Error: Token indices sequence length is longer than the specified maximum sequence length for this model

[mariosconsta]

Hello everyone, I've been trying to figure out why I get this error/warning when I run my Pipeline. I am not even sure if this is the correct place to add this question, but here we go.

Let me give you a small background on what I want to do first:

I want to create a RAG pipeline using Milvus as a vector database and docling as a document converter while using haystack as the backend.

Here's a snippet of the code:

#IMPORT ALL THE LIBRARIES

#############################

DOCUMENTS_DIR = Path("./dummy_data")
FILES = [{"uri": file.resolve()} for file in DOCUMENTS_DIR.iterdir() if file.is_file()]
EXPORT_TYPE = ExportType.DOC_CHUNKS
EMBED_MODEL_ID = "BAAI/bge-m3"#"sentence-transformers/all-MiniLM-L6-v2" "BAAI/bge-m3"
MAX_TOKENS = 2500

# Initialize Milvus DB

document_store = MilvusDocumentStore(
    connection_args={"uri": "./milvus.db"},# Milvus Lite
    index_params={"index_type": "AUTOINDEX", "metric_type": "L2"},
    # connection_args={"uri": "http://localhost:19530"},  # Milvus standalone docker service.
    drop_old=True,
    text_field="txt"  # set for preventing conflict with same-name metadata field
)


# SETUP INDEX PIPELINE

idx_pipe = Pipeline()
idx_pipe.add_component(
    "converter",
    DoclingConverter(
        export_type=EXPORT_TYPE,
        chunker=HybridChunker(
            tokenizer=EMBED_MODEL_ID,  # instance or model name, defaults to "sentence-transformers/all-MiniLM-L6-v2"
            max_tokens=MAX_TOKENS
        )
    )
)

idx_pipe.add_component(
    "embedder",
    SentenceTransformersDocumentEmbedder()
    #OpenAIDocumentEmbedder(model="text-embedding-3-large"),
)

idx_pipe.add_component("writer", DocumentWriter(document_store=document_store))


idx_pipe.connect("converter", "embedder")

idx_pipe.connect("embedder", "writer")


# RUN THE INDEXER

print(f"Indexing {len(FILES)} files...")

pipe_out = idx_pipe.run(data={
    "converter": {"paths": list(Path(DOCUMENTS_DIR).glob("**/*"))}
    },
                        include_outputs_from="converter")
print(f'Total Chunks in Milvus: {document_store.count_documents()}')

Output

Token indices sequence length is longer than the specified maximum sequence length for this model (26838 > 8192). Running this sequence through the model will result in indexing errors
Indexing 1 files...
Batches: 100%|██████████| 1/1 [00:00<00:00,  4.28it/s]
Total Chunks in Milvus: 22

I tried both openAI embedder and HF embedder and I get the same results.

My main question is, where does it find a sequence of length 26838? Why is there a sequence of that length while my max_tokens param is at 2500? Shouldn't all chunks be at max 2500 tokens?

Also from the output, I get the sequence warning before the first print statement "Indexing 1 files..." which means that the warning comes from somewhere outside that cell?

I am very confused and I've been looking at this for days.

The tokenizer is BAAI/bge-m3, the embedder I use is text-embedding-3-large from OpenAI, both have the same Max input size.

I cannot narrow down on which step of the code I get this warning and how to solve it.

Should I use Haystack's splitter into the pipeline to split the chunks down? I am under the impression that HybridChunker handles that, and I have it setup correctly with the max_tokens parameter.

Checking the tokens of each chunk, I get this:

Chunk 1: 16 tokens
Chunk 2: 2502 tokens
Chunk 3: 2500 tokens
Chunk 4: 2501 tokens
Chunk 5: 2500 tokens
Chunk 6: 2502 tokens
Chunk 7: 2499 tokens
Chunk 8: 2502 tokens
Chunk 9: 2502 tokens
Chunk 10: 2497 tokens
Chunk 11: 2501 tokens
Chunk 12: 1854 tokens
Chunk 13: 89 tokens
Chunk 14: 56 tokens
Chunk 15: 1125 tokens
...

Sorry for the long text, thank you for reading!

EDIT:

If I run the last cell again without restarting the notebook, I do not get the sequence length warning/error. But if I restart the notebook and run the cell, I get the warning. So this error pops up only once per notebook session. Running it again and again does not make it appear.

Furtherdown I have a RAG pipeline which works, so I do not know wether do ignore the error or find a solution.

[sanhuezapablo]

You can ignore the error. Screenshot was taken from FAQ page: https://docling-project.github.io/docling/faq/

[mariosconsta]

I checked everywhere but the FAQ.

Thank you mate, you saved me a lot of headache.

[sanhuezapablo]

No problem! I remember seeing the same error and looking everywhere for it
Good luck with the project!