vllm/examples/pooling/embed/openai_embedding_long_text/README.md

Long Text Embedding with Chunked Processing

This directory contains examples for using vLLM's chunked processing feature to handle long text embedding that exceeds the model's maximum context length.

🚀 Quick Start

Start the Server

Use the provided script to start a vLLM server with chunked processing enabled:

# Basic usage (supports very long texts up to ~3M tokens)
./service.sh

# Custom configuration with different models
MODEL_NAME="jinaai/jina-embeddings-v3" \
MAX_EMBED_LEN=1048576 \
./service.sh

# For extremely long documents
MODEL_NAME="intfloat/multilingual-e5-large" \
MAX_EMBED_LEN=3072000 \
./service.sh

Test Long Text Embedding

Run the comprehensive test client:

python client.py

📁 Files

File	Description
service.sh	Server startup script with chunked processing enabled
client.py	Comprehensive test client for long text embedding

⚙️ Configuration

Server Configuration

The key parameters for chunked processing are in the --pooler-config:

{
  "pooling_type": "auto",
  "normalize": true,
  "enable_chunked_processing": true,
  "max_embed_len": 3072000
}

!!! note pooling_type sets the model's own pooling strategy for processing within each chunk. The cross-chunk aggregation automatically uses MEAN strategy when input exceeds the model's native maximum length.

Chunked Processing Behavior

Chunked processing uses MEAN aggregation for cross-chunk combination when input exceeds the model's native maximum length:

Component	Behavior	Description
Within chunks	Model's native pooling	Uses the model's configured pooling strategy
Cross-chunk aggregation	Always MEAN	Weighted averaging based on chunk token counts
Performance	Optimal	All chunks processed for complete semantic coverage

Environment Variables

Variable	Default	Description
MODEL_NAME	intfloat/multilingual-e5-large	Embedding model to use (supports multiple models)
PORT	31090	Server port
GPU_COUNT	1	Number of GPUs to use
MAX_EMBED_LEN	3072000	Maximum embedding input length (supports very long documents)
POOLING_TYPE	auto	Model's native pooling type: auto, MEAN, CLS, LAST (only affects within-chunk pooling, not cross-chunk aggregation)
API_KEY	EMPTY	API key for authentication

🔧 How It Works

1. Enhanced Input Validation: max_embed_len allows accepting inputs longer than max_model_len without environment variables
2. Smart Chunking: Text is split based on max_position_embeddings to maintain semantic integrity
3. Unified Processing: All chunks processed separately through the model using its configured pooling strategy
4. MEAN Aggregation: When input exceeds model's native length, results combined using token count-based weighted averaging across all chunks
5. Consistent Output: Final embeddings maintain the same dimensionality as standard processing

Input Length Handling

• Within max_embed_len: Input is accepted and processed (up to 3M+ tokens)
• Exceeds max_position_embeddings: Chunked processing is automatically triggered
• Exceeds max_embed_len: Input is rejected with clear error message
• No environment variables required: Works without VLLM_ALLOW_LONG_MAX_MODEL_LEN

Extreme Long Text Support

With MAX_EMBED_LEN=3072000, you can process:

• Academic papers: Full research papers with references
• Legal documents: Complete contracts and legal texts
• Books: Entire chapters or small books
• Code repositories: Large codebases and documentation

📊 Performance Characteristics

Chunked Processing Performance

Aspect	Behavior	Performance
Chunk Processing	All chunks processed with native pooling	Consistent with input length
Cross-chunk Aggregation	MEAN weighted averaging	Minimal overhead
Memory Usage	Proportional to number of chunks	Moderate, scalable
Semantic Quality	Complete text coverage	Optimal for long documents

🧪 Test Cases

The test client demonstrates:

• ✅ Short text: Normal processing (baseline)
• ✅ Medium text: Single chunk processing
• ✅ Long text: Multi-chunk processing with aggregation
• ✅ Very long text: Many chunks processing
• ✅ Extreme long text: Document-level processing (100K+ tokens)
• ✅ Batch processing: Mixed-length inputs in one request
• ✅ Consistency: Reproducible results across runs

🐛 Troubleshooting

Common Issues

1. Chunked processing not enabled:

   ValueError: This model's maximum position embeddings length is 4096 tokens...
   

   Solution: Ensure enable_chunked_processing: true in pooler config

2. Input exceeds max_embed_len:

   ValueError: This model's maximum embedding input length is 3072000 tokens...
   

   Solution: Increase max_embed_len in pooler config or reduce input length

3. Memory errors:

   RuntimeError: CUDA out of memory
   

   Solution: Reduce chunk size by adjusting model's max_position_embeddings or use fewer GPUs

4. Slow processing: Expected: Long text takes more time due to multiple inference calls

Debug Information

Server logs show chunked processing activity:

INFO: Input length 150000 exceeds max_position_embeddings 4096, will use chunked processing
INFO: Split input of 150000 tokens into 37 chunks (max_chunk_size: 4096)

🤝 Contributing

To extend chunked processing support to other embedding models:

1. Check model compatibility with the pooling architecture
2. Test with various text lengths
3. Validate embedding quality compared to single-chunk processing
4. Submit PR with test cases and documentation updates

🆕 Enhanced Features

max_embed_len Parameter

The new max_embed_len parameter provides:

• Simplified Configuration: No need for VLLM_ALLOW_LONG_MAX_MODEL_LEN environment variable
• Flexible Input Validation: Accept inputs longer than max_model_len up to max_embed_len
• Extreme Length Support: Process documents with millions of tokens
• Clear Error Messages: Better feedback when inputs exceed limits
• Backward Compatibility: Existing configurations continue to work