环境问题修复中

CoreRAG/MassageAcupointRAG.py

@@ -1,10 +1,10 @@
 import asyncio
 import numpy as np
 import re
-from lightrag import LightRAG
-from lightrag.base import QueryParam
-from custom_rag_processor import QwenEmbedding, DeepSeekCompletion
-
+from CoreRAG.lightrag.lightrag import LightRAG
+from CoreRAG.lightrag.base import QueryParam
+from CoreRAG.custom_rag_processor import QwenEmbedding, DeepSeekCompletion
+from typing import List,Tuple
 class MassageAcupointRAG:
     def __init__(self, working_dir: str):
         self.working_dir = working_dir
@@ -16,7 +16,7 @@ class MassageAcupointRAG:
         )
 
     @staticmethod
-    async def async_embed(texts: list[str]) -> np.ndarray:
+    async def async_embed(texts: List[str]) -> np.ndarray:
         async with QwenEmbedding() as embedder:
             return await embedder.embed(texts)
 
@@ -26,7 +26,7 @@ class MassageAcupointRAG:
             return await completer.complete(prompt, **kwargs)
 
     @staticmethod
-    def extract_acupoint_list(text: str) -> list[str]:
+    def extract_acupoint_list(text: str) -> List[str]:
         """
         从 LLM 返回文本中提取穴位名称列表（形如 ['肩井穴', ...]）
         """
@@ -39,7 +39,7 @@ class MassageAcupointRAG:
     async def initialize(self):
         await self.rag.initialize_storages()
 
-    async def query_acupoints(self, user_query: str) -> tuple[str, list[str]]:
+    async def query_acupoints(self, user_query: str) -> Tuple[str, List[str]]:
         """
         执行 RAG 查询并提取穴位列表
 

CoreRAG/custom_rag_processor.py

@@ -4,10 +4,11 @@ import logging
 import logging.config
 import aiohttp
 import numpy as np
-from lightrag import LightRAG, QueryParam
-from lightrag.kg.shared_storage import initialize_pipeline_status
-from lightrag.utils import logger, set_verbose_debug
+from CoreRAG.lightrag import LightRAG, QueryParam
+from CoreRAG.lightrag.kg.shared_storage import initialize_pipeline_status
+from CoreRAG.lightrag.utils import logger, set_verbose_debug
 import tiktoken
+from typing import List
 WORKING_DIR = "./dickens"
 
 # API配置
@@ -33,7 +34,7 @@ class QwenEmbedding:
         if self.session:
             await self.session.close()
             
-    async def embed(self, texts: list[str]) -> np.ndarray:
+    async def embed(self, texts: List[str]) -> np.ndarray:
         """自动分批请求嵌入"""
         if not self.session:
             raise RuntimeError("Session not initialized. Use async with.")
@@ -114,7 +115,7 @@ class TextChunker:
         self.max_tokens = max_tokens
         self.overlap = overlap
 
-    def chunk_text(self, text: str) -> list[str]:
+    def chunk_text(self, text: str) -> List[str]:
         """将长文本按token分块"""
         tokens = self.tokenizer.encode(text)
         chunks = []
@@ -193,7 +194,7 @@ if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
 
-async def Qwen_embed(texts: list[str]) -> np.ndarray:
+async def Qwen_embed(texts: List[str]) -> np.ndarray:
     async with QwenEmbedding() as embedder:
         return await embedder.embed(texts)
 Qwen_embed.embedding_dim = 1024
@@ -303,7 +304,7 @@ class CustomRAGProcessor:
         await initialize_pipeline_status()
         return rag
 
-    async def qwen_embed_wrapper(self, texts: list[str]) -> np.ndarray:
+    async def qwen_embed_wrapper(self, texts: List[str]) -> np.ndarray:
         """Qwen嵌入函数包装器，带重试机制"""
         async with QwenEmbedding(self.qwen_api_key) as embedder:
             return await embedder.embed(texts)

CoreRAG/lightrag/base.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-
+from typing import List, Union, Callable, Dict, Tuple
 from abc import ABC, abstractmethod
 from enum import Enum
 import os
@@ -70,13 +70,13 @@ class QueryParam:
     max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
     """Maximum number of tokens allocated for entity descriptions in local retrieval."""
 
-    hl_keywords: list[str] = field(default_factory=list)
+    hl_keywords: List[str] = field(default_factory=list)
     """List of high-level keywords to prioritize in retrieval."""
 
-    ll_keywords: list[str] = field(default_factory=list)
+    ll_keywords: List[str] = field(default_factory=list)
     """List of low-level keywords to refine retrieval focus."""
 
-    conversation_history: list[dict[str, str]] = field(default_factory=list)
+    conversation_history: List[Dict[str, str]] = field(default_factory=list)
     """Stores past conversation history to maintain context.
     Format: [{"role": "user/assistant", "content": "message"}].
     """
@@ -84,16 +84,16 @@ class QueryParam:
     history_turns: int = 3
     """Number of complete conversation turns (user-assistant pairs) to consider in the response context."""
 
-    ids: list[str] | None = None
+    ids: Union[List[str], None] = None
     """List of ids to filter the results."""
 
-    model_func: Callable[..., object] | None = None
+    model_func: Union[Callable[..., object], None] = None
     """Optional override for the LLM model function to use for this specific query.
     If provided, this will be used instead of the global model function.
     This allows using different models for different query modes.
     """
 
-    user_prompt: str | None = None
+    user_prompt: Union[str,None] = None
     """User-provided prompt for the query.
     If proivded, this will be use instead of the default vaulue from prompt template.
     """
@@ -102,7 +102,7 @@ class QueryParam:
 @dataclass
 class StorageNameSpace(ABC):
     namespace: str
-    global_config: dict[str, Any]
+    global_config: Dict[str, Any]
 
     async def initialize(self):
         """Initialize the storage"""
@@ -117,7 +117,7 @@ class StorageNameSpace(ABC):
         """Commit the storage operations after indexing"""
 
     @abstractmethod
-    async def drop(self) -> dict[str, str]:
+    async def drop(self) -> Dict[str, str]:
         """Drop all data from storage and clean up resources
 
         This abstract method defines the contract for dropping all data from a storage implementation.
@@ -151,12 +151,12 @@ class BaseVectorStorage(StorageNameSpace, ABC):
 
     @abstractmethod
     async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+        self, query: str, top_k: int, ids: Union[List[str],None] = None
+    ) -> List[Dict[str, Any]]:
         """Query the vector storage and retrieve top_k results."""
 
     @abstractmethod
-    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
+    async def upsert(self, data: Dict[str, Dict[str, Any]]) -> None:
         """Insert or update vectors in the storage.
 
         Importance notes for in-memory storage:
@@ -186,7 +186,7 @@ class BaseVectorStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def get_by_id(self, id: str) -> dict[str, Any] | None:
+    async def get_by_id(self, id: str) -> Union[Dict[str, Any],None]:
         """Get vector data by its ID
 
         Args:
@@ -198,7 +198,7 @@ class BaseVectorStorage(StorageNameSpace, ABC):
         pass
 
     @abstractmethod
-    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
+    async def get_by_ids(self, ids: List[str]) -> List[Dict[str, Any]]:
         """Get multiple vector data by their IDs
 
         Args:
@@ -210,7 +210,7 @@ class BaseVectorStorage(StorageNameSpace, ABC):
         pass
 
     @abstractmethod
-    async def delete(self, ids: list[str]):
+    async def delete(self, ids: List[str]):
         """Delete vectors with specified IDs
 
         Importance notes for in-memory storage:
@@ -228,11 +228,11 @@ class BaseKVStorage(StorageNameSpace, ABC):
     embedding_func: EmbeddingFunc
 
     @abstractmethod
-    async def get_by_id(self, id: str) -> dict[str, Any] | None:
+    async def get_by_id(self, id: str) -> Union[Dict[str, Any],None]:
         """Get value by id"""
 
     @abstractmethod
-    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
+    async def get_by_ids(self, ids: List[str]) -> List[Dict[str, Any]]:
         """Get values by ids"""
 
     @abstractmethod
@@ -249,7 +249,7 @@ class BaseKVStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def delete(self, ids: list[str]) -> None:
+    async def delete(self, ids: List[str]) -> None:
         """Delete specific records from storage by their IDs
 
         Importance notes for in-memory storage:
@@ -263,7 +263,7 @@ class BaseKVStorage(StorageNameSpace, ABC):
             None
         """
 
-    async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool:
+    async def drop_cache_by_modes(self, modes: Union[List[str],None] = None) -> bool:
         """Delete specific records from storage by cache mode
 
         Importance notes for in-memory storage:
@@ -330,7 +330,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def get_node(self, node_id: str) -> dict[str, str] | None:
+    async def get_node(self, node_id: str) -> Union[Dict[str, str],None]:
         """Get node by its ID, returning only node properties.
 
         Args:
@@ -343,7 +343,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
     @abstractmethod
     async def get_edge(
         self, source_node_id: str, target_node_id: str
-    ) -> dict[str, str] | None:
+    ) -> Union[Dict[str, str],None]:
         """Get edge properties between two nodes.
 
         Args:
@@ -355,7 +355,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
+    async def get_node_edges(self, source_node_id: str) -> Union[List[Tuple[str, str]],None]:
         """Get all edges connected to a node.
 
         Args:
@@ -366,7 +366,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
             or None if the node doesn't exist
         """
 
-    async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]:
+    async def get_nodes_batch(self, node_ids: List[str]) -> Dict[str, Dict]:
         """Get nodes as a batch using UNWIND
 
         Default implementation fetches nodes one by one.
@@ -380,7 +380,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
                 result[node_id] = node
         return result
 
-    async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]:
+    async def node_degrees_batch(self, node_ids: List[str]) -> Dict[str, int]:
         """Node degrees as a batch using UNWIND
 
         Default implementation fetches node degrees one by one.
@@ -394,8 +394,8 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         return result
 
     async def edge_degrees_batch(
-        self, edge_pairs: list[tuple[str, str]]
-    ) -> dict[tuple[str, str], int]:
+        self, edge_pairs: List[Tuple[str, str]]
+    ) -> Dict[Tuple[str, str], int]:
         """Edge degrees as a batch using UNWIND also uses node_degrees_batch
 
         Default implementation calculates edge degrees one by one.
@@ -409,8 +409,8 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         return result
 
     async def get_edges_batch(
-        self, pairs: list[dict[str, str]]
-    ) -> dict[tuple[str, str], dict]:
+        self, pairs: List[Dict[str, str]]
+    ) -> Dict[Tuple[str, str], Dict]:
         """Get edges as a batch using UNWIND
 
         Default implementation fetches edges one by one.
@@ -427,8 +427,8 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         return result
 
     async def get_nodes_edges_batch(
-        self, node_ids: list[str]
-    ) -> dict[str, list[tuple[str, str]]]:
+        self, node_ids: List[str]
+    ) -> Dict[str, List[Tuple[str, str]]]:
         """Get nodes edges as a batch using UNWIND
 
         Default implementation fetches node edges one by one.
@@ -442,7 +442,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         return result
 
     @abstractmethod
-    async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
+    async def upsert_node(self, node_id: str, node_data: Dict[str, str]) -> None:
         """Insert a new node or update an existing node in the graph.
 
         Importance notes for in-memory storage:
@@ -457,7 +457,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
 
     @abstractmethod
     async def upsert_edge(
-        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
+        self, source_node_id: str, target_node_id: str, edge_data: Dict[str, str]
     ) -> None:
         """Insert a new edge or update an existing edge in the graph.
 
@@ -486,7 +486,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def remove_nodes(self, nodes: list[str]):
+    async def remove_nodes(self, nodes: List[str]):
         """Delete multiple nodes
 
         Importance notes:
@@ -499,7 +499,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def remove_edges(self, edges: list[tuple[str, str]]):
+    async def remove_edges(self, edges: List[Tuple[str, str]]):
         """Delete multiple edges
 
         Importance notes:
@@ -512,7 +512,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
         """
 
     @abstractmethod
-    async def get_all_labels(self) -> list[str]:
+    async def get_all_labels(self) -> List[str]:
         """Get all labels in the graph.
 
         Returns:
@@ -564,11 +564,11 @@ class DocProcessingStatus:
     """ISO format timestamp when document was created"""
     updated_at: str
     """ISO format timestamp when document was last updated"""
-    chunks_count: int | None = None
+    chunks_count: Union[int,None] = None
     """Number of chunks after splitting, used for processing"""
-    error: str | None = None
+    error: Union[str,None] = None
     """Error message if failed"""
-    metadata: dict[str, Any] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=Dict)
     """Additional metadata"""
 
 
@@ -577,16 +577,16 @@ class DocStatusStorage(BaseKVStorage, ABC):
     """Base class for document status storage"""
 
     @abstractmethod
-    async def get_status_counts(self) -> dict[str, int]:
+    async def get_status_counts(self) -> Dict[str, int]:
         """Get counts of documents in each status"""
 
     @abstractmethod
     async def get_docs_by_status(
         self, status: DocStatus
-    ) -> dict[str, DocProcessingStatus]:
+    ) -> Dict[str, DocProcessingStatus]:
         """Get all documents with a specific status"""
 
-    async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool:
+    async def drop_cache_by_modes(self, modes: Union[List[str],None] = None) -> bool:
         """Drop cache is not supported for Doc Status storage"""
         return False
 

CoreRAG/lightrag/kg/__init__.py

@@ -1,3 +1,5 @@
+from typing import List
+from typing import Dict
 STORAGE_IMPLEMENTATIONS = {
     "KV_STORAGE": {
         "implementations": [
@@ -45,7 +47,7 @@ STORAGE_IMPLEMENTATIONS = {
 }
 
 # Storage implementation environment variable without default value
-STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
+STORAGE_ENV_REQUIREMENTS: Dict[str, List[str]] = {
     # KV Storage Implementations
     "JsonKVStorage": [],
     "MongoKVStorage": [],

CoreRAG/lightrag/kg/json_kv_impl.py

@@ -1,11 +1,11 @@
 import os
 from dataclasses import dataclass
-from typing import Any, final
+from typing import Any, final,Dict,List,Union,Set
 
-from lightrag.base import (
+from ..base import (
     BaseKVStorage,
 )
-from lightrag.utils import (
+from ..utils import (
     load_json,
     logger,
     write_json,
@@ -85,7 +85,7 @@ class JsonKVStorage(BaseKVStorage):
                 write_json(data_dict, self._file_name)
                 await clear_all_update_flags(self.namespace)
 
-    async def get_all(self) -> dict[str, Any]:
+    async def get_all(self) -> Dict[str, Any]:
         """Get all data from storage
 
         Returns:
@@ -94,11 +94,11 @@ class JsonKVStorage(BaseKVStorage):
         async with self._storage_lock:
             return dict(self._data)
 
-    async def get_by_id(self, id: str) -> dict[str, Any] | None:
+    async def get_by_id(self, id: str) -> Union[Dict[str, Any] , None]:
         async with self._storage_lock:
             return self._data.get(id)
 
-    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
+    async def get_by_ids(self, ids: List[str]) -> List[Dict[str, Any]]:
         async with self._storage_lock:
             return [
                 (
@@ -109,11 +109,11 @@ class JsonKVStorage(BaseKVStorage):
                 for id in ids
             ]
 
-    async def filter_keys(self, keys: set[str]) -> set[str]:
+    async def filter_keys(self, keys: Set[str]) -> Set[str]:
         async with self._storage_lock:
-            return set(keys) - set(self._data.keys())
+            return Set(keys) - Set(self._data.keys())
 
-    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
+    async def upsert(self, data: Dict[str, Dict[str, Any]]) -> None:
         """
         Importance notes for in-memory storage:
         1. Changes will be persisted to disk during the next index_done_callback
@@ -126,7 +126,7 @@ class JsonKVStorage(BaseKVStorage):
             self._data.update(data)
             await set_all_update_flags(self.namespace)
 
-    async def delete(self, ids: list[str]) -> None:
+    async def delete(self, ids: List[str]) -> None:
         """Delete specific records from storage by their IDs
 
         Importance notes for in-memory storage:
@@ -149,7 +149,7 @@ class JsonKVStorage(BaseKVStorage):
             if any_deleted:
                 await set_all_update_flags(self.namespace)
 
-    async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool:
+    async def drop_cache_by_modes(self, modes: Union[List[str] , None] = None) -> bool:
         """Delete specific records from storage by by cache mode
 
         Importance notes for in-memory storage:
@@ -172,7 +172,7 @@ class JsonKVStorage(BaseKVStorage):
         except Exception:
             return False
 
-    async def drop(self) -> dict[str, str]:
+    async def drop(self) -> Dict[str, str]:
         """Drop all data from storage and clean up resources
            This action will persistent the data to disk immediately.
 

CoreRAG/lightrag/kg/nano_vector_db_impl.py

@@ -5,15 +5,15 @@ from dataclasses import dataclass
 import numpy as np
 import time
 
-from lightrag.utils import (
+from ..utils import (
     logger,
     compute_mdhash_id,
 )
 import pipmaster as pm
-from lightrag.base import BaseVectorStorage
+from ..base import BaseVectorStorage
 
-if not pm.is_installed("nano-vectordb"):
-    pm.install("nano-vectordb")
+# if not pm.is_installed("nano-vectordb"):
+#     pm.install("nano-vectordb")
 
 from nano_vectordb import NanoVectorDB
 from .shared_storage import (

CoreRAG/lightrag/kg/networkx_impl.py

@@ -1,10 +1,10 @@
 import os
 from dataclasses import dataclass
-from typing import final
+from typing import final,Dict,List,Union,Tuple
 
-from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
-from lightrag.utils import logger
-from lightrag.base import BaseGraphStorage
+from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
+from ..utils import logger
+from ..base import BaseGraphStorage
 
 import pipmaster as pm
 
@@ -98,7 +98,7 @@ class NetworkXStorage(BaseGraphStorage):
         graph = await self._get_graph()
         return graph.has_edge(source_node_id, target_node_id)
 
-    async def get_node(self, node_id: str) -> dict[str, str] | None:
+    async def get_node(self, node_id: str) -> Union[Dict[str, str],None]:
         graph = await self._get_graph()
         return graph.nodes.get(node_id)
 
@@ -112,17 +112,17 @@ class NetworkXStorage(BaseGraphStorage):
 
     async def get_edge(
         self, source_node_id: str, target_node_id: str
-    ) -> dict[str, str] | None:
+    ) -> Union[Dict[str, str],None]:
         graph = await self._get_graph()
         return graph.edges.get((source_node_id, target_node_id))
 
-    async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
+    async def get_node_edges(self, source_node_id: str) -> Union[List[Tuple[str, str]],None]:
         graph = await self._get_graph()
         if graph.has_node(source_node_id):
             return list(graph.edges(source_node_id))
         return None
 
-    async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
+    async def upsert_node(self, node_id: str, node_data: Dict[str, str]) -> None:
         """
         Importance notes:
         1. Changes will be persisted to disk during the next index_done_callback
@@ -133,7 +133,7 @@ class NetworkXStorage(BaseGraphStorage):
         graph.add_node(node_id, **node_data)
 
     async def upsert_edge(
-        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
+        self, source_node_id: str, target_node_id: str, edge_data: Dict[str, str]
     ) -> None:
         """
         Importance notes:
@@ -158,7 +158,7 @@ class NetworkXStorage(BaseGraphStorage):
         else:
             logger.warning(f"Node {node_id} not found in the graph for deletion.")
 
-    async def remove_nodes(self, nodes: list[str]):
+    async def remove_nodes(self, nodes: List[str]):
         """Delete multiple nodes
 
         Importance notes:
@@ -174,7 +174,7 @@ class NetworkXStorage(BaseGraphStorage):
             if graph.has_node(node):
                 graph.remove_node(node)
 
-    async def remove_edges(self, edges: list[tuple[str, str]]):
+    async def remove_edges(self, edges: List[Tuple[str, str]]):
         """Delete multiple edges
 
         Importance notes:
@@ -190,7 +190,7 @@ class NetworkXStorage(BaseGraphStorage):
             if graph.has_edge(source, target):
                 graph.remove_edge(source, target)
 
-    async def get_all_labels(self) -> list[str]:
+    async def get_all_labels(self) -> List[str]:
         """
         Get all node labels in the graph
         Returns:
@@ -389,7 +389,7 @@ class NetworkXStorage(BaseGraphStorage):
 
         return True
 
-    async def drop(self) -> dict[str, str]:
+    async def drop(self) -> Dict[str, str]:
         """Drop all graph data from storage and clean up resources
 
         This method will:

CoreRAG/lightrag/lightrag.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-
 import traceback
 import asyncio
 import configparser
@@ -21,18 +20,18 @@ from typing import (
     List,
     Dict,
 )
-from lightrag.constants import (
+from .constants import (
     DEFAULT_MAX_TOKEN_SUMMARY,
     DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE,
 )
-from lightrag.utils import get_env_value
+from .utils import get_env_value
 
-from lightrag.kg import (
+from .kg import (
     STORAGES,
     verify_storage_implementation,
 )
 
-from lightrag.kg.shared_storage import (
+from .kg.shared_storage import (
     get_namespace_data,
     get_pipeline_status_lock,
 )
@@ -199,7 +198,7 @@ class LightRAG:
     )
     """Maximum number of concurrent embedding function calls."""
 
-    embedding_cache_config: dict[str, Any] = field(
+    embedding_cache_config: Dict[str, Any] = field(
         default_factory=lambda: {
             "enabled": False,
             "similarity_threshold": 0.95,
@@ -283,7 +282,7 @@ class LightRAG:
     _storages_status: StoragesStatus = field(default=StoragesStatus.NOT_CREATED)
 
     def __post_init__(self):
-        from lightrag.kg.shared_storage import (
+        from .kg.shared_storage import (
             initialize_share_data,
         )
 

CoreRAG/lightrag/types.py

@@ -1,18 +1,18 @@
 from __future__ import annotations
 
 from pydantic import BaseModel
-from typing import Any, Optional
+from typing import Any, Optional,List,Dict
 
 
 class GPTKeywordExtractionFormat(BaseModel):
-    high_level_keywords: list[str]
-    low_level_keywords: list[str]
+    high_level_keywords: List[str]
+    low_level_keywords: List[str]
 
 
 class KnowledgeGraphNode(BaseModel):
     id: str
-    labels: list[str]
-    properties: dict[str, Any]  # anything else goes here
+    labels: List[str]
+    properties: Dict[str, Any]  # anything else goes here
 
 
 class KnowledgeGraphEdge(BaseModel):
@@ -20,10 +20,10 @@ class KnowledgeGraphEdge(BaseModel):
     type: Optional[str]
     source: str  # id of source node
     target: str  # id of target node
-    properties: dict[str, Any]  # anything else goes here
+    properties: Dict[str, Any]  # anything else goes here
 
 
 class KnowledgeGraph(BaseModel):
-    nodes: list[KnowledgeGraphNode] = []
-    edges: list[KnowledgeGraphEdge] = []
+    nodes: List[KnowledgeGraphNode] = []
+    edges: List[KnowledgeGraphEdge] = []
     is_truncated: bool = False

CoreRAG/lightrag/utils.py

@@ -14,9 +14,9 @@ from functools import wraps
 from hashlib import md5
 from typing import Any, Protocol, Callable, TYPE_CHECKING, List
 import numpy as np
-from lightrag.prompt import PROMPTS
+from .prompt import PROMPTS
 from dotenv import load_dotenv
-from lightrag.constants import (
+from .constants import (
     DEFAULT_LOG_MAX_BYTES,
     DEFAULT_LOG_BACKUP_COUNT,
     DEFAULT_LOG_FILENAME,
@@ -1696,7 +1696,7 @@ def check_storage_env_vars(storage_name: str) -> None:
     Raises:
         ValueError: If required environment variables are missing
     """
-    from lightrag.kg import STORAGE_ENV_REQUIREMENTS
+    from .kg import STORAGE_ENV_REQUIREMENTS
 
     required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, [])
     missing_vars = [var for var in required_vars if var not in os.environ]

end2end.py

@@ -20,7 +20,7 @@ class autoGenerator():
         
     async def query(self):
         self.rag_client = MassageAcupointRAG(
-            working_dir="C:/Users/ZIWEI/Documents/work/向量化/CoreRAG/Massage_10216"
+            working_dir="CoreRAG/Massage_10216"
         )
 
         await self.rag_client.initialize()

scripts/APF_global_demo.py

@@ -1,6 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
-
+from typing import List
 # ======================= 参数配置 ======================= #
 GRID_SIZE = (40, 130)  # 行x列
 TOTAL_STEPS = 50
@@ -166,14 +166,14 @@ class Agent:
 
 # ======================= 可视化模块 ======================= #
 class Visualizer:
-    def __init__(self, grid_size, full_path_ref: list[tuple]):
+    def __init__(self, grid_size, full_path_ref: List[tuple]):
         self.grid_size = grid_size
         self.full_path_ref = full_path_ref
         plt.ion()
         self.fig, self.ax = plt.subplots(figsize=(4, 10))  # 调整大小
         self.im = self.ax.imshow(np.zeros(grid_size).T, origin='lower', cmap='viridis')
         self.colorbar = plt.colorbar(self.im, ax=self.ax, label='Height')
-        self._artists: list = []
+        self._artists: List = []
 
         # 保持横纵比一致，避免变形
         self.ax.set_aspect('equal')

scripts/planner.py

@@ -1,8 +1,11 @@
 try:from scripts.APF_global_demo import GaussianSchedule,GaussianPathSchedule,TimedGaussianSchedule,FieldScheduler,Agent,Visualizer
 except:from APF_global_demo import GaussianSchedule,GaussianPathSchedule,TimedGaussianSchedule,FieldScheduler,Agent,Visualizer
+
 import numpy as np
+
 try:from scripts.sorter import sorter
 except:from sorter import sorter
+
 import matplotlib.pyplot as plt
 import json
 import time

scripts/sorter.py

@@ -1,6 +1,7 @@
 import re
 import json
 import numpy as np
+from typing import List
 
 class sorter:
     ''' 重点穴位按摩排列器 '''
@@ -18,7 +19,7 @@ class sorter:
             self.massage_side = massage_side # 默认为双边'both'
         else:
             raise ValueError("未指定按摩在左侧、右侧或两侧")
-    def _extract_acupoints(self,respnse_from_llm:str)->list[str]:
+    def _extract_acupoints(self,respnse_from_llm:str)->List[str]:
         pattern = r"[0-9]+\.\s*([\u4e00-\u9fa5]{2,5}穴)"
         matches = re.findall(pattern, respnse_from_llm)
         # 去重 & 排除空值
@@ -26,7 +27,7 @@ class sorter:
         print(unique_names)
         return unique_names
     
-    def sort_acupoints(self,respnse_from_llm:list[str])->list[str]:
+    def sort_acupoints(self,respnse_from_llm:List[str])->List[str]:
         if self.body_part == 'back':
             allowed_names = []
         if self.body_part == 'shoulder':
@@ -38,7 +39,7 @@ class sorter:
                              "志室右","肓门右","胃仓右","意舍右","阳纲右","胞肓右","气海右俞","大肠右俞","小肠右俞",
                              "中膂右俞","肾俞右","关元右俞","膀胱右俞","白环右俞","秩边右","京门右"]
 
-        def __filter_acupoints(acupoints:list[str],allowed_names: list[str])->list[str]:
+        def __filter_acupoints(acupoints:List[str],allowed_names: List[str])->List[str]:
              acupoints_cleaned = []
              matched_keys = []
              for acupoint in acupoints: