- Update extract_entities function in operate.py to use 1.0 as default weight
- Fix Neo4j implementation to use 1.0 instead of 0.0 for missing edge weights
- Fix Memgraph implementation to use 1.0 instead of 0.0 for missing edge weights
- Ensures consistent non-zero default weights across all graph storage backends
This commit refactors query parameter management by consolidating settings like `top_k`, token limits, and thresholds into the `LightRAG` class, and consistently sourcing parameters from a single location.
- Remove MAX_TOKEN_SUMMARY parameter and related configurations
- Eliminate forced token-based truncation in entity/relationship descriptions
- Switch to fragment-count based summarization logic using FORCE_LLM_SUMMARY_ON_MERGE
- Update FORCE_LLM_SUMMARY_ON_MERGE default from 6 to 4 for better summarization
- Clean up documentation, environment examples, and API display code
- Preserve backward compatibility by graceful parameter removal
This change resolves issues where LLMs were forcibly truncating entity relationship
descriptions mid-sentence, leading to incomplete and potentially inaccurate knowledge
graph content. The new approach allows LLMs to generate complete descriptions while
still providing summarization when multiple fragments need to be merged.
Breaking Change: None - parameter removal is backward compatible
Fixes: Entity relationship description truncation issues
- Refactor the trigger condition for LLM-based summarization of entities and relations. Instead of relying on character length, the summary is now triggered when the number of merged description fragments exceeds a configured threshold. This provides a more robust and logical condition for consolidation.
- Introduce the `OLLAMA_NUM_CTX` environment variable to explicitly configure the context window size (`num_ctx`) for Ollama models. This decouples the model's context length from the `MAX_TOKENS` parameter, which is now specifically used to limit input for summary generation, making the configuration clearer and more flexible.
- Updated `README` files, `env.example`, and default values to reflect these changes.
Move knowledge_graph_inst.has_node check inside get_storage_keyed_lock
in _merge_edges_then_upsert to ensure atomic check-then-act operations
and prevent duplicate node creation during concurrent updates.
Refactored the `KeyedUnifiedLock` to be generic and support dynamic namespaces. This decouples the locking mechanism from a specific "GraphDB" implementation, allowing it to be reused across different components and workspaces safely.
Key changes:
- `KeyedUnifiedLock` now takes a `namespace` parameter on lock acquisition.
- Renamed `_graph_db_lock_keyed` to a more generic _storage_keyed_lock`
- Replaced `get_graph_db_lock_keyed` with get_storage_keyed_lock` to support namespaces
Added validation checks in entity and relationship extraction functions to filter out entities that become empty strings after normalize_extracted_info processing. This prevents empty labels from appearing in get_all_labels() results and maintains knowledge graph data integrity.
Refactored the LLM cache to a flat Key-Value (KV) structure, replacing the previous nested format. The old structure used the 'mode' as a key and stored specific cache content as JSON nested under it. This change significantly enhances cache recall efficiency.
- Logs detailed progress, including warnings and failures, to the pipeline status.
- Adds counters to report the total number of successfully rebuilt entities and relationships upon completion.
