Add NLP capabilities with local models - adds multi-lingual support and improves evaluation results

[tmuskal]

What does this PR do?

increases the results of the benchmarks while removing all constant, hardcoded keywords and regexes from all NLP mechanisms. eliminates benchmark overfitting.

What	Added Size	Key Gap Closed
pySBD + negation detection	~50 KB	G3 (sentences), G4 partial (negation)
spaCy xx_ent_wiki_sm + coreferee	~15 MB	G1 (NER), G6 (multilingual), G7 (coref)
GLiNER2 ONNX	~400 MB model	G2 (KG triples), G4 (classification)
wtpsplit sat-3l-sm ONNX	~20 MB model	G3 best-in-class, G6 (85 langs)
phi-3.5-mini onnxruntime-genai	1.5 GB model	G5 (sentiment), all enhanced, triples and knowledge extractions

All share onnxruntime which chromadb already installs. Every phase is backward compatible -- legacy mode produces identical output to current version. Each phase is gated behind a config flag so users opt in explicitly.

Add multilingual support, better NLP capabilities and better quality of results overall.
No more regexes and predefined lists in benchmarks and in implementation. Implementation is no longer overfitted.

Speed test shows that the model-powered implementation is 40% slower when running on github runners CPUs.
All previous tests pass.

How to test

See new test pipelines in the ci (test-nlp , benchmark-quality , benchmark-speed )

Checklist

• Tests pass (python -m pytest tests/ -v)
• No hardcoded paths
• Linter passes (ruff check .)

[web3guru888]

This is a significant piece of work and the architecture is well-considered. The graduated backend levels (legacy → pysbd → spacy → gliner → full) with cumulative capability gating, and all-off-by-default behavior, is exactly the right way to introduce optional ML dependencies into a project like this.

A few observations from reading through the diff:

nlp-coref vs nlp-full version conflict
nlp-coref requires spacy>=3.5,<3.6 (coreferee compatibility), but nlp-full requires spacy>=3.7. A user who installs nlp-full and then tries to add coreference will hit a version incompatibility. The README or install docs should call this out explicitly, and it's worth documenting whether coreferee support will ever land in the full tier.

Model downloads at runtime
The ModelManager lazily downloads models on first use. In sandboxed or air-gapped environments (CI, Docker with no outbound HTTP, corporate proxies), this will silently fail and fall back to legacy rather than erroring loudly. A mempalace nlp prefetch command (or equivalent) that can be run at container build time would be a useful operational addition.

Gemma 3 1B as SLM for triples
We've been extracting KG triples ourselves using a lightweight approach (sentence-level entity pairs + co-occurrence). We've found that instruction-following prompts to small models for triple extraction are brittle — the JSON format compliance rate drops significantly for complex sentences. GLiNER2 for relation extraction is probably more reliable for triples than the SLM prompt approach. The TRIPLES_PROMPT in slm_provider.py may produce inconsistent output on out-of-domain content.

Test coverage on model download paths
The test suite for gliner_provider and nlp_e2e looks thorough. Is the ModelManager download/verify path tested with mocked HTTP? Model download failures (corrupt download, 404, timeout) are a common source of silent degradation.

40% slowdown note
The PR notes 40% slower on GitHub runner CPUs. For interactive use (Claude Code sessions, MCP hooks), this is likely invisible. For batch mining of large session logs it could be noticeable. A per-operation timing note in the speed benchmark output would help users decide which backend level is appropriate for their workload.

Overall: the opt-in architecture is sound, the legacy path is preserved, and the feature gating is clean. The coreferee version conflict and the runtime download behavior are the two things I'd want resolved or documented before merge.

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[igorls]

Nice job @tmuskal Great move forward! The big win here is multilingual support and fixing the benchmark overfitting. @bensig @milla-jovovich

[bensig]

Code review

Found 2 issues:

1. huggingface_hub is used in _download_from_hf() but is not declared in any optional dependency group in pyproject.toml — not even nlp-slm, which is the only tier that triggers HuggingFace downloads (Phi-3.5 Mini). Users who install mempalace[nlp-slm] will get a silent failure when trying to download the model, since the ImportError is caught and logged but no install hint is printed. huggingface_hub should be added to the nlp-slm (and probably nlp-full) extras.

https://github.com/milla-jovovich/mempalace/blob/0423cbdc06e2bdcbeabc06d594e5d555d2aec9c7/mempalace/nlp_providers/model_manager.py#L314-L330

2. The --nlp-backend CLI flag is parsed but never consumed. args.nlp_backend (set by parser.add_argument("--nlp-backend", ...)) is never read in main() or passed to NLPConfig.resolve(cli_backend=...). Running mempalace --nlp-backend spacy mine ... silently ignores the flag and uses the default legacy backend. NLPConfig.resolve already supports a cli_backend parameter — it just needs to be wired up at the call sites.

https://github.com/milla-jovovich/mempalace/blob/0423cbdc06e2bdcbeabc06d594e5d555d2aec9c7/mempalace/cli.py#L553-L559

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[web3guru888]

Both of @bensig's finds look correct — I can validate from our side:

huggingface_hub missing from nlp-slm: Yes, this would cause a silent failure. The _download_from_hf() function catches ImportError and logs it, but the user sees nothing actionable when they installed mempalace[nlp-slm] expecting the download to work. Easy fix — add huggingface-hub>=0.20 to the nlp-slm and nlp-full extras in pyproject.toml.

--nlp-backend flag not wired up: This is a footgun for anyone who reads the help text and tries to override the backend per-invocation. The wiring looks straightforward given NLPConfig.resolve(cli_backend=...) already exists — just needs NLPConfig.resolve(cli_backend=args.nlp_backend) at each call site in main().

[tmuskal]

Thanks @bensig and @web3guru888 for the review! Both issues are now fixed:

1. huggingface_hub missing from optional deps — Added huggingface_hub>=0.20 to both nlp-full and nlp-slm extras in pyproject.toml. Users installing mempalace[nlp-slm] will now get the dependency automatically instead of hitting a silent ImportError during model download.

2. --nlp-backend CLI flag silently ignored — Wired args.nlp_backend to set MEMPALACE_NLP_BACKEND env var right after arg parsing, before command dispatch. NLPConfig.resolve() now picks up the CLI flag everywhere.

Commit: 9205d8b

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[web3guru888]

Both fixes look correct from here.

huggingface_hub in both nlp-full and nlp-slm — right call to add it to both extras. Users who install the lighter nlp-slm tier are exactly the ones most likely to hit the silent failure, so explicit coverage there matters.

--nlp-backend env var injection — wiring it right after arg parsing (before command dispatch) is the correct placement. Setting MEMPALACE_NLP_BACKEND means NLPConfig.resolve() picks it up regardless of how deep the call stack goes, without threading args into every submodule. Clean.

LGTM on both. Would be good to land this — the graduated backend levels are genuinely useful for environments where you can't control which ML libraries are installed.

[web3guru888]

Review: Add NLP Capabilities with Local Models

This is a big PR — 6,385 lines added across 38 files — but the architecture is sound and the implementation is much more careful than most "add AI to everything" PRs I've reviewed.

What's done well

Opt-in by default is the right call. Everything is off unless an env var is set. The NLPConfig feature gate system is clean: per-feature overrides beat backend-level beats config beats default. Users who don't install any extras won't see any behaviour change.

Lazy loading everywhere. Every provider wraps its import in _ensure_loaded() with a double-checked lock. This means import time stays clean and the penalty is paid only when the feature is actually used.

Graceful degradation. Every call site wraps NLP use in try/except Exception: pass and falls back to the regex pipeline. We use a similar pattern in our integration and it's proven robust through 540+ discovery cycles.

Provider protocol abstraction. The NLPProvider base Protocol lets providers opt in to only the capabilities they support. The registry's fallback chain (NLP provider → legacy provider → regex inline) is explicit and easy to reason about.

Issues found

nlp-coref version conflict is buried. The pyproject.toml note says nlp-coref requires spacy<3.6 while nlp/nlp-full require >=3.7. This isn't just a comment issue — a user who runs pip install -e ".[nlp,nlp-coref]" will get a resolver error or silent downgrade. Consider adding a pip install guard in the CLI or at least a mempalace nlp install --coref warning.

NLPConfig.resolve() is called on every extraction. In entity_detector.py, general_extractor.py, and dialect.py, each call creates a fresh NLPConfig.resolve() and may call get_registry(). For high-throughput mining (we regularly mine 10K+ files), this cold-path overhead adds up. Consider caching the config as a module-level singleton, invalidated only when env vars change.

_extract_triples_if_enabled in miner.py creates a new KnowledgeGraph per file. Each KnowledgeGraph(db_path=...) call opens a new SQLite connection. With our workloads this causes connection churn. The process_file signature could accept an optional kg parameter so the caller can pass a long-lived instance.

general_extractor.py single-label override. When NLP classify returns a result, scores = {classification["label"]: 5} completely bypasses the multi-marker regex pipeline. If the NLP model misclassifies, there's no blending with the lower-confidence regex signals. A weighted blend (e.g., NLP score × 3 + regex scores) would be more robust.

pySBD language="en" hardcoded in pysbd_provider.py. If the corpus is Brazilian Portuguese (new in PR #156), sentence boundaries will be worse than the regex fallback for certain constructions. Consider accepting a language param or reading from the NLP config.

test_nlp_integration.py is continue-on-error: true in CI. That's pragmatic for a first pass but means NLP regressions won't block merges. A separate required CI job for at least the pySBD + spaCy tiers would be worthwhile.

Suggestions

1. Cache NLPConfig.resolve() at module level (lazily, re-read if MEMPALACE_NLP_* env vars change)
2. Pass a kg instance into _extract_triples_if_enabled instead of creating one per file
3. Add a --coref flag warning in mempalace nlp install about the spaCy version conflict
4. Consider NLP+regex blending in general_extractor instead of NLP-wins-completely
5. Add pySBD language param to config so pt-BR content gets appropriate sentence splitting

Overall

This is excellent foundational work. The architecture is extensible, the defaults are safe, and the benchmarks give concrete before/after numbers. The issues above are real but none are blockers — the biggest one (config caching) is a performance concern rather than a correctness bug. Would love to see this land and iterate on the performance story in a follow-up.

APPROVED with the suggestions above as non-blocking follow-ups.

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