What this type of data looks like
Shipbuilding ledger data originates from sectional construction process records, material requisition slips, quality inspection reports, sectional assembly ledgers, and similar sources. Update cycles are split by construction milestones: process ledgers are updated daily, material ledgers are summarized weekly, and complete quality inspection ledgers are submitted monthly. Each individual document entry includes fields such as sectional number, process name, operation time, operator, material model, inspection result, and qualification judgment. Units include standardized industrial measurement units like millimeters, tons, and hours.
What constraints these characteristics impose on vector models and indexes
The length of individual ledger entries varies widely. Short process records are only tens of characters long, while long material summaries can reach thousands of characters. This affects the consistency of vector encoding after chunking. The incremental update feature based on construction milestones requires indexes to support on-demand incremental synchronization, to avoid resource consumption from full reconstruction. Fields include structured numerical values with units. Vector models must support encoding of such unit-containing fields, otherwise retrieval matching accuracy will decrease. Multi-process ledgers have cross-document association relationships. Indexes must support cross-sectional associated recall, to avoid generating isolated local results.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Balance between long texts and short entries in shipbuilding ledgers, avoid breaking process logic during chunking |
| Overlap Length | 50–100 characters | Retain continuous operation descriptions across chunks, ensure complete context for associated processes |
| Similarity Threshold | 0.72–0.80 | Filter low-match results for structured data with units, improve retrieval accuracy |
| Single Search Limit | Top 10 entries | Align with the core data volume required for ledger summary reports, avoid exceeding context window limits |
maxToken | 4000 token | Match the default context window of FastGPT v4.17.0, cover the summary requirements for a complete single ledger |
| Reranking Model | Calibrate based on actual testing | Adaptability varies across privately deployed reranking models, effects must be verified with local data |
Three Common Configuration Mistakes
- Symptom: A "connection timed out" error occurs when starting external index synchronization, with a 504 status code returned. Cause: Permissions for the incremental pull interface of shipbuilding ledgers are not configured, causing the index service to fail to normally obtain source data.
- Symptom: Vector retrieval results only cover a single sectional, and cannot associate other process ledgers for the same ship. Cause: Cross-document associated recall configuration is not enabled, and the multi-process association feature of shipbuilding ledgers is not matched.
- Symptom: Material units are confused in reranking model results, with incorrect matches between "kilograms" and "tons". Cause: Standardized cleaning is not performed for unit fields in ledgers, causing vector encoding to lose unit association information.
How to Confirm Successful Configuration
- View index synchronization logs, confirm that daily incremental updates and full reconstruction tasks are triggered normally, with no error records.
- Enter retrieval keywords for a specific process, verify that returned fields such as sectional number and operation time match the source ledger.
- Adjust the similarity threshold and single search limit, confirm that the number and matching degree of returned results meet business requirements.
- Test cross-sectional associated retrieval, verify that multi-process ledger data for the same ship can be recalled.
The values provided are common starting points and should be measured against local sample datasets.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.