What This Type of Data Looks Like
Data for urban gas utility application materials and case review originates primarily from gas operating enterprises’ business ticket systems, safety management platforms, and digitized paper archive files. Updates trigger in real time alongside business processes, including application submission, safety inspection completion, and case archiving. Documents include both structured fields and unstructured attachments. Structured fields include user ID, gas usage address, and application installation area. Unstructured attachments include installation application forms, completion acceptance reports, and scanned safety inspection records. Field units are fixed: area uses square meters, gas volume uses cubic meters. Some case materials contain multi-page long text.
Constraints for Vector Models and Indexing
Mixed structured and unstructured data structures require the vectorization step to distinguish field types. This prevents fixed-unit fields from interfering with semantic vectorization results. Real-time updated business data requires indexes to support incremental updates. This avoids performance loss from full reindexing. Long-text case materials require chunking strategies that balance semantic completeness and index granularity. Fixed-unit fields require retaining field metadata in configurations. This ensures semantic accuracy during queries. Duplicate submissions of identical attachment types carry a risk of duplicate indexing. Deduplication logic must be configured to mitigate this risk.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to single-paragraph semantic completeness of gas case files, avoids breaking long text |
| Overlap Length | 100–150 characters | Retains semantic connection between adjacent chunks, ensures contextual coherence during case review |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance search results, matches accuracy requirements for gas application material semantic matching |
| Single Search Limit | Top 20 entries | Covers retrieval needs for multiple reference cases in gas review scenarios |
defaultToken | 4096 | Adapts to context length of single-round review queries, falls within FastGPT v4.17.0 default configuration range |
maxToken | 8192 | Accommodates complete vectorized content of long case materials, avoids truncating key review information |
Three Common Misconfigurations
- Phenomenon: After uploading a gas installation application form, the interface displays 8 chunks, but the count increases to 13 after indexing completes, with duplicate segments. Cause: Document hash deduplication configuration is not enabled. Duplicate uploads of the same form are identified as separate documents, generating duplicate segments during chunking.
- Phenomenon: After upgrading to FastGPT v4.17.0, existing safety inspection case query results no longer return matches. Cause: Original chunk length configuration was not retained. New index chunk granularity does not match the original index, causing semantic matching to fail.
- Phenomenon: After uploading an Excel-format payment detail file, vectorization results omit field content such as "payment amount" and "payment cycle". Cause: Structured data extraction switch is not enabled. Table fields in the Excel file are not included in the vectorized text scope.
How to Confirm Proper Configuration
- Upload a single 5-10 page gas completion acceptance report. Check if the number of chunks displayed by the system matches the preset chunk length range.
- Initiate a query containing "XX Community 2024 Safety Inspection Records". Verify that returned chunk content is semantically consistent with the original document.
- Upload two identical gas installation application forms. Check for duplicate segment content in the index.
- Adjust the similarity threshold to 0.8. Verify that search result relevance meets business review screening standards.
The values provided are common starting points, and should be evaluated against the sample data relevant to the target deployment.
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.