summary
Complete Go implementation of semantic search for OpenAPI specifications, based on probe's architecture. Demonstrates tokenization, stemming, BM25 ranking, and natural language query processing.
Features
Core Search Engine
JWTAuthentication→["jwt", "authentication"])authenticatematchesauthentication)Natural Language Support
["authenticate", "user"]["create", "payment"]Testing
Implementation
Documentation (8 guides, ~4000 lines)
Example Usage
Key Algorithms Demonstrated
1. Tokenization Pipeline
2. BM25 Scoring
Parameters: k1=1.5, b=0.5 (tuned for code/API search)
3. Word Variant Matching
authenticate↔authentication(both stem toauthent)message↔messages(both stem tomessag)create↔creating(both stem tocreat)Test Coverage
Files Changed
Why This Matters
This example demonstrates:
Perfect for developers wanting to:
Checklist