- Remove optional 'modes' parameter from aclear_cache() and clear_cache() methods
- Replace deprecated drop_cache_by_modes() with drop() method for complete cache clearing
- Update API endpoint to ignore mode-specific parameters and clear all cache
- Simplify frontend clearCache() function to send empty request body
This change ensures all LLM cache is cleared together.
- Add global --temperature command line argument with env fallback
- Implement temperature priority for Ollama LLM binding:
1. --ollama-llm-temperature (highest)
2. OLLAMA_LLM_TEMPERATURE env var
3. --temperature command arg
4. TEMPERATURE env var (lowest)
- Implement same priority logic for OpenAI/Azure OpenAI LLM binding
- Ensure command line args always override environment variables
- Maintain backward compatibility with existing configurations
- Add OpenAILLMOptions dataclass with full OpenAI API parameter support
- Integrate OpenAI options in config.py for automatic binding detection
- Update server functions to inject OpenAI options for openai/azure_openai bindings
- Implement OLLAMA_LLM_TEMPERATURE env var
- Fallback to global TEMPERATURE if unset
- Remove redundant OllamaLLMOptions logic
- Update env.example with new setting
- The `initialize_storages` method must be explicitly called after LightRAG creation.
The `finalize_storages` method should be called before LightRAG destyoyed.
- Added explicit data migration check
- Add file_path sorting support to all database backends (JSON, Redis, PostgreSQL, MongoDB)
- Implement smart column header switching between "ID" and "File Name" based on display mode
- Add automatic sort field switching when toggling between ID and file name display
- Create composite indexes for workspace+file_path in PostgreSQL and MongoDB for better query performance
- Update frontend to maintain sort state when switching display modes
- Add internationalization support for "fileName" in English and Chinese locales
This enhancement improves user experience by providing intuitive file-based sorting
while maintaining performance through optimized database indexes.
- Add pagination support to BaseDocStatusStorage interface and all implementations (PostgreSQL, MongoDB, Redis, JSON)
- Implement RESTful API endpoints for paginated document queries and status counts
- Create reusable pagination UI components with internationalization support
- Optimize performance with database-level pagination and efficient in-memory processing
- Maintain backward compatibility while adding configurable page sizes (10-200 items)
- Add metadata field to doc_status storage with Unix timestamps for processing start/end times
- Update frontend API types: error -> error_msg, add track_id and metadata support
- Add getTrackStatus API method for document tracking functionality
- Fix frontend DocumentManager to use error_msg field for proper error display
- Ensure full compatibility between backend metadata changes and frontend UI
This parameter is no longer used. Its removal simplifies the API and clarifies that token length management is handled by upstream text chunking logic rather than the embedding wrapper.
Fix line length
Create binding_options.py
Remove test property
Add dynamic binding options to CLI and environment config
Automatically generate command-line arguments and environment variable
support for all LLM provider bindings using BindingOptions. Add sample
.env generation and extensible framework for new providers.
Add example option definitions and fix test arg check in OllamaOptions
Add options_dict method to BindingOptions for argument parsing
Add comprehensive Ollama binding configuration options
ruff formatting Apply ruff formatting to binding_options.py
Add Ollama separate options for embedding and LLM
Refactor Ollama binding options and fix class var handling
The changes improve how class variables are handled in binding options
and better organize the Ollama-specific options into LLM and embedding
subclasses.
Fix typo in arg test.
Rename cls parameter to klass to avoid keyword shadowing
Fix Ollama embedding binding name typo
Fix ollama embedder context param name
Split Ollama options into LLM and embedding configs with mixin base
Add Ollama option configuration to LLM and embeddings in lightrag_server
Update sample .env generation and environment handling
Conditionally add env vars and cmdline options only when ollama bindings
are used. Add example env file for Ollama binding options.
- Add ollama_server_infos attribute to LightRAG class with default initialization
- Move default values to constants.py for centralized configuration
- Refactor OllamaServerInfos class with property accessors and CLI support
- Update OllamaAPI to get configuration through rag object instead of direct import
- Add command line arguments for simulated model name and tag
- Fix type imports to avoid circular dependencies
