8 Commits (d0dc36ac0b014d27580f1a3dc87af7b981f18afa)

Author	SHA1	Message	Date
Rene Cannao	d0dc36ac0b	feat: Add structured logging with timing and request IDs ... Add comprehensive structured logging for NL2SQL LLM API calls with request correlation, timing metrics, and detailed error context. Changes: - Add request_id field to NL2SQLRequest with UUID-like auto-generation - Add structured logging macros: * LOG_LLM_REQUEST: Logs URL, model, prompt length with request ID * LOG_LLM_RESPONSE: Logs HTTP status, duration_ms, response preview * LOG_LLM_ERROR: Logs error phase, message, and status code - Update call_generic_openai() signature to accept req_id parameter - Update call_generic_anthropic() signature to accept req_id parameter - Add timing metrics to both LLM call functions using clock_gettime() - Replace existing debug logging with structured logging macros - Update convert() to pass request_id to LLM calls Request IDs are generated as UUID-like strings (e.g., "12345678-9abc-def0-1234-567890abcdef") and are included in all log messages for correlation. This allows tracking a single NL2SQL request through all log lines from request to response. Timing is measured using CLOCK_MONOTONIC for accurate duration tracking of LLM API calls, reported in milliseconds. This provides much better debugging capability when troubleshooting NL2SQL issues, as administrators can now: - Correlate all log lines for a single request - See exact timing of LLM API calls - Identify which phase of processing failed - Track request/response metrics Fixes #2 - Add Structured Logging	1 month ago
Rene Cannao	45e592b623	feat: Add structured error messages with context to NL2SQL ... Add comprehensive error details to help users debug NL2SQL conversion issues. Changes: - Add error_code, error_details, http_status_code, provider_used fields to NL2SQLResult - Add NL2SQLErrorCode enum with structured error codes: * SUCCESS, ERR_API_KEY_MISSING, ERR_API_KEY_INVALID, ERR_TIMEOUT * ERR_CONNECTION_FAILED, ERR_RATE_LIMITED, ERR_SERVER_ERROR * ERR_EMPTY_RESPONSE, ERR_INVALID_RESPONSE, ERR_SQL_INJECTION_DETECTED * ERR_VALIDATION_FAILED, ERR_UNKNOWN_PROVIDER, ERR_REQUEST_TOO_LARGE - Add nl2sql_error_code_to_string() function for error code conversion - Add format_error_context() helper to create detailed error messages including: * Query (truncated if too long) * Schema name * Provider attempted * Endpoint URL * Specific error message - Add set_error_details() helper to populate error fields - Update error handling in convert() to use new error details - Track provider_used in successful conversions This provides much better debugging information when NL2SQL conversions fail, making it easier to identify misconfigurations and connectivity issues. Fixes #1 - Improve Error Messages	1 month ago
Rene Cannao	36b11223b2	feat: Improve SQL validation with multi-factor scoring ... Add comprehensive SQL validation with confidence scoring based on: - SQL keyword detection (17 keywords covering DDL/DML/transactions) - Structural validation (balanced parentheses and quotes) - SQL injection pattern detection - Length and quality checks Confidence scoring: - Base 0.4 for valid SQL keyword - +0.15 for balanced parentheses - +0.15 for balanced quotes - +0.1 for minimum length - +0.1 for FROM clause in SELECT statements - +0.1 for no injection patterns - -0.3 penalty for injection patterns detected Low confidence (< 0.5) results are logged with detailed info. Cache storage threshold updated to 0.5 confidence (from implicit valid_sql). This improves detection of malformed or potentially malicious SQL while providing granular confidence scores for downstream use.	1 month ago
Rene Cannao	897d306d2d	Refactor: Simplify NL2SQL to use only generic providers ... Remove Ollama-specific provider code and use only generic OpenAI-compatible and Anthropic-compatible providers. Ollama is now used via its OpenAI-compatible endpoint at /v1/chat/completions. Changes: - Remove LOCAL_OLLAMA from ModelProvider enum - Remove ai_nl2sql_ollama_model and ai_nl2sql_ollama_url variables - Remove call_ollama() function from LLM_Clients.cpp - Update default configuration to use OpenAI provider with Ollama URL - Update all documentation to reflect generic-only approach Configuration: - ai_nl2sql_provider: 'openai' or 'anthropic' (default: 'openai') - ai_nl2sql_provider_url: endpoint URL (default: Ollama OpenAI-compatible) - ai_nl2sql_provider_model: model name - ai_nl2sql_provider_key: API key (optional for local endpoints) This simplifies the codebase by removing a separate code path for Ollama and aligns with the goal of avoiding provider-specific variables.	1 month ago
Rene Cannao	637b2a669c	feat: Implement NL2SQL vector cache and complete Anomaly threat pattern management ... NL2SQL_Converter improvements: - Implement get_query_embedding() using GenAI module - Implement check_vector_cache() with KNN search via sqlite-vec - Implement store_in_vector_cache() with embedding storage - All stub methods now fully functional Anomaly_Detector improvements: - Implement add_threat_pattern() with embedding generation - Stores patterns in both main table and virtual vec table - Returns pattern ID on success, -1 on error Documentation: - Add comprehensive VECTOR_FEATURES documentation - README.md (471 lines): User guide and quick start - API.md (736 lines): Complete API reference - ARCHITECTURE.md (358 lines): System architecture - TESTING.md (767 lines): Testing guide and procedures This completes the vector features implementation, enabling: - Semantic similarity caching for NL2SQL queries - Embedding-based threat pattern detection - Full CRUD operations for threat patterns	1 month ago
Rene Cannao	fec7d64093	feat: Implement NL2SQL vector cache with GenAI embedding generation ... Implemented semantic caching for NL2SQL using sqlite-vec and GenAI module: Changes to lib/AI_Features_Manager.cpp: - Create virtual vec0 tables for similarity search: * nl2sql_cache_vec for NL2SQL cache * anomaly_patterns_vec for threat patterns * query_history_vec for query history Changes to include/NL2SQL_Converter.h: - Add get_query_embedding() method declaration Changes to lib/NL2SQL_Converter.cpp: - Add GenAI_Thread.h include and GloGATH extern - Implement get_query_embedding() - calls GloGATH->embed_documents() - Implement check_vector_cache() - sqlite-vec KNN search with cosine distance - Implement store_in_vector_cache() - stores embedding and updates vec table - Implement clear_cache() - deletes from both main and vec tables - Implement get_cache_stats() - returns cache entry/hit counts - Add vector_to_json() helper for sqlite-vec MATCH queries Features: - Uses GenAI module (llama-server) for embedding generation - Cosine similarity search via sqlite-vec vec_distance_cosine() - Configurable similarity threshold (ai_nl2sql_cache_similarity_threshold) - Automatic hit counting and timestamp tracking	1 month ago
Rene Cannao	4f45c25945	docs: Add comprehensive doxygen comments to NL2SQL headers and LLM_Clients ... - Add file-level doxygen documentation with @file, @brief, @date, @version - Add detailed class and method documentation with @param, @return, @note, @see - Document data structures (NL2SQLRequest, NL2SQLResult, ModelProvider) - Add section comments and inline documentation for implementation files - Document all three LLM provider APIs (Ollama, OpenAI, Anthropic)	1 month ago
Rene Cannao	147a059781	feat: Add NL2SQL converter with hybrid LLM support ... - Add NL2SQL_Converter with prompt building and model selection - Add LLM clients for Ollama, OpenAI, Anthropic APIs - Update Makefile for new source files	1 month ago