add: to optionally replace default tiktoken Tokenizer with a custom one

README-zh.md

@@ -1090,7 +1090,8 @@ rag.clear_cache(modes=["local"])
 | **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` |
 | **chunk_token_size** | `int` | 拆分文档时每个块的最大令牌大小 | `1200` |
 | **chunk_overlap_token_size** | `int` | 拆分文档时两个块之间的重叠令牌大小 | `100` |
-| **tiktoken_model_name** | `str` | 用于计算令牌数的Tiktoken编码器的模型名称 | `gpt-4o-mini` |
+| **tokenizer** | `Tokenizer` | 用于将文本转换为 tokens（数字）以及使用遵循 TokenizerInterface 协议的 .encode() 和 .decode() 函数将 tokens 转换回文本的函数。 如果您不指定，它将使用默认的 Tiktoken tokenizer。 | `TiktokenTokenizer` |
+| **tiktoken_model_name** | `str` | 如果您使用的是默认的 Tiktoken tokenizer，那么这是要使用的特定 Tiktoken 模型的名称。如果您提供自己的 tokenizer，则忽略此设置。 | `gpt-4o-mini` |
 | **entity_extract_max_gleaning** | `int` | 实体提取过程中的循环次数，附加历史消息 | `1` |
 | **entity_summary_to_max_tokens** | `int` | 每个实体摘要的最大令牌大小 | `500` |
 | **node_embedding_algorithm** | `str` | 节点嵌入算法（当前未使用） | `node2vec` |

README.md

@@ -1156,7 +1156,8 @@ Valid modes are:
 | **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` |
 | **chunk_token_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
 | **chunk_overlap_token_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
-| **tiktoken_model_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
+| **tokenizer** | `Tokenizer` | The function used to convert text into tokens (numbers) and back using .encode() and .decode() functions following `TokenizerInterface` protocol. If you don't specify one, it will use the default Tiktoken tokenizer. | `TiktokenTokenizer` |
+| **tiktoken_model_name** | `str` | If you're using the default Tiktoken tokenizer, this is the name of the specific Tiktoken model to use. This setting is ignored if you provide your own tokenizer. | `gpt-4o-mini` |
 | **entity_extract_max_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
 | **entity_summary_to_max_tokens** | `int` | Maximum token size for each entity summary | `500` |
 | **node_embedding_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |

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 encode_string_by_tiktoken
+from lightrag.utils import TiktokenTokenizer 
 from lightrag.api.utils_api import ollama_server_infos, get_combined_auth_dependency
 from fastapi import Depends
 
@@ -97,7 +97,7 @@ class OllamaTagResponse(BaseModel):
 
 def estimate_tokens(text: str) -> int:
     """Estimate the number of tokens in text using tiktoken"""
-    tokens = encode_string_by_tiktoken(text)
+    tokens = TiktokenTokenizer().encode(text)
     return len(tokens)
 
 

lightrag/lightrag.py

@@ -41,11 +41,12 @@ from .operate import (
 )
 from .prompt import GRAPH_FIELD_SEP, PROMPTS
 from .utils import (
+    Tokenizer,
+    TiktokenTokenizer,
     EmbeddingFunc,
     always_get_an_event_loop,
     compute_mdhash_id,
     convert_response_to_json,
-    encode_string_by_tiktoken,
     lazy_external_import,
     limit_async_func_call,
     get_content_summary,
@@ -122,33 +123,38 @@ class LightRAG:
     )
     """Number of overlapping tokens between consecutive text chunks to preserve context."""
 
-    tiktoken_model_name: str = field(default="gpt-4o-mini")
+    tokenizer: Optional[Tokenizer] = field(default=None)
-    """Model name used for tokenization when chunking text."""
+    """
+    A function that returns a Tokenizer instance.
+    If None, and a `tiktoken_model_name` is provided, a TiktokenTokenizer will be created.
+    If both are None, the default TiktokenTokenizer is used.
+    """
 
-    """Maximum number of tokens used for summarizing extracted entities."""
+    tiktoken_model_name: str = field(default="gpt-4o-mini")
+    """Model name used for tokenization when chunking text with tiktoken. Defaults to `gpt-4o-mini`."""
 
     chunking_func: Callable[
         [
+            Tokenizer,
             str,
-            str | None,
+            Optional[str],
             bool,
             int,
             int,
-            str,
         ],
-        list[dict[str, Any]],
+        List[Dict[str, Any]],
     ] = field(default_factory=lambda: chunking_by_token_size)
     """
     Custom chunking function for splitting text into chunks before processing.
 
     The function should take the following parameters:
 
+        - `tokenizer`: A Tokenizer instance to use for tokenization.
         - `content`: The text to be split into chunks.
         - `split_by_character`: The character to split the text on. If None, the text is split into chunks of `chunk_token_size` tokens.
         - `split_by_character_only`: If True, the text is split only on the specified character.
         - `chunk_token_size`: The maximum number of tokens per chunk.
         - `chunk_overlap_token_size`: The number of overlapping tokens between consecutive chunks.
-        - `tiktoken_model_name`: The name of the tiktoken model to use for tokenization.
 
     The function should return a list of dictionaries, where each dictionary contains the following keys:
         - `tokens`: The number of tokens in the chunk.
@@ -310,7 +316,15 @@ class LightRAG:
         _print_config = ",\n  ".join([f"{k} = {v}" for k, v in global_config.items()])
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
-        # Init LLM
+        # Init Tokenizer
+        # Post-initialization hook to handle backward compatabile tokenizer initialization based on provided parameters
+        if self.tokenizer is None:
+            if self.tiktoken_model_name:
+                self.tokenizer = TiktokenTokenizer(self.tiktoken_model_name)
+            else:
+                self.tokenizer = TiktokenTokenizer()
+
+        # Init Embedding
         self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(  # type: ignore
             self.embedding_func
         )
@@ -900,12 +914,12 @@ class LightRAG:
                                 "file_path": file_path,  # Add file path to each chunk
                             }
                             for dp in self.chunking_func(
+                                self.tokenizer,
                                 status_doc.content,
                                 split_by_character,
                                 split_by_character_only,
                                 self.chunk_overlap_token_size,
                                 self.chunk_token_size,
-                                self.tiktoken_model_name,
                             )
                         }
 
@@ -1134,8 +1148,8 @@ class LightRAG:
                 chunk_content = clean_text(chunk_data["content"])
                 source_id = chunk_data["source_id"]
                 tokens = len(
-                    encode_string_by_tiktoken(
+                    self.tokenizer.encode(
-                        chunk_content, model_name=self.tiktoken_model_name
+                        chunk_content
                     )
                 )
                 chunk_order_index = (

lightrag/operate.py

@@ -12,8 +12,7 @@ from .utils import (
     logger,
     clean_str,
     compute_mdhash_id,
-    decode_tokens_by_tiktoken,
+    Tokenizer,
-    encode_string_by_tiktoken,
     is_float_regex,
     list_of_list_to_csv,
     normalize_extracted_info,
@@ -46,32 +45,31 @@ load_dotenv(dotenv_path=".env", override=False)
 
 
 def chunking_by_token_size(
+    tokenizer: Tokenizer,
     content: str,
     split_by_character: str | None = None,
     split_by_character_only: bool = False,
     overlap_token_size: int = 128,
     max_token_size: int = 1024,
-    tiktoken_model: str = "gpt-4o",
 ) -> list[dict[str, Any]]:
-    tokens = encode_string_by_tiktoken(content, model_name=tiktoken_model)
+    tokens = tokenizer.encode(content)
     results: list[dict[str, Any]] = []
     if split_by_character:
         raw_chunks = content.split(split_by_character)
         new_chunks = []
         if split_by_character_only:
             for chunk in raw_chunks:
-                _tokens = encode_string_by_tiktoken(chunk, model_name=tiktoken_model)
+                _tokens = tokenizer.encode(chunk)
                 new_chunks.append((len(_tokens), chunk))
         else:
             for chunk in raw_chunks:
-                _tokens = encode_string_by_tiktoken(chunk, model_name=tiktoken_model)
+                _tokens = tokenizer.encode(chunk)
                 if len(_tokens) > max_token_size:
                     for start in range(
                         0, len(_tokens), max_token_size - overlap_token_size
                     ):
-                        chunk_content = decode_tokens_by_tiktoken(
+                        chunk_content = tokenizer.decode(
-                            _tokens[start : start + max_token_size],
+                            _tokens[start : start + max_token_size]
-                            model_name=tiktoken_model,
                         )
                         new_chunks.append(
                             (min(max_token_size, len(_tokens) - start), chunk_content)
@@ -90,8 +88,8 @@ def chunking_by_token_size(
         for index, start in enumerate(
             range(0, len(tokens), max_token_size - overlap_token_size)
         ):
-            chunk_content = decode_tokens_by_tiktoken(
+            chunk_content = tokenizer.decode(
-                tokens[start : start + max_token_size], model_name=tiktoken_model
+                tokens[start : start + max_token_size]
             )
             results.append(
                 {
@@ -116,6 +114,7 @@ async def _handle_entity_relation_summary(
     If too long, use LLM to summarize.
     """
     use_llm_func: callable = global_config["llm_model_func"]
+    tokenizer: Tokenizer = global_config["tokenizer"]
     llm_max_tokens = global_config["llm_model_max_token_size"]
     tiktoken_model_name = global_config["tiktoken_model_name"]
     summary_max_tokens = global_config["summary_to_max_tokens"]
@@ -124,10 +123,12 @@ async def _handle_entity_relation_summary(
         "language", PROMPTS["DEFAULT_LANGUAGE"]
     )
 
-    tokens = encode_string_by_tiktoken(description, model_name=tiktoken_model_name)
+    tokens = tokenizer.encode(description)
+    if len(tokens) < summary_max_tokens:  # No need for summary
+        return description
     prompt_template = PROMPTS["summarize_entity_descriptions"]
-    use_description = decode_tokens_by_tiktoken(
+    use_description = tokenizer.decode(
-        tokens[:llm_max_tokens], model_name=tiktoken_model_name
+        tokens[:llm_max_tokens]
     )
     context_base = dict(
         entity_name=entity_or_relation_name,
@@ -865,7 +866,8 @@ async def kg_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
-    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    tokenizer: Tokenizer = global_config["tokenizer"]
+    len_of_prompts = len(tokenizer.encode(query + sys_prompt))
     logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
 
     response = await use_model_func(
@@ -987,7 +989,8 @@ async def extract_keywords_only(
         query=text, examples=examples, language=language, history=history_context
     )
 
-    len_of_prompts = len(encode_string_by_tiktoken(kw_prompt))
+    tokenizer: Tokenizer = global_config["tokenizer"]
+    len_of_prompts = len(tokenizer.encode(kw_prompt))
     logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
 
     # 5. Call the LLM for keyword extraction
@@ -1210,7 +1213,8 @@ async def mix_kg_vector_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
-    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    tokenizer: Tokenizer = global_config["tokenizer"]
+    len_of_prompts = len(tokenizer.encode(query + sys_prompt))
     logger.debug(f"[mix_kg_vector_query]Prompt Tokens: {len_of_prompts}")
 
     # 6. Generate response
@@ -1978,7 +1982,8 @@ async def naive_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
-    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    tokenizer: Tokenizer = global_config["tokenizer"]
+    len_of_prompts = len(tokenizer.encode(query + sys_prompt))
     logger.debug(f"[naive_query]Prompt Tokens: {len_of_prompts}")
 
     response = await use_model_func(
@@ -2125,7 +2130,8 @@ async def kg_query_with_keywords(
     if query_param.only_need_prompt:
         return sys_prompt
 
-    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    tokenizer: Tokenizer = global_config["tokenizer"]
+    len_of_prompts = len(tokenizer.encode(query + sys_prompt))
     logger.debug(f"[kg_query_with_keywords]Prompt Tokens: {len_of_prompts}")
 
     # 6. Generate response

lightrag/utils.py

@@ -12,10 +12,9 @@ import re
 from dataclasses import dataclass
 from functools import wraps
 from hashlib import md5
-from typing import Any, Callable, TYPE_CHECKING
+from typing import Any, Protocol, Callable, TYPE_CHECKING, List, Optional, Union
 import xml.etree.ElementTree as ET
 import numpy as np
-import tiktoken
 from lightrag.prompt import PROMPTS
 from dotenv import load_dotenv
 
@@ -193,9 +192,6 @@ class UnlimitedSemaphore:
         pass
 
 
-ENCODER = None
-
-
 @dataclass
 class EmbeddingFunc:
     embedding_dim: int
@@ -311,20 +307,87 @@ def write_json(json_obj, file_name):
         json.dump(json_obj, f, indent=2, ensure_ascii=False)
 
 
-def encode_string_by_tiktoken(content: str, model_name: str = "gpt-4o"):
+class TokenizerInterface(Protocol):
-    global ENCODER
+    """
-    if ENCODER is None:
+    Defines the interface for a tokenizer, requiring encode and decode methods.
-        ENCODER = tiktoken.encoding_for_model(model_name)
+    """
-    tokens = ENCODER.encode(content)
+    def encode(self, content: str) -> List[int]:
-    return tokens
+        """Encodes a string into a list of tokens."""
+        ...
+
+    def decode(self, tokens: List[int]) -> str:
+        """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.
+
+        Args:
+            model_name: The associated model name for the tokenizer.
+            tokenizer: An instance of a class implementing the TokenizerInterface.
+        """
+        self.model_name: str = model_name
+        self.tokenizer: TokenizerInterface = tokenizer
+
+    def encode(self, content: str) -> List[int]:
+        """
+        Encodes a string into a list of tokens using the underlying tokenizer.
+
+        Args:
+            content: The string to encode.
+
+        Returns:
+            A list of integer tokens.
+        """
+        return self.tokenizer.encode(content)
+
+    def decode(self, tokens: List[int]) -> str:
+        """
+        Decodes a list of tokens into a string using the underlying tokenizer.
+
+        Args:
+            tokens: A list of integer tokens to decode.
+
+        Returns:
+            The decoded string.
+        """
+        return self.tokenizer.decode(tokens)
 
 
-def decode_tokens_by_tiktoken(tokens: list[int], model_name: str = "gpt-4o"):
+class TiktokenTokenizer(Tokenizer):
-    global ENCODER
+    """
-    if ENCODER is None:
+    A Tokenizer implementation using the tiktoken library.
-        ENCODER = tiktoken.encoding_for_model(model_name)
+    """
-    content = ENCODER.decode(tokens)
+    def __init__(self, model_name: str = "gpt-4o-mini"):
-    return content
+        """
+        Initializes the TiktokenTokenizer with a specified model name.
+
+        Args:
+            model_name: The model name for the tiktoken tokenizer to use.  Defaults to "gpt-4o-mini".
+
+        Raises:
+            ImportError: If tiktoken is not installed.
+            ValueError: If the model_name is invalid.
+        """
+        try:
+            import tiktoken
+        except ImportError:
+            raise ImportError(
+                "tiktoken is not installed. Please install it with `pip install tiktoken` or define custom `tokenizer_func`."
+            )
+
+        try:
+            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}."
+            )
 
 
 def pack_user_ass_to_openai_messages(*args: str):
@@ -368,7 +431,7 @@ def truncate_list_by_token_size(
         return []
     tokens = 0
     for i, data in enumerate(list_data):
-        tokens += len(encode_string_by_tiktoken(key(data)))
+        tokens += len(tokenizer.encode(key(data)))
         if tokens > max_token_size:
             return list_data[:i]
     return list_data