Nihaisha RAG Prototype

Nihaisha RAG Prototype

Answer from the legacy bundled PDF corpus. Authoritative runtime evidence is a retrieved original PDF paragraph with portable filename, page, and excerpt; retrieval aids are navigation only. Run in this Skill directory; DB: data/pdf_rag_bge_m3/rag.sqlite.

First use

git lfs pull
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[runtime]"
python3 -m nihaisha_kg doctor

Retrieve only after status: ok. The 159,286-unit dense DB requires FAISS for vector/hybrid; no full-scan fallback exists. text/knowledge/graph need no key. graph uses only structurally accepted or reviewed relations and returns their original paragraphs. vector/hybrid need FAISS plus query embeddings: recommended SiliconFlow BAAI/bge-m3, or .[local] with --embedding local-bge-m3. The nearest .env is parsed without shell execution; exported values win.

Commands

python3 -m nihaisha_kg search "原词" --mode text --limit 5
python3 -m nihaisha_kg search "一钱是多少克" --mode knowledge --limit 5
python3 -m nihaisha_kg search "麻黄汤对应什么方证" --mode graph --limit 5
python3 -m nihaisha_kg search "桂枝汤和麻黄汤的方证如何鉴别？" --mode hybrid --limit 8
python3 -m nihaisha_kg answer "木香饼热熨法来自哪一本书哪一段？" --mode hybrid --limit 8
python3 -m nihaisha_kg evaluate --cases evals/golden_v1.jsonl --mode hybrid --limit 10

Hybrid fuses text, vector, legacy knowledge, and a 0.35-weight graph navigation channel. Graph integration improves MRR, nDCG@10, and context precision@10 on the current seven-case seed without changing recall or forbidden hits, but hybrid still does not beat text on this tiny seed. Measure mode choice; seven cases are regression seeds, not accuracy proof.

CLI reranking is auto: SiliconFlow only when a key exists; --reranker none disables it. For questionable citations rerun search/answer with --json --trace, inspect selected IDs, channels/ranks and degradation, then verify PDF/page/quote. Plain output never displays trace.

Trace allowlists diagnostics; it does not copy provider credentials, headers, env dumps, vectors, or full evidence, and recognized credential/error patterns are sanitized. normalized_query retains query text: never query with keys, passwords, patient/private text, or secrets. Trace is neither evidence nor a confidentiality boundary. Public source_path must be portable (pdfs/<basename>), never an absolute machine path.

Evidence policy

1. Cite only retrieved PDF original paragraphs. If evidence is insufficient, say so; never fill from memory. The current SQLite stores document source_layer, paragraph-level evidence, candidate entities and candidate relations.
2. Ten course documents are classified course_primary; 黄帝内经原文和翻译.pdf is classified classic_primary as a candidate document. This classification is structural provenance, not edition verification or expert review.
3. derived triples, accepted graph relations, guide nodes, expansions, flows, and follow-ups only navigate to originals; never cite them standalone or assert their subject/object as facts or formula names. auto_accepted means deterministic extraction checks passed, not expert validation. Follow-ups must arise from the query and retrieved evidence, not a fixed checklist.
4. This runtime has no external retrieval. It may retrieve classical material already present in the legacy bundle, but a separately versioned and verified authoritative classic layer is not implemented. Never reconstruct an absent ancient quotation from model memory.
5. The current classic_primary candidate is not yet independently version-verified, and reference_secondary remains future work. User-supplied or explicitly authorized outside research must be labeled “external / not retrieved from bundled DB,” verified and cited separately, without bundled authority.
6. Cite lecture claims and classical originals separately; never turn a paraphrase into an ancient quote.

Use the separate nihaisha-rag-builder repository/Skill (often ../nihaisha-rag-builder, or configured path) for incremental PDFs. Stage, audit, validate, and atomically publish the full asset set; runtime must not mutate production DB.

Safety

Frame output as course study/source lookup. Never provide individualized diagnosis, prescription, dosage decision, purchasing advice, acupuncture/external-treatment instruction, or self-treatment plan.

涉及剂量、方药或处方线索时必须谨慎：不同人的体质不同，病情阶段、兼证、年龄、基础病和用药史都不同；现代药材来源、炮制、浓度和药效也和以前差很多。建议去线下正规中医渠道面诊辨证，不要私自购药有风险。