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.
- To enhance performance during document deletion, new batch-get methods, `get_nodes_by_chunk_ids` and `get_edges_by_chunk_ids`, have been added to the graph storage layer (`BaseGraphStorage` and its implementations). The [`adelete_by_doc_id`](lightrag/lightrag.py:1681) function now leverages these methods to avoid unnecessary iteration over the entire knowledge graph, significantly improving efficiency.
- Graph storage updated: Networkx, Neo4j, Postgres AGE
- Add validation in _find_most_related_edges_from_entities and _get_edge_data function during edge data construction
- Add warning logs when 'weight' attribute is missing and set default value of 0.0
- Merges 'mix' mode query handling into 'hybrid' mode, simplifying query logic by removing the dedicated `mix_kg_vector_query` function
- Standardizes vector search result by using JSON string format to build context
- Fixes a bug in `query_with_keywords` ensuring `hl_keywords` and `ll_keywords` are correctly passed to `kg_query_with_keywords`
