File size: 5,127 Bytes
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<style>
.block-container {
max-width: 1080px;
padding-top: 2rem;
padding-bottom: 4rem;
}
.doc-hero {
padding: 1.45rem 1.55rem;
border: 1px solid rgba(120, 120, 120, 0.25);
border-radius: 18px;
margin-bottom: 1rem;
}
.doc-eyebrow {
font-size: 0.78rem;
font-weight: 700;
letter-spacing: 0.08em;
text-transform: uppercase;
opacity: 0.72;
margin-bottom: 0.35rem;
}
.doc-hero h1 {
font-size: 2.15rem;
line-height: 1.15;
margin: 0 0 0.65rem 0;
}
.doc-hero p {
font-size: 1.05rem;
line-height: 1.6;
margin: 0;
opacity: 0.9;
}
.principle-card, .boundary-card {
padding: 1rem 1.1rem;
border: 1px solid rgba(120, 120, 120, 0.22);
border-radius: 14px;
min-height: 100%;
}
.principle-card strong, .boundary-card strong {
display: block;
margin-bottom: 0.3rem;
}
.pipeline-step {
padding: 0.85rem 1rem;
margin: 0.45rem 0;
border-left: 4px solid rgba(100, 120, 220, 0.75);
background: rgba(120, 120, 120, 0.06);
border-radius: 0 12px 12px 0;
}
.summary-callout {
padding: 1rem 1.1rem;
border: 1px solid rgba(120, 120, 120, 0.22);
border-radius: 14px;
margin-bottom: 0.75rem;
}
.lineage-note {
padding: 0.9rem 1rem;
border-radius: 12px;
background: rgba(80, 130, 210, 0.08);
border: 1px solid rgba(80, 130, 210, 0.22);
}
</style>
"""
HERO_HTML = """
<div class="doc-hero">
<div class="doc-eyebrow">Agent 5 · Document Intelligence</div>
<h1>What did this document say — and can every extracted item be traced back to the source?</h1>
<p>
Long documents become useful only when people can turn them into structured work products without losing
the connection to the original text. This agent parses a document, creates page-aware chunks, extracts
lightweight structured intelligence, preserves source lineage, supports search, and exports an auditable record.
</p>
</div>
"""
WHY_IT_MATTERS_HTML = """
<div class="principle-card">
<strong>The business problem</strong>
Analysts often re-read documents, copy facts into spreadsheets, and lose track of which page supported a conclusion.
</div>
"""
CONTROL_BOUNDARY_HTML = """
<div class="boundary-card">
<strong>What this agent does</strong>
Converts selectable PDF/TXT text into structured, searchable, source-linked outputs using transparent local rules.
</div>
<div style="height: 0.65rem"></div>
<div class="boundary-card">
<strong>What this agent does not claim</strong>
It does not prove that the document is true, perform OCR on scanned pages, or replace expert review of consequential documents.
</div>
"""
PIPELINE_HTML = """
<div class="pipeline-step"><strong>1 · Parse</strong><br>Read selectable PDF or plain-text content and record extraction notes.</div>
<div class="pipeline-step"><strong>2 · Structure</strong><br>Create validated metadata, page-aware chunks, and lightweight summary fields.</div>
<div class="pipeline-step"><strong>3 · Preserve lineage</strong><br>Attach extracted facts, dates, entities, risks, and actions to their source page and chunk.</div>
<div class="pipeline-step"><strong>4 · Search</strong><br>Search the original document chunks rather than searching only a rewritten summary.</div>
<div class="pipeline-step"><strong>5 · Export</strong><br>Produce structured JSON/CSV records that preserve the extraction and source references.</div>
"""
LIMITS_MARKDOWN = """
### Interpretation boundary
The structured findings are **extractions from the supplied document**, not independent verification that the document is correct.
- Summary generation uses transparent deterministic rules, not a hosted LLM.
- Source lineage means an extracted item can be traced back to the page/chunk where it appeared.
- Source lineage does **not** mean the source statement is true.
- Scanned PDFs without selectable text may require OCR and are intentionally reported as a limitation rather than silently guessed.
- Tables, complex layouts, nuanced entity resolution, and multi-document comparison remain production-upgrade opportunities.
- High-stakes legal, medical, financial, compliance, or client documents should receive expert review.
"""
ARCHITECTURE_MARKDOWN = """
### Engineering architecture
```text
PDF / TXT
↓
Deterministic text extraction
↓
Page-aware normalized text
↓
Overlapping document chunks
↓
Transparent structured extraction
├─ executive summary
├─ key facts
├─ entities
├─ dates
├─ risks
└─ action items
↓
Source-lineage mapping
├─ page number
└─ chunk ID
↓
Validated Pydantic report
├─ search
├─ JSON export
└─ CSV export
```
**Control principle:** parsing and extraction are deterministic application logic. The system does not ask an LLM to invent structure or provenance.
"""
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