corpus-core 0.1.0

Shared infrastructure for corpus indexing + MCP search (used by arxiv-radar-mcp + lab-corpus-mcp).

corpus-core

Shared infrastructure for corpus indexing + MCP search. Powers both arxiv-radar-mcp (the public-data arXiv topical radar) and lab-corpus-mcp (the private multi-source PDF / video / lab-notes server).

What's inside:

Module	Role
embeddings.py	Encoder — lazy SentenceTransformer wrapper with model-aware query/passage prefixes, bf16 on CUDA, matryoshka truncation. Encoder.unload() drops the in-process model + frees CUDA VRAM (idempotent; next encode lazily re-loads). EmbeddingIndex — mmap'd float32 matrix + row_for mapping + metadata, atomic save/load.
chunker.py	chunk_markdown(text, max_tokens) → list[Chunk]. Section-aware split + paragraph overlap; rough but fast token estimator.
corpus_index.py	Chunk-level corpus search. reindex(parse_dir, encoder, *, incremental) — incremental encode + atomic swap. search_paper_text / search_paper_semantic / similar_to_paper. is_junk_section filter.
search.py	Abstract-level search primitives over EmbeddingIndex: search_text / search_semantic / similar_to. Paper-shaped records via Protocol — no host-project dep.
jobs.py	JobRegistry — ThreadPoolExecutor + persistent jobs/<id>.json. Disk-truth fallback in get() so a stuck-running cell doesn't lie about completed jobs.
proxy.py	Local stdio↔remote-HTTP bridge. run_proxy(target, port, ssh_binary) opens an SSH tunnel and forwards MCP traffic; _bridge_loop reconnects on backend disconnect.
reranker.py	Reranker — lazy CrossEncoder wrapper for hybrid-search re-scoring. Reranker.unload() mirrors Encoder.unload() for symmetric VRAM control after a batch. Local RerankerConfig dataclass.
mcp_scaffold.py	Generic MCP server scaffold: make_method_dispatcher(handler, allowlist) → Dispatcher, build_mcp_app(server_name, tool_specs, dispatcher) → mcp.server.Server, serve_stdio / serve_streamable_http transports with optional BackgroundTaskFactory list.
http_fetch.py	fetch_url(url, dest_path) → FetchResult — throttled GET with 429/503 retry + Retry-After + atomic file write. fetch_arxiv_pdf(arxiv_id, dest_dir) convenience wrapper. get_arxiv_throttle() singleton — process-wide 1 req / 3 sec budget shared by arxiv-radar's HTML/LaTeX fetcher and lab-corpus's ingest_url / ingest_arxiv_pdf, so the combined image never double-spams arxiv.org.

Install

pip install corpus-core            # once published to PyPI
# or, during dev:
pip install -e ../corpus-core

Quick start

from corpus_core import (
    Encoder, EmbeddingIndex,
    Chunk, chunk_markdown,
    search_text, search_semantic, similar_to,
    search_paper_text, search_paper_semantic, similar_to_paper,
    load_chunk_texts, reindex, is_junk_section,
    JobRegistry, JobHandle, JobError, Job,
    make_method_dispatcher, build_mcp_app,
    serve_stdio, serve_streamable_http,
    Dispatcher, BackgroundTaskFactory,
    fetch_url, fetch_arxiv_pdf,
    Throttle, get_arxiv_throttle,
    request_with_retry, FetchResult,
    ARXIV_RATE_LIMIT_S, DEFAULT_USER_AGENT,
)

# Submodule access also fine:
from corpus_core.embeddings import Encoder
from corpus_core.proxy import run_proxy
from corpus_core.reranker import Reranker, RerankerConfig
from corpus_core.http_fetch import fetch_url, get_arxiv_throttle

Invariants downstream packages must honour

• Embedding cache layout:
  • <cache_dir>/embeddings.npy — float32, L2-normalized, shape (N, D).
  • <cache_dir>/index.json — {model, dims, n, row_for, ...metadata}.
  • Both written atomically (*.tmp → os.replace).
• Job persistence schema: <cache_dir>/jobs/<job_id>.json with fields {job_id, kind, state, progress, n_total, n_done, started_at, finished_at, result, error, args}. State ∈ {pending, running, done, failed, orphaned}.
• Chunk metadata: each chunk in EmbeddingIndex.metadata["chunks"] has {arxiv_id, section, chunk_idx, n_chars, n_tokens_est}. The arxiv_id field is the corpus-wide paper id — DOI / PMID / sha256 / any string the host project chooses.
• Encoder config duck-type: Encoder.__init__(config) reads config.embeddings.{model, batch_size, target_dim, cache_dir}. Pass any object with that shape. See corpus_core.embeddings.Config Protocol for the formal type.
• HTTP fetch invariants (http_fetch.py):
  • fetch_url writes atomically (<dest>.tmp → os.replace); on any failure (transport error, non-2xx, empty body) dest_path is not created or overwritten.
  • Throttle is one instance per source domain; all callers that share an instance share the budget. Use get_arxiv_throttle() for every arxiv.org GET so the combined image enforces 1 req / 3 sec across both downstream servers.
  • request_with_retry retries only on 429/503; other status codes fall through after the first attempt. Honours Retry-After if present, else exponential backoff 3→6→12 sec.

Used by

• arxiv-radar-mcp — arxiv-only topical radar over the daily-arxiv-* fork family.
• lab-corpus-mcp — private PDF / DOCX / PPTX / image corpus parsed via MinerU; can also run combined with arxiv-radar-mcp on one Qwen instance to fit a 12 GB GPU.

Status

v0.2.0, in production as of 2026-05-24. Both downstream projects (arxiv-radar-mcp and lab-corpus-mcp) install corpus-core editable from the sibling repo. The combined exopoiesis/lab-corpus-gpu image on gomer holds:

• 34,627 abstract embeddings (Qwen3-Embedding-4B native 2560 dims, L2-normalized) over 5 arxiv-radar fork sources.
• 466 fulltext chunks across 51 fetched arxiv papers + 54 chunks across 2 MinerU-parsed lab PDFs.
• All in one corpus_core.embeddings.EmbeddingIndex cache layout, encoded by a single shared Encoder instance.

Build-time audit_image.py in both downstream projects checks the no-duplicate-distribution invariant — pip never ends up with two copies of any package, including torch.

HTTP fetch primitives added (2026-05-13) to close arxiv-radar-mcp's U14: http_fetch.py extracts the throttled GET + 429/503-retry pattern that previously lived inside arxiv-radar's fulltext.py. Both servers now share the singleton arxiv throttle — arxiv-radar uses it for HTML/LaTeX, lab-corpus uses it for ingest_url / ingest_arxiv_pdf PDF downloads. Same module-global lock across the whole combined image.

VRAM unload added (2026-05-24, v0.2.0). Encoder.unload() and Reranker.unload() drop the in-process model and free CUDA VRAM via torch.cuda.empty_cache() + gc.collect(). Both are idempotent and guarded by the existing model-load lock so concurrent encodes either complete first or re-load on next call. Downstream projects call Encoder.unload() after heavy one-shot work (reindex, refresh, bulk ingest) so a shared GPU host can use the freed VRAM for unrelated compute. 119 corpus-core tests green.

Phase 3 extraction is complete; PyPI publication of corpus-core deferred until the API stabilises through real-world ingest of more than the current 53 papers.

Tests

pytest -q from the repo root. The standalone test suite covers chunker, jobs, mcp_scaffold, proxy invocation, reranker config + lazy load, and basic embedding-index roundtrip with a deterministic stub encoder. Heavier integration testing (host-project Configs, real Qwen weights, real MCP sessions) lives in the arxiv-radar-mcp and lab-corpus-mcp test suites.

License

MIT.