48c7732edc
Implement automatic entity resolution to prevent duplicate nodes in the knowledge graph. The system uses a 3-layer approach: 1. Case-insensitive exact matching (free, instant) 2. Fuzzy string matching >85% threshold (free, instant) 3. Vector similarity + LLM verification (for acronyms/synonyms) Key features: - Pre-resolution phase prevents race conditions in parallel processing - Numeric suffix detection blocks false matches (IL-4 ≠ IL-13) - PostgreSQL alias cache for fast lookups on subsequent ingestion - Configurable thresholds via environment variables Bug fixes included: - Fix fuzzy matching false positives for numbered entities - Fix alias cache not being populated (missing db parameter) - Skip entity_aliases table from generic id index creation New files: - lightrag/entity_resolution/ - Core resolution module - tests/test_entity_resolution/ - Unit tests - docker/postgres-age-vector/ - Custom PG image with pgvector + AGE - docker-compose.test.yml - Integration test environment Configuration (env.example): - ENTITY_RESOLUTION_ENABLED=true - ENTITY_RESOLUTION_FUZZY_THRESHOLD=0.85 - ENTITY_RESOLUTION_VECTOR_THRESHOLD=0.5 - ENTITY_RESOLUTION_MAX_CANDIDATES=3 |
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| .. | ||
| unofficial-sample | ||
| generate_query.py | ||
| graph_visual_with_html.py | ||
| graph_visual_with_neo4j.py | ||
| insert_custom_kg.py | ||
| lightrag_azure_openai_demo.py | ||
| lightrag_ollama_demo.py | ||
| lightrag_openai_compatible_demo.py | ||
| lightrag_openai_demo.py | ||
| lightrag_openai_mongodb_graph_demo.py | ||
| modalprocessors_example.py | ||
| raganything_example.py | ||
| rerank_example.py | ||
| test_entity_resolution.py | ||