---

name: index-free-reranking description: "Use RAGatouille for index-free ColBERT reranking, in-memory document encoding, transient encoded search, and encoded-state clearing." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Index-Free Reranking

Use this sub-skill when a task needs RAGatouille without creating or loading a persisted index: rerank candidate documents from another retriever, encode a small transient document set in memory, search those encoded documents, or clear stale in-memory encodings.

Route Here For

• Reranking external retriever results with RAGPretrainedModel.rerank(query, documents, k=..., zero_index_ranks=..., bsize=...).
• Encoding small, already-chunked text lists with RAGPretrainedModel.encode(documents, document_metadatas=..., max_document_length=...).
• Querying transient encoded documents with RAGPretrainedModel.search_encoded_docs(query, k=..., bsize=...).
• Clearing expired in-memory encodings with RAGPretrainedModel.clear_encoded_docs(force=True).
• Explaining single-query versus multi-query result shapes, metadata propagation, ranks, and small-dataset limits.

Route Elsewhere

• Persisted indexes, index(), search(), add_to_index(), delete_from_index(), index roots, document IDs, and disk-backed metadata: ../pretrained-indexing-search/SKILL.md.
• LangChain retriever/compressor wrappers and export helpers: ../integrations-export/SKILL.md.
• Training, fine-tuning, data processors, and hard negative mining: ../training-data-finetuning/SKILL.md.

Fast Decision Guide

• Use rerank() when another retriever already produced a short candidate list and you only need ColBERT to rescore those candidate strings.
• Use encode() plus search_encoded_docs() when the whole working set is small, temporary, and should live only in the current RAGPretrainedModel instance.
• Use a persisted index instead when document counts are large, documents must survive process restart, document IDs are important, or repeated searches need optimized latency.

References

• references/api-reference.md: verified signatures, return keys, rank semantics, and source-backed edge cases.
• references/workflows.md: rerank, encode/search/append, metadata, and clear-state recipes.
• references/troubleshooting.md: common failures and unsafe/expensive operations to avoid during lightweight checks.

Offline Helper

Run scripts/check_result_shapes.py --help to inspect the bundled JSON shape checker. It validates sample rerank() and search_encoded_docs()-like results without importing RAGatouille, loading models, downloading checkpoints, or using GPUs.