The article outlines a multi-gate architecture for a high-accuracy semantic evidence and RAG system for financial documents, emphasizing traceability, reconciliation, and hybrid retrieval, and seeks feedback on its design.
I'm designing a local, single-user semantic evidence/knowledge system for a growing library of financial and economic PDFs and structured Excel files. The system has two objectives: Evidence integrity: important numbers and claims must be traceable to the original source, and unsupported information should not be returned confidently. Semantic retrieval: users should be able to ask questions in natural language and retrieve relevant evidence even when the source uses different terminology. For example, a document says: "Net interest income increased 8% due to higher average loan balances." I should be able to ask: "What drove NII growth?" and retrieve that evidence. Architecture I'm considering PDF / EXCEL │ ┌────────────┴────────────┐ │ │ ▼ ▼ GATE 1 — PRIMARY READ GATE 1 — EXCEL READ ───────────────────── ─────────────────── PDF: PyMuPDF + openpyxl pdfplumber Extract: • text • numbers • coordinates • text / notes • tables • formulas • images / vectors • dates • sheets / cells │ │ ▼ ▼ GATE 2 — SECOND READ STRUCTURE CHECK ───────────────────── ───────────────── Docling / Camelot formulas / totals / Tesseract if needed row-column consistency │ │ └────────────┬────────────┘ ▼ GATE 3 — EXTRACTION RECONCILIATION ───────────────────── Compare independent readings of the same source: • values • text • coordinates • table structure • arithmetic │ ┌─────────┴─────────┐ ▼ ▼ AGREE CONFLICT │ │ ▼ ▼ ACCEPT QUARANTINE │ Human review if material ▼ GATE 4 — SEMANTIC INTERPRETATION ───────────────────── GPT-5.6 Luna via OpenRouter • concept classification • terminology mapping • entity / period identification • narrative understanding Never invent or alter source values. │ ▼ GATE 5 — CROSS-SOURCE RECONCILIATION ───────────────────── Compare PDF ↔ Excel ↔ other documents • same metric? • same period? • same definition? • restatement? • genuine conflict? │ ▼ GATE 6 — KNOWLEDGE / RETRIEVAL ───────────────────── Numeric → DuckDB Lexical → BM25 Semantic → vector search Return: evidence + provenance + conflicts + gaps │ ▼ LAYER 2 Reasoning / synthesis Some principles I'm trying to enforce Extract first, interpret second. Don't trust a single PDF extraction engine; reconcile independent readings. Don't rasterize every chart by default as many PDFs contain recoverable text/vector data. OCR is an escalation path for scanned/unreadable pages, not the default. Vision/LLM interpretation of a chart is a last resort; visually estimated numbers are not automatically trusted. Excel is a full evidence source: financial numbers and analyst notes/text are ingested with sheet/cell provenance. Financial numbers are stored structurally rather than relying on vector similarity. Narrative evidence uses both lexical and semantic retrieval. Conflicting sources are preserved rather than silently resolved. This system establishes and retrieves evidence I'd really appreciate feedback on: 1. Is this multi-gate architecture sensible, or am I over-engineering the ingestion process? 2. Is independent extraction + reconciliation a good practical control for silent PDF/OCR errors? 3. Would you use PyMuPDF + pdfplumber + Docling, or simplify the PDF extraction stack? 4. Is hybrid retrieval, structured numeric + BM25 + vector/semantic search, the right approach for financial/economic documents? 5. What important failure modes am I missing, particularly around financial tables, charts, OCR, restatements, conflicting sources, and Excel-based analyst notes? 'm looking for architectural criticism before going too far down the implementation path. Thanks, u/terrible_Put8617 for some early guidance.
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