FlagEmbedding

Use this repo skill when a task involves FlagEmbedding, BGE embedding models, BGE-M3 dense/sparse/ColBERT retrieval, BGE rerankers, retrieval evaluation, or FlagEmbedding fine-tuning data and launch commands.

Read references/repo-provenance.md when checking whether this skill matches a current checkout or package version. Read references/model-overview.md when choosing model families or deciding whether automatic model routing is likely to work. Read references/troubleshooting.md for install/import, optional dependency, backend, model-cache, and remote-code problems.

Install And Import

Base package install:

python -m pip install -U FlagEmbedding

Fine-tuning extras:

python -m pip install -U "FlagEmbedding[finetune]"

Evaluation workflows also need retrieval metric/index dependencies that are not always installed by the base package metadata:

python -m pip install faiss-cpu pytrec_eval

Use GPU-specific FAISS, CUDA PyTorch, DeepSpeed, or flash-attn only after the runtime backend is deliberately prepared. Do not treat a CPU import as proof of GPU training or flash-attn compatibility.

Minimal import check:

python - <<'PY'
from FlagEmbedding import FlagAutoModel, FlagAutoReranker
print(FlagAutoModel, FlagAutoReranker)
PY

Bundled environment/API probe from this skill directory:

python scripts/check_flag_embedding_env.py

Route Map

Use sub-skills/inference/SKILL.md when the task is to load embedders or rerankers, encode queries/corpus, compute BGE-M3 dense/sparse/ColBERT scores, rerank query-passage pairs, choose model_class, handle instructions, or smoke-check inference APIs without running training or benchmark jobs.

Use sub-skills/fine-tuning/SKILL.md when the task is to prepare or validate training JSONL, mine or reason about hard negatives, add teacher-score fields, split long data, choose an embedder/reranker fine-tuning module, build a torchrun command, or diagnose DeepSpeed/flash-attn/training-data issues.

Use sub-skills/evaluation/SKILL.md when the task is to run or prepare retrieval evaluation with FlagEmbedding, create custom corpus.jsonl / test_queries.jsonl / test_qrels.jsonl, choose MTEB/BEIR/MSMARCO/MIRACL/MLDR /MKQA/AIR-Bench/BRIGHT commands, add a reranker to evaluation, or interpret metrics and output directories.

Common Decisions

Prefer auto loaders for mapped checkpoints:

from FlagEmbedding import FlagAutoModel, FlagAutoReranker

embedder = FlagAutoModel.from_finetuned(
    "BAAI/bge-base-en-v1.5",
    query_instruction_for_retrieval="Represent this sentence for searching relevant passages:",
    devices="cpu",
    use_fp16=False,
)

reranker = FlagAutoReranker.from_finetuned(
    "BAAI/bge-reranker-base",
    devices="cpu",
    use_fp16=False,
)

For custom or unmapped checkpoints, set model_class explicitly instead of retrying the same auto call. Embedder ids include encoder-only-base, encoder-only-m3, decoder-only-base, decoder-only-icl, and decoder-only-pseudo_moe. Reranker ids include encoder-only-base, decoder-only-base, decoder-only-layerwise, and decoder-only-lightweight.

Use CPU and full precision for cheap smoke checks. Move to CUDA, fp16, bf16, large batch sizes, remote model ids, or benchmark downloads only after the user approves the runtime, cache, and budget.

Verification Anchors

The generated skill is grounded in these public surfaces:

• Public imports and mappings under FlagEmbedding.inference.
• Fine-tuning module entry points under FlagEmbedding.finetune.
• Evaluation module entry points under FlagEmbedding.evaluation.
• Maintained package examples distilled into bundled references and scripts.
• Small native candidates and synthetic fixtures recorded in the integration reports under the review/test artifact directory.

Runtime files do not require the original checkout. Source examples and scripts were distilled into the generated skill references or adapted as bundled helper scripts.