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name: colbert description: "Use ColBERT/colbert-ai for late-interaction retrieval: prepare data, inspect configs, train/fine-tune, index collections, search rankings, update indexes, serve search, evaluate MS MARCO/LoTTE outputs, or reason about Baleen multi-hop retrieval." disable-model-invocation: true metadata: disco-role: operating license: MIT

ColBERT Repo Skill

Use this skill when a task involves the colbert-ai Python package, the ColBERTv2/PLAID retrieval workflow, ColBERT data formats, or the optional Baleen multi-hop extension.

ColBERT is a late-interaction neural retrieval system. Typical workflows prepare TSV data, choose or train a checkpoint, build an index, search queries, save rankings, and evaluate retrieval quality.

Start Here

1. Install the public package with the needed backend, for example pip install "colbert-ai[torch,faiss-cpu]" for CPU-oriented inspection or pip install "colbert-ai[torch,faiss-gpu]" when CUDA/FAISS GPU is available and intended.
2. Run python scripts/check_colbert_env.py to verify imports, package versions, torch/CUDA visibility, and important public API signatures.
3. Read references/troubleshooting.md if imports fail, FAISS/Torch extras are missing, CUDA is unavailable, or old torch/setuptools stacks report pkg_resources errors.
4. Read references/repo-provenance.md before deciding whether this generated skill matches a current checkout or should be refreshed.

Route Tasks

• Use sub-skills/data-and-evaluation/ for collection.tsv, queries.tsv, rankings, qrels, LoTTE layouts, preprocessing, validation, and metric evaluation.
• Use sub-skills/modeling-and-tokenization/ for Checkpoint, ColBERTConfig, tokenizer behavior, marker tokens, max lengths, dimensions, and safe model/config inspection.
• Use sub-skills/training-and-distillation/ for Trainer, triples/examples JSONL, ColBERTv1/v2-style fine-tuning, distillation/scored examples, and GPU/resource planning.
• Use sub-skills/indexing-and-search/ for Indexer, Searcher, RunConfig, index roots, single-query or batch search, ranking save behavior, and search tuning.
• Use sub-skills/index-updates-and-serving/ for IndexUpdater, add/remove/persist workflows, coalescing updated artifacts, and lightweight JSON search serving.
• Use sub-skills/baleen-multihop/ for optional Baleen HopSearcher, Condenser, collectionX, multi-hop retrieval plans, and static diagnostics.

Verified Package Facts

• Distribution name: colbert-ai; import package: colbert; generated against package version 0.2.22.
• Public imports verified during creation: colbert, colbert.infra, colbert.data, colbert.modeling.checkpoint, utility, and baleen.
• Important public constructors: Indexer(checkpoint, config=None, verbose=3), Searcher(index, checkpoint=None, collection=None, config=None, index_root=None, verbose=3), Trainer(triples, queries, collection, config=None), and IndexUpdater(config, searcher, checkpoint=None).
• CPU imports and validation helpers are safe for inspection; practical indexing, training, updating, and full Baleen runs often need local checkpoints, indexes, datasets, and CUDA/GPU resources.

Core Workflow

1. Validate data with the data/evaluation sub-skill before starting expensive indexing or training.
2. Inspect checkpoint/config/tokenization assumptions with the modeling sub-skill when changing query_maxlen, doc_maxlen, dim, marker tokens, or checkpoint sources.
3. Train or fine-tune only after validating triples/examples and planning GPU resources.
4. Index with explicit RunConfig(root=..., experiment=...), ColBERTConfig(...), checkpoint, index name, and overwrite/resume policy.
5. Search with the same root/experiment/index naming assumptions or pass explicit index_root; save rankings with an explicit output path when possible.
6. Treat mutable index updates and serving as operations on an existing index; back up artifacts before persist_to_disk().

Bundled Root Files

• references/repo-provenance.md records the source repository snapshot, package version, evidence paths, and refresh checks.
• references/troubleshooting.md covers cross-cutting install/import/backend/package issues shared by all sub-skills.
• scripts/check_colbert_env.py performs a deterministic environment and public-signature inspection without loading checkpoints, downloading models, or running retrieval.

Safety Boundaries

• Do not assume a successful import means a user has working checkpoints, indexes, datasets, FAISS GPU, or CUDA.
• Do not start long training, indexing, Hugging Face downloads, benchmark evaluation, or server processes unless the user explicitly wants that side effect.
• Do not mutate an existing index with IndexUpdater.persist_to_disk() until the target index is backed up or disposable.
• Prefer bundled validation/template scripts in this skill over copying commands from old notebooks or generated docs.