[Question]: ModelError: Your input is too long. Max input length is 4096 tokens, but you supplied 5441 tokens.

[axz91]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

ModelError: Your input is too long. Max input length is 4096 tokens, but you supplied 5441 tokens.]

how to fix this,,

[dosubot[bot]]

🤖

Hello,

The error you're encountering is due to the fact that the input you're providing exceeds the maximum token limit of the model. In your case, you're supplying 5441 tokens, while the maximum limit is 4096 tokens.

To resolve this issue, you can use the TokenTextSplitter class from the llama_index.text_splitter module. This class allows you to split your text into smaller chunks based on a specified chunk size and overlap. Here is an example of how you can use this class:

from llama_index.text_splitter import TokenTextSplitter

# Your input text
text = "Your very long text..."

# Create a text splitter
text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=0)

# Split the text into chunks
chunks = text_splitter.split_text(text)

# Now you can process each chunk separately
for chunk in chunks:
    # Your processing code here
    pass

Please replace "Your very long text..." with your actual text. The chunk_size parameter specifies the maximum number of tokens in each chunk, and the chunk_overlap parameter specifies the number of tokens that should overlap between consecutive chunks. Adjust these parameters as needed for your specific use case.

In the context shared, there are also examples of how to use the TokenTextSplitter class with a tokenizer from the tiktoken library. If your text is tokenized in a specific way, you can pass the tokenizer's encoding function to the TokenTextSplitter constructor.

You can find more details about this in the LlamaIndex repository:

• llama_index/text_splitter/utils.py
• tests/text_splitter/test_token_splitter.py

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• Token indices sequence length is longer than the specified maximum sequence length for this model (3793 > 1024). Running this sequence through the model will result in indexing errors
• [Question]: Is token count correct?
• GPTSimpleVectorIndex azure embedding error
• llama_index/text_splitter/utils.py
• tests/text_splitter/test_token_splitter.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[axz91]

how can i set the limit when doing inquiry

query_engine = index.as_query_engine()

ModelError: Your input is too long. Max input length is 4096 tokens, but you supplied 5086 tokens.

[dosubot[bot]]

🤖

Hello,

Based on the error message you're encountering, it seems like the input you're trying to process exceeds the maximum token limit. You're correct in considering the use of TokenTextSplitter to split your text into smaller chunks.

Here's an example of how you can use TokenTextSplitter:

from llama_index.text_splitter.token_splitter import TokenTextSplitter
from tiktoken import get_encoding

# Create an instance of the TokenTextSplitter class
splitter = TokenTextSplitter(chunk_size=4096, chunk_overlap=20, tokenizer=get_encoding("gpt2").encode)

# Your text
text = "Your long text goes here..."

# Split the text into smaller chunks
chunks = splitter.split_text(text)

# Now you can process each chunk separately
for chunk in chunks:
    # Perform your inquiry here...
    pass

In this example, the text is split into chunks of 4096 tokens each, with an overlap of 20 tokens between each chunk. You can adjust these parameters according to your needs. The tokenizer parameter is used to encode your text into tokens using the GPT-2 encoding.

If you're dealing with large documents, you might also consider splitting your documents into smaller chunks before passing them to the from_documents method. Here's an example of how you can do this:

from llama_index.schema import Document

def split_document(document: Document, chunk_size: int):
    text = document.text
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
    return [Document(text=chunk, metadata=document.metadata) for chunk in chunks]

# Assume `documents` is your list of large documents
smaller_documents = []
for document in documents:
    smaller_documents.extend(split_document(document, chunk_size=1000))

index = VectorStoreIndex.from_documents(smaller_documents)

In this example, split_document is a function that splits the text of a document into chunks of a specified size (in this case, 1000 characters), and creates a new Document object for each chunk. The metadata of the original document is preserved for each chunk.

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• Token indices sequence length is longer than the specified maximum sequence length for this model (2503 > 1024). Running this sequence through the model will result in indexing errors?
• [Question]: How to split or seperate nodes Doucment data?
• Token Limit Issue.
• llama_index/text_splitter/token_splitter.py
• llama_index/text_splitter/init.py
• tests/text_splitter/test_token_splitter.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.