gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
drahnreb
2025-04-17 10:56:23 +02:00
parent 4fd40fd798
commit 20ba1eb9c2
6 changed files with 138 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
README-zh.md
View File

@@ -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` |

3
README.md
View File

@@ -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` |

4
lightrag/api/routers/ollama_api.py
View File

@@ -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)

38
lightrag/lightrag.py
View File

@@ -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 = (

44
lightrag/operate.py
View File

@@ -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

99
lightrag/utils.py
View File

@@ -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