diff --git a/README-zh.md b/README-zh.md index 47859f91..11f1a753 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 088f345d..165c891d 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/examples/lightrag_gemini_demo_no_tiktoken.py b/examples/lightrag_gemini_demo_no_tiktoken.py new file mode 100644 index 00000000..92c74201 --- /dev/null +++ b/examples/lightrag_gemini_demo_no_tiktoken.py @@ -0,0 +1,230 @@ +# pip install -q -U google-genai to use gemini as a client + +import os +from typing import Optional +import dataclasses +from pathlib import Path +import hashlib +import numpy as np +from google import genai +from google.genai import types +from dotenv import load_dotenv +from lightrag.utils import EmbeddingFunc, Tokenizer +from lightrag import LightRAG, QueryParam +from sentence_transformers import SentenceTransformer +from lightrag.kg.shared_storage import initialize_pipeline_status +import sentencepiece as spm +import requests + +import asyncio +import nest_asyncio + +# Apply nest_asyncio to solve event loop issues +nest_asyncio.apply() + +load_dotenv() +gemini_api_key = os.getenv("GEMINI_API_KEY") + +WORKING_DIR = "./dickens" + +if os.path.exists(WORKING_DIR): + import shutil + + shutil.rmtree(WORKING_DIR) + +os.mkdir(WORKING_DIR) + + +class GemmaTokenizer(Tokenizer): + # adapted from google-cloud-aiplatform[tokenization] + + @dataclasses.dataclass(frozen=True) + class _TokenizerConfig: + tokenizer_model_url: str + tokenizer_model_hash: str + + _TOKENIZERS = { + "google/gemma2": _TokenizerConfig( + tokenizer_model_url="https://raw.githubusercontent.com/google/gemma_pytorch/33b652c465537c6158f9a472ea5700e5e770ad3f/tokenizer/tokenizer.model", + tokenizer_model_hash="61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2", + ), + "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 + ): + # 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 + # https://github.com/googleapis/python-aiplatform/blob/main/vertexai/tokenization/_tokenizer_loading.py + tokenizer_name = "google/gemma2" + else: + # for gemini > 2.0 gemma3 was used + tokenizer_name = "google/gemma3" + + file_url = self._TOKENIZERS[tokenizer_name].tokenizer_model_url + tokenizer_model_name = file_url.rsplit("/", 1)[1] + expected_hash = self._TOKENIZERS[tokenizer_name].tokenizer_model_hash + + tokenizer_dir = Path(tokenizer_dir) + if tokenizer_dir.is_dir(): + file_path = tokenizer_dir / tokenizer_model_name + model_data = self._maybe_load_from_cache( + file_path=file_path, expected_hash=expected_hash + ) + else: + model_data = None + if not model_data: + 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() + tokenizer.LoadFromSerializedProto(model_data) + super().__init__(model_name=model_name, tokenizer=tokenizer) + + def _is_valid_model(self, model_data: bytes, expected_hash: str) -> bool: + """Returns true if the content is valid by checking the hash.""" + return hashlib.sha256(model_data).hexdigest() == expected_hash + + def _maybe_load_from_cache(self, file_path: Path, expected_hash: str) -> bytes: + """Loads the model data from the cache path.""" + if not file_path.is_file(): + return + with open(file_path, "rb") as f: + content = f.read() + if self._is_valid_model(model_data=content, expected_hash=expected_hash): + return content + + # Cached file corrupted. + self._maybe_remove_file(file_path) + + def _load_from_url(self, file_url: str, expected_hash: str) -> bytes: + """Loads model bytes from the given file url.""" + resp = requests.get(file_url) + resp.raise_for_status() + content = resp.content + + if not self._is_valid_model(model_data=content, expected_hash=expected_hash): + actual_hash = hashlib.sha256(content).hexdigest() + raise ValueError( + f"Downloaded model file is corrupted." + f" Expected hash {expected_hash}. Got file hash {actual_hash}." + ) + return content + + @staticmethod + def save_tokenizer_to_cache(cache_path: Path, model_data: bytes) -> None: + """Saves the model data to the cache path.""" + try: + if not cache_path.is_file(): + cache_dir = cache_path.parent + cache_dir.mkdir(parents=True, exist_ok=True) + with open(cache_path, "wb") as f: + f.write(model_data) + except OSError: + # Don't raise if we cannot write file. + pass + + @staticmethod + def _maybe_remove_file(file_path: Path) -> None: + """Removes the file if exists.""" + if not file_path.is_file(): + return + try: + file_path.unlink() + except OSError: + # Don't raise if we cannot remove file. + pass + + # def encode(self, content: str) -> list[int]: + # return self.tokenizer.encode(content) + + # def decode(self, tokens: list[int]) -> str: + # return self.tokenizer.decode(tokens) + + +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + # 1. Initialize the GenAI Client with your Gemini API Key + client = genai.Client(api_key=gemini_api_key) + + # 2. Combine prompts: system prompt, history, and user prompt + if history_messages is None: + history_messages = [] + + combined_prompt = "" + if system_prompt: + combined_prompt += f"{system_prompt}\n" + + for msg in history_messages: + # Each msg is expected to be a dict: {"role": "...", "content": "..."} + combined_prompt += f"{msg['role']}: {msg['content']}\n" + + # Finally, add the new user prompt + combined_prompt += f"user: {prompt}" + + # 3. Call the Gemini model + response = client.models.generate_content( + model="gemini-1.5-flash", + contents=[combined_prompt], + config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1), + ) + + # 4. Return the response text + return response.text + + +async def embedding_func(texts: list[str]) -> np.ndarray: + model = SentenceTransformer("all-MiniLM-L6-v2") + embeddings = model.encode(texts, convert_to_numpy=True) + return embeddings + + +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", + ), + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=384, + max_token_size=8192, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + file_path = "story.txt" + with open(file_path, "r") as file: + text = file.read() + + rag.insert(text) + + response = rag.query( + query="What is the main theme of the story?", + param=QueryParam(mode="hybrid", top_k=5, response_type="single line"), + ) + + print(response) + + +if __name__ == "__main__": + main() diff --git a/lightrag/api/routers/ollama_api.py b/lightrag/api/routers/ollama_api.py index 088cd02c..3aabfe35 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..90a68aa9 100644 --- a/lightrag/lightrag.py +++ b/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 +from typing import ( + Any, + AsyncIterator, + Callable, + Iterator, + cast, + final, + Literal, + Optional, + List, + Dict, +) from lightrag.kg import ( STORAGES, @@ -41,11 +52,12 @@ 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 +134,38 @@ 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 +327,15 @@ 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 ) @@ -603,11 +628,7 @@ class LightRAG: inserting_chunks: dict[str, Any] = {} for index, chunk_text in enumerate(text_chunks): chunk_key = compute_mdhash_id(chunk_text, prefix="chunk-") - tokens = len( - encode_string_by_tiktoken( - chunk_text, model_name=self.tiktoken_model_name - ) - ) + tokens = len(self.tokenizer.encode(chunk_text)) inserting_chunks[chunk_key] = { "content": chunk_text, "full_doc_id": doc_key, @@ -900,12 +921,12 @@ 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, ) } @@ -1133,11 +1154,7 @@ 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( - encode_string_by_tiktoken( - chunk_content, model_name=self.tiktoken_model_name - ) - ) + tokens = len(self.tokenizer.encode(chunk_content)) chunk_order_index = ( 0 if "chunk_order_index" not in chunk_data.keys() diff --git a/lightrag/operate.py b/lightrag/operate.py index 7040ae2e..8eb7cf24 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) @@ -90,9 +88,7 @@ def chunking_by_token_size( for index, start in enumerate( range(0, len(tokens), max_token_size - overlap_token_size) ): - chunk_content = 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( { "tokens": min(max_token_size, len(tokens) - start), @@ -116,19 +112,19 @@ 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"] language = global_config["addon_params"].get( "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, description_list=use_description.split(GRAPH_FIELD_SEP), @@ -865,7 +861,8 @@ 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 +984,8 @@ 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 @@ -1054,6 +1052,8 @@ async def mix_kg_vector_query( 2. Retrieving relevant text chunks through vector similarity 3. Combining both results for comprehensive answer generation """ + # get tokenizer + tokenizer: Tokenizer = global_config["tokenizer"] # 1. Cache handling use_model_func = ( query_param.model_func @@ -1153,6 +1153,7 @@ async def mix_kg_vector_query( valid_chunks, key=lambda x: x["content"], max_token_size=query_param.max_token_for_text_unit, + tokenizer=tokenizer, ) if not maybe_trun_chunks: @@ -1210,7 +1211,7 @@ 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)) + len_of_prompts = len(tokenizer.encode(query + sys_prompt)) logger.debug(f"[mix_kg_vector_query]Prompt Tokens: {len_of_prompts}") # 6. Generate response @@ -1373,17 +1374,24 @@ 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") len_node_datas = len(node_datas) node_datas = truncate_list_by_token_size( node_datas, key=lambda x: x["description"] if x["description"] is not None else "", max_token_size=query_param.max_token_for_local_context, + tokenizer=tokenizer, ) logger.debug( f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})" @@ -1558,14 +1566,15 @@ async def _find_most_related_text_unit_from_entities( logger.warning("No valid text units found") return [] + tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer") all_text_units = sorted( all_text_units, key=lambda x: (x["order"], -x["relation_counts"]) ) - all_text_units = truncate_list_by_token_size( all_text_units, key=lambda x: x["data"]["content"], max_token_size=query_param.max_token_for_text_unit, + tokenizer=tokenizer, ) logger.debug( @@ -1619,6 +1628,7 @@ async def _find_most_related_edges_from_entities( } all_edges_data.append(combined) + tokenizer: Tokenizer = knowledge_graph_inst.global_config.get("tokenizer") all_edges_data = sorted( all_edges_data, key=lambda x: (x["rank"], x["weight"]), reverse=True ) @@ -1626,6 +1636,7 @@ async def _find_most_related_edges_from_entities( all_edges_data, key=lambda x: x["description"] if x["description"] is not None else "", max_token_size=query_param.max_token_for_global_context, + tokenizer=tokenizer, ) logger.debug( @@ -1681,6 +1692,7 @@ async def _get_edge_data( } edge_datas.append(combined) + tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer") edge_datas = sorted( edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True ) @@ -1688,13 +1700,19 @@ async def _get_edge_data( edge_datas, key=lambda x: x["description"] if x["description"] is not None else "", max_token_size=query_param.max_token_for_global_context, + tokenizer=tokenizer, ) 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( @@ -1804,11 +1822,13 @@ async def _find_most_related_entities_from_relationships( combined = {**node, "entity_name": entity_name, "rank": degree} node_datas.append(combined) + tokenizer: Tokenizer = knowledge_graph_inst.global_config.get("tokenizer") len_node_datas = len(node_datas) node_datas = truncate_list_by_token_size( node_datas, key=lambda x: x["description"] if x["description"] is not None else "", max_token_size=query_param.max_token_for_local_context, + tokenizer=tokenizer, ) logger.debug( f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})" @@ -1863,10 +1883,12 @@ async def _find_related_text_unit_from_relationships( logger.warning("No valid text chunks after filtering") return [] + tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer") truncated_text_units = truncate_list_by_token_size( valid_text_units, key=lambda x: x["data"]["content"], max_token_size=query_param.max_token_for_text_unit, + tokenizer=tokenizer, ) logger.debug( @@ -1937,10 +1959,12 @@ async def naive_query( logger.warning("No valid chunks found after filtering") return PROMPTS["fail_response"] + tokenizer: Tokenizer = global_config["tokenizer"] maybe_trun_chunks = truncate_list_by_token_size( valid_chunks, key=lambda x: x["content"], max_token_size=query_param.max_token_for_text_unit, + tokenizer=tokenizer, ) if not maybe_trun_chunks: @@ -1978,7 +2002,7 @@ async def naive_query( if query_param.only_need_prompt: return sys_prompt - len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt)) + 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 +2149,8 @@ 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..c6991629 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 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 +307,89 @@ 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.""" + ... -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 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) + + +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): @@ -361,14 +426,17 @@ 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 + 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: 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