pyr0ball
f941ebdeeb
Extends Pagepiper's document shelving pipeline (renamed from "ingest" — see below) to cover the formats most likely to appear in a real-world engineering document corpus, prompted by scoping a STERIS licensing pitch that needs DOCX/ODT coverage. - Rename the ingest pipeline to "shelve" throughout (scripts/, app/api, tests, docs, frontend). "Glean" (Turnstone's term) was considered and rejected — that's a harvest metaphor for log/knowledge extraction, not a fit for documents entering a library. Documented as a general CF naming principle in the org-level CLAUDE.md. - Wire DOCX into the upload/scan UI, README, and docs — the extraction logic (heading-based chunking, table serialization) already existed but wasn't exposed to users or covered by tests. - Add ODT support via odfpy, mirroring DOCX's chunking strategy. - Add Apple Pages support via headless LibreOffice conversion to ODT. No maintained Python library parses the IWA format directly; libreoffice bundles libetonyek, the only real open-source Pages parser. Adds libreoffice-writer to the Docker image (~300-400MB) for this. - 24 new/updated tests across shelve_docx, shelve_odt, and shelve_pages; full suite (72 tests) passing. Known gaps not addressed here: no Windchill/DocPortal connector exists yet (metadata-only PowerShell recon only), Excel/.xlsx is unsupported, and circuitforge_core.tasks.dispatch_task does not currently exist in circuitforge-core — cf-orch dispatch is dead code, always falling through to local BackgroundTasks. See circuitforge-plans/pagepiper/superpowers/plans/2026-07-10-steris-licensing-pitch.md for the full writeup.
60 lines
1.9 KiB
Markdown
60 lines
1.9 KiB
Markdown
# Architecture
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## Overview
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```
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Browser (Vue 3 SPA)
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nginx (static + /api proxy)
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FastAPI backend
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├── BM25Index (in-process, rank-bm25)
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├── Retriever (BM25 + optional vector)
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├── Synthesizer (LLMRouter → Ollama)
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└── SQLite (page_chunks + metadata)
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+
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sqlite-vec (vectors)
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```
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## Shelve pipeline
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```
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PDF / EPUB / DOCX / ODT / Pages file
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│
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├─ PDFExtractor (pdfminer + OCR fallback) ← circuitforge_core
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│ or
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└─ EPUBExtractor (BeautifulSoup + heading chunking)
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│
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text_clean.py (strip artifacts)
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│
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INSERT INTO page_chunks
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│
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Ollama embed (batches of 64) ← BYOK gate
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│
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sqlite-vec upsert
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```
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## Retrieval
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Hybrid search merges BM25 and semantic results with a 50/50 score blend:
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1. BM25 queries the in-process index (no round-trip to DB)
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2. Semantic query embeds the user query via Ollama, fetches `top_k * 20` nearest vectors, filters by `doc_id` in Python
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3. Hits are merged: BM25 scores and vector scores combined; BM25 hits take priority
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4. Top `k` results are ranked, then adjacent pages (page ± 1) are fetched to restore context for mid-sentence chunk boundaries
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## Storage
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| File | Format | Contents |
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|------|--------|---------|
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| `pagepiper.db` | SQLite | `documents`, `page_chunks`, `chat_feedback` |
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| `pagepiper_vecs.db` | sqlite-vec | `page_vecs` virtual table + `page_vecs_meta` |
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The vector database stores one row per page chunk. If the embedding model changes, Pagepiper detects the dimension mismatch at startup (reads `CREATE VIRTUAL TABLE` DDL from `sqlite_master`), deletes the vec DB, and queues a background re-embed.
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## Licensing boundary
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| Component | License |
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|-----------|---------|
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| BM25 search, shelve pipeline, library API | MIT |
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| Hybrid vector search, RAG chat, embedding | BSL 1.1 (BYOK unlocked on Free tier) |
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