add custom tokenizer

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` |

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()

lightrag/api/routers/ollama_api.py

@@ -10,7 +10,7 @@
 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)