proxysql

Commit Graph

Author	SHA1	Message	Date
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.	5 months ago
Rene Cannao	c5a7fc31f7	Add external LLM setup guide and live testing script - test_external_live.sh: Interactive script for testing with custom LLM - EXTERNAL_LLM_SETUP.md: Complete guide for external model configuration Covers: - Custom LLM endpoint configuration for NL2SQL - llama-server configuration for embeddings - Architecture overview - Configuration variables - Testing procedures - Troubleshooting tips	5 months 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	5 months ago

Author

SHA1

Message

Date

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.

Rene Cannao

c5a7fc31f7

Add external LLM setup guide and live testing script

- test_external_live.sh: Interactive script for testing with custom LLM
- EXTERNAL_LLM_SETUP.md: Complete guide for external model configuration

Covers:
- Custom LLM endpoint configuration for NL2SQL
- llama-server configuration for embeddings
- Architecture overview
- Configuration variables
- Testing procedures
- Troubleshooting tips

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

3 Commits (6cdd4e697eb86982e3dfd2ceab009ead5da0efcb)