Merge branch 'main' into main

env.example

@@ -148,3 +148,10 @@ QDRANT_URL=http://localhost:16333
 
 ### Redis
 REDIS_URI=redis://localhost:6379
+
+# For jwt auth
+AUTH_USERNAME=admin      # login name
+AUTH_PASSWORD=admin123   # password
+TOKEN_SECRET=your-key # JWT key
+TOKEN_EXPIRE_HOURS=4     # expire duration
+WHITELIST_PATHS=/login,/health  # white list

examples/test_postgres.py

@@ -0,0 +1,51 @@
+import os
+import asyncio
+from lightrag.kg.postgres_impl import PGGraphStorage
+from lightrag.llm.ollama import ollama_embedding
+from lightrag.utils import EmbeddingFunc
+
+#########
+# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
+# import nest_asyncio
+# nest_asyncio.apply()
+#########
+
+WORKING_DIR = "./local_neo4jWorkDir"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+# AGE
+os.environ["AGE_GRAPH_NAME"] = "dickens"
+
+os.environ["POSTGRES_HOST"] = "localhost"
+os.environ["POSTGRES_PORT"] = "15432"
+os.environ["POSTGRES_USER"] = "rag"
+os.environ["POSTGRES_PASSWORD"] = "rag"
+os.environ["POSTGRES_DATABASE"] = "rag"
+
+
+async def main():
+    graph_db = PGGraphStorage(
+        namespace="dickens",
+        embedding_func=EmbeddingFunc(
+            embedding_dim=1024,
+            max_token_size=8192,
+            func=lambda texts: ollama_embedding(
+                texts, embed_model="bge-m3", host="http://localhost:11434"
+            ),
+        ),
+        global_config={},
+    )
+    await graph_db.initialize()
+    labels = await graph_db.get_all_labels()
+    print("all labels", labels)
+
+    res = await graph_db.get_knowledge_graph("FEZZIWIG")
+    print("knowledge graphs", res)
+
+    await graph_db.finalize()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

lightrag/api/README.md

@@ -387,6 +387,19 @@ Note: If you don't need the API functionality, you can install the base package
 pip install lightrag-hku
 ```
 
+## Authentication Endpoints
+
+### JWT Authentication Mechanism
+LightRAG API Server implements JWT-based authentication using HS256 algorithm. To enable secure access control, the following environment variables are required:
+```bash
+# For jwt auth
+AUTH_USERNAME=admin      # login name
+AUTH_PASSWORD=admin123   # password
+TOKEN_SECRET=your-key # JWT key
+TOKEN_EXPIRE_HOURS=4     # expire duration
+WHITELIST_PATHS=/api1,/api2  # white list. /login,/health,/docs,/redoc,/openapi.json are whitelisted by default.
+```
+
 ## API Endpoints
 
 All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality. When API Server is running, visit:

lightrag/api/auth.py

@@ -0,0 +1,41 @@
+import os
+from datetime import datetime, timedelta
+import jwt
+from fastapi import HTTPException, status
+from pydantic import BaseModel
+
+
+class TokenPayload(BaseModel):
+    sub: str
+    exp: datetime
+
+
+class AuthHandler:
+    def __init__(self):
+        self.secret = os.getenv("TOKEN_SECRET", "4f85ds4f56dsf46")
+        self.algorithm = "HS256"
+        self.expire_hours = int(os.getenv("TOKEN_EXPIRE_HOURS", 4))
+
+    def create_token(self, username: str) -> str:
+        expire = datetime.utcnow() + timedelta(hours=self.expire_hours)
+        payload = TokenPayload(sub=username, exp=expire)
+        return jwt.encode(payload.dict(), self.secret, algorithm=self.algorithm)
+
+    def validate_token(self, token: str) -> str:
+        try:
+            payload = jwt.decode(token, self.secret, algorithms=[self.algorithm])
+            expire_timestamp = payload["exp"]
+            expire_time = datetime.utcfromtimestamp(expire_timestamp)
+
+            if datetime.utcnow() > expire_time:
+                raise HTTPException(
+                    status_code=status.HTTP_401_UNAUTHORIZED, detail="Token expired"
+                )
+            return payload["sub"]
+        except jwt.PyJWTError:
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token"
+            )
+
+
+auth_handler = AuthHandler()

lightrag/api/lightrag_server.py

@@ -2,10 +2,7 @@
 LightRAG FastAPI Server
 """
 
-from fastapi import (
+from fastapi import FastAPI, Depends, HTTPException, status
-    FastAPI,
-    Depends,
-)
 import asyncio
 import os
 import logging
@@ -45,6 +42,8 @@ from lightrag.kg.shared_storage import (
     initialize_pipeline_status,
     get_all_update_flags_status,
 )
+from fastapi.security import OAuth2PasswordRequestForm
+from .auth import auth_handler
 
 # Load environment variables
 # Updated to use the .env that is inside the current folder
@@ -372,6 +371,27 @@ def create_app(args):
     ollama_api = OllamaAPI(rag, top_k=args.top_k)
     app.include_router(ollama_api.router, prefix="/api")
 
+    @app.post("/login")
+    async def login(form_data: OAuth2PasswordRequestForm = Depends()):
+        username = os.getenv("AUTH_USERNAME")
+        password = os.getenv("AUTH_PASSWORD")
+
+        if not (username and password):
+            raise HTTPException(
+                status_code=status.HTTP_501_NOT_IMPLEMENTED,
+                detail="Authentication not configured",
+            )
+
+        if form_data.username != username or form_data.password != password:
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED, detail="Incorrect credentials"
+            )
+
+        return {
+            "access_token": auth_handler.create_token(username),
+            "token_type": "bearer",
+        }
+
     @app.get("/health", dependencies=[Depends(optional_api_key)])
     async def get_status():
         """Get current system status"""

lightrag/api/requirements.txt

@@ -1,10 +1,20 @@
 aiofiles
 ascii_colors
+asyncpg
+distro
 fastapi
+httpcore
+httpx
+jiter
 numpy
+openai
+passlib[bcrypt]
 pipmaster
+PyJWT
 python-dotenv
+python-jose[cryptography]
 python-multipart
+pytz
 tenacity
 tiktoken
 uvicorn

lightrag/api/routers/document_routes.py

@@ -18,8 +18,11 @@ from lightrag import LightRAG
 from lightrag.base import DocProcessingStatus, DocStatus
 from lightrag.api.utils_api import get_api_key_dependency, global_args
 
-
+router = APIRouter(
-router = APIRouter(prefix="/documents", tags=["documents"])
+    prefix="/documents",
+    tags=["documents"],
+    dependencies=[Depends(get_auth_dependency())],
+)
 
 # Temporary file prefix
 temp_prefix = "__tmp__"

lightrag/api/routers/graph_routes.py

@@ -3,12 +3,11 @@ This module contains all graph-related routes for the LightRAG API.
 """
 
 from typing import Optional
-
 from fastapi import APIRouter, Depends
 
-from ..utils_api import get_api_key_dependency
+from ..utils_api import get_api_key_dependency, get_auth_dependency
 
-router = APIRouter(tags=["graph"])
+router = APIRouter(tags=["graph"], dependencies=[Depends(get_auth_dependency())])
 
 
 def create_graph_routes(rag, api_key: Optional[str] = None):
@@ -25,23 +24,33 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
         return await rag.get_graph_labels()
 
     @router.get("/graphs", dependencies=[Depends(optional_api_key)])
-    async def get_knowledge_graph(label: str, max_depth: int = 3):
+    async def get_knowledge_graph(
+        label: str, max_depth: int = 3, min_degree: int = 0, inclusive: bool = False
+    ):
         """
         Retrieve a connected subgraph of nodes where the label includes the specified label.
         Maximum number of nodes is constrained by the environment variable `MAX_GRAPH_NODES` (default: 1000).
         When reducing the number of nodes, the prioritization criteria are as follows:
-            1. Label matching nodes take precedence
+            1. min_degree does not affect nodes directly connected to the matching nodes
-            2. Followed by nodes directly connected to the matching nodes
+            2. Label matching nodes take precedence
-            3. Finally, the degree of the nodes
+            3. Followed by nodes directly connected to the matching nodes
+            4. Finally, the degree of the nodes
         Maximum number of nodes is limited to env MAX_GRAPH_NODES(default: 1000)
 
         Args:
             label (str): Label to get knowledge graph for
             max_depth (int, optional): Maximum depth of graph. Defaults to 3.
+            inclusive_search (bool, optional): If True, search for nodes that include the label. Defaults to False.
+            min_degree (int, optional): Minimum degree of nodes. Defaults to 0.
 
         Returns:
             Dict[str, List[str]]: Knowledge graph for label
         """
-        return await rag.get_knowledge_graph(node_label=label, max_depth=max_depth)
+        return await rag.get_knowledge_graph(
+            node_label=label,
+            max_depth=max_depth,
+            inclusive=inclusive,
+            min_degree=min_degree,
+        )
 
     return router

lightrag/api/routers/query_routes.py

@@ -8,12 +8,12 @@ from typing import Any, Dict, List, Literal, Optional
 
 from fastapi import APIRouter, Depends, HTTPException
 from lightrag.base import QueryParam
-from ..utils_api import get_api_key_dependency
+from ..utils_api import get_api_key_dependency, get_auth_dependency
 from pydantic import BaseModel, Field, field_validator
 
 from ascii_colors import trace_exception
 
-router = APIRouter(tags=["query"])
+router = APIRouter(tags=["query"], dependencies=[Depends(get_auth_dependency())])
 
 
 class QueryRequest(BaseModel):

lightrag/api/utils_api.py

@@ -9,10 +9,11 @@ import sys
 import logging
 from ascii_colors import ASCIIColors
 from lightrag.api import __api_version__
-from fastapi import HTTPException, Security
+from fastapi import HTTPException, Security, Depends, Request
 from dotenv import load_dotenv
-from fastapi.security import APIKeyHeader
+from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
 from starlette.status import HTTP_403_FORBIDDEN
+from .auth import auth_handler
 
 # Load environment variables
 load_dotenv(override=True)
@@ -33,6 +34,24 @@ class OllamaServerInfos:
 ollama_server_infos = OllamaServerInfos()
 
 
+def get_auth_dependency():
+    whitelist = os.getenv("WHITELIST_PATHS", "").split(",")
+
+    async def dependency(
+        request: Request,
+        token: str = Depends(OAuth2PasswordBearer(tokenUrl="login", auto_error=False)),
+    ):
+        if request.url.path in whitelist:
+            return
+
+        if not (os.getenv("AUTH_USERNAME") and os.getenv("AUTH_PASSWORD")):
+            return
+
+        auth_handler.validate_token(token)
+
+    return dependency
+
+
 def get_api_key_dependency(api_key: Optional[str]):
     """
     Create an API key dependency for route protection.

lightrag/api/webui/index.html

@@ -5,8 +5,8 @@
     <link rel="icon" type="image/svg+xml" href="./logo.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>
-    <script type="module" crossorigin src="./assets/index-DbuMPJAD.js"></script>
+    <script type="module" crossorigin src="./assets/index-CJz72b6Q.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-rP-YlyR1.css">
+    <link rel="stylesheet" crossorigin href="./assets/index-CH-3l4_Z.css">
   </head>
   <body>
     <div id="root"></div>

lightrag/base.py

@@ -204,7 +204,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
 
     @abstractmethod
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int = 5
+        self, node_label: str, max_depth: int = 3
     ) -> KnowledgeGraph:
         """Retrieve a subgraph of the knowledge graph starting from a given node."""
 

lightrag/kg/chroma_impl.py

@@ -229,3 +229,43 @@ class ChromaVectorDBStorage(BaseVectorStorage):
         except Exception as e:
             logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
             raise
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Get all records from the collection
+            # Since ChromaDB doesn't directly support prefix search on IDs,
+            # we'll get all records and filter in Python
+            results = self._collection.get(
+                include=["metadatas", "documents", "embeddings"]
+            )
+
+            matching_records = []
+
+            # Filter records where ID starts with the prefix
+            for i, record_id in enumerate(results["ids"]):
+                if record_id.startswith(prefix):
+                    matching_records.append(
+                        {
+                            "id": record_id,
+                            "content": results["documents"][i],
+                            "vector": results["embeddings"][i],
+                            **results["metadatas"][i],
+                        }
+                    )
+
+            logger.debug(
+                f"Found {len(matching_records)} records with prefix '{prefix}'"
+            )
+            return matching_records
+
+        except Exception as e:
+            logger.error(f"Error during prefix search in ChromaDB: {str(e)}")
+            raise

lightrag/kg/faiss_impl.py

@@ -371,3 +371,24 @@ class FaissVectorDBStorage(BaseVectorStorage):
                 return False  # Return error
 
         return True  # Return success
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        matching_records = []
+
+        # Search for records with IDs starting with the prefix
+        for faiss_id, meta in self._id_to_meta.items():
+            if "__id__" in meta and meta["__id__"].startswith(prefix):
+                # Create a copy of all metadata and add "id" field
+                record = {**meta, "id": meta["__id__"]}
+                matching_records.append(record)
+
+        logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'")
+        return matching_records

lightrag/kg/milvus_impl.py

@@ -206,3 +206,28 @@ class MilvusVectorDBStorage(BaseVectorStorage):
 
         except Exception as e:
             logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use Milvus query with expression to find IDs with the given prefix
+            expression = f'id like "{prefix}%"'
+            results = self._client.query(
+                collection_name=self.namespace,
+                filter=expression,
+                output_fields=list(self.meta_fields) + ["id"],
+            )
+
+            logger.debug(f"Found {len(results)} records with prefix '{prefix}'")
+            return results
+
+        except Exception as e:
+            logger.error(f"Error searching for records with prefix '{prefix}': {e}")
+            return []

lightrag/kg/mongo_impl.py

@@ -1045,6 +1045,32 @@ class MongoVectorDBStorage(BaseVectorStorage):
         except PyMongoError as e:
             logger.error(f"Error deleting relations for {entity_name}: {str(e)}")
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use MongoDB regex to find documents where _id starts with the prefix
+            cursor = self._data.find({"_id": {"$regex": f"^{prefix}"}})
+            matching_records = await cursor.to_list(length=None)
+
+            # Format results
+            results = [{**doc, "id": doc["_id"]} for doc in matching_records]
+
+            logger.debug(
+                f"Found {len(results)} records with prefix '{prefix}' in {self.namespace}"
+            )
+            return results
+
+        except PyMongoError as e:
+            logger.error(f"Error searching by prefix in {self.namespace}: {str(e)}")
+            return []
+
 
 async def get_or_create_collection(db: AsyncIOMotorDatabase, collection_name: str):
     collection_names = await db.list_collection_names()

lightrag/kg/nano_vector_db_impl.py

@@ -236,3 +236,23 @@ class NanoVectorDBStorage(BaseVectorStorage):
                 return False  # Return error
 
         return True  # Return success
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        storage = await self.client_storage
+        matching_records = []
+
+        # Search for records with IDs starting with the prefix
+        for record in storage["data"]:
+            if "__id__" in record and record["__id__"].startswith(prefix):
+                matching_records.append({**record, "id": record["__id__"]})
+
+        logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'")
+        return matching_records

lightrag/kg/networkx_impl.py

@@ -232,19 +232,26 @@ class NetworkXStorage(BaseGraphStorage):
         return sorted(list(labels))
 
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int = 5
+        self,
+        node_label: str,
+        max_depth: int = 3,
+        min_degree: int = 0,
+        inclusive: bool = False,
     ) -> KnowledgeGraph:
         """
         Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
         Maximum number of nodes is constrained by the environment variable `MAX_GRAPH_NODES` (default: 1000).
         When reducing the number of nodes, the prioritization criteria are as follows:
-            1. Label matching nodes take precedence
+            1. min_degree does not affect nodes directly connected to the matching nodes
-            2. Followed by nodes directly connected to the matching nodes
+            2. Label matching nodes take precedence
-            3. Finally, the degree of the nodes
+            3. Followed by nodes directly connected to the matching nodes
+            4. Finally, the degree of the nodes
 
         Args:
             node_label: Label of the starting node
             max_depth: Maximum depth of the subgraph
+            min_degree: Minimum degree of nodes to include. Defaults to 0
+            inclusive: Do an inclusive search if true
 
         Returns:
             KnowledgeGraph object containing nodes and edges
@@ -255,6 +262,10 @@ class NetworkXStorage(BaseGraphStorage):
 
         graph = await self._get_graph()
 
+        # Initialize sets for start nodes and direct connected nodes
+        start_nodes = set()
+        direct_connected_nodes = set()
+
         # Handle special case for "*" label
         if node_label == "*":
             # For "*", return the entire graph including all nodes and edges
@@ -262,10 +273,15 @@ class NetworkXStorage(BaseGraphStorage):
                 graph.copy()
             )  # Create a copy to avoid modifying the original graph
         else:
-            # Find nodes with matching node id (partial match)
+            # Find nodes with matching node id based on search_mode
             nodes_to_explore = []
             for n, attr in graph.nodes(data=True):
-                if node_label in str(n):  # Use partial matching
+                node_str = str(n)
+                if not inclusive:
+                    if node_label == node_str:  # Use exact matching
+                        nodes_to_explore.append(n)
+                else:  # inclusive mode
+                    if node_label in node_str:  # Use partial matching
                         nodes_to_explore.append(n)
 
             if not nodes_to_explore:
@@ -277,26 +293,37 @@ class NetworkXStorage(BaseGraphStorage):
             for start_node in nodes_to_explore:
                 node_subgraph = nx.ego_graph(graph, start_node, radius=max_depth)
                 combined_subgraph = nx.compose(combined_subgraph, node_subgraph)
+
+            # Get start nodes and direct connected nodes
+            if nodes_to_explore:
+                start_nodes = set(nodes_to_explore)
+                # Get nodes directly connected to all start nodes
+                for start_node in start_nodes:
+                    direct_connected_nodes.update(
+                        combined_subgraph.neighbors(start_node)
+                    )
+
+                # Remove start nodes from directly connected nodes (avoid duplicates)
+                direct_connected_nodes -= start_nodes
+
             subgraph = combined_subgraph
 
+        # Filter nodes based on min_degree, but keep start nodes and direct connected nodes
+        if min_degree > 0:
+            nodes_to_keep = [
+                node
+                for node, degree in subgraph.degree()
+                if node in start_nodes
+                or node in direct_connected_nodes
+                or degree >= min_degree
+            ]
+            subgraph = subgraph.subgraph(nodes_to_keep)
+
         # Check if number of nodes exceeds max_graph_nodes
         if len(subgraph.nodes()) > MAX_GRAPH_NODES:
             origin_nodes = len(subgraph.nodes())
-
             node_degrees = dict(subgraph.degree())
 
-            start_nodes = set()
-            direct_connected_nodes = set()
-
-            if node_label != "*" and nodes_to_explore:
-                start_nodes = set(nodes_to_explore)
-                # Get nodes directly connected to all start nodes
-                for start_node in start_nodes:
-                    direct_connected_nodes.update(subgraph.neighbors(start_node))
-
-                # Remove start nodes from directly connected nodes (avoid duplicates)
-                direct_connected_nodes -= start_nodes
-
             def priority_key(node_item):
                 node, degree = node_item
                 # Priority order: start(2) > directly connected(1) > other nodes(0)
@@ -356,7 +383,7 @@ class NetworkXStorage(BaseGraphStorage):
             result.edges.append(
                 KnowledgeGraphEdge(
                     id=edge_id,
-                    type="RELATED",
+                    type="DIRECTED",
                     source=str(source),
                     target=str(target),
                     properties=edge_data,

lightrag/kg/oracle_impl.py

@@ -494,6 +494,41 @@ class OracleVectorDBStorage(BaseVectorStorage):
             logger.error(f"Error deleting relations for entity {entity_name}: {e}")
             raise
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Determine the appropriate table based on namespace
+            table_name = namespace_to_table_name(self.namespace)
+
+            # Create SQL query to find records with IDs starting with prefix
+            search_sql = f"""
+                SELECT * FROM {table_name}
+                WHERE workspace = :workspace
+                AND id LIKE :prefix_pattern
+                ORDER BY id
+            """
+
+            params = {"workspace": self.db.workspace, "prefix_pattern": f"{prefix}%"}
+
+            # Execute query and get results
+            results = await self.db.query(search_sql, params, multirows=True)
+
+            logger.debug(
+                f"Found {len(results) if results else 0} records with prefix '{prefix}'"
+            )
+            return results or []
+
+        except Exception as e:
+            logger.error(f"Error searching records with prefix '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass

lightrag/kg/postgres_impl.py

@@ -575,6 +575,41 @@ class PGVectorStorage(BaseVectorStorage):
         except Exception as e:
             logger.error(f"Error deleting relations for entity {entity_name}: {e}")
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        table_name = namespace_to_table_name(self.namespace)
+        if not table_name:
+            logger.error(f"Unknown namespace for prefix search: {self.namespace}")
+            return []
+
+        search_sql = f"SELECT * FROM {table_name} WHERE workspace=$1 AND id LIKE $2"
+        params = {"workspace": self.db.workspace, "prefix": f"{prefix}%"}
+
+        try:
+            results = await self.db.query(search_sql, params, multirows=True)
+            logger.debug(f"Found {len(results)} records with prefix '{prefix}'")
+
+            # Format results to match the expected return format
+            formatted_results = []
+            for record in results:
+                formatted_record = dict(record)
+                # Ensure id field is available (for consistency with NanoVectorDB implementation)
+                if "id" not in formatted_record:
+                    formatted_record["id"] = record["id"]
+                formatted_results.append(formatted_record)
+
+            return formatted_results
+        except Exception as e:
+            logger.error(f"Error during prefix search for '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass
@@ -775,6 +810,14 @@ class PGGraphStorage(BaseGraphStorage):
             v = record[k]
             # agtype comes back '{key: value}::type' which must be parsed
             if isinstance(v, str) and "::" in v:
+                if v.startswith("[") and v.endswith("]"):
+                    if "::vertex" not in v:
+                        continue
+                    v = v.replace("::vertex", "")
+                    vertexes = json.loads(v)
+                    for vertex in vertexes:
+                        vertices[vertex["id"]] = vertex.get("properties")
+                else:
                     dtype = v.split("::")[-1]
                     v = v.split("::")[0]
                     if dtype == "vertex":
@@ -785,17 +828,49 @@ class PGGraphStorage(BaseGraphStorage):
         for k in record.keys():
             v = record[k]
             if isinstance(v, str) and "::" in v:
+                if v.startswith("[") and v.endswith("]"):
+                    if "::vertex" in v:
+                        v = v.replace("::vertex", "")
+                        vertexes = json.loads(v)
+                        dl = []
+                        for vertex in vertexes:
+                            prop = vertex.get("properties")
+                            if not prop:
+                                prop = {}
+                            prop["label"] = PGGraphStorage._decode_graph_label(
+                                prop["node_id"]
+                            )
+                            dl.append(prop)
+                        d[k] = dl
+
+                    elif "::edge" in v:
+                        v = v.replace("::edge", "")
+                        edges = json.loads(v)
+                        dl = []
+                        for edge in edges:
+                            dl.append(
+                                (
+                                    vertices[edge["start_id"]],
+                                    edge["label"],
+                                    vertices[edge["end_id"]],
+                                )
+                            )
+                        d[k] = dl
+                    else:
+                        print("WARNING: unsupported type")
+                        continue
+
+                else:
                     dtype = v.split("::")[-1]
                     v = v.split("::")[0]
-            else:
-                dtype = ""
-
                     if dtype == "vertex":
                         vertex = json.loads(v)
                         field = vertex.get("properties")
                         if not field:
                             field = {}
-                field["label"] = PGGraphStorage._decode_graph_label(field["node_id"])
+                        field["label"] = PGGraphStorage._decode_graph_label(
+                            field["node_id"]
+                        )
                         d[k] = field
                     # convert edge from id-label->id by replacing id with node information
                     # we only do this if the vertex was also returned in the query
@@ -809,6 +884,9 @@ class PGGraphStorage(BaseGraphStorage):
                             ],  # we don't use decode_graph_label(), since edge label is always "DIRECTED"
                             vertices.get(edge["end_id"], {}),
                         )
+            else:
+                if v is None or (v.count("{") < 1 and v.count("[") < 1):
+                    d[k] = v
                 else:
                     d[k] = json.loads(v) if isinstance(v, str) else v
 
@@ -1284,7 +1362,7 @@ class PGGraphStorage(BaseGraphStorage):
                          OPTIONAL MATCH p = (n)-[*..%d]-(m)
                          RETURN nodes(p) AS nodes, relationships(p) AS relationships
                          LIMIT %d
-                       $$) AS (nodes agtype[], relationships agtype[])""" % (
+                       $$) AS (nodes agtype, relationships agtype)""" % (
                 self.graph_name,
                 encoded_node_label,
                 max_depth,
@@ -1293,17 +1371,23 @@ class PGGraphStorage(BaseGraphStorage):
 
         results = await self._query(query)
 
-        nodes = set()
+        nodes = {}
         edges = []
+        unique_edge_ids = set()
 
         for result in results:
             if node_label == "*":
                 if result["n"]:
                     node = result["n"]
-                    nodes.add(self._decode_graph_label(node["node_id"]))
+                    node_id = self._decode_graph_label(node["node_id"])
+                    if node_id not in nodes:
+                        nodes[node_id] = node
+
                 if result["m"]:
                     node = result["m"]
-                    nodes.add(self._decode_graph_label(node["node_id"]))
+                    node_id = self._decode_graph_label(node["node_id"])
+                    if node_id not in nodes:
+                        nodes[node_id] = node
                 if result["r"]:
                     edge = result["r"]
                     src_id = self._decode_graph_label(edge["start_id"])
@@ -1312,16 +1396,36 @@ class PGGraphStorage(BaseGraphStorage):
             else:
                 if result["nodes"]:
                     for node in result["nodes"]:
-                        nodes.add(self._decode_graph_label(node["node_id"]))
+                        node_id = self._decode_graph_label(node["node_id"])
+                        if node_id not in nodes:
+                            nodes[node_id] = node
+
                 if result["relationships"]:
-                    for edge in result["relationships"]:
+                    for edge in result["relationships"]:  # src --DIRECTED--> target
-                        src_id = self._decode_graph_label(edge["start_id"])
+                        src_id = self._decode_graph_label(edge[0]["node_id"])
-                        tgt_id = self._decode_graph_label(edge["end_id"])
+                        tgt_id = self._decode_graph_label(edge[2]["node_id"])
-                        edges.append((src_id, tgt_id))
+                        id = src_id + "," + tgt_id
+                        if id in unique_edge_ids:
+                            continue
+                        else:
+                            unique_edge_ids.add(id)
+                        edges.append(
+                            (id, src_id, tgt_id, {"source": edge[0], "target": edge[2]})
+                        )
 
         kg = KnowledgeGraph(
-            nodes=[KnowledgeGraphNode(id=node_id) for node_id in nodes],
+            nodes=[
-            edges=[KnowledgeGraphEdge(source=src, target=tgt) for src, tgt in edges],
+                KnowledgeGraphNode(
+                    id=node_id, labels=[node_id], properties=nodes[node_id]
+                )
+                for node_id in nodes
+            ],
+            edges=[
+                KnowledgeGraphEdge(
+                    id=id, type="DIRECTED", source=src, target=tgt, properties=props
+                )
+                for id, src, tgt, props in edges
+            ],
         )
 
         return kg

lightrag/kg/qdrant_impl.py

@@ -233,3 +233,45 @@ class QdrantVectorDBStorage(BaseVectorStorage):
                 logger.debug(f"No relations found for entity {entity_name}")
         except Exception as e:
             logger.error(f"Error deleting relations for {entity_name}: {e}")
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use scroll method to find records with IDs starting with the prefix
+            results = self._client.scroll(
+                collection_name=self.namespace,
+                scroll_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="id", match=models.MatchText(text=prefix, prefix=True)
+                        )
+                    ]
+                ),
+                with_payload=True,
+                with_vectors=False,
+                limit=1000,  # Adjust as needed for your use case
+            )
+
+            # Extract matching points
+            matching_records = results[0]
+
+            # Format the results to match expected return format
+            formatted_results = [
+                {**point.payload, "id": point.id} for point in matching_records
+            ]
+
+            logger.debug(
+                f"Found {len(formatted_results)} records with prefix '{prefix}'"
+            )
+            return formatted_results
+
+        except Exception as e:
+            logger.error(f"Error searching for prefix '{prefix}': {e}")
+            return []

lightrag/kg/tidb_impl.py

@@ -414,6 +414,55 @@ class TiDBVectorDBStorage(BaseVectorStorage):
         # Ti handles persistence automatically
         pass
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        # Determine which table to query based on namespace
+        if self.namespace == NameSpace.VECTOR_STORE_ENTITIES:
+            sql_template = """
+                SELECT entity_id as id, name as entity_name, entity_type, description, content
+                FROM LIGHTRAG_GRAPH_NODES
+                WHERE entity_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        elif self.namespace == NameSpace.VECTOR_STORE_RELATIONSHIPS:
+            sql_template = """
+                SELECT relation_id as id, source_name as src_id, target_name as tgt_id,
+                       keywords, description, content
+                FROM LIGHTRAG_GRAPH_EDGES
+                WHERE relation_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        elif self.namespace == NameSpace.VECTOR_STORE_CHUNKS:
+            sql_template = """
+                SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id
+                FROM LIGHTRAG_DOC_CHUNKS
+                WHERE chunk_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        else:
+            logger.warning(
+                f"Namespace {self.namespace} not supported for prefix search"
+            )
+            return []
+
+        # Add prefix pattern parameter with % for SQL LIKE
+        prefix_pattern = f"{prefix}%"
+        params = {"prefix_pattern": prefix_pattern, "workspace": self.db.workspace}
+
+        try:
+            results = await self.db.query(sql_template, params=params, multirows=True)
+            logger.debug(
+                f"Found {len(results) if results else 0} records with prefix '{prefix}'"
+            )
+            return results if results else []
+        except Exception as e:
+            logger.error(f"Error searching records with prefix '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass
@@ -968,4 +1017,20 @@ SQL_TEMPLATES = {
         WHERE (source_name = :source AND target_name = :target)
         AND workspace = :workspace
     """,
+    # Search by prefix SQL templates
+    "search_entity_by_prefix": """
+        SELECT entity_id as id, name as entity_name, entity_type, description, content
+        FROM LIGHTRAG_GRAPH_NODES
+        WHERE entity_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
+    "search_relationship_by_prefix": """
+        SELECT relation_id as id, source_name as src_id, target_name as tgt_id, keywords, description, content
+        FROM LIGHTRAG_GRAPH_EDGES
+        WHERE relation_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
+    "search_chunk_by_prefix": """
+        SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id
+        FROM LIGHTRAG_DOC_CHUNKS
+        WHERE chunk_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
 }

lightrag/lightrag.py

@@ -504,11 +504,39 @@ class LightRAG:
         return text
 
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int
+        self,
+        node_label: str,
+        max_depth: int = 3,
+        min_degree: int = 0,
+        inclusive: bool = False,
     ) -> KnowledgeGraph:
-        return await self.chunk_entity_relation_graph.get_knowledge_graph(
+        """Get knowledge graph for a given label
-            node_label=node_label, max_depth=max_depth
+
-        )
+        Args:
+            node_label (str): Label to get knowledge graph for
+            max_depth (int): Maximum depth of graph
+            min_degree (int, optional): Minimum degree of nodes to include. Defaults to 0.
+            inclusive (bool, optional): Whether to use inclusive search mode. Defaults to False.
+
+        Returns:
+            KnowledgeGraph: Knowledge graph containing nodes and edges
+        """
+        # get params supported by get_knowledge_graph of specified storage
+        import inspect
+
+        storage_params = inspect.signature(
+            self.chunk_entity_relation_graph.get_knowledge_graph
+        ).parameters
+
+        kwargs = {"node_label": node_label, "max_depth": max_depth}
+
+        if "min_degree" in storage_params and min_degree > 0:
+            kwargs["min_degree"] = min_degree
+
+        if "inclusive" in storage_params:
+            kwargs["inclusive"] = inclusive
+
+        return await self.chunk_entity_relation_graph.get_knowledge_graph(**kwargs)
 
     def _get_storage_class(self, storage_name: str) -> Callable[..., Any]:
         import_path = STORAGES[storage_name]
@@ -2016,6 +2044,9 @@ class LightRAG:
                 # Delete old entity record from vector database
                 old_entity_id = compute_mdhash_id(entity_name, prefix="ent-")
                 await self.entities_vdb.delete([old_entity_id])
+                logger.info(
+                    f"Deleted old entity '{entity_name}' and its vector embedding from database"
+                )
 
                 # Update relationship vector representations
                 for src, tgt, edge_data in relations_to_update:
@@ -2143,6 +2174,15 @@ class LightRAG:
                     f"Relation from '{source_entity}' to '{target_entity}' does not exist"
                 )
 
+            # Important: First delete the old relation record from the vector database
+            old_relation_id = compute_mdhash_id(
+                source_entity + target_entity, prefix="rel-"
+            )
+            await self.relationships_vdb.delete([old_relation_id])
+            logger.info(
+                f"Deleted old relation record from vector database for relation {source_entity} -> {target_entity}"
+            )
+
             # 2. Update relation information in the graph
             new_edge_data = {**edge_data, **updated_data}
             await self.chunk_entity_relation_graph.upsert_edge(
@@ -2641,12 +2681,29 @@ class LightRAG:
 
             # 9. Delete source entities
             for entity_name in source_entities:
-                # Delete entity node
+                # Delete entity node from knowledge graph
                 await self.chunk_entity_relation_graph.delete_node(entity_name)
-                # Delete record from vector database
+
+                # Delete entity record from vector database
                 entity_id = compute_mdhash_id(entity_name, prefix="ent-")
                 await self.entities_vdb.delete([entity_id])
-                logger.info(f"Deleted source entity '{entity_name}'")
+
+                # Also ensure any relationships specific to this entity are deleted from vector DB
+                # This is a safety check, as these should have been transformed to the target entity already
+                entity_relation_prefix = compute_mdhash_id(entity_name, prefix="rel-")
+                relations_with_entity = await self.relationships_vdb.search_by_prefix(
+                    entity_relation_prefix
+                )
+                if relations_with_entity:
+                    relation_ids = [r["id"] for r in relations_with_entity]
+                    await self.relationships_vdb.delete(relation_ids)
+                    logger.info(
+                        f"Deleted {len(relation_ids)} relation records for entity '{entity_name}' from vector database"
+                    )
+
+                logger.info(
+                    f"Deleted source entity '{entity_name}' and its vector embedding from database"
+                )
 
             # 10. Save changes
             await self._merge_entities_done()

lightrag/operate.py

@@ -1152,7 +1152,8 @@ async def _get_node_data(
             "entity",
             "type",
             "description",
-            "rank" "created_at",
+            "rank",
+            "created_at",
         ]
     ]
     for i, n in enumerate(node_datas):

lightrag_webui/src/api/lightrag.ts

@@ -161,8 +161,12 @@ axiosInstance.interceptors.response.use(
 )
 
 // API methods
-export const queryGraphs = async (label: string, maxDepth: number): Promise<LightragGraphType> => {
+export const queryGraphs = async (
-  const response = await axiosInstance.get(`/graphs?label=${label}&max_depth=${maxDepth}`)
+  label: string,
+  maxDepth: number,
+  minDegree: number
+): Promise<LightragGraphType> => {
+  const response = await axiosInstance.get(`/graphs?label=${encodeURIComponent(label)}&max_depth=${maxDepth}&min_degree=${minDegree}`)
   return response.data
 }
 

lightrag_webui/src/components/graph/GraphControl.tsx

@@ -40,18 +40,21 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
   const focusedEdge = useGraphStore.use.focusedEdge()
 
   /**
-   * When component mount
+   * When component mount or maxIterations changes
-   * => load the graph
+   * => load the graph and apply layout
    */
   useEffect(() => {
     // Create & load the graph
     const graph = lightrageGraph()
     loadGraph(graph)
-    if (!(graph as any).__force_applied) {
     assignLayout()
-      Object.assign(graph, { __force_applied: true })
+  }, [assignLayout, loadGraph, lightrageGraph, maxIterations])
-    }
 
+  /**
+   * When component mount
+   * => register events
+   */
+  useEffect(() => {
     const { setFocusedNode, setSelectedNode, setFocusedEdge, setSelectedEdge, clearSelection } =
       useGraphStore.getState()
 
@@ -87,7 +90,7 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
       },
       clickStage: () => clearSelection()
     })
-  }, [assignLayout, loadGraph, registerEvents, lightrageGraph])
+  }, [registerEvents])
 
   /**
    * When component mount or hovered node change

lightrag_webui/src/components/graph/Settings.tsx

@@ -90,9 +90,12 @@ const LabeledNumberInput = ({
         {label}
       </label>
       <Input
-        value={currentValue || ''}
+        type="number"
+        value={currentValue === null ? '' : currentValue}
         onChange={onValueChange}
-        className="h-6 w-full min-w-0"
+        className="h-6 w-full min-w-0 pr-1"
+        min={min}
+        max={max}
         onBlur={onBlur}
         onKeyDown={(e) => {
           if (e.key === 'Enter') {
@@ -119,6 +122,7 @@ export default function Settings() {
   const enableHideUnselectedEdges = useSettingsStore.use.enableHideUnselectedEdges()
   const showEdgeLabel = useSettingsStore.use.showEdgeLabel()
   const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth()
+  const graphMinDegree = useSettingsStore.use.graphMinDegree()
   const graphLayoutMaxIterations = useSettingsStore.use.graphLayoutMaxIterations()
 
   const enableHealthCheck = useSettingsStore.use.enableHealthCheck()
@@ -177,6 +181,11 @@ export default function Settings() {
     useSettingsStore.setState({ graphQueryMaxDepth: depth })
   }, [])
 
+  const setGraphMinDegree = useCallback((degree: number) => {
+    if (degree < 0) return
+    useSettingsStore.setState({ graphMinDegree: degree })
+  }, [])
+
   const setGraphLayoutMaxIterations = useCallback((iterations: number) => {
     if (iterations < 1) return
     useSettingsStore.setState({ graphLayoutMaxIterations: iterations })
@@ -266,6 +275,12 @@ export default function Settings() {
             value={graphQueryMaxDepth}
             onEditFinished={setGraphQueryMaxDepth}
           />
+          <LabeledNumberInput
+            label="Minimum Degree"
+            min={0}
+            value={graphMinDegree}
+            onEditFinished={setGraphMinDegree}
+          />
           <LabeledNumberInput
             label="Max Layout Iterations"
             min={1}

lightrag_webui/src/components/ui/Input.tsx

@@ -7,7 +7,7 @@ const Input = React.forwardRef<HTMLInputElement, React.ComponentProps<'input'>>(
       <input
         type={type}
         className={cn(
-          'border-input file:text-foreground placeholder:text-muted-foreground focus-visible:ring-ring flex h-9 rounded-md border bg-transparent px-3 py-1 text-base shadow-sm transition-colors file:border-0 file:bg-transparent file:text-sm file:font-medium focus-visible:ring-1 focus-visible:outline-none disabled:cursor-not-allowed disabled:opacity-50 md:text-sm',
+          'border-input file:text-foreground placeholder:text-muted-foreground focus-visible:ring-ring flex h-9 rounded-md border bg-transparent px-3 py-1 text-base shadow-sm transition-colors file:border-0 file:bg-transparent file:text-sm file:font-medium focus-visible:ring-1 focus-visible:outline-none disabled:cursor-not-allowed disabled:opacity-50 md:text-sm [&::-webkit-inner-spin-button]:opacity-100 [&::-webkit-outer-spin-button]:opacity-100',
           className
         )}
         ref={ref}

lightrag_webui/src/hooks/useLightragGraph.tsx

@@ -50,11 +50,11 @@ export type NodeType = {
 }
 export type EdgeType = { label: string }
 
-const fetchGraph = async (label: string, maxDepth: number) => {
+const fetchGraph = async (label: string, maxDepth: number, minDegree: number) => {
   let rawData: any = null
 
   try {
-    rawData = await queryGraphs(label, maxDepth)
+    rawData = await queryGraphs(label, maxDepth, minDegree)
   } catch (e) {
     useBackendState.getState().setErrorMessage(errorMessage(e), 'Query Graphs Error!')
     return null
@@ -161,13 +161,14 @@ const createSigmaGraph = (rawGraph: RawGraph | null) => {
   return graph
 }
 
-const lastQueryLabel = { label: '', maxQueryDepth: 0 }
+const lastQueryLabel = { label: '', maxQueryDepth: 0, minDegree: 0 }
 
 const useLightrangeGraph = () => {
   const queryLabel = useSettingsStore.use.queryLabel()
   const rawGraph = useGraphStore.use.rawGraph()
   const sigmaGraph = useGraphStore.use.sigmaGraph()
   const maxQueryDepth = useSettingsStore.use.graphQueryMaxDepth()
+  const minDegree = useSettingsStore.use.graphMinDegree()
 
   const getNode = useCallback(
     (nodeId: string) => {
@@ -185,13 +186,16 @@ const useLightrangeGraph = () => {
 
   useEffect(() => {
     if (queryLabel) {
-      if (lastQueryLabel.label !== queryLabel || lastQueryLabel.maxQueryDepth !== maxQueryDepth) {
+      if (lastQueryLabel.label !== queryLabel ||
+          lastQueryLabel.maxQueryDepth !== maxQueryDepth ||
+          lastQueryLabel.minDegree !== minDegree) {
         lastQueryLabel.label = queryLabel
         lastQueryLabel.maxQueryDepth = maxQueryDepth
+        lastQueryLabel.minDegree = minDegree
 
         const state = useGraphStore.getState()
         state.reset()
-        fetchGraph(queryLabel, maxQueryDepth).then((data) => {
+        fetchGraph(queryLabel, maxQueryDepth, minDegree).then((data) => {
           // console.debug('Query label: ' + queryLabel)
           state.setSigmaGraph(createSigmaGraph(data))
           data?.buildDynamicMap()
@@ -203,7 +207,7 @@ const useLightrangeGraph = () => {
       state.reset()
       state.setSigmaGraph(new DirectedGraph())
     }
-  }, [queryLabel, maxQueryDepth])
+  }, [queryLabel, maxQueryDepth, minDegree])
 
   const lightrageGraph = useCallback(() => {
     if (sigmaGraph) {

lightrag_webui/src/stores/settings.ts

@@ -22,6 +22,9 @@ interface SettingsState {
   graphQueryMaxDepth: number
   setGraphQueryMaxDepth: (depth: number) => void
 
+  graphMinDegree: number
+  setGraphMinDegree: (degree: number) => void
+
   graphLayoutMaxIterations: number
   setGraphLayoutMaxIterations: (iterations: number) => void
 
@@ -66,6 +69,7 @@ const useSettingsStoreBase = create<SettingsState>()(
       enableEdgeEvents: false,
 
       graphQueryMaxDepth: 3,
+      graphMinDegree: 0,
       graphLayoutMaxIterations: 10,
 
       queryLabel: defaultQueryLabel,
@@ -107,6 +111,8 @@ const useSettingsStoreBase = create<SettingsState>()(
 
       setGraphQueryMaxDepth: (depth: number) => set({ graphQueryMaxDepth: depth }),
 
+      setGraphMinDegree: (degree: number) => set({ graphMinDegree: degree }),
+
       setEnableHealthCheck: (enable: boolean) => set({ enableHealthCheck: enable }),
 
       setApiKey: (apiKey: string | null) => set({ apiKey }),

lightrag_webui/src/vite-env.d.ts

@@ -1 +1,11 @@
 /// <reference types="vite/client" />
+
+interface ImportMetaEnv {
+  readonly VITE_API_PROXY: string
+  readonly VITE_API_ENDPOINTS: string
+  readonly VITE_BACKEND_URL: string
+}
+
+interface ImportMeta {
+  readonly env: ImportMetaEnv
+}

lightrag_webui/tsconfig.json

@@ -26,5 +26,5 @@
       "@/*": ["./src/*"]
     }
   },
-  "include": ["src"]
+  "include": ["src", "vite.config.ts"]
 }

lightrag_webui/vite.config.ts

@@ -14,6 +14,21 @@ export default defineConfig({
   },
   base: './',
   build: {
-    outDir: path.resolve(__dirname, '../lightrag/api/webui')
+    outDir: path.resolve(__dirname, '../lightrag/api/webui'),
+    emptyOutDir: true
+  },
+  server: {
+    proxy: import.meta.env.VITE_API_PROXY === 'true' && import.meta.env.VITE_API_ENDPOINTS ?
+      Object.fromEntries(
+        import.meta.env.VITE_API_ENDPOINTS.split(',').map(endpoint => [
+          endpoint,
+          {
+            target: import.meta.env.VITE_BACKEND_URL || 'http://localhost:9621',
+            changeOrigin: true,
+            rewrite: endpoint === '/api' ?
+              (path) => path.replace(/^\/api/, '') : undefined
+          }
+        ])
+      ) : {}
   }
 })