feat(sparse): allow MiniCOIL sequence length limit

[Coding-Professional]

Long MiniCOIL inputs currently use the model tokenizer's full context window, so callers cannot reduce sequence length to control memory use. This adds an optional construction-time max_sequence_length for Qdrant/minicoil-v1:

model = SparseTextEmbedding(model_name="Qdrant/minicoil-v1", max_sequence_length=512)
embeddings = list(model.embed(documents))

The limit is applied at tokenizer load, so document embeddings, query embeddings, token counts, and parallel workers use the same setting. Omitting it preserves the existing model limit. A larger value is capped at that model limit; non-positive values and values too small to fit special tokens plus one text token raise ValueError. Failed validation clears the tokenizer and runs before ONNX session startup, so retries cannot bypass the limit. The README documents the option.

Validation:

• pytest -q tests/test_minicoil_sequence_length.py tests/test_sparse_embeddings.py -k 'minicoil or sequence_limit': 18 passed, 24 deselected.
• Ruff 0.3.4 (the repository pre-commit version): ruff check and ruff format --check passed for both changed Python files.
• With the real Qdrant/minicoil-v1 model, a 16-token cap produced 16 counted tokens and successful document/query embeddings; a 3-token cap also produced successful document/query embeddings.
• The full model suite was not run locally.

Fixes #544

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Reviewing files that changed from the base of the PR and between 1b0b40d and 3793741.

📒 Files selected for processing (2)

• fastembed/sparse/minicoil.py
• tests/test_minicoil_sequence_length.py

🚧 Files skipped from review as they are similar to previous changes (1)

• tests/test_minicoil_sequence_length.py

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📝 Walkthrough

Walkthrough

MiniCOIL adds an optional max_sequence_length setting. It validates the value and checks that it allows at least one text token alongside special tokens. Tokenization uses the smaller of the configured value and tokenizer limit. The setting is passed to parallel workers. Tests cover validation, truncation, token counts, and worker arguments. The README adds a MiniCOIL example.

Priority: ➖ Normal

Estimated code review effort: 2 (Simple) | ~12 minutes

Change: Feature · Severity of issue fixed: Medium

Merge Risk: ⚪ Minimal · up to 37937

The sequence-length limit is applied across token counting, document and query embeddings, and parallel workers. No merge-blocking issue is established.

Security Architecture Review

Security architecture risk: 🔵 Low · up to 37937

The new length limit is enforced during normal operation without an identified expansion of access or privileges. Simultaneous first-use calls could briefly observe the original limit instead; whether applications support that usage remains unconfirmed.

Retained concerns

• Low · reliability · inferred: The new cap is applied after the tokenizer becomes visible to consumers. During simultaneous first use of one instance, a token-count or embedding operation could observe the original model limit rather than the configured smaller limit. This conditionally weakens resource containment for untrusted text, but supported concurrent usage and attacker reachability are not established. Normal eager initialization and separate worker instances avoid this shared-initialization scenario.

Security review details

Security Blast Radius

• inferred — The demonstrated scope is an embedding instance and its separately initialized workers. The conditional cap bypass requires overlapping first-use operations on one instance and can expose the existing model limit, not a newly enlarged limit. Any downstream tenant, service, or environment exposure depends on deployment arrangements not supplied here.

Trust Boundaries and Controls

• observed — The new configuration is validated before base construction and applied to the tokenizer loaded from the existing model directory. The diff does not add credentials, identities, network destinations, model paths, or privileged loading arguments; it changes tokenization policy and initialization order.

Resilience and Maintainability Implications

• inferred — Sequential recovery preserves the cap: invalid tokenizer configuration is discarded, and a later ONNX session-loading failure leaves the already configured tokenizer available for retry. Concurrent initialization lacks an equivalent atomic publication boundary. Tests inspect cap enforcement and worker arguments but do not exercise concurrent initialization, injected session failures, or real spawned workers.

Hardening Proposals

• proposed — If concurrent first use is supported, serialize initialization or publish tokenizer state only after validation and truncation are complete. Otherwise, explicitly require initialization before sharing an instance across concurrent callers.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 21.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 14 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Issue #544 requests native MiniCOIL sequence-length control. MiniCOIL adds max_sequence_length and applies it through tokenizer truncation without truncating input strings. The limit applies to do…
Out of Scope Changes check	✅ Passed	The changes stay within Issue #544. The README documents the new option. The tests verify sequence limits, validation, token counts, ONNX inputs, repeated calls, and worker propagation. The tokenizer-…
Title check	✅ Passed	The title clearly and concisely describes the main change: adding a sequence-length limit to MiniCOIL.
Description check	✅ Passed	The description directly explains the new MiniCOIL option, its validation rules, affected operations, documentation changes, and validation results.

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Inline comments:
Review comments at @fastembed/sparse/minicoil.py:
- Around line 182-185: Clear self.tokenizer before raising when the
minimum-length check finds max_sequence_length too small, so later calls re-run
validation instead of using a cached tokenizer with its model-level truncation
limit.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Advanced

Run ID: f38349e7-5ee7-42bc-a299-cc116d60394a

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Reviewing files that changed from the base of the PR and between 7d36728 and 1b0b40d.

📒 Files selected for processing (3)

• README.md
• fastembed/sparse/minicoil.py
• tests/test_minicoil_sequence_length.py

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