Document xrag artifacts v1

README.md

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+---
+language:
+- en
+task_categories:
+- text-retrieval
+tags:
+- rag
+- minirag
+- faiss
+- diskann
+- reproducibility
+size_categories:
+- 100K<n<1M
+---
+
+# xrag prebuilt artifacts
+
+Eight audited artifact bundles for LihuaWorld, ConcurrentQA, and
+ConcurrentQA-Extended. Download tooling and documentation:
+[lar0129/xrag-artifacts](https://github.com/lar0129/xrag-artifacts).
+
+`manifest.json` pins an immutable commit containing the archives under
+`releases/v1/` and records per-archive/per-file SHA-256 checksums, install paths,
+corpus fingerprints, validation results, and known provenance limits.
+
+| Dataset | Bundles |
+|---|---|
+| LihuaWorld | 425-document TXT corpus + QA, complete historical MiniRAG graph, Flat |
+| ConcurrentQA | Complete 2,743-document MiniRAG graph, Flat, matching rebuilt DiskANN |
+| ConcurrentQA-Extended | Flat (991,397 vectors, 991,376 distinct texts), DiskANN (991,376 passages) |
+
+No standalone ConcurrentQA/Extended corpus dumps, Extended graph, or LihuaWorld
+DiskANN are distributed. Retrieval indexes contain the text needed to return
+passages; upstream data terms still apply. Model weights are not included.
+Compact DiskANN requires a matching query-time embedding service.
+
+See [REPRODUCING.md](REPRODUCING.md) for source revisions, reconstruction rules,
+version selection, data terms, and the difference between structural validation
+and full end-to-end reproduction.

REPRODUCING.md

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+# Reproduction and provenance
+
+## Release v1 decisions
+
+Completeness is measured against the reference corpus, not directory size or date.
+
+- **LihuaWorld graph:** the historical `minirag_v002` worktree's
+  `LiHuaWorld-MiniRAG-Graph-nothinking-qwen3.5-4B` covers exactly all 425 week
+  documents. April 18/19 candidates cover only 368/407. Older 442-document graphs
+  contain 17 additional documents. The selected graph has 425 chunk vectors,
+  1,212 graph nodes and 507 edges. Its original build commit could not be recovered
+  from the relocated worktree. Adjacent historical config is supporting evidence,
+  not a guarantee of every historical runtime option.
+- **ConcurrentQA graph:** `Conc-MiniRAG-Graph-nothinking-qwen3.5-4B-0430-keywordFIX`
+  covers all 2,743 documents. Other existing candidates cover 2,700, 2,708, or 2,740.
+- **ConcurrentQA DiskANN:** rebuilt from the 2,743 documents and existing 1,024-D
+  embeddings in SQLite, without calling an embedding model. The old 18,551-document
+  backup exactly matches the HF train split, shares only 1,288 texts with this test
+  corpus, and is excluded.
+- **Extended:** Flat contains 991,397 vectors, with exactly 21 duplicate-text
+  occurrences. Its 991,376 distinct texts exactly match SQLite and DiskANN.
+  The original Flat vectors, IDs and duplicates are preserved. Scores between Flat
+  and DiskANN are not claimed identical: duplicate multiplicity and ANN behavior differ.
+
+All published FAISS indexes were loaded with their document stores and passed
+three self-query probes. GraphML, all vector matrix shapes/finite values, document
+coverage, chunk IDs and graph source references were checked. All DiskANN passage
+IDs and byte offsets, corpus coverage, required files, and PQ shapes were checked.
+Both DiskANN indexes also loaded successfully through the native backend.
+These checks do not establish end-to-end answer accuracy. Query-time embedding and
+generation still require model services.
+
+## Corpus reconstruction
+
+### LihuaWorld
+
+The corpus archive installs `data/LiHuaWorld/data/week*/*.txt` and
+`qa/query_set.{json,csv}`. All 425 week files are byte-identical between the inspected
+old and new checkouts. `bakery_deliver` is excluded, matching the integrator.
+MiniRAG strips surrounding whitespace; Flat preserves the original text, so their
+text fingerprints differ even though document coverage matches.
+The supplied QA set has 637 entries. Existing placeholder evidence such as `N/A`
+and `What` does not resolve to TXT files; the integrator's warnings are preserved.
+
+### ConcurrentQA
+
+- HF dataset: `stanfordnlp/concurrentqa`.
+- Cached source revision: `2cc9db602b10a561026c905403a641422f066369`.
+- Split: `test`, 1,600 questions, full range.
+- Traverse each question's `sp` in dataset order. For each nonempty `title` and
+  `sents`, form `title + "\n" + "\n".join(sents)`. Keep the first text for each title.
+  Assign document IDs in insertion order. This gives 2,743 documents.
+- The original integrator downloads via HF and does not save a standalone corpus
+  file. Its QA pairs refer to these document IDs; do not reorder documents when
+  reconstructing them.
+
+Example source download:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "stanfordnlp/concurrentqa",
+    revision="2cc9db602b10a561026c905403a641422f066369",
+    split="test",
+)
+```
+
+The online runner currently uses xrag's integrator and verifies the resulting
+evidence-text fingerprint. If upstream changes, use the source revision above;
+do not silently accept another corpus.
+
+### ConcurrentQA-Extended
+
+This is a constructed dataset, not a single downloadable HF corpus:
+
+1. Start with the ConcurrentQA test documents above, preserving their IDs.
+2. Append parsed CMU Enron `enron_mail_20150507` emails from
+   `https://www.cs.cmu.edu/~enron/enron_mail_20150507.tar.gz`.
+   The repository constructs text from Subject, Sender, Recipients, maildir path,
+   a separator and the decoded body. Empty bodies and parse failures are skipped.
+3. Stream `wikimedia/wikipedia`, config `20231101.en`, split `train`, shuffle
+   seed 42, buffer size 10,000, and take 500,000 rows. Skip empty texts and repeated
+   titles; rename titles colliding with base evidence using `__ext`.
+   The locally cached Wikipedia revision is
+   `b04c8d1ceb2f5cd4588862100d08de323dccfbaa`; the historical integrator did not pin
+   a revision, so this is observed cache provenance, not proof of its exact input.
+4. Keep whole documents (`identical_chunking`) and drop texts longer than 20,000
+   characters. SQLite deduplicates by text hash. The stored release corpus has
+   991,376 distinct texts.
+
+Maildir traversal used `os.walk` without sorting. Exact historical ID order cannot
+be guaranteed from source downloads alone, even with the same sampling seed.
+The released indexes retain their own mappings and texts. For byte-identical
+reproduction use the published artifacts; for source rebuilds compare corpus
+fingerprints and preserve the base QA document IDs.
+
+## Index and runtime configuration
+
+- Flat: FAISS `IndexFlatIP`, `normalize_l2: true`, 1,024 dimensions.
+- Flat/DiskANN embeddings: `Qwen/Qwen3-Embedding-0.6B`, truncation 2,048 tokens,
+  local OpenAI-compatible provider. The release manifest records the complete
+  xrag embedding configuration and the observed dependency versions.
+- MiniRAG: Qwen3-Embedding-0.6B, 1,024 dimensions; Qwen3.5-4B extraction. LihuaWorld
+  predates later keyword-extraction fixes. Its larger coverage does not imply the
+  same extraction behavior as the April 30 ConcurrentQA graph.
+- DiskANN: LEANN compact/recompute, inner product (`mips`), degree 32, build
+  complexity 64, 8 threads, disk PQ 128 bytes, search/build memory budgets 8/16 GiB.
+  The published `leann.index` is a prefix: the compact graph is stored in
+  `leann_disk_graph.index`, not a file named `leann.index`.
+- Runtime DiskANN config matching directory hash `0ac1b1dfe7b9d7ca`:
+  `num_nodes_to_cache: 1000`, `search_complexity: 16`, `beam_width: 4`,
+  `prune_ratio: 0.5`, `pruning_strategy: global`, `batch_recompute: true`,
+  `dedup_node_dis: true`, `recompute_neighbors: false`. Use the audited
+  `configs/pipelines/eta/qwen/agentic_rag_leann_diskann.yml`.
+- LEANN requires native DiskANN/partition support from xrag's pinned dependency
+  checkout. A plain upstream `diskannpy` installation is not sufficient.
+- Compact DiskANN recomputes vectors at query time; the matching embedding model
+  endpoint must be running. xrag refreshes the endpoint binding from your runtime
+  configuration. Published metadata only contains localhost and the `EMPTY` placeholder.
+
+The manifest's xrag/dependency commits identify the inspected code snapshot.
+They are not asserted to be the original build commits for historical artifacts.
+Model repository revisions and exact historical server settings were not recorded;
+the release therefore preserves artifacts rather than claiming bit-for-bit rebuilds.
+
+## Data terms
+
+Index packages include document text in FAISS pickle stores, MiniRAG JSON, and
+LEANN passages. This release does not assign a new blanket license to those texts.
+Consult the original dataset cards and sources for applicable terms and attribution:
+
+- https://huggingface.co/datasets/stanfordnlp/concurrentqa
+- https://huggingface.co/datasets/wikimedia/wikipedia
+- https://www.cs.cmu.edu/~enron/
+
+LihuaWorld is supplied from the xrag project's local corpus. Use the artifacts for
+the intended research reproduction and retain upstream attribution. Metadata,
+sample text, logs, and credentials beyond the required retrieval payload are not
+added to the release.