When using model groups (e.g. 'deepseek-auto'), the dashboard logged the
group name instead of the concrete resolved model (e.g. 'deepseek-reasoner').
Now:
- logRequest passes the resolved modelID (concrete) + modelGroup (group name)
- RequestLog struct has a new ModelGroup field (omitempty)
- Dashboard displays resolved model (via group) when a group was used
Files changed:
internal/server/logging.go - add ModelGroup field
internal/server/server.go - pass resolved modelID, capture modelGroup
static/js/websocket.js - show group annotation in Recent Activity
static/js/pages/overview.js - show group annotation in overview table
static/js/pages/monitoring.js - show group annotation in stream
30-second resty client timeout was killing long streaming responses
mid-generation. Models with large output windows (e.g. deepseek-v4-pro
at 384K max_tokens) routinely exceed 30s. Raised all providers to
10 minutes (Ollama already at 15min, unchanged). Circuit breaker
recovery timeout raised from 30s to 5min.
Logs what max_tokens the client sends, whether gophergate injects
one from the registry, and the final value forwarded to the provider.
Helps trace output truncation issues.
Go 1.26 changed NullTime.Scan to delegate to convertAssign,
which has no string->time.Time conversion path. The
modernc.org/sqlite driver returns raw strings for aggregate
expressions like MAX(last_used_at), causing silent scan failures
that made all clients/providers show 'Never' for last used.
Fixes by scanning into a string and parsing with time.Parse.
When a client omits max_tokens, providers (DeepSeek, etc.) apply
a low server-side default output cap. Now gophergate looks up the
model in the models.dev registry and injects the model's output
limit, preventing silent truncation.
- Add CachedContentTokenCount to UsageMetadata parsing for both
streaming (helpers.go) and non-streaming (gemini.go) requests
- CacheReadTokens now populated from Gemini cachedContentTokenCount
- Add uint32Ptr helper for nil-safe uint32 pointer creation
Add step between exact ID match and forward fuzzy match that checks
if registry model ID starts with the requested name. Fixes models like
'gpt-5.4-mini' not matching 'gpt-5.4-mini-2026-04-01' in registry.
- 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 configuration instructions to README.md
- Update API usage section with Ollama examples
- Add Ollama to provider list in BACKEND_ARCHITECTURE.md
- All documentation now reflects complete Ollama support
- 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
hobokenchicken