diff --git a/lightrag/kg/age_impl.py b/lightrag/kg/age_impl.py index d6a8c64a..097b7b0b 100644 --- a/lightrag/kg/age_impl.py +++ b/lightrag/kg/age_impl.py @@ -88,11 +88,6 @@ class AGEStorage(BaseGraphStorage): return None - def __post_init__(self): - self._node_embed_algorithms = { - "node2vec": self._node2vec_embed, - } - async def close(self): if self._driver: await self._driver.close() @@ -592,9 +587,6 @@ class AGEStorage(BaseGraphStorage): logger.error("Error during edge upsert: {%s}", e) raise - async def _node2vec_embed(self): - print("Implemented but never called.") - @asynccontextmanager async def _get_pool_connection(self, timeout: Optional[float] = None): """Workaround for a psycopg_pool bug""" diff --git a/lightrag/kg/gremlin_impl.py b/lightrag/kg/gremlin_impl.py index 486eb224..32dbcc4e 100644 --- a/lightrag/kg/gremlin_impl.py +++ b/lightrag/kg/gremlin_impl.py @@ -69,11 +69,6 @@ class GremlinStorage(BaseGraphStorage): transport_factory=lambda: AiohttpTransport(call_from_event_loop=True), ) - def __post_init__(self): - self._node_embed_algorithms = { - "node2vec": self._node2vec_embed, - } - async def close(self): if self._driver: self._driver.close() @@ -389,9 +384,6 @@ class GremlinStorage(BaseGraphStorage): logger.error("Error during edge upsert: {%s}", e) raise - async def _node2vec_embed(self): - print("Implemented but never called.") - async def delete_node(self, node_id: str) -> None: """Delete a node with the specified entity_name diff --git a/lightrag/kg/neo4j_impl.py b/lightrag/kg/neo4j_impl.py index 51284562..da6a94cb 100644 --- a/lightrag/kg/neo4j_impl.py +++ b/lightrag/kg/neo4j_impl.py @@ -50,11 +50,6 @@ class Neo4JStorage(BaseGraphStorage): ) self._driver = None - def __post_init__(self): - self._node_embed_algorithms = { - "node2vec": self._node2vec_embed, - } - async def initialize(self): URI = os.environ.get("NEO4J_URI", config.get("neo4j", "uri", fallback=None)) USERNAME = os.environ.get( @@ -634,9 +629,6 @@ class Neo4JStorage(BaseGraphStorage): logger.error(f"Error during edge upsert: {str(e)}") raise - async def _node2vec_embed(self): - print("Implemented but never called.") - async def get_knowledge_graph( self, node_label: str, diff --git a/lightrag/kg/networkx_impl.py b/lightrag/kg/networkx_impl.py index 0841d2ef..50b3d34a 100644 --- a/lightrag/kg/networkx_impl.py +++ b/lightrag/kg/networkx_impl.py @@ -58,10 +58,6 @@ class NetworkXStorage(BaseGraphStorage): logger.info("Created new empty graph") self._graph = preloaded_graph or nx.Graph() - self._node_embed_algorithms = { - "node2vec": self._node2vec_embed, - } - async def initialize(self): """Initialize storage data""" # Get the update flag for cross-process update notification diff --git a/lightrag/kg/postgres_impl.py b/lightrag/kg/postgres_impl.py index 388ea3a1..c067a0d0 100644 --- a/lightrag/kg/postgres_impl.py +++ b/lightrag/kg/postgres_impl.py @@ -1021,9 +1021,6 @@ class PGGraphQueryException(Exception): class PGGraphStorage(BaseGraphStorage): def __post_init__(self): self.graph_name = self.namespace or os.environ.get("AGE_GRAPH_NAME", "lightrag") - self._node_embed_algorithms = { - "node2vec": self._node2vec_embed, - } self.db: PostgreSQLDB | None = None async def initialize(self): @@ -1396,9 +1393,6 @@ class PGGraphStorage(BaseGraphStorage): ) raise - async def _node2vec_embed(self): - print("Implemented but never called.") - async def delete_node(self, node_id: str) -> None: """ Delete a node from the graph. diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 5647799b..cd801305 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -155,31 +155,6 @@ class LightRAG: Defaults to `chunking_by_token_size` if not specified. """ - # Node embedding - # --- - - node_embedding_algorithm: str = field(default="node2vec") - """Algorithm used for node embedding in knowledge graphs.""" - - node2vec_params: dict[str, int] = field( - default_factory=lambda: { - "dimensions": 1536, - "num_walks": 10, - "walk_length": 40, - "window_size": 2, - "iterations": 3, - "random_seed": 3, - } - ) - """Configuration for the node2vec embedding algorithm: - - dimensions: Number of dimensions for embeddings. - - num_walks: Number of random walks per node. - - walk_length: Number of steps per random walk. - - window_size: Context window size for training. - - iterations: Number of iterations for training. - - random_seed: Seed value for reproducibility. - """ - # Embedding # ---