Merge branch 'main' into merge_lock_with_key

README-zh.md

@@ -30,7 +30,7 @@
       <a href="https://github.com/HKUDS/LightRAG/issues/285"><img src="https://img.shields.io/badge/💬微信群-交流-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e"></a>
     </p>
     <p>
-      <a href="README_zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a>
+      <a href="README-zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a>
       <a href="README.md"><img src="https://img.shields.io/badge/🇺🇸English-1a1a2e?style=for-the-badge"></a>
     </p>
   </div>

README.md

@@ -860,6 +860,41 @@ rag = LightRAG(
 
 </details>
 
+<details>
+<summary> <b>Using Memgraph for Storage</b> </summary>
+
+* Memgraph is a high-performance, in-memory graph database compatible with the Neo4j Bolt protocol.
+* You can run Memgraph locally using Docker for easy testing:
+* See: https://memgraph.com/download
+
+```python
+export MEMGRAPH_URI="bolt://localhost:7687"
+
+# Setup logger for LightRAG
+setup_logger("lightrag", level="INFO")
+
+# When you launch the project, override the default KG: NetworkX
+# by specifying kg="MemgraphStorage".
+
+# Note: Default settings use NetworkX
+# Initialize LightRAG with Memgraph implementation.
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
+        graph_storage="MemgraphStorage", #<-----------override KG default
+    )
+
+    # Initialize database connections
+    await rag.initialize_storages()
+    # Initialize pipeline status for document processing
+    await initialize_pipeline_status()
+
+    return rag
+```
+
+</details>
+
 ## Edit Entities and Relations
 
 LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.

config.ini.example

@@ -21,3 +21,6 @@ password = your_password
 database = your_database
 workspace = default  # 可选,默认为default
 max_connections = 12
+
+[memgraph]
+uri = bolt://localhost:7687

env.example

@@ -134,13 +134,14 @@ EMBEDDING_BINDING_HOST=http://localhost:11434
 # LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage
 ### Graph Storage (Recommended for production deployment)
 # LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
+# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
 
 ####################################################################
 ### Default workspace for all storage types
 ### For the purpose of isolation of data for each LightRAG instance
 ### Valid characters: a-z, A-Z, 0-9, and _
 ####################################################################
-# WORKSPACE=doc—
+# WORKSPACE=space1
 
 ### PostgreSQL Configuration
 POSTGRES_HOST=localhost
@@ -179,3 +180,10 @@ QDRANT_URL=http://localhost:6333
 ### Redis
 REDIS_URI=redis://localhost:6379
 # REDIS_WORKSPACE=forced_workspace_name
+
+### Memgraph Configuration
+MEMGRAPH_URI=bolt://localhost:7687
+MEMGRAPH_USERNAME=
+MEMGRAPH_PASSWORD=
+MEMGRAPH_DATABASE=memgraph
+# MEMGRAPH_WORKSPACE=forced_workspace_name

lightrag/kg/__init__.py

@@ -15,6 +15,7 @@ STORAGE_IMPLEMENTATIONS = {
             "Neo4JStorage",
             "PGGraphStorage",
             "MongoGraphStorage",
+            "MemgraphStorage",
             # "AGEStorage",
             # "TiDBGraphStorage",
             # "GremlinStorage",
@@ -57,6 +58,7 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
     "NetworkXStorage": [],
     "Neo4JStorage": ["NEO4J_URI", "NEO4J_USERNAME", "NEO4J_PASSWORD"],
     "MongoGraphStorage": [],
+    "MemgraphStorage": ["MEMGRAPH_URI"],
     # "TiDBGraphStorage": ["TIDB_USER", "TIDB_PASSWORD", "TIDB_DATABASE"],
     "AGEStorage": [
         "AGE_POSTGRES_DB",
@@ -111,6 +113,7 @@ STORAGES = {
     "PGDocStatusStorage": ".kg.postgres_impl",
     "FaissVectorDBStorage": ".kg.faiss_impl",
     "QdrantVectorDBStorage": ".kg.qdrant_impl",
+    "MemgraphStorage": ".kg.memgraph_impl",
 }
 
 

lightrag/kg/memgraph_impl.py

@@ -0,0 +1,906 @@
+import os
+from dataclasses import dataclass
+from typing import final
+import configparser
+
+from ..utils import logger
+from ..base import BaseGraphStorage
+from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
+from ..constants import GRAPH_FIELD_SEP
+import pipmaster as pm
+
+if not pm.is_installed("neo4j"):
+    pm.install("neo4j")
+
+from neo4j import (
+    AsyncGraphDatabase,
+    AsyncManagedTransaction,
+)
+
+from dotenv import load_dotenv
+
+# use the .env that is inside the current folder
+load_dotenv(dotenv_path=".env", override=False)
+
+MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000))
+
+config = configparser.ConfigParser()
+config.read("config.ini", "utf-8")
+
+
+@final
+@dataclass
+class MemgraphStorage(BaseGraphStorage):
+    def __init__(self, namespace, global_config, embedding_func, workspace=None):
+        memgraph_workspace = os.environ.get("MEMGRAPH_WORKSPACE")
+        if memgraph_workspace and memgraph_workspace.strip():
+            workspace = memgraph_workspace
+        super().__init__(
+            namespace=namespace,
+            workspace=workspace or "",
+            global_config=global_config,
+            embedding_func=embedding_func,
+        )
+        self._driver = None
+
+    def _get_workspace_label(self) -> str:
+        """Get workspace label, return 'base' for compatibility when workspace is empty"""
+        workspace = getattr(self, "workspace", None)
+        return workspace if workspace else "base"
+
+    async def initialize(self):
+        URI = os.environ.get(
+            "MEMGRAPH_URI",
+            config.get("memgraph", "uri", fallback="bolt://localhost:7687"),
+        )
+        USERNAME = os.environ.get(
+            "MEMGRAPH_USERNAME", config.get("memgraph", "username", fallback="")
+        )
+        PASSWORD = os.environ.get(
+            "MEMGRAPH_PASSWORD", config.get("memgraph", "password", fallback="")
+        )
+        DATABASE = os.environ.get(
+            "MEMGRAPH_DATABASE", config.get("memgraph", "database", fallback="memgraph")
+        )
+
+        self._driver = AsyncGraphDatabase.driver(
+            URI,
+            auth=(USERNAME, PASSWORD),
+        )
+        self._DATABASE = DATABASE
+        try:
+            async with self._driver.session(database=DATABASE) as session:
+                # Create index for base nodes on entity_id if it doesn't exist
+                try:
+                    workspace_label = self._get_workspace_label()
+                    await session.run(
+                        f"""CREATE INDEX ON :{workspace_label}(entity_id)"""
+                    )
+                    logger.info(
+                        f"Created index on :{workspace_label}(entity_id) in Memgraph."
+                    )
+                except Exception as e:
+                    # Index may already exist, which is not an error
+                    logger.warning(
+                        f"Index creation on :{workspace_label}(entity_id) may have failed or already exists: {e}"
+                    )
+                await session.run("RETURN 1")
+                logger.info(f"Connected to Memgraph at {URI}")
+        except Exception as e:
+            logger.error(f"Failed to connect to Memgraph at {URI}: {e}")
+            raise
+
+    async def finalize(self):
+        if self._driver is not None:
+            await self._driver.close()
+            self._driver = None
+
+    async def __aexit__(self, exc_type, exc, tb):
+        await self.finalize()
+
+    async def index_done_callback(self):
+        # Memgraph handles persistence automatically
+        pass
+
+    async def has_node(self, node_id: str) -> bool:
+        """
+        Check if a node exists in the graph.
+
+        Args:
+            node_id: The ID of the node to check.
+
+        Returns:
+            bool: True if the node exists, False otherwise.
+
+        Raises:
+            Exception: If there is an error checking the node existence.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists"
+                result = await session.run(query, entity_id=node_id)
+                single_result = await result.single()
+                await result.consume()  # Ensure result is fully consumed
+                return (
+                    single_result["node_exists"] if single_result is not None else False
+                )
+            except Exception as e:
+                logger.error(f"Error checking node existence for {node_id}: {str(e)}")
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
+        """
+        Check if an edge exists between two nodes in the graph.
+
+        Args:
+            source_node_id: The ID of the source node.
+            target_node_id: The ID of the target node.
+
+        Returns:
+            bool: True if the edge exists, False otherwise.
+
+        Raises:
+            Exception: If there is an error checking the edge existence.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = (
+                    f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) "
+                    "RETURN COUNT(r) > 0 AS edgeExists"
+                )
+                result = await session.run(
+                    query,
+                    source_entity_id=source_node_id,
+                    target_entity_id=target_node_id,
+                )  # type: ignore
+                single_result = await result.single()
+                await result.consume()  # Ensure result is fully consumed
+                return (
+                    single_result["edgeExists"] if single_result is not None else False
+                )
+            except Exception as e:
+                logger.error(
+                    f"Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}"
+                )
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def get_node(self, node_id: str) -> dict[str, str] | None:
+        """Get node by its label identifier, return only node properties
+
+        Args:
+            node_id: The node label to look up
+
+        Returns:
+            dict: Node properties if found
+            None: If node not found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = (
+                    f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN n"
+                )
+                result = await session.run(query, entity_id=node_id)
+                try:
+                    records = await result.fetch(
+                        2
+                    )  # Get 2 records for duplication check
+
+                    if len(records) > 1:
+                        logger.warning(
+                            f"Multiple nodes found with label '{node_id}'. Using first node."
+                        )
+                    if records:
+                        node = records[0]["n"]
+                        node_dict = dict(node)
+                        # Remove workspace label from labels list if it exists
+                        if "labels" in node_dict:
+                            node_dict["labels"] = [
+                                label
+                                for label in node_dict["labels"]
+                                if label != workspace_label
+                            ]
+                        return node_dict
+                    return None
+                finally:
+                    await result.consume()  # Ensure result is fully consumed
+            except Exception as e:
+                logger.error(f"Error getting node for {node_id}: {str(e)}")
+                raise
+
+    async def node_degree(self, node_id: str) -> int:
+        """Get the degree (number of relationships) of a node with the given label.
+        If multiple nodes have the same label, returns the degree of the first node.
+        If no node is found, returns 0.
+
+        Args:
+            node_id: The label of the node
+
+        Returns:
+            int: The number of relationships the node has, or 0 if no node found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                    MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+                    OPTIONAL MATCH (n)-[r]-()
+                    RETURN COUNT(r) AS degree
+                """
+                result = await session.run(query, entity_id=node_id)
+                try:
+                    record = await result.single()
+
+                    if not record:
+                        logger.warning(f"No node found with label '{node_id}'")
+                        return 0
+
+                    degree = record["degree"]
+                    return degree
+                finally:
+                    await result.consume()  # Ensure result is fully consumed
+            except Exception as e:
+                logger.error(f"Error getting node degree for {node_id}: {str(e)}")
+                raise
+
+    async def get_all_labels(self) -> list[str]:
+        """
+        Get all existing node labels in the database
+        Returns:
+            ["Person", "Company", ...]  # Alphabetically sorted label list
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (n:`{workspace_label}`)
+                WHERE n.entity_id IS NOT NULL
+                RETURN DISTINCT n.entity_id AS label
+                ORDER BY label
+                """
+                result = await session.run(query)
+                labels = []
+                async for record in result:
+                    labels.append(record["label"])
+                await result.consume()
+                return labels
+            except Exception as e:
+                logger.error(f"Error getting all labels: {str(e)}")
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
+        """Retrieves all edges (relationships) for a particular node identified by its label.
+
+        Args:
+            source_node_id: Label of the node to get edges for
+
+        Returns:
+            list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges
+            None: If no edges found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                try:
+                    workspace_label = self._get_workspace_label()
+                    query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+                            OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`)
+                            WHERE connected.entity_id IS NOT NULL
+                            RETURN n, r, connected"""
+                    results = await session.run(query, entity_id=source_node_id)
+
+                    edges = []
+                    async for record in results:
+                        source_node = record["n"]
+                        connected_node = record["connected"]
+
+                        # Skip if either node is None
+                        if not source_node or not connected_node:
+                            continue
+
+                        source_label = (
+                            source_node.get("entity_id")
+                            if source_node.get("entity_id")
+                            else None
+                        )
+                        target_label = (
+                            connected_node.get("entity_id")
+                            if connected_node.get("entity_id")
+                            else None
+                        )
+
+                        if source_label and target_label:
+                            edges.append((source_label, target_label))
+
+                    await results.consume()  # Ensure results are consumed
+                    return edges
+                except Exception as e:
+                    logger.error(
+                        f"Error getting edges for node {source_node_id}: {str(e)}"
+                    )
+                    await results.consume()  # Ensure results are consumed even on error
+                    raise
+        except Exception as e:
+            logger.error(f"Error in get_node_edges for {source_node_id}: {str(e)}")
+            raise
+
+    async def get_edge(
+        self, source_node_id: str, target_node_id: str
+    ) -> dict[str, str] | None:
+        """Get edge properties between two nodes.
+
+        Args:
+            source_node_id: Label of the source node
+            target_node_id: Label of the target node
+
+        Returns:
+            dict: Edge properties if found, default properties if not found or on error
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (start:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(end:`{workspace_label}` {{entity_id: $target_entity_id}})
+                RETURN properties(r) as edge_properties
+                """
+                result = await session.run(
+                    query,
+                    source_entity_id=source_node_id,
+                    target_entity_id=target_node_id,
+                )
+                records = await result.fetch(2)
+                await result.consume()
+                if records:
+                    edge_result = dict(records[0]["edge_properties"])
+                    for key, default_value in {
+                        "weight": 0.0,
+                        "source_id": None,
+                        "description": None,
+                        "keywords": None,
+                    }.items():
+                        if key not in edge_result:
+                            edge_result[key] = default_value
+                            logger.warning(
+                                f"Edge between {source_node_id} and {target_node_id} is missing property: {key}. Using default value: {default_value}"
+                            )
+                    return edge_result
+                return None
+            except Exception as e:
+                logger.error(
+                    f"Error getting edge between {source_node_id} and {target_node_id}: {str(e)}"
+                )
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
+        """
+        Upsert a node in the Neo4j database.
+
+        Args:
+            node_id: The unique identifier for the node (used as label)
+            node_data: Dictionary of node properties
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        properties = node_data
+        entity_type = properties["entity_type"]
+        if "entity_id" not in properties:
+            raise ValueError("Neo4j: node properties must contain an 'entity_id' field")
+
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                workspace_label = self._get_workspace_label()
+
+                async def execute_upsert(tx: AsyncManagedTransaction):
+                    query = f"""
+                    MERGE (n:`{workspace_label}` {{entity_id: $entity_id}})
+                    SET n += $properties
+                    SET n:`{entity_type}`
+                    """
+                    result = await tx.run(
+                        query, entity_id=node_id, properties=properties
+                    )
+                    await result.consume()  # Ensure result is fully consumed
+
+                await session.execute_write(execute_upsert)
+        except Exception as e:
+            logger.error(f"Error during upsert: {str(e)}")
+            raise
+
+    async def upsert_edge(
+        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
+    ) -> None:
+        """
+        Upsert an edge and its properties between two nodes identified by their labels.
+        Ensures both source and target nodes exist and are unique before creating the edge.
+        Uses entity_id property to uniquely identify nodes.
+
+        Args:
+            source_node_id (str): Label of the source node (used as identifier)
+            target_node_id (str): Label of the target node (used as identifier)
+            edge_data (dict): Dictionary of properties to set on the edge
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            edge_properties = edge_data
+            async with self._driver.session(database=self._DATABASE) as session:
+
+                async def execute_upsert(tx: AsyncManagedTransaction):
+                    workspace_label = self._get_workspace_label()
+                    query = f"""
+                    MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})
+                    WITH source
+                    MATCH (target:`{workspace_label}` {{entity_id: $target_entity_id}})
+                    MERGE (source)-[r:DIRECTED]-(target)
+                    SET r += $properties
+                    RETURN r, source, target
+                    """
+                    result = await tx.run(
+                        query,
+                        source_entity_id=source_node_id,
+                        target_entity_id=target_node_id,
+                        properties=edge_properties,
+                    )
+                    try:
+                        await result.fetch(2)
+                    finally:
+                        await result.consume()  # Ensure result is consumed
+
+                await session.execute_write(execute_upsert)
+        except Exception as e:
+            logger.error(f"Error during edge upsert: {str(e)}")
+            raise
+
+    async def delete_node(self, node_id: str) -> None:
+        """Delete a node with the specified label
+
+        Args:
+            node_id: The label of the node to delete
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        async def _do_delete(tx: AsyncManagedTransaction):
+            workspace_label = self._get_workspace_label()
+            query = f"""
+            MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+            DETACH DELETE n
+            """
+            result = await tx.run(query, entity_id=node_id)
+            logger.debug(f"Deleted node with label {node_id}")
+            await result.consume()
+
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                await session.execute_write(_do_delete)
+        except Exception as e:
+            logger.error(f"Error during node deletion: {str(e)}")
+            raise
+
+    async def remove_nodes(self, nodes: list[str]):
+        """Delete multiple nodes
+
+        Args:
+            nodes: List of node labels to be deleted
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        for node in nodes:
+            await self.delete_node(node)
+
+    async def remove_edges(self, edges: list[tuple[str, str]]):
+        """Delete multiple edges
+
+        Args:
+            edges: List of edges to be deleted, each edge is a (source, target) tuple
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        for source, target in edges:
+
+            async def _do_delete_edge(tx: AsyncManagedTransaction):
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(target:`{workspace_label}` {{entity_id: $target_entity_id}})
+                DELETE r
+                """
+                result = await tx.run(
+                    query, source_entity_id=source, target_entity_id=target
+                )
+                logger.debug(f"Deleted edge from '{source}' to '{target}'")
+                await result.consume()  # Ensure result is fully consumed
+
+            try:
+                async with self._driver.session(database=self._DATABASE) as session:
+                    await session.execute_write(_do_delete_edge)
+            except Exception as e:
+                logger.error(f"Error during edge deletion: {str(e)}")
+                raise
+
+    async def drop(self) -> dict[str, str]:
+        """Drop all data from the current workspace and clean up resources
+
+        This method will delete all nodes and relationships in the Memgraph database.
+
+        Returns:
+            dict[str, str]: Operation status and message
+            - On success: {"status": "success", "message": "data dropped"}
+            - On failure: {"status": "error", "message": "<error details>"}
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                workspace_label = self._get_workspace_label()
+                query = f"MATCH (n:`{workspace_label}`) DETACH DELETE n"
+                result = await session.run(query)
+                await result.consume()
+                logger.info(
+                    f"Dropped workspace {workspace_label} from Memgraph database {self._DATABASE}"
+                )
+                return {"status": "success", "message": "workspace data dropped"}
+        except Exception as e:
+            logger.error(
+                f"Error dropping workspace {workspace_label} from Memgraph database {self._DATABASE}: {e}"
+            )
+            return {"status": "error", "message": str(e)}
+
+    async def edge_degree(self, src_id: str, tgt_id: str) -> int:
+        """Get the total degree (sum of relationships) of two nodes.
+
+        Args:
+            src_id: Label of the source node
+            tgt_id: Label of the target node
+
+        Returns:
+            int: Sum of the degrees of both nodes
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        src_degree = await self.node_degree(src_id)
+        trg_degree = await self.node_degree(tgt_id)
+
+        # Convert None to 0 for addition
+        src_degree = 0 if src_degree is None else src_degree
+        trg_degree = 0 if trg_degree is None else trg_degree
+
+        degrees = int(src_degree) + int(trg_degree)
+        return degrees
+
+    async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Get all nodes that are associated with the given chunk_ids.
+
+        Args:
+            chunk_ids: List of chunk IDs to find associated nodes for
+
+        Returns:
+            list[dict]: A list of nodes, where each node is a dictionary of its properties.
+                        An empty list if no matching nodes are found.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            query = f"""
+            UNWIND $chunk_ids AS chunk_id
+            MATCH (n:`{workspace_label}`)
+            WHERE n.source_id IS NOT NULL AND chunk_id IN split(n.source_id, $sep)
+            RETURN DISTINCT n
+            """
+            result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP)
+            nodes = []
+            async for record in result:
+                node = record["n"]
+                node_dict = dict(node)
+                node_dict["id"] = node_dict.get("entity_id")
+                nodes.append(node_dict)
+            await result.consume()
+            return nodes
+
+    async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Get all edges that are associated with the given chunk_ids.
+
+        Args:
+            chunk_ids: List of chunk IDs to find associated edges for
+
+        Returns:
+            list[dict]: A list of edges, where each edge is a dictionary of its properties.
+                        An empty list if no matching edges are found.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            query = f"""
+            UNWIND $chunk_ids AS chunk_id
+            MATCH (a:`{workspace_label}`)-[r]-(b:`{workspace_label}`)
+            WHERE r.source_id IS NOT NULL AND chunk_id IN split(r.source_id, $sep)
+            WITH a, b, r, a.entity_id AS source_id, b.entity_id AS target_id
+            // Ensure we only return each unique edge once by ordering the source and target
+            WITH a, b, r,
+                 CASE WHEN source_id <= target_id THEN source_id ELSE target_id END AS ordered_source,
+                 CASE WHEN source_id <= target_id THEN target_id ELSE source_id END AS ordered_target
+            RETURN DISTINCT ordered_source AS source, ordered_target AS target, properties(r) AS properties
+            """
+            result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP)
+            edges = []
+            async for record in result:
+                edge_properties = record["properties"]
+                edge_properties["source"] = record["source"]
+                edge_properties["target"] = record["target"]
+                edges.append(edge_properties)
+            await result.consume()
+            return edges
+
+    async def get_knowledge_graph(
+        self,
+        node_label: str,
+        max_depth: int = 3,
+        max_nodes: int = MAX_GRAPH_NODES,
+    ) -> KnowledgeGraph:
+        """
+        Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
+
+        Args:
+            node_label: Label of the starting node, * means all nodes
+            max_depth: Maximum depth of the subgraph, Defaults to 3
+            max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
+
+        Returns:
+            KnowledgeGraph object containing nodes and edges, with an is_truncated flag
+            indicating whether the graph was truncated due to max_nodes limit
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        result = KnowledgeGraph()
+        seen_nodes = set()
+        seen_edges = set()
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                if node_label == "*":
+                    # First check if database has any nodes
+                    count_query = "MATCH (n) RETURN count(n) as total"
+                    count_result = None
+                    total_count = 0
+                    try:
+                        count_result = await session.run(count_query)
+                        count_record = await count_result.single()
+                        if count_record:
+                            total_count = count_record["total"]
+                            if total_count == 0:
+                                logger.debug("No nodes found in database")
+                                return result
+                            if total_count > max_nodes:
+                                result.is_truncated = True
+                                logger.info(
+                                    f"Graph truncated: {total_count} nodes found, limited to {max_nodes}"
+                                )
+                    finally:
+                        if count_result:
+                            await count_result.consume()
+
+                    # Run the main query to get nodes with highest degree
+                    main_query = f"""
+                    MATCH (n:`{workspace_label}`)
+                    OPTIONAL MATCH (n)-[r]-()
+                    WITH n, COALESCE(count(r), 0) AS degree
+                    ORDER BY degree DESC
+                    LIMIT $max_nodes
+                    WITH collect(n) AS kept_nodes
+                    MATCH (a)-[r]-(b)
+                    WHERE a IN kept_nodes AND b IN kept_nodes
+                    RETURN [node IN kept_nodes | {{node: node}}] AS node_info,
+                        collect(DISTINCT r) AS relationships
+                    """
+                    result_set = None
+                    try:
+                        result_set = await session.run(
+                            main_query, {"max_nodes": max_nodes}
+                        )
+                        record = await result_set.single()
+                        if not record:
+                            logger.debug("No record returned from main query")
+                            return result
+                    finally:
+                        if result_set:
+                            await result_set.consume()
+
+                else:
+                    bfs_query = f"""
+                    MATCH (start:`{workspace_label}`)
+                    WHERE start.entity_id = $entity_id
+                    WITH start
+                    CALL {{
+                        WITH start
+                        MATCH path = (start)-[*0..{max_depth}]-(node)
+                        WITH nodes(path) AS path_nodes, relationships(path) AS path_rels
+                        UNWIND path_nodes AS n
+                        WITH collect(DISTINCT n) AS all_nodes, collect(DISTINCT path_rels) AS all_rel_lists
+                        WITH all_nodes, reduce(r = [], x IN all_rel_lists | r + x) AS all_rels
+                        RETURN all_nodes, all_rels
+                    }}
+                    WITH all_nodes AS nodes, all_rels AS relationships, size(all_nodes) AS total_nodes
+                    WITH
+                    CASE
+                        WHEN total_nodes <= {max_nodes} THEN nodes
+                        ELSE nodes[0..{max_nodes}]
+                    END AS limited_nodes,
+                    relationships,
+                    total_nodes,
+                    total_nodes > {max_nodes} AS is_truncated
+                    RETURN
+                    [node IN limited_nodes | {{node: node}}] AS node_info,
+                    relationships,
+                    total_nodes,
+                    is_truncated
+                    """
+                    result_set = None
+                    try:
+                        result_set = await session.run(
+                            bfs_query,
+                            {
+                                "entity_id": node_label,
+                            },
+                        )
+                        record = await result_set.single()
+                        if not record:
+                            logger.debug(f"No nodes found for entity_id: {node_label}")
+                            return result
+
+                        # Check if the query indicates truncation
+                        if "is_truncated" in record and record["is_truncated"]:
+                            result.is_truncated = True
+                            logger.info(
+                                f"Graph truncated: breadth-first search limited to {max_nodes} nodes"
+                            )
+
+                    finally:
+                        if result_set:
+                            await result_set.consume()
+
+                # Process the record if it exists
+                if record and record["node_info"]:
+                    for node_info in record["node_info"]:
+                        node = node_info["node"]
+                        node_id = node.id
+                        if node_id not in seen_nodes:
+                            seen_nodes.add(node_id)
+                            result.nodes.append(
+                                KnowledgeGraphNode(
+                                    id=f"{node_id}",
+                                    labels=[node.get("entity_id")],
+                                    properties=dict(node),
+                                )
+                            )
+
+                    for rel in record["relationships"]:
+                        edge_id = rel.id
+                        if edge_id not in seen_edges:
+                            seen_edges.add(edge_id)
+                            start = rel.start_node
+                            end = rel.end_node
+                            result.edges.append(
+                                KnowledgeGraphEdge(
+                                    id=f"{edge_id}",
+                                    type=rel.type,
+                                    source=f"{start.id}",
+                                    target=f"{end.id}",
+                                    properties=dict(rel),
+                                )
+                            )
+
+                logger.info(
+                    f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
+                )
+
+            except Exception as e:
+                logger.error(f"Error getting knowledge graph: {str(e)}")
+                # Return empty but properly initialized KnowledgeGraph on error
+                return KnowledgeGraph()
+
+        return result

lightrag/lightrag.py

@@ -900,9 +900,15 @@ class LightRAG:
                 # Get first document's file path and total count for job name
                 first_doc_id, first_doc = next(iter(to_process_docs.items()))
                 first_doc_path = first_doc.file_path
-                path_prefix = first_doc_path[:20] + (
+
-                    "..." if len(first_doc_path) > 20 else ""
+                # Handle cases where first_doc_path is None
-                )
+                if first_doc_path:
+                    path_prefix = first_doc_path[:20] + (
+                        "..." if len(first_doc_path) > 20 else ""
+                    )
+                else:
+                    path_prefix = "unknown_source"
+
                 total_files = len(to_process_docs)
                 job_name = f"{path_prefix}[{total_files} files]"
                 pipeline_status["job_name"] = job_name

lightrag/llm/openai.py

@@ -210,9 +210,18 @@ async def openai_complete_if_cache(
         async def inner():
             # Track if we've started iterating
             iteration_started = False
+            final_chunk_usage = None
+
             try:
                 iteration_started = True
                 async for chunk in response:
+                    # Check if this chunk has usage information (final chunk)
+                    if hasattr(chunk, "usage") and chunk.usage:
+                        final_chunk_usage = chunk.usage
+                        logger.debug(
+                            f"Received usage info in streaming chunk: {chunk.usage}"
+                        )
+
                     # Check if choices exists and is not empty
                     if not hasattr(chunk, "choices") or not chunk.choices:
                         logger.warning(f"Received chunk without choices: {chunk}")
@@ -222,16 +231,31 @@ async def openai_complete_if_cache(
                     if not hasattr(chunk.choices[0], "delta") or not hasattr(
                         chunk.choices[0].delta, "content"
                     ):
-                        logger.warning(
+                        # This might be the final chunk, continue to check for usage
-                            f"Received chunk without delta content: {chunk.choices[0]}"
-                        )
                         continue
+
                     content = chunk.choices[0].delta.content
                     if content is None:
                         continue
                     if r"\u" in content:
                         content = safe_unicode_decode(content.encode("utf-8"))
+
                     yield content
+
+                # After streaming is complete, track token usage
+                if token_tracker and final_chunk_usage:
+                    # Use actual usage from the API
+                    token_counts = {
+                        "prompt_tokens": getattr(final_chunk_usage, "prompt_tokens", 0),
+                        "completion_tokens": getattr(
+                            final_chunk_usage, "completion_tokens", 0
+                        ),
+                        "total_tokens": getattr(final_chunk_usage, "total_tokens", 0),
+                    }
+                    token_tracker.add_usage(token_counts)
+                    logger.debug(f"Streaming token usage (from API): {token_counts}")
+                elif token_tracker:
+                    logger.debug("No usage information available in streaming response")
             except Exception as e:
                 logger.error(f"Error in stream response: {str(e)}")
                 # Try to clean up resources if possible

lightrag/operate.py

@@ -26,6 +26,7 @@ from .utils import (
     get_conversation_turns,
     use_llm_func_with_cache,
     update_chunk_cache_list,
+    remove_think_tags,
 )
 from .base import (
     BaseGraphStorage,
@@ -1708,7 +1709,8 @@ async def extract_keywords_only(
     result = await use_model_func(kw_prompt, keyword_extraction=True)
 
     # 6. Parse out JSON from the LLM response
-    match = re.search(r"\{.*\}", result, re.DOTALL)
+    result = remove_think_tags(result)
+    match = re.search(r"\{.*?\}", result, re.DOTALL)
     if not match:
         logger.error("No JSON-like structure found in the LLM respond.")
         return [], []

lightrag/utils.py

@@ -1465,6 +1465,11 @@ async def update_chunk_cache_list(
         )
 
 
+def remove_think_tags(text: str) -> str:
+    """Remove <think> tags from the text"""
+    return re.sub(r"^(<think>.*?</think>|<think>)", "", text, flags=re.DOTALL).strip()
+
+
 async def use_llm_func_with_cache(
     input_text: str,
     use_llm_func: callable,
@@ -1531,6 +1536,7 @@ async def use_llm_func_with_cache(
             kwargs["max_tokens"] = max_tokens
 
         res: str = await use_llm_func(input_text, **kwargs)
+        res = remove_think_tags(res)
 
         if llm_response_cache.global_config.get("enable_llm_cache_for_entity_extract"):
             await save_to_cache(
@@ -1557,8 +1563,9 @@ async def use_llm_func_with_cache(
     if max_tokens is not None:
         kwargs["max_tokens"] = max_tokens
 
-    logger.info(f"Call LLM function with query text lenght: {len(input_text)}")
+    logger.info(f"Call LLM function with query text length: {len(input_text)}")
-    return await use_llm_func(input_text, **kwargs)
+    res = await use_llm_func(input_text, **kwargs)
+    return remove_think_tags(res)
 
 
 def get_content_summary(content: str, max_length: int = 250) -> str:

tests/test_graph_storage.py

@@ -10,6 +10,7 @@
 - Neo4JStorage
 - MongoDBStorage
 - PGGraphStorage
+- MemgraphStorage
 """
 
 import asyncio