ac01fffd9a
Architectural snapshot of the lakehouse codebase at the point where the
full matrix-driven agent loop with Mem0 versioning + deletion was
validated end-to-end.
WHAT THIS REPO IS
A clean single-commit snapshot of the lakehouse code. Heavy test data
(.parquet datasets, vector indexes) excluded — see REPLICATION.md for
regen path. Full lakehouse history at git.agentview.dev/profit/lakehouse.
WHAT WAS PROVEN
- Vector retrieval across multi-corpora matrix (chicago_permits + entity
briefs + sec_tickers + distilled procedural + llm_team runs)
- Observer hand-review (cloud + heuristic fallback) gating each candidate
- Local-model agent loop (qwen3.5:latest) with tool use + scratchpad
- Playbook seal on success → next-iter retrieval surfaces it as preamble
- Mem0 versioning + deletion in pathway_memory:
* UPSERT: ADD on new workflow, UPDATE bumps replay_count on identical
* REVISE: chains versions, parent.superseded_at + superseded_by stamped
* RETIRE: marks specific trace retired with reason, excluded from retrieval
* HISTORY: walks chain root→tip, cycle-safe
KEY DIRECTORIES
- crates/vectord/src/pathway_memory.rs — Mem0 ops live here
- crates/vectord/src/playbook_memory.rs — original Mem0 reference
- tests/agent_test/ — local-model agent harness + PRD + session archives
- scripts/dump_raw_corpus.sh — MinIO bucket dump (raw test corpus)
- scripts/vectorize_raw_corpus.ts — corpus → vector indexes
- scripts/analyze_chicago_contracts.ts — real inference pipeline
- scripts/seal_agent_playbook.ts — Mem0 upsert from agent traces
Replication: see REPLICATION.md for Debian 13 clean install + cloud-only
adaptation (no local Ollama).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
415 lines
19 KiB
HTML
415 lines
19 KiB
HTML
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<html lang="en"><head>
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<meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
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<title>Lakehouse — Connect Your Data</title>
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<style>
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input[type=text]:focus{border-color:#388bfd}
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table.preview{width:100%;border-collapse:collapse;font-size:11px;margin-top:8px;font-family:ui-monospace,Menlo,monospace}
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table.preview th{text-align:left;padding:8px 10px;color:#8b949e;background:#0d1117;border-bottom:1px solid #21262d;font-weight:600;font-size:10px;text-transform:uppercase;letter-spacing:0.8px}
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.col{display:flex;align-items:center;gap:8px;padding:8px 12px;background:#161b22;border-radius:6px;font-size:12px}
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.col .name{color:#e6edf3;font-weight:500;min-width:140px}
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.pii.phone{background:#2d1b00;color:#d29922;border:1px solid #854d0e}
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.pii.ssn{background:#3a1a1a;color:#f85149;border:1px solid #7f1d1d}
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.pii.addr{background:#2d1b00;color:#d29922;border:1px solid #854d0e}
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.col .sample{color:#8b949e;font-size:11px;font-family:ui-monospace,Menlo,monospace;flex:1;min-width:0;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
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.note{color:#8b949e;font-size:12px;line-height:1.7;padding:10px 14px;border-left:2px solid #21262d;margin:10px 0}
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.note strong{color:#c9d1d9}
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.result{padding:16px;border-radius:10px;margin:10px 0}
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.result.ok{background:#0d2818;border:1px solid #2ea04360;color:#86efac}
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.result.err{background:#3a1a1a;border:1px solid #f8514960;color:#fca5a5}
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.result .big{font-size:16px;font-weight:700;margin-bottom:4px}
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.result .detail{font-size:12px}
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.spin{display:inline-block;width:14px;height:14px;border:2px solid #30363d;border-top-color:#58a6ff;border-radius:50%;animation:spin 0.7s linear infinite;vertical-align:middle;margin-right:6px}
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@keyframes spin{to{transform:rotate(360deg)}}
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.footer{border-top:1px solid #171d27;padding:20px;text-align:center;color:#3d444d;font-size:11px}
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@media(max-width:720px){
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.wrap{padding:20px 12px 40px}
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.step{padding-left:36px}
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.bar nav{display:none}
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}
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</style></head>
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<body>
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<div class="bar">
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<h1>Lakehouse — Connect Your Data</h1>
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<nav>
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<a href=".">Dashboard</a>
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<a href="console">Walkthrough</a>
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<a href="proof">Architecture</a>
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<a href="spec">Spec</a>
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<a href="onboard" class="active">Onboard</a>
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<a href="alerts">Alerts</a>
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<a href="workspaces">Workspaces</a>
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</nav>
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<div class="rt">30 minutes from CSV to live search</div>
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</div>
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<div class="wrap">
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<div style="margin-bottom:24px">
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<h1 style="color:#e6edf3;font-size:26px;font-weight:700;letter-spacing:-0.5px;margin-bottom:8px">Ingest any staffing CSV in three steps</h1>
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<p style="color:#8b949e;font-size:14px;line-height:1.6;max-width:720px">
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Upload your ATS export, your worker roster, or any CSV with a <code>name</code> column.
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The wizard auto-detects columns, flags PII, previews the first rows, then ingests
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as a queryable Parquet dataset. Everything that follows — hybrid search,
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playbook ranking, pattern discovery — works against your data automatically.
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</p>
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</div>
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<!-- Step 1 -->
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<div class="step active" data-n="1" id="step-1">
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<h2>Pick a file</h2>
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<div class="lede">Drag a CSV in, pick from disk, or use the sample roster to see the flow without any real data.</div>
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<label class="drop" id="drop">
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<input type="file" id="file" accept=".csv,text/csv">
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<div class="title">Drop a CSV here or click to choose</div>
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<div class="sub">Max 50MB · UTF-8 · comma-separated with a header row</div>
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</label>
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<div class="actions">
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<a class="btn ghost" href="samples/staffing_roster_sample.csv" download>Download sample roster (25 workers)</a>
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<button class="btn ghost" id="use-sample" type="button">Use the sample as my data</button>
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</div>
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</div>
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<!-- Step 2 -->
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<div class="step" data-n="2" id="step-2" style="display:none">
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<h2>Review what we detected</h2>
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<div class="lede">
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Columns auto-typed. PII columns flagged. First rows previewed. Nothing is written to the system yet — this is a read-only dry-run.
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</div>
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<div class="card">
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<div style="color:#545d68;font-size:11px;text-transform:uppercase;letter-spacing:1.2px;font-weight:600;margin-bottom:10px">Columns detected</div>
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<div class="col-list" id="col-list"></div>
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</div>
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<div class="card">
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<div style="color:#545d68;font-size:11px;text-transform:uppercase;letter-spacing:1.2px;font-weight:600;margin-bottom:10px">First rows</div>
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<div style="overflow-x:auto"><table class="preview" id="preview-table"></table></div>
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<div style="color:#545d68;font-size:11px;margin-top:8px" id="row-count"></div>
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</div>
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<div class="note">
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<strong>What happens next.</strong> On commit, the file is sent to <code>/ingest/file</code> — the same
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endpoint every other ingest path uses. The Rust gateway writes it to object storage as
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Parquet, computes a schema fingerprint, registers it in the catalog, and auto-detects PII
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columns server-side. Re-uploading the same file is a no-op (deduplicated by content hash).
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</div>
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</div>
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<!-- Step 3 -->
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<div class="step" data-n="3" id="step-3" style="display:none">
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<h2>Name it and commit</h2>
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<div class="lede">Give the dataset a queryable name. This becomes the table you can <code>SELECT * FROM</code> immediately after commit.</div>
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<div class="card">
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<label style="display:block;color:#8b949e;font-size:12px;margin-bottom:6px">Dataset name</label>
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<input type="text" id="dataset-name" placeholder="e.g. acme_staffing_roster" value="client_workers">
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<div style="color:#545d68;font-size:11px;margin-top:6px">
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Use lowercase + underscores. Once committed: queryable via <code>/query/sql</code>,
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searchable via <code>/search</code>, indexable via <code>/vectors/index</code>.
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</div>
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<div class="actions" style="margin-top:14px">
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<button class="btn green" id="commit-btn" type="button">Commit dataset</button>
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<button class="btn ghost" id="back-btn" type="button">Back</button>
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</div>
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</div>
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<div id="commit-result"></div>
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</div>
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<!-- Step 4: Post-commit next steps (hidden until success) -->
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<div class="step" data-n="4" id="step-4" style="display:none">
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<h2>Your dataset is live</h2>
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<div class="lede">From here, the rest of the system applies to your data with zero additional setup.</div>
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<div class="card">
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<div style="color:#e6edf3;font-size:14px;font-weight:600;margin-bottom:12px">What you can do right now</div>
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<div style="display:flex;flex-direction:column;gap:10px">
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<div style="padding:10px 14px;background:#161b22;border-radius:6px;font-size:12px"><strong style="color:#e6edf3">Query via SQL.</strong> <code>POST /query/sql</code> with <code>SELECT * FROM <span id="ds-name-1">your_dataset</span> LIMIT 10</code>.</div>
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<div style="padding:10px 14px;background:#161b22;border-radius:6px;font-size:12px"><strong style="color:#e6edf3">Build a vector index.</strong> <code>POST /vectors/index</code> with <code>{"dataset":"<span id="ds-name-2">your_dataset</span>","text_column":"skills"}</code>. Embeddings stream in; queryable progressively.</div>
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<div style="padding:10px 14px;background:#161b22;border-radius:6px;font-size:12px"><strong style="color:#e6edf3">Search via the dashboard.</strong> Open the <a href=".">dashboard</a> and the "Search all workers" box. Results will come from your data.</div>
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<div style="padding:10px 14px;background:#161b22;border-radius:6px;font-size:12px"><strong style="color:#e6edf3">Track with playbook memory.</strong> Every Call/SMS/No-show click on a worker card trains the system on your data.</div>
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</div>
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</div>
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</div>
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</div>
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<div class="footer">Lakehouse · onboarding wizard · <a href="spec">full architecture spec</a></div>
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<script>
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var P=location.pathname.indexOf('/lakehouse')>=0?'/lakehouse':'';
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var A=location.origin+P;
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var parsedFile=null; // { name, rows, columns, sample }
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var rawBlob=null;
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function el(tag,cls,text){
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var e=document.createElement(tag);
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if(cls) e.className=cls;
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if(text!==undefined && text!==null) e.textContent=String(text);
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return e;
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}
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function setStep(n){
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for(var i=1;i<=4;i++){
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var s=document.getElementById('step-'+i);
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if(!s) continue;
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s.classList.remove('active','done');
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if(i<n) s.classList.add('done');
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if(i===n) { s.classList.add('active'); s.style.display='block'; }
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else if(i>n) s.style.display='none';
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else s.style.display='block';
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}
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}
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// Basic CSV parser. Handles quoted fields with commas + escaped quotes.
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function parseCSV(text){
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var lines=[], i=0, cur='', row=[], inQ=false;
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while(i<text.length){
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var c=text[i];
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if(inQ){
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if(c==='"' && text[i+1]==='"'){ cur+='"'; i+=2; continue; }
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if(c==='"'){ inQ=false; i++; continue; }
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cur+=c; i++;
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} else {
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if(c==='"'){ inQ=true; i++; continue; }
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if(c===','){ row.push(cur); cur=''; i++; continue; }
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if(c==='\n'){ row.push(cur); lines.push(row); row=[]; cur=''; i++; continue; }
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if(c==='\r'){ i++; continue; }
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cur+=c; i++;
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}
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}
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if(cur.length>0||row.length>0){ row.push(cur); lines.push(row); }
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return lines;
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}
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// Type + PII inference from a sample of values for a column.
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function inferColumn(name, values){
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var nonEmpty=values.filter(function(v){return v!=null && v!==''});
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var n=nonEmpty.length;
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var lname=(name||'').toLowerCase();
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// PII signals by name and sample content
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var pii=null;
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if(/email|e-mail|mail_address/.test(lname)) pii='email';
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else if(/phone|mobile|cell|tel/.test(lname)) pii='phone';
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else if(/ssn|social|tax_id/.test(lname)) pii='ssn';
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else if(/address|street|zip|postal/.test(lname)) pii='addr';
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// Content-based PII scan on sample
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if(!pii){
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var emailRe=/^[^\s@]+@[^\s@]+\.[^\s@]+$/;
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var phoneRe=/^[\s\-\(\)\+]*\d[\s\-\(\)\+\d]{6,}$/;
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var emailHits=nonEmpty.filter(function(v){return emailRe.test(v)}).length;
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var phoneHits=nonEmpty.filter(function(v){return phoneRe.test(v)}).length;
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if(emailHits>n*0.5) pii='email';
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else if(phoneHits>n*0.5) pii='phone';
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}
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// Type inference
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var type='text';
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if(n>0){
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var intHits=nonEmpty.filter(function(v){return /^-?\d+$/.test(v)}).length;
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var numHits=nonEmpty.filter(function(v){return /^-?\d+(\.\d+)?$/.test(v)}).length;
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if(intHits===n) type='int';
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else if(numHits===n) type='decimal';
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else if(nonEmpty.every(function(v){return /^\d{4}-\d{2}-\d{2}/.test(v)})) type='date';
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}
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return { name: name, type: type, pii: pii, sample: nonEmpty.slice(0,3).join(' · ') || '(empty)' };
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}
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function readFile(file){
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rawBlob=file;
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var reader=new FileReader();
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reader.onload=function(e){
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var text=String(e.target.result||'');
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var lines=parseCSV(text);
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if(lines.length<2){
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alert('CSV is empty or has no data rows.');
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return;
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}
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var header=lines[0].map(function(h){return String(h||'').trim()});
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var rows=lines.slice(1).filter(function(r){return r.length>0 && r.some(function(c){return c!=null&&c!==''})});
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// Build columns with inference
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var columns=header.map(function(h,idx){
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var colValues=rows.map(function(r){return r[idx]});
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return inferColumn(h, colValues.slice(0,50)); // sample first 50 for perf
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});
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parsedFile={name: file.name, rowCount: rows.length, header: header, rows: rows, columns: columns};
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renderPreview();
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setStep(2);
|
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};
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reader.readAsText(file);
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}
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function renderPreview(){
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var colList=document.getElementById('col-list'); colList.textContent='';
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parsedFile.columns.forEach(function(c){
|
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var row=el('div','col');
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row.appendChild(el('span','name',c.name));
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row.appendChild(el('span','type',c.type));
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if(c.pii){
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var chip=el('span','pii '+c.pii,c.pii.toUpperCase());
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row.appendChild(chip);
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}
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row.appendChild(el('span','sample',c.sample));
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colList.appendChild(row);
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});
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var table=document.getElementById('preview-table');table.textContent='';
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var thead=document.createElement('thead');
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var htr=document.createElement('tr');
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parsedFile.header.forEach(function(h){ htr.appendChild(el('th',null,h)) });
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thead.appendChild(htr);table.appendChild(thead);
|
|
var tbody=document.createElement('tbody');
|
|
parsedFile.rows.slice(0,5).forEach(function(r){
|
|
var tr=document.createElement('tr');
|
|
parsedFile.header.forEach(function(_,idx){
|
|
tr.appendChild(el('td',null,r[idx]||''));
|
|
});
|
|
tbody.appendChild(tr);
|
|
});
|
|
table.appendChild(tbody);
|
|
|
|
document.getElementById('row-count').textContent=parsedFile.rowCount.toLocaleString()+' data rows · '+parsedFile.header.length+' columns · source: '+parsedFile.name;
|
|
|
|
// Auto-advance button: a Next button at the bottom of step-2
|
|
var step2=document.getElementById('step-2');
|
|
var existing=document.getElementById('step-2-next');
|
|
if(!existing){
|
|
var wrap=el('div','actions');wrap.id='step-2-next';
|
|
var next=el('button','btn','Continue to commit');next.type='button';
|
|
next.onclick=function(){ setStep(3); };
|
|
var reset=el('button','btn ghost','Pick a different file');reset.type='button';
|
|
reset.onclick=function(){ parsedFile=null; rawBlob=null; document.getElementById('file').value=''; setStep(1); };
|
|
wrap.appendChild(next);wrap.appendChild(reset);
|
|
step2.appendChild(wrap);
|
|
}
|
|
}
|
|
|
|
function commit(){
|
|
var dsName=(document.getElementById('dataset-name').value||'').trim();
|
|
if(!dsName){ alert('Dataset name required.'); return; }
|
|
if(!/^[a-z][a-z0-9_]*$/.test(dsName)){ alert('Dataset name: lowercase letters, numbers, underscores only; start with a letter.'); return; }
|
|
if(!rawBlob){ alert('No file loaded.'); return; }
|
|
|
|
var btn=document.getElementById('commit-btn');
|
|
btn.disabled=true;btn.textContent='';
|
|
var spin=el('span','spin');btn.appendChild(spin);
|
|
btn.appendChild(document.createTextNode('Ingesting…'));
|
|
|
|
var form=new FormData();
|
|
form.append('file', rawBlob, parsedFile.name);
|
|
|
|
fetch(A+'/onboard/ingest?name='+encodeURIComponent(dsName), {
|
|
method: 'POST', body: form,
|
|
}).then(function(r){
|
|
return r.text().then(function(body){ return {status:r.status, body:body}; });
|
|
}).then(function(res){
|
|
btn.disabled=false;btn.textContent='Commit dataset';
|
|
var out=document.getElementById('commit-result');out.textContent='';
|
|
if(res.status>=200 && res.status<300){
|
|
var ok=el('div','result ok');
|
|
ok.appendChild(el('div','big','Dataset "'+dsName+'" is live.'));
|
|
ok.appendChild(el('div','detail',parsedFile.rowCount.toLocaleString()+' rows · '+parsedFile.header.length+' columns · registered in catalog'));
|
|
out.appendChild(ok);
|
|
document.getElementById('ds-name-1').textContent=dsName;
|
|
document.getElementById('ds-name-2').textContent=dsName;
|
|
setStep(4);
|
|
} else {
|
|
var err=el('div','result err');
|
|
err.appendChild(el('div','big','Ingest failed ('+res.status+')'));
|
|
err.appendChild(el('div','detail',res.body.slice(0,400)));
|
|
out.appendChild(err);
|
|
}
|
|
}).catch(function(e){
|
|
btn.disabled=false;btn.textContent='Commit dataset';
|
|
var out=document.getElementById('commit-result');out.textContent='';
|
|
var err=el('div','result err');
|
|
err.appendChild(el('div','big','Network error'));
|
|
err.appendChild(el('div','detail',e.message||String(e)));
|
|
out.appendChild(err);
|
|
});
|
|
}
|
|
|
|
document.addEventListener('DOMContentLoaded',function(){
|
|
var fileInput=document.getElementById('file');
|
|
var drop=document.getElementById('drop');
|
|
fileInput.addEventListener('change',function(e){
|
|
if(e.target.files.length) readFile(e.target.files[0]);
|
|
});
|
|
drop.addEventListener('dragover',function(e){e.preventDefault();drop.classList.add('active')});
|
|
drop.addEventListener('dragleave',function(){drop.classList.remove('active')});
|
|
drop.addEventListener('drop',function(e){
|
|
e.preventDefault();drop.classList.remove('active');
|
|
if(e.dataTransfer.files.length) readFile(e.dataTransfer.files[0]);
|
|
});
|
|
|
|
document.getElementById('use-sample').addEventListener('click',function(){
|
|
// Fetch the sample CSV and feed it through the same pipeline
|
|
fetch(A+'/samples/staffing_roster_sample.csv').then(function(r){return r.blob()}).then(function(blob){
|
|
var file=new File([blob],'staffing_roster_sample.csv',{type:'text/csv'});
|
|
readFile(file);
|
|
}).catch(function(e){ alert('Sample unavailable: '+e.message); });
|
|
});
|
|
|
|
document.getElementById('commit-btn').addEventListener('click',commit);
|
|
document.getElementById('back-btn').addEventListener('click',function(){ setStep(2); });
|
|
});
|
|
</script>
|
|
</body></html>
|