Principle:AnswerDotAI RAGatouille In Memory Document Encoding

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

Overview

An index-free document encoding mechanism that computes and stores ColBERT token-level embeddings in GPU/CPU memory for immediate search without building a persistent PLAID index.

Description

In-Memory Document Encoding provides a lightweight alternative to full PLAID indexing. Instead of building a compressed on-disk index, documents are encoded into dense token-level embedding tensors that are held in memory. This enables fast prototyping, small-collection search, and reranking workflows where the overhead of building a full index is unnecessary.

The encoding process:

• Documents are tokenized and encoded through the ColBERT checkpoint to produce per-token embeddings
• Embeddings are padded to uniform length for efficient batched MaxSim computation
• Document attention masks are created to distinguish real tokens from padding
• Results are stored as tensors in memory (in_memory_embed_docs, doc_masks)
• Supports incremental encoding — calling encode multiple times appends to existing tensors
• Auto-adjusts batch size for long documents to manage memory

Usage

Use this principle when:

• Working with small document collections (performance degrades with more documents)
• Prototyping search without the overhead of building a full index
• Documents change frequently and rebuilding an index each time is impractical
• You need to search a temporary collection that won't be persisted

For collections larger than ~1000 documents, prefer building a PLAID index instead.

Theoretical Basis

In-memory encoding computes the same token-level representations as PLAID indexing but without the compression step:

$E_d={\text{ColBERT}}_{\text{doc}}(d)\in {\mathbb{ℝ}}^{n\times h}$

Where n is the padded token count and h is the embedding dimension. The full dense tensors are stored, enabling exact MaxSim computation without the approximation inherent in PLAID's centroid-based search.

The tradeoff is memory: storing full float tensors uses significantly more memory than quantized PLAID indexes, but provides exact scoring.

Related Pages

Implemented By

• Implementation:AnswerDotAI_RAGatouille_RAGPretrainedModel_Encode

Uses Heuristic

• Heuristic:AnswerDotAI_RAGatouille_Auto_Batch_Size_For_Long_Documents