- Gemini requires function results to immediately follow the model message that called them
- Implemented look-ahead grouping to pair assistant calls with their tool results
- Standardized system and orphaned tool message handling for Gemini compatibility
- Group consecutive 'tool' messages into a single Gemini content message with multiple 'functionResponse' parts
- Ensure assistant tool calls are properly mapped and sent
- Maintain v1beta for preview and newer models
- Added debug logging for API errors
- Support tool definitions in Gemini requests
- Map tool role to 'function' in Gemini content
- Ensure tool results are wrapped in JSON objects for Gemini compatibility
- Parse FunctionCall from Gemini response and map to OpenAI-compatible ToolCalls
- Correctly map finish_reason for tool calls
- Explicitly set tool_choice: auto when tools are present to aid gemma/llama models
- Sync stop sequences into the options map for broader compatibility
- Restore gemma/llama to the high-context (32k) optimization list
- Use case-insensitive matching for model names and routing
- Default max_tokens/num_predict to 8192 for all Ollama models to prevent truncation
- Increase default context window and add more large-context model families
- Ensure DeepSeek routing handles Ollama-hosted variants correctly
- Map MaxTokens to num_predict in options map
- Set default num_ctx to 8192 for common models (gemma, llama, etc.)
- This ensures Ollama doesn't cut off responses early due to default limits
- Increase Ollama timeout to 5m for larger models (e.g. gemma4)
- Set default max_tokens to 4096 for common Ollama models
- Expand stream scanner buffer to 10MB to prevent truncation
- Improve model routing and prefix stripping in server
- Add timeouts (30s) and retries to resty client
- Add debug logging for Ollama requests and responses
- Import time package for timeout configuration
- This should fix 500 errors and provide better error messages
- Add Ollama to allowed providers in model list endpoint
- Add model pattern detection for Ollama models (glm-, qwen, gemma, llama, mistral, codellama)
- This fixes 500 errors when using Ollama models via /v1/chat/completions
- Implement OllamaProvider with OpenAI-compatible API integration
- Add Ollama to provider initialization in server.go
- Update config.go to handle Ollama (no API key required)
- Configure .env with Ollama server at 172.20.1.222:11434
- Support models: glm-4.7-flash:latest, qwen3-coder:30b, gemma4:26b
- Added AES-GCM encryption/decryption for provider API keys in the database.
- Implemented RefreshProviders to load provider configs from the database with precedence over environment variables.
- Updated dashboard handlers to encrypt keys on save and trigger in-memory provider refresh.
- Updated Grok test model to grok-3-mini for better compatibility.
- Fixed status indicator UI mapping in websocket.js and index.html.
- Added missing CSS for connection status indicator and pulse animation.
- Made initial model registry fetch asynchronous to prevent blocking server startup.
- Improved configuration loading to correctly handle LLM_PROXY__SERVER__PORT from environment.
Added JSON tags to the User struct to match frontend expectations and excluded sensitive fields.
Updated session management to include and persist DisplayName.
Unified user field names (using display_name) across backend, sessions, and frontend UI.
Updated handleGetProviders to include available models and last-used timestamps. Refined Model Pricing table to strictly filter by core providers and actual usage.
Added /api/system/metrics with CPU/Mem/Disk/Load data using gopsutil. Updated Hub to track active WebSocket listeners. Verified log format for monitoring charts.
Filtered registry iteration to only include openai, gemini, deepseek, and grok. Improved used_only logic to match specific (model, provider) pairs from logs.
Refined CalculateCost to correctly handle cached token discounts. Added fuzzy matching to model lookup. Robustified SQL date extraction using SUBSTR and LIKE for better SQLite compatibility.
Fixed '2025 years ago' issue by correctly handling zero-value timestamps. Improved SQL scanning logic to handle NULL values more safely across all analytics handlers.
Restricted AuthMiddleware to /v1 group to prevent dashboard session interference. Robustified analytics SQL queries with COALESCE to handle empty datasets.
Improved extraction of reasoning and cached tokens from OpenAI and DeepSeek responses (including streams). Ensured accurate cost calculation using registry metadata.
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