cleaned code

lightrag/base.py

@@ -107,9 +107,11 @@ class BaseVectorStorage(StorageNameSpace):
         raise NotImplementedError
 
     async def delete_entity(self, entity_name: str) -> None:
+        """Delete a single entity by its name"""
         raise NotImplementedError
 
     async def delete_entity_relation(self, entity_name: str) -> None:
+        """Delete relations for a given entity by scanning metadata"""
         raise NotImplementedError
 
 

lightrag/lightrag.py

@@ -524,7 +524,6 @@ class LightRAG:
             embedding_func=None,
         )
 
-        # What's for, Is this nessisary ?
         if self.llm_response_cache and hasattr(
             self.llm_response_cache, "global_config"
         ):
@@ -1252,7 +1251,7 @@ class LightRAG:
         """
         return await self.doc_status.get_status_counts()
 
-    async def adelete_by_doc_id(self, doc_id: str):
+    async def adelete_by_doc_id(self, doc_id: str) -> None:
         """Delete a document and all its related data
 
         Args:
@@ -1269,6 +1268,9 @@ class LightRAG:
 
             # 2. Get all related chunks
             chunks = await self.text_chunks.get_by_id(doc_id)
+            if not chunks:
+                return
+
             chunk_ids = list(chunks.keys())
             logger.debug(f"Found {len(chunk_ids)} chunks to delete")
 

lightrag/llm.py

@@ -66,7 +66,11 @@ class MultiModel:
         return self._models[self._current_model]
 
     async def llm_model_func(
-        self, prompt, system_prompt=None, history_messages=[], **kwargs
+        self,
+        prompt: str,
+        system_prompt: str | None = None,
+        history_messages: list[dict[str, Any]] = [],
+        **kwargs: Any,
     ) -> str:
         kwargs.pop("model", None)  # stop from overwriting the custom model name
         kwargs.pop("keyword_extraction", None)

lightrag/operate.py

@@ -1608,7 +1608,7 @@ async def kg_query_with_keywords(
     query_param: QueryParam,
     global_config: dict[str, str],
     hashing_kv: BaseKVStorage | None = None,
-) -> str:
+) -> str | AsyncIterator[str]:
     """
     Refactored kg_query that does NOT extract keywords by itself.
     It expects hl_keywords and ll_keywords to be set in query_param, or defaults to empty.

lightrag/utils.py

@@ -9,7 +9,7 @@ import re
 from dataclasses import dataclass
 from functools import wraps
 from hashlib import md5
-from typing import Any, Union, List, Optional
+from typing import Any, Callable, Union, List, Optional
 import xml.etree.ElementTree as ET
 import bs4
 
@@ -67,7 +67,7 @@ class EmbeddingFunc:
 
 @dataclass
 class ReasoningResponse:
-    reasoning_content: str
+    reasoning_content: str | None
     response_content: str
     tag: str
 
@@ -109,7 +109,7 @@ def convert_response_to_json(response: str) -> dict[str, Any]:
         raise e from None
 
 
-def compute_args_hash(*args, cache_type: str = None) -> str:
+def compute_args_hash(*args: Any, cache_type: str | None = None) -> str:
     """Compute a hash for the given arguments.
     Args:
         *args: Arguments to hash
@@ -220,11 +220,13 @@ def clean_str(input: Any) -> str:
     return re.sub(r"[\x00-\x1f\x7f-\x9f]", "", result)
 
 
-def is_float_regex(value):
+def is_float_regex(value: str) -> bool:
     return bool(re.match(r"^[-+]?[0-9]*\.?[0-9]+$", value))
 
 
-def truncate_list_by_token_size(list_data: list, key: callable, max_token_size: int):
+def truncate_list_by_token_size(
+    list_data: list[Any], key: Callable[[Any], str], max_token_size: int
+) -> list[int]:
     """Truncate a list of data by token size"""
     if max_token_size <= 0:
         return []
@@ -334,7 +336,7 @@ def xml_to_json(xml_file):
         return None
 
 
-def process_combine_contexts(hl, ll):
+def process_combine_contexts(hl: str, ll: str):
     header = None
     list_hl = csv_string_to_list(hl.strip())
     list_ll = csv_string_to_list(ll.strip())
@@ -640,7 +642,9 @@ def exists_func(obj, func_name: str) -> bool:
         return False
 
 
-def get_conversation_turns(conversation_history: list[dict], num_turns: int) -> str:
+def get_conversation_turns(
+    conversation_history: list[dict[str, Any]], num_turns: int
+) -> str:
     """
     Process conversation history to get the specified number of complete turns.
 
@@ -652,8 +656,8 @@ def get_conversation_turns(conversation_history: list[dict], num_turns: int) ->
         Formatted string of the conversation history
     """
     # Group messages into turns
-    turns = []
-    messages = []
+    turns: list[list[dict[str, Any]]] = []
+    messages: list[dict[str, Any]] = []
 
     # First, filter out keyword extraction messages
     for msg in conversation_history:
@@ -687,7 +691,7 @@ def get_conversation_turns(conversation_history: list[dict], num_turns: int) ->
         turns = turns[-num_turns:]
 
     # Format the turns into a string
-    formatted_turns = []
+    formatted_turns: list[str] = []
     for turn in turns:
         formatted_turns.extend(
             [f"user: {turn[0]['content']}", f"assistant: {turn[1]['content']}"]