- 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.
The document processing pipeline would crash with a TypeError when a document was submitted as raw text via the API, as the file_path attribute would be None. This change adds a check to handle the None case gracefully, preventing the crash and allowing text-based documents to be indexed correctly.
- 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 a lock to delete, adelete_by_entity, and adelete_by_relation methods to prevent race conditions and ensure data consistency during concurrent modifications to the knowledge graph.
