From 20ba1eb9c2c7f5a52e1567e9c8d553558f9ede76 Mon Sep 17 00:00:00 2001 From: drahnreb <25883607+drahnreb@users.noreply.github.com> Date: Thu, 17 Apr 2025 10:56:23 +0200 Subject: [PATCH] add: to optionally replace default tiktoken Tokenizer with a custom one --- README-zh.md | 3 +- README.md | 3 +- lightrag/api/routers/ollama_api.py | 4 +- lightrag/lightrag.py | 38 ++++++++---- lightrag/operate.py | 44 +++++++------ lightrag/utils.py | 99 ++++++++++++++++++++++++------ 6 files changed, 138 insertions(+), 53 deletions(-) diff --git a/README-zh.md b/README-zh.md index d345562f..dedd4c49 100644 --- a/README-zh.md +++ b/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` | diff --git a/README.md b/README.md index c3a43650..a8968550 100644 --- a/README.md +++ b/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` | diff --git a/lightrag/api/routers/ollama_api.py b/lightrag/api/routers/ollama_api.py index 088cd02c..e33e3992 100644 --- a/lightrag/api/routers/ollama_api.py +++ b/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) diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 99e3ba41..aa014684 100644 --- a/lightrag/lightrag.py +++ b/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") - """Model name used for tokenization when chunking text.""" + tokenizer: Optional[Tokenizer] = field(default=None) + """ + 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( - chunk_content, model_name=self.tiktoken_model_name + self.tokenizer.encode( + chunk_content ) ) chunk_order_index = ( diff --git a/lightrag/operate.py b/lightrag/operate.py index 7040ae2e..8f79dcf8 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -12,8 +12,7 @@ from .utils import ( logger, clean_str, compute_mdhash_id, - decode_tokens_by_tiktoken, - encode_string_by_tiktoken, + Tokenizer, 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( - _tokens[start : start + max_token_size], - model_name=tiktoken_model, + chunk_content = tokenizer.decode( + _tokens[start : start + max_token_size] ) 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( - tokens[start : start + max_token_size], model_name=tiktoken_model + chunk_content = tokenizer.decode( + 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( - tokens[:llm_max_tokens], model_name=tiktoken_model_name + use_description = tokenizer.decode( + 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 diff --git a/lightrag/utils.py b/lightrag/utils.py index dc717fb7..3aded98a 100644 --- a/lightrag/utils.py +++ b/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"): - global ENCODER - if ENCODER is None: - ENCODER = tiktoken.encoding_for_model(model_name) - tokens = ENCODER.encode(content) - return tokens +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.""" + ... + + 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"): - global ENCODER - if ENCODER is None: - ENCODER = tiktoken.encoding_for_model(model_name) - content = ENCODER.decode(tokens) - return content +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. + + 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