Principle:AnswerDotAI RAGatouille Index Loading

Knowledge Sources	ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT PLAID: An Efficient Engine for Late Interaction Retrieval RAGatouille
Domains	NLP, Information_Retrieval, Index_Management
Last Updated	2026-02-12 12:00 GMT

Overview

A model and index restoration mechanism that loads a ColBERT encoder alongside a previously built document index from disk, enabling immediate search without re-indexing.

Description

Index Loading reconstructs a complete retrieval system from a previously built index directory. Unlike loading just a pretrained model, this principle restores both the ColBERT encoder and the PLAID index state, including the collection of documents, passage-to-document ID mappings, and optional metadata. This enables resuming search operations on an existing index without the computational cost of re-encoding and re-indexing the document collection.

The process involves:

• Loading the ColBERT configuration from the index directory
• Restoring the PLAID model index via ModelIndexFactory
• Deserializing the document collection from collection.json
• Restoring the pid_docid_map for passage-to-document mapping
• Loading optional document metadata from docid_metadata_map.json
• Initializing the inference checkpoint for query encoding

Usage

Use this principle when you need to query or update a previously built index. This is the appropriate entry point when:

• An index has already been built and persisted to disk
• You want to avoid the cost of re-indexing a document collection
• You need to add or remove documents from an existing index
• You are deploying a search service that loads indexes on startup

Theoretical Basis

PLAID (Performance-optimized Late Interaction Driver) indexes store pre-computed document token embeddings in a compressed format using centroid-based quantization. Loading an index restores:

1. Centroids: Cluster centers from k-means over token embeddings
2. Compressed residuals: Quantized differences from centroids (2-bit or 4-bit)
3. Document mappings: Passage ID to document ID associations

This pre-computation eliminates the need to re-encode documents at query time, making search efficient.

Related Pages

Implemented By

• Implementation:AnswerDotAI_RAGatouille_RAGPretrainedModel_From_Index