To set up a custom tokenizer in LlamaIndex, you need to replace the default tokenizer with one that aligns with your specific use case. LlamaIndex uses tokenizers to split text into tokens for processing by language models, and customizing this allows you to control chunking behavior, handle special formats, or optimize for non-OpenAI models. The process involves defining a class that implements tokenization logic and configuring LlamaIndex to use it globally.
First, create a tokenizer class that adheres to LlamaIndex’s interface. For example, if using a Hugging Face tokenizer, import AutoTokenizer from the transformers library and wrap it in a class with encode and decode methods. Here’s a basic example:
from transformers import AutoTokenizer
from llama_index import Tokenizer
class CustomTokenizer:
def __init__(self, model_name):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
def encode(self, text):
return self.tokenizer.encode(text)
def decode(self, tokens):
return self.tokenizer.decode(tokens)
# Configure LlamaIndex to use the custom tokenizer
from llama_index import Settings
Settings.tokenizer = CustomTokenizer("bert-base-uncased")
This class must implement encode (converts text to token IDs) and decode (converts tokens back to text). Ensure the tokenizer matches the requirements of your language model, as inconsistent tokenization can cause errors in downstream tasks.
Next, apply the tokenizer to LlamaIndex components. The global Settings.tokenizer ensures your custom tokenizer is used during data indexing and query execution. For instance, when splitting documents into chunks, the tokenizer’s encode method determines how text is divided. If your tokenizer uses a different vocabulary (e.g., BERT vs. GPT-4), adjust chunk_size in node parsers to align with your model’s context window. For example:
from llama_index.node_parser import SentenceSplitter
node_parser = SentenceSplitter(chunk_size=512, tokenizer=Settings.tokenizer)
This ensures text splitting respects your tokenizer’s output.
Finally, validate the tokenizer’s behavior. Test edge cases like special characters, multilingual text, or domain-specific terms to ensure it handles them correctly. For instance, if processing code snippets, verify that symbols like { or = are tokenized as expected. Monitor token counts during indexing to avoid overflows or underutilized chunks. Custom tokenizers are especially useful when using open-source models (e.g., Llama 2) that require specific tokenization rules, ensuring consistency between preprocessing and model input. By tailoring the tokenizer, you gain precise control over how data is processed, improving performance for specialized applications.