lateness 0.1.0

Modern ColBERT for Late Interaction with native multi-vector support

Lateness - Modern ColBERT for Late Interaction

A Python package for Modern ColBERT (late interaction) embeddings with native multi-vector support for efficient retrieval using Qdrant vector database.

Features

• Dual Backend Architecture: ONNX for fast retrieval, PyTorch for GPU indexing
• Native Multi-Vector Support: Optimized for Qdrant's MaxSim comparator
• Smart Installation: Lightweight retrieval or heavy indexing based on your needs
• Production Ready: Separate deployment targets for different workloads

Quick Start

Installation

# Lightweight retrieval (ONNX + Qdrant)
pip install lateness

# Heavy indexing (PyTorch + Transformers + ONNX + Qdrant)
pip install lateness[index]

Backend Selection

Basic Usage

Default Installation (ONNX Backend):

# pip install lateness
from lateness import ModernColBERT
colbert = ModernColBERT("prithivida/modern_colbert_base_en_v1")
# Output:
# 🚀 Using ONNX backend Using ONNX backend (default, for GPU accelerated indexing, install lateness[index] and set LATENESS_USE_TORCH=true)
# 🔄 Downloading model: prithivida/modern_colbert_base_en_v1
# ✅ ONNX ColBERT loaded with providers: ['CPUExecutionProvider']
# Query max length: 256, Document max length: 300

Index Installation (PyTorch Backend):

# pip install lateness[index]
import os
os.environ['LATENESS_USE_TORCH'] = 'true'
from lateness import ModernColBERT

colbert = ModernColBERT("prithivida/modern_colbert_base_en_v1")
# Output:
# 🚀 Using PyTorch backend (LATENESS_USE_TORCH=true)
# 🔄 Downloading model: prithivida/modern_colbert_base_en_v1
# Loading model from: /root/.cache/huggingface/hub/models--prithivida--modern_colbert_base_en_v1/...
# ✅ PyTorch ColBERT loaded on cuda
# Query max length: 256, Document max length: 300

Complete Example with Qdrant:

For a complete working example with Qdrant integration, environment setup, and testing instructions, see the examples/qdrant folder.

The examples include:

• Environment setup and testing
• Local Qdrant server management
• Complete indexing and retrieval workflows
• Both ONNX and PyTorch backend examples

Architecture

Two Deployment Models

Retrieval Service (Lightweight)

pip install lateness

• ONNX backend (fast CPU inference)
• Qdrant integration
• ~50MB total dependencies
• Perfect for user-facing search APIs

Indexing Service (Heavy)

pip install lateness[index]

• PyTorch backend (GPU acceleration)
• Full Transformers support
• ~2GB+ dependencies
• Perfect for batch document processing

Backend Selection

The package uses environment variables for backend control:

• Default behavior → ONNX backend (CPU retrieval)
• LATENESS_USE_TORCH=true → PyTorch backend (GPU indexing)

Note: PyTorch backend requires pip install lateness[index] to install PyTorch dependencies.

API Reference

ModernColBERT

from lateness import ModernColBERT

# Initialize
colbert = ModernColBERT("prithivida/modern_colbert_base_en_v1")

# Encode queries
query_embeddings = colbert.encode_queries(["What is AI?"])

# Encode documents  
doc_embeddings = colbert.encode_documents(["AI is artificial intelligence"])

# Compute similarity
scores = ModernColBERT.compute_similarity(query_embeddings, doc_embeddings)

Qdrant Integration

from lateness import QdrantIndexer, QdrantRetriever
from qdrant_client import QdrantClient

client = QdrantClient("localhost", port=6333)

# Indexing
indexer = QdrantIndexer(client, "documents")
indexer.create_collection()
indexer.index_documents_simple(documents)

# Retrieval
retriever = QdrantRetriever(client, "documents")
results = retriever.search_simple("query", top_k=10)

License

Apache License 2.0

Contributing

Contributions welcome! Please check our contributing guidelines.