fix linting

examples/lightrag_gemini_demo_no_tiktoken.py

@@ -51,10 +51,12 @@ class GemmaTokenizer(Tokenizer):
         "google/gemma3": _TokenizerConfig(
             tokenizer_model_url="https://raw.githubusercontent.com/google/gemma_pytorch/cb7c0152a369e43908e769eb09e1ce6043afe084/tokenizer/gemma3_cleaned_262144_v2.spiece.model",
             tokenizer_model_hash="1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c",
-        )
-    }            
+        ),
+    }
 
-    def __init__(self, model_name: str = "gemini-2.0-flash", tokenizer_dir: Optional[str] = None):
+    def __init__(
+        self, model_name: str = "gemini-2.0-flash", tokenizer_dir: Optional[str] = None
+    ):
         # https://github.com/google/gemma_pytorch/tree/main/tokenizer
         if "1.5" in model_name or "1.0" in model_name:
             # up to gemini 1.5 gemma2 is a comparable local tokenizer
@@ -77,7 +79,9 @@ class GemmaTokenizer(Tokenizer):
         else:
             model_data = None
         if not model_data:
-            model_data = self._load_from_url(file_url=file_url, expected_hash=expected_hash)
+            model_data = self._load_from_url(
+                file_url=file_url, expected_hash=expected_hash
+            )
             self.save_tokenizer_to_cache(cache_path=file_path, model_data=model_data)
 
         tokenizer = spm.SentencePieceProcessor()
@@ -140,7 +144,7 @@ class GemmaTokenizer(Tokenizer):
 
     # def encode(self, content: str) -> list[int]:
     #     return self.tokenizer.encode(content)
-        
+
     # def decode(self, tokens: list[int]) -> str:
     #     return self.tokenizer.decode(tokens)
 
@@ -187,7 +191,10 @@ async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
         # tiktoken_model_name="gpt-4o-mini",
-        tokenizer=GemmaTokenizer(tokenizer_dir=(Path(WORKING_DIR) / "vertexai_tokenizer_model"), model_name="gemini-2.0-flash"),
+        tokenizer=GemmaTokenizer(
+            tokenizer_dir=(Path(WORKING_DIR) / "vertexai_tokenizer_model"),
+            model_name="gemini-2.0-flash",
+        ),
         llm_model_func=llm_model_func,
         embedding_func=EmbeddingFunc(
             embedding_dim=384,

lightrag/api/routers/ollama_api.py

@@ -10,7 +10,7 @@ from fastapi.responses import StreamingResponse
 import asyncio
 from ascii_colors import trace_exception
 from lightrag import LightRAG, QueryParam
-from lightrag.utils import TiktokenTokenizer 
+from lightrag.utils import TiktokenTokenizer
 from lightrag.api.utils_api import ollama_server_infos, get_combined_auth_dependency
 from fastapi import Depends
 

lightrag/lightrag.py

@@ -7,7 +7,18 @@ import warnings
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
-from typing import Any, AsyncIterator, Callable, Iterator, cast, final, Literal, Optional, List, Dict
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Iterator,
+    cast,
+    final,
+    Literal,
+    Optional,
+    List,
+    Dict,
+)
 
 from lightrag.kg import (
     STORAGES,
@@ -1147,11 +1158,7 @@ class LightRAG:
             for chunk_data in custom_kg.get("chunks", []):
                 chunk_content = clean_text(chunk_data["content"])
                 source_id = chunk_data["source_id"]
-                tokens = len(
-                    self.tokenizer.encode(
-                        chunk_content
-                    )
-                )
+                tokens = len(self.tokenizer.encode(chunk_content))
                 chunk_order_index = (
                     0
                     if "chunk_order_index" not in chunk_data.keys()

lightrag/operate.py

@@ -88,9 +88,7 @@ def chunking_by_token_size(
         for index, start in enumerate(
             range(0, len(tokens), max_token_size - overlap_token_size)
         ):
-            chunk_content = tokenizer.decode(
-                tokens[start : start + max_token_size]
-            )
+            chunk_content = tokenizer.decode(tokens[start : start + max_token_size])
             results.append(
                 {
                     "tokens": min(max_token_size, len(tokens) - start),
@@ -126,9 +124,7 @@ async def _handle_entity_relation_summary(
     if len(tokens) < summary_max_tokens:  # No need for summary
         return description
     prompt_template = PROMPTS["summarize_entity_descriptions"]
-    use_description = tokenizer.decode(
-        tokens[:llm_max_tokens]
-    )
+    use_description = tokenizer.decode(tokens[:llm_max_tokens])
     context_base = dict(
         entity_name=entity_or_relation_name,
         description_list=use_description.split(GRAPH_FIELD_SEP),
@@ -1378,10 +1374,15 @@ async def _get_node_data(
     ]  # what is this text_chunks_db doing.  dont remember it in airvx.  check the diagram.
     # get entitytext chunk
     use_text_units = await _find_most_related_text_unit_from_entities(
-        node_datas, query_param, text_chunks_db, knowledge_graph_inst,
+        node_datas,
+        query_param,
+        text_chunks_db,
+        knowledge_graph_inst,
     )
     use_relations = await _find_most_related_edges_from_entities(
-        node_datas, query_param, knowledge_graph_inst,
+        node_datas,
+        query_param,
+        knowledge_graph_inst,
     )
 
     tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer")
@@ -1703,10 +1704,15 @@ async def _get_edge_data(
     )
     use_entities, use_text_units = await asyncio.gather(
         _find_most_related_entities_from_relationships(
-            edge_datas, query_param, knowledge_graph_inst,
+            edge_datas,
+            query_param,
+            knowledge_graph_inst,
         ),
         _find_related_text_unit_from_relationships(
-            edge_datas, query_param, text_chunks_db, knowledge_graph_inst,
+            edge_datas,
+            query_param,
+            text_chunks_db,
+            knowledge_graph_inst,
         ),
     )
     logger.info(

lightrag/utils.py

@@ -12,7 +12,7 @@ import re
 from dataclasses import dataclass
 from functools import wraps
 from hashlib import md5
-from typing import Any, Protocol, Callable, TYPE_CHECKING, List, Optional, Union
+from typing import Any, Protocol, Callable, TYPE_CHECKING, List
 import xml.etree.ElementTree as ET
 import numpy as np
 from lightrag.prompt import PROMPTS
@@ -311,6 +311,7 @@ class TokenizerInterface(Protocol):
     """
     Defines the interface for a tokenizer, requiring encode and decode methods.
     """
+
     def encode(self, content: str) -> List[int]:
         """Encodes a string into a list of tokens."""
         ...
@@ -319,10 +320,12 @@ class TokenizerInterface(Protocol):
         """Decodes a list of tokens into a string."""
         ...
 
+
 class Tokenizer:
     """
     A wrapper around a tokenizer to provide a consistent interface for encoding and decoding.
     """
+
     def __init__(self, model_name: str, tokenizer: TokenizerInterface):
         """
         Initializes the Tokenizer with a tokenizer model name and a tokenizer instance.
@@ -363,6 +366,7 @@ class TiktokenTokenizer(Tokenizer):
     """
     A Tokenizer implementation using the tiktoken library.
     """
+
     def __init__(self, model_name: str = "gpt-4o-mini"):
         """
         Initializes the TiktokenTokenizer with a specified model name.
@@ -385,9 +389,7 @@ class TiktokenTokenizer(Tokenizer):
             tokenizer = tiktoken.encoding_for_model(model_name)
             super().__init__(model_name=model_name, tokenizer=tokenizer)
         except KeyError:
-            raise ValueError(
-                f"Invalid model_name: {model_name}."
-            )
+            raise ValueError(f"Invalid model_name: {model_name}.")
 
 
 def pack_user_ass_to_openai_messages(*args: str):
@@ -424,7 +426,10 @@ def is_float_regex(value: str) -> bool:
 
 
 def truncate_list_by_token_size(
-    list_data: list[Any], key: Callable[[Any], str], max_token_size: int, tokenizer: Tokenizer
+    list_data: list[Any],
+    key: Callable[[Any], str],
+    max_token_size: int,
+    tokenizer: Tokenizer,
 ) -> list[int]:
     """Truncate a list of data by token size"""
     if max_token_size <= 0: