What the data for this category looks like
Data for this category primarily comes from software asset management systems, procurement systems, terminal scan results, and vendor authorization documents. Daily documents include license ledgers, software usage lists, procurement contracts, and compliance audit reports. Data update frequency is monthly or quarterly. Document fields include software name, authorized quantity, authorization type, terminal installation records, procurement date, renewal deadlines, and other relevant items. Some scanned documents require initial text extraction, and no unified standardized format exists across datasets.
Constraints on vector models and indexing workflows
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. Data sources include structured ledgers, semi-structured scanned documents, and unstructured contracts, so indexes must support multi-format text parsing. Update frequency is monthly or quarterly, so high-frequency incremental indexing is not required. However, index consistency must be maintained during full updates. Documents contain business-specific fields, so vector models must adapt to semantic associations for these specialized keywords. Duplicate installation records generated from terminal scans require indexes to support deduplication configuration. Compliance audit reports must retain complete original source traceability information, and metadata associations cannot be lost.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text density of documents such as license ledgers and contracts, avoiding information overload in single chunks |
| Overlap Length | 100–150 characters | Ensures coherent business context across chunks, such as the association between renewal deadlines and corresponding software names |
| Similarity Threshold | 0.72–0.78 | Distinguishes semantic similarity of business keywords including authorized quantity and software name, preventing incorrect matches |
maxToken | 4000 | Adapts to long-text parsing of compliance audit reports, avoiding truncation of critical authorization information |
| Single Search Limit | Top 8 entries | Balances retrieval efficiency and information completeness for business scenarios such as over-installation identification and renewal reminders |
Three Common Configuration Mistakes
- Symptom: Retrieval results do not return source information for the corresponding document, or the source field is empty. Cause: Document metadata binding configuration is not enabled, and metadata such as file names and generation times of license ledgers and vendor authorization documents are not associated with vector chunks.
- Symptom: An error is returned when calling vector retrieval, indicating that the specified embedding model is not supported. Cause: Compatibility between the selected embedding model and FastGPT v4.17.0 has not been confirmed, such as failing to verify the adaptability of Doubao-embedding.
- Symptom: A large number of duplicate terminal installation record entries appear in retrieval results. Cause: Index deduplication rules are not configured, and duplicate items are not filtered based on fields such as terminal ID and software name.
How to Verify Proper Configuration
- Upload a single license ledger document, run a retrieval test, and confirm that returned vector chunks cover core business fields including authorized quantity and renewal deadlines.
- View the metadata area of retrieval results, and confirm that each result is associated with identification information of the corresponding document.
- Adjust the chunk length parameter, upload a long-text compliance audit report, and confirm that no critical information truncation occurs.
- Simulate batch upload of multiple scanned documents, and check whether duplicate entry prompts appear during index generation.
The values listed here are common starting points, and should be validated against samples from the actual deployment environment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.